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Collins, Gary S., Paula Dhiman, Constanza L. Andaur Navarro, Jie Ma, Lotty Hooft, Johannes B. Reitsma, Patricia Logullo, et al. "Protocol for development of a reporting guideline (TRIPOD-AI) and risk of bias tool (PROBAST-AI) for diagnostic and prognostic prediction model studies based on artificial intelligence." BMJ Open 11, no. 7 (July 2021): e048008. http://dx.doi.org/10.1136/bmjopen-2020-048008.
Повний текст джерелаАнотація:
IntroductionThe Transparent Reporting of a multivariable prediction model of Individual Prognosis Or Diagnosis (TRIPOD) statement and the Prediction model Risk Of Bias ASsessment Tool (PROBAST) were both published to improve the reporting and critical appraisal of prediction model studies for diagnosis and prognosis. This paper describes the processes and methods that will be used to develop an extension to the TRIPOD statement (TRIPOD-artificial intelligence, AI) and the PROBAST (PROBAST-AI) tool for prediction model studies that applied machine learning techniques.Methods and analysisTRIPOD-AI and PROBAST-AI will be developed following published guidance from the EQUATOR Network, and will comprise five stages. Stage 1 will comprise two systematic reviews (across all medical fields and specifically in oncology) to examine the quality of reporting in published machine-learning-based prediction model studies. In stage 2, we will consult a diverse group of key stakeholders using a Delphi process to identify items to be considered for inclusion in TRIPOD-AI and PROBAST-AI. Stage 3 will be virtual consensus meetings to consolidate and prioritise key items to be included in TRIPOD-AI and PROBAST-AI. Stage 4 will involve developing the TRIPOD-AI checklist and the PROBAST-AI tool, and writing the accompanying explanation and elaboration papers. In the final stage, stage 5, we will disseminate TRIPOD-AI and PROBAST-AI via journals, conferences, blogs, websites (including TRIPOD, PROBAST and EQUATOR Network) and social media. TRIPOD-AI will provide researchers working on prediction model studies based on machine learning with a reporting guideline that can help them report key details that readers need to evaluate the study quality and interpret its findings, potentially reducing research waste. We anticipate PROBAST-AI will help researchers, clinicians, systematic reviewers and policymakers critically appraise the design, conduct and analysis of machine learning based prediction model studies, with a robust standardised tool for bias evaluation.Ethics and disseminationEthical approval has been granted by the Central University Research Ethics Committee, University of Oxford on 10-December-2020 (R73034/RE001). Findings from this study will be disseminated through peer-review publications.PROSPERO registration numberCRD42019140361 and CRD42019161764.
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Kaiser, Isabelle, Sonja Mathes, Annette B. Pfahlberg, Wolfgang Uter, Carola Berking, Markus V. Heppt, Theresa Steeb, Katharina Diehl, and Olaf Gefeller. "Using the Prediction Model Risk of Bias Assessment Tool (PROBAST) to Evaluate Melanoma Prediction Studies." Cancers 14, no. 12 (June 20, 2022): 3033. http://dx.doi.org/10.3390/cancers14123033.
Повний текст джерелаАнотація:
Rising incidences of cutaneous melanoma have fueled the development of statistical models that predict individual melanoma risk. Our aim was to assess the validity of published prediction models for incident cutaneous melanoma using a standardized procedure based on PROBAST (Prediction model Risk Of Bias ASsessment Tool). We included studies that were identified by a recent systematic review and updated the literature search to ensure that our PROBAST rating included all relevant studies. Six reviewers assessed the risk of bias (ROB) for each study using the published “PROBAST Assessment Form” that consists of four domains and an overall ROB rating. We further examined a temporal effect regarding changes in overall and domain-specific ROB rating distributions. Altogether, 42 studies were assessed, of which the vast majority (n = 34; 81%) was rated as having high ROB. Only one study was judged as having low ROB. The main reasons for high ROB ratings were the use of hospital controls in case-control studies and the omission of any validation of prediction models. However, our temporal analysis results showed a significant reduction in the number of studies with high ROB for the domain “analysis”. Nevertheless, the evidence base of high-quality studies that can be used to draw conclusions on the prediction of incident cutaneous melanoma is currently much weaker than the high number of studies on this topic would suggest.
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Zheng, Yadi, Jiang Li, Zheng Wu, He Li, Maomao Cao, Ni Li, and Jie He. "Risk prediction models for breast cancer: a systematic review." BMJ Open 12, no. 7 (July 2022): e055398. http://dx.doi.org/10.1136/bmjopen-2021-055398.
Повний текст джерелаАнотація:
ObjectivesTo systematically review and critically appraise published studies of risk prediction models for breast cancer in the general population without breast cancer, and provide evidence for future research in the field.DesignSystematic review using the Prediction model study Risk Of Bias Assessment Tool (PROBAST) framework.Data sourcesPubMed, the Cochrane Library and Embase were searched from inception to 16 December 2021.Eligibility criteriaWe included studies reporting multivariable models to estimate the individualised risk of developing female breast cancer among different ethnic groups. Search was limited to English language only.Data extraction and synthesisTwo reviewers independently screened, reviewed, extracted and assessed studies with discrepancies resolved through discussion or a third reviewer. Risk of bias was assessed according to the PROBAST framework.Results63 894 studies were screened and 40 studies with 47 risk prediction models were included in the review. Most of the studies used logistic regression to develop breast cancer risk prediction models for Caucasian women by case–control data. The most widely used risk factor was reproductive factors and the highest area under the curve was 0.943 (95% CI 0.919 to 0.967). All the models included in the review had high risk of bias.ConclusionsNo risk prediction models for breast cancer were recommended for different ethnic groups and models incorporating mammographic density or single-nucleotide polymorphisms among Asian women are few and poorly needed. High-quality breast cancer risk prediction models assessed by PROBAST should be developed and validated, especially among Asian women.PROSPERO registration numberCRD42020202570.
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Du, M., D. Haag, Y. Song, J. Lynch, and M. Mittinty. "Examining Bias and Reporting in Oral Health Prediction Modeling Studies." Journal of Dental Research 99, no. 4 (February 6, 2020): 374–87. http://dx.doi.org/10.1177/0022034520903725.
Повний текст джерелаАнотація:
Recent efforts to improve the reliability and efficiency of scientific research have caught the attention of researchers conducting prediction modeling studies (PMSs). Use of prediction models in oral health has become more common over the past decades for predicting the risk of diseases and treatment outcomes. Risk of bias and insufficient reporting present challenges to the reproducibility and implementation of these models. A recent tool for bias assessment and a reporting guideline—PROBAST (Prediction Model Risk of Bias Assessment Tool) and TRIPOD (Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis)—have been proposed to guide researchers in the development and reporting of PMSs, but their application has been limited. Following the standards proposed in these tools and a systematic review approach, a literature search was carried out in PubMed to identify oral health PMSs published in dental, epidemiologic, and biostatistical journals. Risk of bias and transparency of reporting were assessed with PROBAST and TRIPOD. Among 2,881 papers identified, 34 studies containing 58 models were included. The most investigated outcomes were periodontal diseases (42%) and oral cancers (30%). Seventy-five percent of the studies were susceptible to at least 4 of 20 sources of bias, including measurement error in predictors ( n = 12) and/or outcome ( n = 7), omitting samples with missing data ( n = 10), selecting variables based on univariate analyses ( n = 9), overfitting ( n = 13), and lack of model performance assessment ( n = 24). Based on TRIPOD, at least 5 of 31 items were inadequately reported in 95% of the studies. These items included sampling approaches ( n = 15), participant eligibility criteria ( n = 6), and model-building procedures ( n = 16). There was a general lack of transparent reporting and identification of bias across the studies. Application of the recommendations proposed in PROBAST and TRIPOD can benefit future research and improve the reproducibility and applicability of prediction models in oral health.
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Geng, Chanyu, Liming Huang, Yi Li, Amanda Ying Wang, Guisen Li, and Yunlin Feng. "Prediction Models of Primary Membranous Nephropathy: A Systematic Review and Meta-Analysis." Journal of Clinical Medicine 12, no. 2 (January 10, 2023): 559. http://dx.doi.org/10.3390/jcm12020559.
Повний текст джерелаАнотація:
Background: Several statistical models for predicting prognosis of primary membranous nephropathy (PMN) have been proposed, most of which have not been as widely accepted in clinical practice. Methods: A systematic search was performed in MEDLINE and EMBASE. English studies that developed any prediction models including two or more than two predictive variables were eligible for inclusion. The study population was limited to adult patients with pathologically confirmed PMN. The outcomes in eligible studies should be events relevant to prognosis of PMN, either disease progression or response profile after treatments. The risk of bias was assessed according to the PROBAST. Results: In all, eight studies with 1237 patients were included. The pooled AUC value of the seven studies with renal function deterioration and/or ESRD as the predicted outcomes was 0.88 (95% CI: 0.85 to 0.90; I2 = 77%, p = 0.006). The paired forest plots for sensitivity and specificity with corresponding 95% CIs for each of these seven studies indicated the combined sensitivity and specificity were 0.76 (95% CI: 0.64 to 0.85) and 0.84 (95% CI: 0.80 to 0.88), respectively. All seven studies included in the meta-analysis were assessed as high risk of bias according to the PROBAST tool. Conclusions: The reported discrimination ability of included models was good; however, the insufficient calibration assessment and lack of validation studies precluded drawing a definitive conclusion on the performance of these prediction models. High-grade evidence from well-designed studies is needed in this field.
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Wolff, Robert F., Karel G. M. Moons, Richard D. Riley, Penny F. Whiting, Marie Westwood, Gary S. Collins, Johannes B. Reitsma, Jos Kleijnen, and Sue Mallett. "PROBAST: A Tool to Assess the Risk of Bias and Applicability of Prediction Model Studies." Annals of Internal Medicine 170, no. 1 (January 1, 2019): 51. http://dx.doi.org/10.7326/m18-1376.
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Crowson, Matthew G., Dana Moukheiber, Aldo Robles Arévalo, Barbara D. Lam, Sreekar Mantena, Aakanksha Rana, Deborah Goss, David W. Bates, and Leo Anthony Celi. "A systematic review of federated learning applications for biomedical data." PLOS Digital Health 1, no. 5 (May 19, 2022): e0000033. http://dx.doi.org/10.1371/journal.pdig.0000033.
Повний текст джерелаАнотація:
Objectives Federated learning (FL) allows multiple institutions to collaboratively develop a machine learning algorithm without sharing their data. Organizations instead share model parameters only, allowing them to benefit from a model built with a larger dataset while maintaining the privacy of their own data. We conducted a systematic review to evaluate the current state of FL in healthcare and discuss the limitations and promise of this technology. Methods We conducted a literature search using PRISMA guidelines. At least two reviewers assessed each study for eligibility and extracted a predetermined set of data. The quality of each study was determined using the TRIPOD guideline and PROBAST tool. Results 13 studies were included in the full systematic review. Most were in the field of oncology (6 of 13; 46.1%), followed by radiology (5 of 13; 38.5%). The majority evaluated imaging results, performed a binary classification prediction task via offline learning (n = 12; 92.3%), and used a centralized topology, aggregation server workflow (n = 10; 76.9%). Most studies were compliant with the major reporting requirements of the TRIPOD guidelines. In all, 6 of 13 (46.2%) of studies were judged at high risk of bias using the PROBAST tool and only 5 studies used publicly available data. Conclusion Federated learning is a growing field in machine learning with many promising uses in healthcare. Few studies have been published to date. Our evaluation found that investigators can do more to address the risk of bias and increase transparency by adding steps for data homogeneity or sharing required metadata and code.
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Ogero, Morris, Rachel Jelagat Sarguta, Lucas Malla, Jalemba Aluvaala, Ambrose Agweyu, Mike English, Nelson Owuor Onyango, and Samuel Akech. "Prognostic models for predicting in-hospital paediatric mortality in resource-limited countries: a systematic review." BMJ Open 10, no. 10 (October 2020): e035045. http://dx.doi.org/10.1136/bmjopen-2019-035045.
Повний текст джерелаАнотація:
ObjectivesTo identify and appraise the methodological rigour of multivariable prognostic models predicting in-hospital paediatric mortality in low-income and middle-income countries (LMICs).DesignSystematic review of peer-reviewed journals.Data sourcesMEDLINE, CINAHL, Google Scholar and Web of Science electronic databases since inception to August 2019.Eligibility criteriaWe included model development studies predicting in-hospital paediatric mortality in LMIC.Data extraction and synthesisThis systematic review followed the Checklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies framework. The risk of bias assessment was conducted using Prediction model Risk of Bias Assessment Tool (PROBAST). No quantitative summary was conducted due to substantial heterogeneity that was observed after assessing the studies included.ResultsOur search strategy identified a total of 4054 unique articles. Among these, 3545 articles were excluded after review of titles and abstracts as they covered non-relevant topics. Full texts of 509 articles were screened for eligibility, of which 15 studies reporting 21 models met the eligibility criteria. Based on the PROBAST tool, risk of bias was assessed in four domains; participant, predictors, outcome and analyses. The domain of statistical analyses was the main area of concern where none of the included models was judged to be of low risk of bias.ConclusionThis review identified 21 models predicting in-hospital paediatric mortality in LMIC. However, most reports characterising these models are of poor quality when judged against recent reporting standards due to a high risk of bias. Future studies should adhere to standardised methodological criteria and progress from identifying new risk scores to validating or adapting existing scores.PROSPERO registration numberCRD42018088599.
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Monahan, Ann Corneille, and Sue S. Feldman. "Models Predicting Hospital Admission of Adult Patients Utilizing Prehospital Data: Systematic Review Using PROBAST and CHARMS." JMIR Medical Informatics 9, no. 9 (September 16, 2021): e30022. http://dx.doi.org/10.2196/30022.
Повний текст джерелаАнотація:
Background Emergency department boarding and hospital exit block are primary causes of emergency department crowding and have been conclusively associated with poor patient outcomes and major threats to patient safety. Boarding occurs when a patient is delayed or blocked from transitioning out of the emergency department because of dysfunctional transition or bed assignment processes. Predictive models for estimating the probability of an occurrence of this type could be useful in reducing or preventing emergency department boarding and hospital exit block, to reduce emergency department crowding. Objective The aim of this study was to identify and appraise the predictive performance, predictor utility, model application, and model utility of hospital admission prediction models that utilized prehospital, adult patient data and aimed to address emergency department crowding. Methods We searched multiple databases for studies, from inception to September 30, 2019, that evaluated models predicting adult patients’ imminent hospital admission, with prehospital patient data and regression analysis. We used PROBAST (Prediction Model Risk of Bias Assessment Tool) and CHARMS (Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies) to critically assess studies. Results Potential biases were found in most studies, which suggested that each model’s predictive performance required further investigation. We found that select prehospital patient data contribute to the identification of patients requiring hospital admission. Biomarker predictors may add superior value and advantages to models. It is, however, important to note that no models had been integrated with an information system or workflow, operated independently as electronic devices, or operated in real time within the care environment. Several models could be used at the site-of-care in real time without digital devices, which would make them suitable for low-technology or no-electricity environments. Conclusions There is incredible potential for prehospital admission prediction models to improve patient care and hospital operations. Patient data can be utilized to act as predictors and as data-driven, actionable tools to identify patients likely to require imminent hospital admission and reduce patient boarding and crowding in emergency departments. Prediction models can be used to justify earlier patient admission and care, to lower morbidity and mortality, and models that utilize biomarker predictors offer additional advantages.
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Petre, Maria-Alexandra, Bibek Saha, Shugo Kasuya, Marina Englesakis, Nan Gai, Arie Peliowski, and Kazuyoshi Aoyama. "Risk prediction models for emergence delirium in paediatric general anaesthesia: a systematic review." BMJ Open 11, no. 1 (January 2021): e043968. http://dx.doi.org/10.1136/bmjopen-2020-043968.
Повний текст джерелаАнотація:
ObjectivesEmergence delirium (ED) occurs in approximately 25% of paediatric general anaesthetics and has significant adverse effects. The goal of the current systematic review was to identify the existing literature investigating performance of predictive models for the development of paediatric ED following general anaesthesia and to determine their usability.DesignSystematic review using the Prediction model study Risk Of Bias Assessment Tool (PROBAST) framework.Data sourcesMedline (Ovid), PubMed, Embase (Ovid), Cochrane Database of Systematic Reviews (Ovid), Cochrane CENTRAL (Ovid), PsycINFO (Ovid), Scopus (Elsevier) and Web of Science (Clarivate Analytics), ClinicalTrials.gov, International Clinical Trials Registry Platform and ProQuest Digital Dissertations and Theses International through 17 November 2020.Eligibility criteria for selecting studiesAll randomised controlled trials and cohort studies investigating predictive models for the development of ED in children undergoing general anaesthesia.Data extraction and synthesisFollowing title, abstract and full-text screening by two reviewers, data were extracted from all eligible studies, including demographic parameters, details of anaesthetics and performance characteristics of the predictive scores for ED. Evidence quality and predictive score usability were assessed according to the PROBAST framework.ResultsThe current systematic review yielded 9242 abstracts, of which only one study detailing the development and validation of the Emergence Agitation Risk Scale (EARS) met the inclusion criteria. EARS had good discrimination with c-index of 0.81 (95% CI 0.72 to 0.89). Calibration showed a non-significant Homer-Lemeshow goodness-of-fit test (p=0.97). Although the EARS demonstrated low concern of applicability, the high risk of bias compromised the overall usability of this model.ConclusionsThe current systematic review concluded that EARS has good discrimination performance but low usability to predict ED in a paediatric population. Further research is warranted to develop novel models for the prediction of ED in paediatric anaesthesia.PROSPERO registration numberCRD42019141950.
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Cooray, Shamil D., Lihini A. Wijeyaratne, Georgia Soldatos, John Allotey, Jacqueline A. Boyle, and Helena J. Teede. "The Unrealised Potential for Predicting Pregnancy Complications in Women with Gestational Diabetes: A Systematic Review and Critical Appraisal." International Journal of Environmental Research and Public Health 17, no. 9 (April 27, 2020): 3048. http://dx.doi.org/10.3390/ijerph17093048.
Повний текст джерелаАнотація:
Gestational diabetes (GDM) increases the risk of pregnancy complications. However, these risks are not the same for all affected women and may be mediated by inter-related factors including ethnicity, body mass index and gestational weight gain. This study was conducted to identify, compare, and critically appraise prognostic prediction models for pregnancy complications in women with gestational diabetes (GDM). A systematic review of prognostic prediction models for pregnancy complications in women with GDM was conducted. Critical appraisal was conducted using the prediction model risk of bias assessment tool (PROBAST). Five prediction modelling studies were identified, from which ten prognostic models primarily intended to predict pregnancy complications related to GDM were developed. While the composition of the pregnancy complications predicted varied, the delivery of a large-for-gestational age neonate was the subject of prediction in four studies, either alone or as a component of a composite outcome. Glycaemic measures and body mass index were selected as predictors in four studies. Model evaluation was limited to internal validation in four studies and not reported in the fifth. Performance was inadequately reported with no useful measures of calibration nor formal evaluation of clinical usefulness. Critical appraisal using PROBAST revealed that all studies were subject to a high risk of bias overall driven by methodologic limitations in statistical analysis. This review demonstrates the potential for prediction models to provide an individualised absolute risk of pregnancy complications for women affected by GDM. However, at present, a lack of external validation and high risk of bias limit clinical application. Future model development and validation should utilise the latest methodological advances in prediction modelling to achieve the evolution required to create a useful clinical tool. Such a tool may enhance clinical decision-making and support a risk-stratified approach to the management of GDM. Systematic review registration: PROSPERO CRD42019115223.
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Moons, Karel G. M., Robert F. Wolff, Richard D. Riley, Penny F. Whiting, Marie Westwood, Gary S. Collins, Johannes B. Reitsma, Jos Kleijnen, and Sue Mallett. "PROBAST: A Tool to Assess Risk of Bias and Applicability of Prediction Model Studies: Explanation and Elaboration." Annals of Internal Medicine 170, no. 1 (January 1, 2019): W1. http://dx.doi.org/10.7326/m18-1377.
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Andaur Navarro, Constanza L., Johanna A. A. G. Damen, Toshihiko Takada, Steven W. J. Nijman, Paula Dhiman, Jie Ma, Gary S. Collins, et al. "Protocol for a systematic review on the methodological and reporting quality of prediction model studies using machine learning techniques." BMJ Open 10, no. 11 (November 2020): e038832. http://dx.doi.org/10.1136/bmjopen-2020-038832.
Повний текст джерелаАнотація:
IntroductionStudies addressing the development and/or validation of diagnostic and prognostic prediction models are abundant in most clinical domains. Systematic reviews have shown that the methodological and reporting quality of prediction model studies is suboptimal. Due to the increasing availability of larger, routinely collected and complex medical data, and the rising application of Artificial Intelligence (AI) or machine learning (ML) techniques, the number of prediction model studies is expected to increase even further. Prediction models developed using AI or ML techniques are often labelled as a ‘black box’ and little is known about their methodological and reporting quality. Therefore, this comprehensive systematic review aims to evaluate the reporting quality, the methodological conduct, and the risk of bias of prediction model studies that applied ML techniques for model development and/or validation.Methods and analysisA search will be performed in PubMed to identify studies developing and/or validating prediction models using any ML methodology and across all medical fields. Studies will be included if they were published between January 2018 and December 2019, predict patient-related outcomes, use any study design or data source, and available in English. Screening of search results and data extraction from included articles will be performed by two independent reviewers. The primary outcomes of this systematic review are: (1) the adherence of ML-based prediction model studies to the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD), and (2) the risk of bias in such studies as assessed using the Prediction model Risk Of Bias ASsessment Tool (PROBAST). A narrative synthesis will be conducted for all included studies. Findings will be stratified by study type, medical field and prevalent ML methods, and will inform necessary extensions or updates of TRIPOD and PROBAST to better address prediction model studies that used AI or ML techniques.Ethics and disseminationEthical approval is not required for this study because only available published data will be analysed. Findings will be disseminated through peer-reviewed publications and scientific conferences.Systematic review registrationPROSPERO, CRD42019161764.
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Lu, Sheng-Chieh, Cai Xu, Chandler Nguyen, Larissa Meyer, and Chris Sidey-Gibbons. "A systematic evaluation of models predicting short-term mortality for cancer patients to highlight uncertainty about model performance and high risk of bias." Journal of Clinical Oncology 39, no. 15_suppl (May 20, 2021): e13559-e13559. http://dx.doi.org/10.1200/jco.2021.39.15_suppl.e13559.
Повний текст джерелаАнотація:
e13559 Background: Short-term cancer mortality prediction has many implications concerning care planning. An accurate prognosis allows healthcare providers to adjust care plans and take appropriate actions, such as initiating end-of-life conversations. Machine learning (ML) techniques demonstrated promising capability to support clinical decision-making via providing reliable predictions for a variety of clinical outcomes, including cancer mortality. However, the evidence has not yet been systematically synthesized and evaluated. The objective of this review was to examine the performance and risk-of-bias for ML models trained to predict short-term (≤ 12 months) cancer mortality. Methods: We identified relevant literature from five electronic databases: Ovid Medline, Ovid EMBASE, Scopus, Web of Science, and IEEE Xplore. We searched each database with predefined MeSH terms and keywords of oncology, machine learning, and mortality using AND/OR statements. Inclusion criteria included: 1) developed/validated ML models for predicting oncology patient mortality within one year using electronic health record data; 2) reported model performance within a dataset that was not used to train the models; 3) original research; 4) peer-reviewed full paper in English; 5) published before 1/10/2020. We conducted risk of bias assessment using prediction model risk of bias assessment tool (PROBAST). Results: Ten articles were included in this review. Most studies focused on predicting 1-year mortality (n = 6) for multiple types of cancer (n = 5). Most studies (n = 7) used a single metric, the area under the receiver operating characteristic curve (AUROC), to examine their models. The AUROC ranged from .69 to .91, with a median of .85. Information on samples (n = 10), resampling methods (n = 6), model tuning approaches (n = 9), censoring (n = 10), and sample size determinations (n = 10) were incomplete or absent. Six studies have a high risk of bias for the analysis domain in the PROBAST. Conclusions: The performance of ML models for short-term cancer mortality appears promising. However, most studies report only a single performance metric that obfuscates evaluation of a model’s true performance. This is especially problematic when predicting rare events such as short-term mortality. We found little-to-no information on a given model’s ability to correctly identify patients at high risk of mortality. The incomplete reporting of model development poses challenges to risk of bias assessment and reduces the confidence in the results. Our findings suggest that future studies should report comprehensive performance metrics using a standard reporting guideline, such as transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD), to ensure sufficient information for replication, justification, and adoption.
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Bektaş, Mustafa, Babs M. Zonderhuis, Henk A. Marquering, Jaime Costa Pereira, George L. Burchell, and Donald L. van der Peet. "Artificial intelligence in hepatFIGopancreaticobiliary surgery: a systematic review." Artificial Intelligence Surgery 2, no. 3 (2022): 132–43. http://dx.doi.org/10.20517/ais.2022.20.
Повний текст джерелаАнотація:
Aim: The aim of this systematic review was to provide an overview of Machine Learning applications within hepatopancreaticobiliary surgery. The secondary aim was to evaluate the predictive performances of applied Machine Learning models. Methods: A systematic search was conducted in PubMed, EMBASE, Cochrane, and Web of Science. Studies were only eligible for inclusion when they described Machine Learning in hepatopancreaticobiliary surgery. The Cochrane and PROBAST risk of bias tools were used to evaluate the quality of studies and included Machine Learning models. Results: Out of 1821 articles, 52 studies have met the inclusion criteria. The majority of Machine Learning models were developed to predict the course of disease, and postoperative complications. The course of disease has been predicted with accuracies up to 99%, and postoperative complications with accuracies up to 89%. Most studies had a retrospective study design, in which external validation was absent for Machine Learning models. Conclusion: Machine learning models have shown promising accuracies in the prediction of short-term and long-term surgical outcomes after hepatopancreaticobiliary surgery. External validation of Machine Learning models is required to facilitate the clinical introduction of Machine Learning.
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Maestre-Muñiz, Modesto M., Ángel Arias, and Alfredo J. Lucendo. "Predicting In-Hospital Mortality in Severe COVID-19: A Systematic Review and External Validation of Clinical Prediction Rules." Biomedicines 10, no. 10 (September 27, 2022): 2414. http://dx.doi.org/10.3390/biomedicines10102414.
Повний текст джерелаАнотація:
Multiple prediction models for risk of in-hospital mortality from COVID-19 have been developed, but not applied, to patient cohorts different to those from which they were derived. The MEDLINE, EMBASE, Scopus, and Web of Science (WOS) databases were searched. Risk of bias and applicability were assessed with PROBAST. Nomograms, whose variables were available in a well-defined cohort of 444 patients from our site, were externally validated. Overall, 71 studies, which derived a clinical prediction rule for mortality outcome from COVID-19, were identified. Predictive variables consisted of combinations of patients′ age, chronic conditions, dyspnea/taquipnea, radiographic chest alteration, and analytical values (LDH, CRP, lymphocytes, D-dimer); and markers of respiratory, renal, liver, and myocardial damage, which were mayor predictors in several nomograms. Twenty-five models could be externally validated. Areas under receiver operator curve (AUROC) in predicting mortality ranged from 0.71 to 1 in derivation cohorts; C-index values ranged from 0.823 to 0.970. Overall, 37/71 models provided very-good-to-outstanding test performance. Externally validated nomograms provided lower predictive performances for mortality in their respective derivation cohorts, with the AUROC being 0.654 to 0.806 (poor to acceptable performance). We can conclude that available nomograms were limited in predicting mortality when applied to different populations from which they were derived.
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Gerry, Stephen, Jacqueline Birks, Timothy Bonnici, Peter J. Watkinson, Shona Kirtley, and Gary S. Collins. "Early warning scores for detecting deterioration in adult hospital patients: a systematic review protocol." BMJ Open 7, no. 12 (December 2017): e019268. http://dx.doi.org/10.1136/bmjopen-2017-019268.
Повний текст джерелаАнотація:
IntroductionEarly warning scores (EWSs) are used extensively to identify patients at risk of deterioration in hospital. Previous systematic reviews suggest that studies which develop EWSs suffer methodological shortcomings and consequently may fail to perform well. The reviews have also identified that few validation studies exist to test whether the scores work in other settings. We will aim to systematically review papers describing the development or validation of EWSs, focusing on methodology, generalisability and reporting.MethodsWe will identify studies that describe the development or validation of EWSs for adult hospital inpatients. Each study will be assessed for risk of bias using the Prediction model Risk of Bias ASsessment Tool (PROBAST). Two reviewers will independently extract information. A narrative synthesis and descriptive statistics will be used to answer the main aims of the study which are to assess and critically appraise the methodological quality of the EWS, to describe the predictors included in the EWSs and to describe the reported performance of EWSs in external validation.Ethics and disseminationThis systematic review will only investigate published studies and therefore will not directly involve patient data. The review will help to establish whether EWSs are fit for purpose and make recommendations to improve the quality of future research in this area.PROSPERO registration numberCRD42017053324.
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Venema, Esmee, Benjamin S. Wessler, Jessica K. Paulus, Rehab Salah, Gowri Raman, Lester Y. Leung, Benjamin C. Koethe, et al. "Large-scale validation of the prediction model risk of bias assessment Tool (PROBAST) using a short form: high risk of bias models show poorer discrimination." Journal of Clinical Epidemiology 138 (October 2021): 32–39. http://dx.doi.org/10.1016/j.jclinepi.2021.06.017.
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Brinza, Crischentian, Alexandru Burlacu, Grigore Tinica, Adrian Covic, and Liviu Macovei. "A Systematic Review on Bleeding Risk Scores’ Accuracy after Percutaneous Coronary Interventions in Acute and Elective Settings." Healthcare 9, no. 2 (February 2, 2021): 148. http://dx.doi.org/10.3390/healthcare9020148.
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Dual antiplatelet therapy (DAT) is recommended for all patients undergoing percutaneous coronary intervention (PCI), as it significantly reduces the ischemic risk at the cost of increasing the incidence of bleeding events. Several clinical predictive models were developed to better stratify the bleeding risk associated with DAT. This systematic review aims to perform a literature survey of both standard and emerging bleeding risk scores and report their performance on predicting hemorrhagic events, especially in the era of second-generation drug-eluting stents and more potent P2Y12 inhibitors. We searched PubMed, ScienceDirect, and Cochrane databases for full-text studies that developed or validated bleeding risk scores in adult patients undergoing PCI with subsequent DAT. The risk of bias for each study was assessed using the prediction model risk of bias assessment tool (PROBAST). Eighteen studies were included in the present systematic review. Bleeding risk scores showed a modest to good discriminatory power with c-statistic ranging from 0.49 (95% CI, 0.45–0.53) to 0.82 (95% CI, 0.80–0.85). Clinical models that predict in-hospital bleeding events had a relatively good predictive performance, with c-statistic ranging from 0.70 (95% CI, 0.67–0.72) to 0.80 (95% CI, 0.73–0.87), depending on the risk scores and major hemorrhagic event definition used. The knowledge and utilization of the current bleeding risk scores in appropriate clinical contexts could improve the prediction of bleeding events.
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Li, Suli, Yihang Chu, Ying Wang, Yantong Wang, Shipeng Hu, Xiangye Wu, and Xinwei Qi. "Distinguish the Value of the Benign Nevus and Melanomas Using Machine Learning: A Meta-Analysis and Systematic Review." Mediators of Inflammation 2022 (October 14, 2022): 1–8. http://dx.doi.org/10.1155/2022/1734327.
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Background. Melanomas, the most common human malignancy, are primarily diagnosed visually, beginning with an initial clinical screening and followed potentially by dermoscopic analysis, a biopsy, and histopathological examination. We aimed to systematically review the performance and quality of machine learning-based methods in distinguishing melanoma and benign nevus in the relevant literature. Method. Four databases (Web of Science, PubMed, Embase, and the Cochrane library) were searched to retrieve the relevant studies published until March 26, 2022. The Predictive model Deviation Risk Assessment tool (PROBAST) was used to assess the deviation risk of opposing law. Result. This systematic review included thirty researches with 114007 subjects and 71 machine learning models. The convolutional neural network was the main machine learning method. The pooled sensitivity was 85% (95% CI 82–87%), the specificity was 86% (82–88%), and the C -index was 0.87 (0.84–0.90). Conclusion. The findings of our study showed that ML algorithms had high sensitivity and specificity for distinguishing between melanoma and benign nevi. This suggests that state-of-the-art ML-based algorithms for distinguishing melanoma from benign nevi may be ready for clinical use. However, a large proportion of the earlier published studies had methodological flaws, such as lack of external validation and lack of clinician comparisons. The results of these studies should be interpreted with caution.
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Dretzke, Janine, Naomi Chuchu, Ridhi Agarwal, Clare Herd, Winnie Chua, Larissa Fabritz, Susan Bayliss, et al. "Predicting recurrent atrial fibrillation after catheter ablation: a systematic review of prognostic models." EP Europace 22, no. 5 (March 30, 2020): 748–60. http://dx.doi.org/10.1093/europace/euaa041.
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Abstract Aims We assessed the performance of modelsf (risk scores) for predicting recurrence of atrial fibrillation (AF) in patients who have undergone catheter ablation. Methods and results Systematic searches of bibliographic databases were conducted (November 2018). Studies were eligible for inclusion if they reported the development, validation, or impact assessment of a model for predicting AF recurrence after ablation. Model performance (discrimination and calibration) measures were extracted. The Prediction Study Risk of Bias Assessment Tool (PROBAST) was used to assess risk of bias. Meta-analysis was not feasible due to clinical and methodological differences between studies, but c-statistics were presented in forest plots. Thirty-three studies developing or validating 13 models were included; eight studies compared two or more models. Common model variables were left atrial parameters, type of AF, and age. Model discriminatory ability was highly variable and no model had consistently poor or good performance. Most studies did not assess model calibration. The main risk of bias concern was the lack of internal validation which may have resulted in overly optimistic and/or biased model performance estimates. No model impact studies were identified. Conclusion Our systematic review suggests that clinical risk prediction of AF after ablation has potential, but there remains a need for robust evaluation of risk factors and development of risk scores.
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Burlacu, Alexandru, Adrian Iftene, Iolanda Valentina Popa, Radu Crisan-Dabija, Crischentian Brinza, and Adrian Covic. "Computational Models Used to Predict Cardiovascular Complications in Chronic Kidney Disease Patients: A Systematic Review." Medicina 57, no. 6 (May 27, 2021): 538. http://dx.doi.org/10.3390/medicina57060538.
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Background and objectives: cardiovascular complications (CVC) are the leading cause of death in patients with chronic kidney disease (CKD). Standard cardiovascular disease risk prediction models used in the general population are not validated in patients with CKD. We aim to systematically review the up-to-date literature on reported outcomes of computational methods such as artificial intelligence (AI) or regression-based models to predict CVC in CKD patients. Materials and methods: the electronic databases of MEDLINE/PubMed, EMBASE, and ScienceDirect were systematically searched. The risk of bias and reporting quality for each study were assessed against transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) and the prediction model risk of bias assessment tool (PROBAST). Results: sixteen papers were included in the present systematic review: 15 non-randomized studies and 1 ongoing clinical trial. Twelve studies were found to perform AI or regression-based predictions of CVC in CKD, either through single or composite endpoints. Four studies have come up with computational solutions for other CV-related predictions in the CKD population. Conclusions: the identified studies represent palpable trends in areas of clinical promise with an encouraging present-day performance. However, there is a clear need for more extensive application of rigorous methodologies. Following the future prospective, randomized clinical trials, and thorough external validations, computational solutions will fill the gap in cardiovascular predictive tools for chronic kidney disease.
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Harrison, Hannah, Juliet A. Usher-Smith, Lanxin Li, Lydia Roberts, Zhiyuan Lin, Rachel E. Thompson, Sabrina H. Rossi, et al. "Risk prediction models for symptomatic patients with bladder and kidney cancer: a systematic review." British Journal of General Practice 72, no. 714 (September 24, 2021): e11-e18. http://dx.doi.org/10.3399/bjgp.2021.0319.
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BackgroundTimely diagnosis of bladder and kidney cancer is key to improving clinical outcomes. Given the challenges of early diagnosis, models incorporating clinical symptoms and signs may be helpful to primary care clinicians when triaging at-risk patients.AimTo identify and compare published models that use clinical signs and symptoms to predict the risk of undiagnosed prevalent bladder or kidney cancer.Design and settingSystematic review.MethodA search identified primary research reporting or validating models predicting the risk of bladder or kidney cancer in MEDLINE and EMBASE. After screening identified studies for inclusion, data were extracted onto a standardised form. The risk models were classified using TRIPOD guidelines and evaluated using the PROBAST assessment tool.ResultsThe search identified 20 661 articles. Twenty studies (29 models) were identified through screening. All the models included haematuria (visible, non-visible, or unspecified), and seven included additional signs and symptoms (such as abdominal pain). The models combined clinical features with other factors (including demographic factors and urinary biomarkers) to predict the risk of undiagnosed prevalent cancer. Several models (n = 13) with good discrimination (area under the receiver operating curve >0.8) were identified; however, only eight had been externally validated. All of the studies had either high or unclear risk of bias.ConclusionModels were identified that could be used in primary care to guide referrals, with potential to identify lower-risk patients with visible haematuria and to stratify individuals who present with non-visible haematuria. However, before application in general practice, external validations in appropriate populations are required.
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Drozdowska, Bogna A., Kris McGill, Michael McKay, Roisin Bartlam, Peter Langhorne, and Terence J. Quinn. "Prognostic rules for predicting cognitive syndromes following stroke: A systematic review." European Stroke Journal 6, no. 1 (February 23, 2021): 18–27. http://dx.doi.org/10.1177/2396987321997045.
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Purpose Stroke survivors are at high risk of developing cognitive syndromes, such as delirium and dementia. Accurate prediction of future cognitive outcomes may aid timely diagnosis, intervention planning, and stratification in clinical trials. We aimed to identify, describe and appraise existing multivariable prognostic rules for prediction of post-stroke cognitive status. Method We systematically searched four electronic databases from inception to November 2019 for publications describing a method to estimate individual probability of developing a cognitive syndrome following stroke. We extracted data from selected studies using a pre-specified proforma and applied the Prediction model Risk Of Bias Assessment Tool (PROBAST) for critical appraisal. Findings Of 17,390 titles, we included 10 studies (3143 participants), presenting the development of 11 prognostic rules – 7 for post-stroke cognitive impairment and 4 for delirium. Most commonly incorporated predictors were: demographics, imaging findings, stroke type and symptom severity. Among studies assessing predictive discrimination, the area under the receiver operating characteristic (AUROC) in apparent validation ranged from 0.80 to 0.91. The overall risk of bias for each study was high. Only one prognostic rule had been externally validated. Discussion/conclusion: Research into the prognosis of cognitive outcomes following stroke is an expanding field, still at its early stages. Recommending use of specific prognostic rules is limited by the high risk of bias in all identified studies, and lack of supporting evidence from external validation. To ensure the quality of future research, investigators should adhere to current, endorsed best practice guidelines for conduct of prediction model studies.
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Pandor, Abdullah, Jahnavi Daru, Beverley J. Hunt, Gill Rooney, Jean Hamilton, Mark Clowes, Steve Goodacre, Catherine Nelson-Piercy, and Sarah Davis. "Risk assessment models for venous thromboembolism in pregnancy and in the puerperium: a systematic review." BMJ Open 12, no. 10 (October 2022): e065892. http://dx.doi.org/10.1136/bmjopen-2022-065892.
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ObjectivesTo assess the comparative accuracy of risk assessment models (RAMs) to identify women during pregnancy and the early postnatal period who are at increased risk of venous thromboembolism (VTE).DesignSystematic review following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines.Data sourcesMEDLINE, Embase, Cochrane Library and two research registers were searched until February 2021.Eligibility criteriaAll validation studies that examined the accuracy of a multivariable RAM (or scoring system) for predicting the risk of developing VTE in women who are pregnant or in the puerperium (within 6 weeks post-delivery).Data extraction and synthesisTwo authors independently selected and extracted data. Risk of bias was appraised using PROBAST (Prediction model Risk Of Bias ASsessment Tool). Data were synthesised without meta-analysis.ResultsSeventeen studies, comprising 19 externally validated RAMs and 1 internally validated model, met the inclusion criteria. The most widely evaluated RAMs were the Royal College of Obstetricians and Gynaecologists guidelines (six studies), American College of Obstetricians and Gynecologists guidelines (two studies), Swedish Society of Obstetrics and Gynecology guidelines (two studies) and the Lyon score (two studies). In general, estimates of sensitivity and specificity were highly variable with sensitivity estimates ranging from 0% to 100% for RAMs that were applied to antepartum women to predict antepartum or postpartum VTE and 0% to 100% for RAMs applied postpartum to predict postpartum VTE. Specificity estimates were similarly diverse ranging from 28% to 98% and 5% to 100%, respectively.ConclusionsAvailable data suggest that external validation studies have weak designs and limited generalisability, so estimates of prognostic accuracy are very uncertain.PROSPERO registration numberCRD42020221094.
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Beyer, Katharina, Lisa Moris, Michael Lardas, Anna Haire, Francesco Barletta, Simone Scuderi, Megan Molnar, et al. "Diagnostic and prognostic factors in patients with prostate cancer: a systematic review." BMJ Open 12, no. 4 (April 2022): e058267. http://dx.doi.org/10.1136/bmjopen-2021-058267.
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ObjectivesAs part of the PIONEER Consortium objectives, we have explored which diagnostic and prognostic factors (DPFs) are available in relation to our previously defined clinician and patient-reported outcomes for prostate cancer (PCa).DesignWe performed a systematic review to identify validated and non-validated studies.Data sourcesMEDLINE, Embase and the Cochrane Library were searched on 21 January 2020.Eligibility criteriaOnly quantitative studies were included. Single studies with fewer than 50 participants, published before 2014 and looking at outcomes which are not prioritised in the PIONEER core outcome set were excluded.Data extraction and synthesisAfter initial screening, we extracted data following the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of prognostic factor studies (CHARMS-PF) criteria and discussed the identified factors with a multidisciplinary expert group. The quality of the included papers was scored for applicability and risk of bias using validated tools such as PROBAST, Quality in Prognostic Studies and Quality Assessment of Diagnostic Accuracy Studies 2.ResultsThe search identified 6604 studies, from which 489 DPFs were included. Sixty-four of those were internally or externally validated. However, only three studies on diagnostic and seven studies on prognostic factors had a low risk of bias and a low risk concerning applicability.ConclusionMost of the DPFs identified require additional evaluation and validation in properly designed studies before they can be recommended for use in clinical practice. The PIONEER online search tool for DPFs for PCa will enable researchers to understand the quality of the current research and help them design future studies.Ethics and disseminationThere are no ethical implications.
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Dao Phuoc, Thang, Long Khuong Quynh, Linh Vien Dang Khanh, Thinh Ong Phuc, Hieu Le Sy, Tu Le Ngoc, and Lam Phung Khanh. "Clinical prognostic models for severe dengue: a systematic review protocol." Wellcome Open Research 4 (January 24, 2019): 12. http://dx.doi.org/10.12688/wellcomeopenres.15033.1.
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Background: Dengue is a common mosquito-borne, with high morbidity rates recorded in the annually. Dengue contributes a major disease burden in many tropical countries. This demonstrates the urgent need in developing effective approaches to identify severe cases early. For this purpose, many multivariable prognostic models using multiple prognostic variables were developed to predict the risk of progression to severe outcomes. The aim of the planned systematic review is to identify and describe the existing clinical multivariable prognostic models for severe dengue as well as examine the possibility of combining them. These findings will suggest directions for further research of this field. Methods: This protocol has followed the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta – Analyses Protocol (PRISMA-P). We will conduct a comprehensive search of Pubmed, Embase and Web of Science. Eligiblity criteria include being published in peer-review journals, focusing on human subjects and developing the multivariable prognostic model for severe dengue, without any restriction on language, location and period of publication, and study design. The reference list will be captured and removed from duplications. We will use the Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS) checklist to extract data and Prediction study risk of bias assessment tool (PROBAST) to assess the study quality. Discussion: This systematic review will describe the existing prediction models, summarize the current status of prognostic research on dengue, and report the possibility to combine the models to optimize the power of each paradigm. PROSPERO registration: CRD42018102907
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Dao Phuoc, Thang, Long Khuong Quynh, Linh Vien Dang Khanh, Thinh Ong Phuc, Hieu Le Sy, Tu Le Ngoc, and Lam Phung Khanh. "Clinical prognostic models for severe dengue: a systematic review protocol." Wellcome Open Research 4 (August 2, 2019): 12. http://dx.doi.org/10.12688/wellcomeopenres.15033.2.
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Background: Dengue is a common mosquito-borne, with high morbidity rates recorded in the annual. Dengue contributes to a major disease burden in many tropical countries. This demonstrates the urgent need in developing effective approaches to identify severe cases early. For this purpose, many multivariable prognostic models using multiple prognostic variables were developed to predict the risk of progression to severe outcomes. The aim of the planned systematic review is to identify and describe the existing clinical multivariable prognostic models for severe dengue as well as examine the possibility of combining them. These findings will suggest directions for further research of this field. Methods: This protocol has followed the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta – Analyses Protocol (PRISMA-P). We will conduct a comprehensive search of Pubmed, Embase, and Web of Science. Eligibility criteria include being published in peer-review journals, focusing on human subjects and developing the multivariable prognostic model for severe dengue, without any restriction on language, location and period of publication, and study design. The reference list will be captured and removed from duplications. We will use the Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS) checklist to extract data and Prediction study risk of bias assessment tool (PROBAST) to assess the study quality. Discussion: This systematic review will describe the existing prediction models, summarize the current status of prognostic research on dengue, and report the possibility to combine the models to optimize the power of each paradigm. PROSPERO registration: CRD42018102907
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Carrillo-Larco, Rodrigo M., Diego J. Aparcana-Granda, Jhonatan R. Mejia, and Antonio Bernabé-Ortiz. "FINDRISC in Latin America: a systematic review of diagnosis and prognosis models." BMJ Open Diabetes Research & Care 8, no. 1 (April 2020): e001169. http://dx.doi.org/10.1136/bmjdrc-2019-001169.
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This review aimed to assess whether the FINDRISC, a risk score for type 2 diabetes mellitus (T2DM), has been externally validated in Latin America and the Caribbean (LAC). We conducted a systematic review following the CHARMS (CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies) framework. Reports were included if they validated or re-estimated the FINDRISC in population-based samples, health facilities or administrative data. Reports were excluded if they only studied patients or at-risk individuals. The search was conducted in Medline, Embase, Global Health, Scopus and LILACS. Risk of bias was assessed with the PROBAST (Prediction model Risk of Bias ASsessment Tool) tool. From 1582 titles and abstracts, 4 (n=7502) reports were included for qualitative summary. All reports were from South America; there were slightly more women, and the mean age ranged from 29.5 to 49.7 years. Undiagnosed T2DM prevalence ranged from 2.6% to 5.1%. None of the studies conducted an independent external validation of the FINDRISC; conversely, they used the same (or very similar) predictors to fit a new model. None of the studies reported calibration metrics. The area under the receiver operating curve was consistently above 65.0%. All studies had high risk of bias. There has not been any external validation of the FINDRISC model in LAC. Selected reports re-estimated the FINDRISC, although they have several methodological limitations. There is a need for big data to develop—or improve—T2DM diagnostic and prognostic models in LAC. This could benefit T2DM screening and early diagnosis.
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Heestermans, Tessa, Beth Payne, Gbenga Ayodele Kayode, Mary Amoakoh-Coleman, Ewoud Schuit, Marcus J. Rijken, Kerstin Klipstein-Grobusch, Kitty Bloemenkamp, Diederick E. Grobbee, and Joyce L. Browne. "Prognostic models for adverse pregnancy outcomes in low-income and middle-income countries: a systematic review." BMJ Global Health 4, no. 5 (October 2019): e001759. http://dx.doi.org/10.1136/bmjgh-2019-001759.
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IntroductionNinety-nine per cent of all maternal and neonatal deaths occur in low-income and middle-income countries (LMIC). Prognostic models can provide standardised risk assessment to guide clinical management and can be vital to reduce and prevent maternal and perinatal mortality and morbidity. This review provides a comprehensive summary of prognostic models for adverse maternal and perinatal outcomes developed and/or validated in LMIC.MethodsA systematic search in four databases (PubMed/Medline, EMBASE, Global Health Library and The Cochrane Library) was conducted from inception (1970) up to 2 May 2018. Risk of bias was assessed with the PROBAST tool and narratively summarised.Results1741 articles were screened and 21 prognostic models identified. Seventeen models focused on maternal outcomes and four on perinatal outcomes, of which hypertensive disorders of pregnancy (n=9) and perinatal death including stillbirth (n=4) was most reported. Only one model was externally validated. Thirty different predictors were used to develop the models. Risk of bias varied across studies, with the item ‘quality of analysis’ performing the least.ConclusionPrognostic models can be easy to use, informative and low cost with great potential to improve maternal and neonatal health in LMIC settings. However, the number of prognostic models developed or validated in LMIC settings is low and mirrors the 10/90 gap in which only 10% of resources are dedicated to 90% of the global disease burden. External validation of existing models developed in both LMIC and high-income countries instead of developing new models should be encouraged.PROSPERO registration numberCRD42017058044.
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Chowdhury, Mohammad Ziaul Islam, Iffat Naeem, Hude Quan, Alexander A. Leung, Khokan C. Sikdar, Maeve O’Beirne, and Tanvir C. Turin. "Prediction of hypertension using traditional regression and machine learning models: A systematic review and meta-analysis." PLOS ONE 17, no. 4 (April 7, 2022): e0266334. http://dx.doi.org/10.1371/journal.pone.0266334.
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Objective We aimed to identify existing hypertension risk prediction models developed using traditional regression-based or machine learning approaches and compare their predictive performance. Methods We systematically searched MEDLINE, EMBASE, Web of Science, Scopus, and the grey literature for studies predicting the risk of hypertension among the general adult population. Summary statistics from the individual studies were the C-statistic, and a random-effects meta-analysis was used to obtain pooled estimates. The predictive performance of pooled estimates was compared between traditional regression-based models and machine learning-based models. The potential sources of heterogeneity were assessed using meta-regression, and study quality was assessed using the PROBAST (Prediction model Risk Of Bias ASsessment Tool) checklist. Results Of 14,778 articles, 52 articles were selected for systematic review and 32 for meta-analysis. The overall pooled C-statistics was 0.75 [0.73–0.77] for the traditional regression-based models and 0.76 [0.72–0.79] for the machine learning-based models. High heterogeneity in C-statistic was observed. The age (p = 0.011), and sex (p = 0.044) of the participants and the number of risk factors considered in the model (p = 0.001) were identified as a source of heterogeneity in traditional regression-based models. Conclusion We attempted to provide a comprehensive evaluation of hypertension risk prediction models. Many models with acceptable-to-good predictive performance were identified. Only a few models were externally validated, and the risk of bias and applicability was a concern in many studies. Overall discrimination was similar between models derived from traditional regression analysis and machine learning methods. More external validation and impact studies to implement the hypertension risk prediction model in clinical practice are required.
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Chifor, Radu, Tudor Arsenescu, Laura Monica Dascalu (Rusu), and Alexandru Florin Badea. "Automated diagnosis using artificial intelligence a step forward for preventive dentistry: A systematic review." Romanian Journal of Stomatology 68, no. 3 (September 30, 2022): 106–15. http://dx.doi.org/10.37897/rjs.2022.3.7.
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Background. Early diagnosis and monitoring the evolution of the patients is required to be able to have effective preventive attitudes. An easy and cost-effective way of diagnosis is needed for this purpose. The aim of the study was to evaluate the AI level of use in dentistry diagnosis and the fields of its applicability especially for early diagnosis purposes. A secondary objective was to point out the measured performances for automated AI diagnosis by comparison with standard diagnosis procedures. Material and methods. A comprehensive electronic search was performed in November 2022 through PubMed, Scopus, and Web of Science databases. The following keywords were used to search the databases: (”Artificial Intelligence” OR ”neural network” OR ”Deep learning” OR “Machine learning”) AND (”Dentistry” OR “Dental medicine”) AND (” periodontal disease” OR ”periodontics” OR ”Carious lesions” OR ”oral cancer” OR ”restorative” or “early diagnosis”). The risk of bias (RoB) of the included studies was assessed using PROBAST tool. Results. A total of 334 publications were collected after searching the databases. For 218 remaining publications the title and the abstract were assessed. The reviewers agreed to continue with 69 studies for full text assessment. Because 49 studies had not completely fulfilled the inclusion criteria only 20 publications were included in the final analysis. AI automatic data processing for diagnostic purposes was implemented in the field of dental radiology, oral pathology, restorative dentistry, pedodontics, oncology, endodontics, and periodontics. Conclusion. AI based automatic diagnostic is a powerful and reliable tool that has a great future potential for different fields of dental medicine like periodontal disease, oral cancer, and carious lesions.
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Schramm, Katharina. "Probst, Peter." Journal of Religion in Africa 44, no. 1 (February 25, 2014): 127–28. http://dx.doi.org/10.1163/15700666-12341275.
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Oliveira Ascef, Bruna, Gustavo Laine Araújo de Oliveira, Carmelita Ribeiro Filha Coriolano, and Haliton Alves De Oliveira Junior. "Forecasting models for leprosy cases: a scoping review protocol." BMJ Open 12, no. 7 (July 2022): e062828. http://dx.doi.org/10.1136/bmjopen-2022-062828.
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IntroductionLeprosy is a neglected tropical disease caused by Mycobacterium leprae that mainly affects the skin, the peripheral nerves, the mucosa of the upper respiratory tract and the eyes. Mathematical models and statistical methodologies could play an important role in decision-making and help maintain the gains in elimination programmes. Various models for predicting leprosy cases have been reported in the literature, but they have different settings and distinct approaches to predicting the cases. This study describes the protocol for a scoping review to identify and synthesise information from studies using models to forecast leprosy cases.Methods and analysisA scoping review methodology will be applied following the Joanna Briggs Institute methodology for scoping reviews and will be reported according to Preferred Reporting Items for Systematic Reviews and Meta-analysis Extension for Scoping Reviews. We will perform a systematic search from when each database started until April 2022 and we will include the following electronic databases: MEDLINE via PubMed, Embase, Cochrane Library and Latin American and Caribbean Health Science Literature Database. Data will be extracted and recorded on a calibrated predefined data form and will be presented in a tabular form accompanied by a descriptive summary. The Prediction Model Study Risk of Bias Assessment Tool (PROBAST) will be used.Ethics and disseminationNo ethical approval is required for this study. This scoping review will identify and map the methodological and other characteristics of modelling studies predicting leprosy cases. We hope that the review will contribute to scientific knowledge in this area and act as a basis for researchers designing and conducting leprosy models. This information can also be used to enhance national surveillance systems and to target specific policies. The protocol and consequent publications of this scoping review will be disseminated through peer-reviewed publications and policy briefs.Systematic review registrationThis scoping review was registered in the Open Science Framework (https://doi.org/10.17605/OSF.IO/W9375).
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Klunder, Jet H., Sofie L. Panneman, Emma Wallace, Ralph de Vries, Karlijn J. Joling, Otto R. Maarsingh, and Hein P. J. van Hout. "Prediction models for the prediction of unplanned hospital admissions in community-dwelling older adults: A systematic review." PLOS ONE 17, no. 9 (September 23, 2022): e0275116. http://dx.doi.org/10.1371/journal.pone.0275116.
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Background Identification of community-dwelling older adults at risk of unplanned hospitalizations is of importance to facilitate preventive interventions. Our objective was to review and appraise the methodological quality and predictive performance of prediction models for predicting unplanned hospitalizations in community-dwelling older adults Methods and findings We searched MEDLINE, EMBASE and CINAHL from August 2013 to January 2021. Additionally, we checked references of the identified articles for the inclusion of relevant publications and added studies from two previous reviews that fulfilled the eligibility criteria. We included prospective and retrospective studies with any follow-up period that recruited adults aged 65 and over and developed a prediction model predicting unplanned hospitalizations. We included models with at least one (internal or external) validation cohort. The models had to be intended to be used in a primary care setting. Two authors independently assessed studies for inclusion and undertook data extraction following recommendations of the CHARMS checklist, while quality assessment was performed using the PROBAST tool. A total of 19 studies met the inclusion criteria. Prediction horizon ranged from 4.5 months to 4 years. Most frequently included variables were specific medical diagnoses (n = 11), previous hospital admission (n = 11), age (n = 11), and sex or gender (n = 8). Predictive performance in terms of area under the curve ranged from 0.61 to 0.78. Models developed to predict potentially preventable hospitalizations tended to have better predictive performance than models predicting hospitalizations in general. Overall, risk of bias was high, predominantly in the analysis domain. Conclusions Models developed to predict preventable hospitalizations tended to have better predictive performance than models to predict all-cause hospitalizations. There is however substantial room for improvement on the reporting and analysis of studies. We recommend better adherence to the TRIPOD guidelines.
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Braun, Cordula, Nigel C. Hanchard, Alan M. Batterham, Helen H. Handoll, and Andreas Betthäuser. "Prognostic Models in Adults Undergoing Physical Therapy for Rotator Cuff Disorders: Systematic Review." Physical Therapy 96, no. 7 (July 1, 2016): 961–71. http://dx.doi.org/10.2522/ptj.20150475.
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Abstract Background Rotator cuff–related disorders represent the largest subgroup of shoulder complaints. Despite the availability of various conservative and surgical treatment options, the precise indications for these options remain unclear. Purpose The purpose of this systematic review was to synthesize the available research on prognostic models for predicting outcomes in adults undergoing physical therapy for painful rotator cuff disorders. Data Sources The MEDLINE, EMBASE, CINAHL, Cochrane CENTRAL, and PEDro databases and the World Health Organization (WHO) International Clinical Trials Registry Platform (ICTRP) up to October 2015 were searched. Study Selection The review included primary studies exploring prognostic models in adults undergoing physical therapy, with or without other conservative measures, for painful rotator cuff disorders. Primary outcomes were pain, disability, and adverse events. Inclusion was limited to prospective investigations of prognostic factors elicited at the baseline assessment. Study selection was independently performed by 2 reviewers. Data Extraction A pilot-tested form was used to extract data on key aspects of study design, characteristics, analyses, and results. Risk of bias and applicability were independently assessed by 2 reviewers using the Prediction Study Risk of Bias Assessment tool (PROBAST). Data Synthesis Five studies were included in the review. These studies were extremely heterogeneous in many aspects of design, conduct, and analysis. The findings were analyzed narratively. Limitations All included studies were rated as at high risk of bias, and none of the resulting prognostic models was found to be usable in clinical practice. Conclusions There are no prognostic models ready to inform clinical practice in the context of the review question, highlighting the need for further research on prognostic models for predicting outcomes in adults who undergo physical therapy for painful rotator cuff disorders. The design and conduct of future studies should be receptive to developing methods.
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Akyea, Ralph K., Jo Leonardi-Bee, Folkert W. Asselbergs, Riyaz S. Patel, Paul Durrington, Anthony S. Wierzbicki, Oluwaseun H. Ibiwoye, Joe Kai, Nadeem Qureshi, and Stephen F. Weng. "Predicting major adverse cardiovascular events for secondary prevention: protocol for a systematic review and meta-analysis of risk prediction models." BMJ Open 10, no. 7 (July 2020): e034564. http://dx.doi.org/10.1136/bmjopen-2019-034564.
Повний текст джерелаАнотація:
IntroductionCardiovascular disease (CVD) is the leading cause of morbidity and mortality globally. With advances in early diagnosis and treatment of CVD and increasing life expectancy, more people are surviving initial CVD events. However, models for stratifying disease severity risk in patients with established CVD for effective secondary prevention strategies are inadequate. Multivariable prognostic models to stratify CVD risk may allow personalised treatment interventions. This review aims to systematically review the existing multivariable prognostic models for the recurrence of CVD or major adverse cardiovascular events in adults with established CVD diagnosis.Methods and analysisBibliographic databases (Ovid MEDLINE, EMBASE, PsycINFO and Web of Science) will be searched, from database inception to April 2020, using terms relating to the clinical area and prognosis. A hand search of the reference lists of included studies will also be done to identify additional published studies. No restrictions on language of publications will be applied. Eligible studies present multivariable models (derived or validated) of adults (aged 16 years and over) with an established diagnosis of CVD, reporting at least one of the components of the primary outcome of major adverse cardiovascular events (defined as either coronary heart disease, stroke, peripheral artery disease, heart failure or CVD-related mortality). Reviewing will be done by two reviewers independently using the pre-defined criteria. Data will be extracted for included full-text articles. Risk of bias will be assessed using the Prediction model study Risk Of Bias ASsessment Tool (PROBAST). Prognostic models will be summarised narratively. If a model is tested in multiple validation studies, the predictive performance will be summarised using a random-effects meta-analysis model to account for any between-study heterogeneity.Ethics and disseminationEthics approval is not required. The results of this study will be submitted to relevant conferences for presentation and a peer-reviewed journal for publication.PROSPERO registration numberCRD42019149111.
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Aladwani, Mohammad, Artitaya Lophatananon, William Ollier, and Kenneth Muir. "Prediction models for prostate cancer to be used in the primary care setting: a systematic review." BMJ Open 10, no. 7 (July 2020): e034661. http://dx.doi.org/10.1136/bmjopen-2019-034661.
Повний текст джерелаАнотація:
ObjectiveTo identify risk prediction models for prostate cancer (PCa) that can be used in the primary care and community health settings.DesignSystematic review.Data sourcesMEDLINE and Embase databases combined from inception and up to the end of January 2019.EligibilityStudies were included based on satisfying all the following criteria: (i) presenting an evaluation of PCa risk at initial biopsy in patients with no history of PCa, (ii) studies not incorporating an invasive clinical assessment or expensive biomarker/genetic tests, (iii) inclusion of at least two variables with prostate-specific antigen (PSA) being one of them, and (iv) studies reporting a measure of predictive performance. The quality of the studies and risk of bias was assessed by using the Prediction model Risk Of Bias ASsessment Tool (PROBAST).Data extraction and synthesisRelevant information extracted for each model included: the year of publication, source of data, type of model, number of patients, country, age, PSA range, mean/median PSA, other variables included in the model, number of biopsy cores to assess outcomes, study endpoint(s), cancer detection, model validation and model performance.ResultsAn initial search yielded 109 potential studies, of which five met the set criteria. Four studies were cohort-based and one was a case-control study. PCa detection rate was between 20.6% and 55.8%. Area under the curve (AUC) was reported in four studies and ranged from 0.65 to 0.75. All models showed significant improvement in predicting PCa compared with being based on PSA alone. The difference in AUC between extended models and PSA alone was between 0.06 and 0.21.ConclusionOnly a few PCa risk prediction models have the potential to be readily used in the primary healthcare or community health setting. Further studies are needed to investigate other potential variables that could be integrated into models to improve their clinical utility for PCa testing in a community setting.
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Roqué, Marta, Laura Martínez-García, Ivan Solà, Pablo Alonso-Coello, Xavier Bonfill, and Javier Zamora. "Toolkit of methodological resources to conduct systematic reviews." F1000Research 9 (February 4, 2020): 82. http://dx.doi.org/10.12688/f1000research.22032.1.
Повний текст джерелаАнотація:
Background: Systematic reviews (SR) can be classified by type depending on the research question they are based on. This work identifies and describes the most relevant methodological resources to conduct high-quality reviews that answer clinical questions regarding prevalence, prognosis, diagnostic accuracy and efficacy of interventions. Methods: Methodological resources have been identified from literature searches and consulting guidelines from institutions that develop SRs. The selected resources are organized by type of SR, and stage of development of the review (formulation of the research question, development of the protocol, literature search, risk of bias assessment, synthesis of findings, assessment of the quality of evidence, and report of SR results and conclusions). Results: Although the different types of SRs are developed following the same steps, each SR type requires specific methods, differing in characteristics and complexity. The extent of methodological development varies by type of SR, with more solid guidelines available for diagnostic accuracy and efficacy of interventions SRs. This methodological toolkit describes the most up-to-date risk of bias instruments: Quality in Prognostic Studies (QUIPS) tool and Prediction model study Risk Of Bias Assessment Tool (PROBAST) for prognostic SRs, Quality assessment of diagnostic accuracy studies tool (QUADAS-2) for diagnostic accuracy SRs, Cochrane risk of bias tool (ROB-2) and Risk of bias in non-randomised studies of interventions studies tool (ROBINS-I) for efficacy of interventions SRs, as well as the latest developments on the Grading of Recommendations Assessment, Development and Evaluation (GRADE) system. Conclusions: This structured compilation of the best methodological resources for each type of SR may prove to be a very useful tool for those researchers that wish to develop SRs or conduct methodological research works on SRs.
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Roqué, Marta, Laura Martínez-García, Ivan Solà, Pablo Alonso-Coello, Xavier Bonfill, and Javier Zamora. "Toolkit of methodological resources to conduct systematic reviews." F1000Research 9 (August 11, 2020): 82. http://dx.doi.org/10.12688/f1000research.22032.2.
Повний текст джерелаАнотація:
Background: Systematic reviews (SR) can be classified by type depending on the research question they are based on. This work identifies and describes the most relevant methodological resources to conduct high-quality reviews that answer health care questions regarding prevalence, prognosis, diagnostic accuracy and effects of interventions. Methods: Methodological resources have been identified from literature searches and consulting guidelines from institutions that develop SRs. The selected resources are organized by type of SR, and stage of development of the review (formulation of the research question, development of the protocol, literature search, risk of bias assessment, synthesis of findings, assessment of the quality of evidence, and report of SR results and conclusions). Results: Although the different types of SRs are developed following the same steps, each SR type requires specific methods, differing in characteristics and complexity. The extent of methodological development varies by type of SR, with more solid guidelines available for diagnostic accuracy and effects of interventions SRs. This methodological toolkit describes the most up-to-date risk of bias instruments: Quality in Prognostic Studies (QUIPS) tool and Prediction model study Risk Of Bias Assessment Tool (PROBAST) for prognostic SRs, Quality assessment of diagnostic accuracy studies tool (QUADAS-2) for diagnostic accuracy SRs, Cochrane risk of bias tool (ROB-2) and Risk of bias in non-randomised studies of interventions studies tool (ROBINS-I) for effects of interventions SRs, as well as the latest developments on the Grading of Recommendations Assessment, Development and Evaluation (GRADE) system. Conclusions: This structured compilation of the best methodological resources for each type of SR may prove to be a very useful tool for those researchers that wish to develop SRs or conduct methodological research works on SRs
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Roqué, Marta, Laura Martínez-García, Ivan Solà, Pablo Alonso-Coello, Xavier Bonfill, and Javier Zamora. "Toolkit of methodological resources to conduct systematic reviews." F1000Research 9 (October 14, 2020): 82. http://dx.doi.org/10.12688/f1000research.22032.3.
Повний текст джерелаАнотація:
Background: Systematic reviews (SR) can be classified by type depending on the research question they are based on. This work identifies and describes the most relevant methodological resources to conduct high-quality reviews that answer health care questions regarding prevalence, prognosis, diagnostic accuracy and effects of interventions. Methods: Methodological resources have been identified from literature searches and consulting guidelines from institutions that develop SRs. The selected resources are organized by type of SR, and stage of development of the review (formulation of the research question, development of the protocol, literature search, risk of bias assessment, synthesis of findings, assessment of the quality of evidence, and report of SR results and conclusions). Results: Although the different types of SRs are developed following the same steps, each SR type requires specific methods, differing in characteristics and complexity. The extent of methodological development varies by type of SR, with more solid guidelines available for diagnostic accuracy and effects of interventions SRs. This methodological toolkit describes the most up-to-date risk of bias instruments: Quality in Prognostic Studies (QUIPS) tool and Prediction model study Risk Of Bias Assessment Tool (PROBAST) for prognostic SRs, Quality assessment of diagnostic accuracy studies tool (QUADAS-2) for diagnostic accuracy SRs, Cochrane risk of bias tool (ROB-2) and Risk of bias in non-randomised studies of interventions studies tool (ROBINS-I) for effects of interventions SRs, as well as the latest developments on the Grading of Recommendations Assessment, Development and Evaluation (GRADE) system. Conclusions: This structured compilation of the best methodological resources for each type of SR may prove to be a very useful tool for those researchers that wish to develop SRs or conduct methodological research works on SRs
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Harnan, Susan, Katy Cooper, John Stevens, Ruth Wong, Paul Tappenden, Alice Bessey, Sue Ward, Rachid Rafia, Rob Stein, and Janet Brown. "OP66 Tumor Profiling Tests In Early Breast Cancer: A Systematic Review." International Journal of Technology Assessment in Health Care 34, S1 (2018): 24. http://dx.doi.org/10.1017/s0266462318001095.
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Introduction:Tumor profiling tests can help to identify whether women with breast cancer need chemotherapy due to their risk of relapse, and some may be able to predict benefit from chemotherapy. We focused on four genetic tests: Oncotype DX (O-DX), MammaPrint (MMP), EndoPredict and Prosigna, and one immunohistochemistry test, IHC4, for the National Institute of Health and Care Excellence as part of their Diagnostic Appraisal Programme.Methods:A systematic review was undertaken, including searching of nine databases in February 2017 plus other sources including a previous review published in 2013. The review included studies assessing clinical effectiveness of the five tumor profiling tests, with or without clinicopathological factors, to guide decisions about adjuvant chemotherapy in people with ER-positive, HER-2 negative, Stage I-II cancer with 0 to 3 positive lymph nodes (LN). The PROBAST tool and Cochrane risk of bias tools were used to assess risk of bias.Results:A total of 153 studies were included; the strength of evidence base for individual tests was varied. Results suggest all tests are prognostic for risk of relapse, though results were more varied in LN positive (+) patients than in LN negative (0) patients. Evidence was limited about whether tests can predict benefit from chemotherapy (available for MMP and O-DX only). Studies that assessed the impact of the tests on clinical decisions indicate that the net change in chemotherapy recommendations or decisions pre-/post-test ranged from an increase of one percent to a decrease of 23 percent among UK studies, and a decrease of zero percent to 64 percent across European studies.Conclusions:The studies included in the review suggest that all of the tests can provide prognostic information on the risk of relapse; however results were more varied in LN+ patients than in LN0 patients. There is limited and varying evidence for prediction of chemotherapy benefit.
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Peetluk, Lauren Saag, Felipe Ridolfi, Valeria Rolla, and Timothy Sterling. "4298 Prediction models for pulmonary tuberculosis treatment outcomes: a systematic review." Journal of Clinical and Translational Science 4, s1 (June 2020): 34. http://dx.doi.org/10.1017/cts.2020.138.
Повний текст джерелаАнотація:
OBJECTIVES/GOALS: Many clinical prediction models have been developed to guide tuberculosis (TB) treatment, but their results and methods have not been formally evaluated. We aimed to identify and synthesize existing models for predicting TB treatment outcomes, including bias and applicability assessment. METHODS/STUDY POPULATION: Our review will adhere to methods that developed specifically for systematic reviews of prediction model studies. We will search PubMed, Embase, Web of Science, and Google Scholar (first 200 citations) to identify studies that internally and/or externally validate a model for TB treatment outcomes (defined as one or multiple of cure, treatment completion, death, treatment failure, relapse, default, and lost to follow-up). Study screening, data extraction, and bias assessment will be conducted independently by two reviewers with a third party to resolve discrepancies. Study quality will be assessed using the Prediction model Risk Of Bias Assessment Tool (PROBAST). RESULTS/ANTICIPATED RESULTS: Our search strategy yielded 6,242 articles in PubMed, 10,585 in Embase, 10,511 in Web of Science, and 200 from Google Scholar, totaling 27,538 articles. After de-duplication, 14,029 articles remain. After screening titles, abstracts, and full-text, we will extract data from relevant studies, including publication details, study characteristics, methods, and results. Data will be summarized with narrative review and in detailed tables with descriptive statistics. We anticipate finding disparate outcome definitions, contrasting predictors across models, and high risk of bias in methods. Meta-analysis of performance measures for model validation studies will be performed if possible. DISCUSSION/SIGNIFICANCE OF IMPACT: TB outcome prediction models are important but existing ones have not been rigorously evaluated. This systematic review will synthesize TB outcome prediction models and serve as guidance to future studies that aim to use or develop TB outcome prediction models.
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Boujarzadeh, Banafsheh, Amene Ranjbar, Farzaneh Banihashemi, Vahid Mehrnoush, Fatemeh Darsareh, and Mozhgan Saffari. "Machine learning approach to predict postpartum haemorrhage: a systematic review protocol." BMJ Open 13, no. 1 (January 2023): e067661. http://dx.doi.org/10.1136/bmjopen-2022-067661.
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IntroductionPostpartum haemorrhage (PPH) is the most serious clinical problem of childbirth that contributes significantly to maternal mortality worldwide. This systematic review aims to identify predictors of PPH based on a machine learning (ML) approach.Methods and analysisThis review adhered to the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocol. The review is scheduled to begin on 10 January 2023 and end on 20 March 2023. The main objective is to identify and summarise the predictive factors associated with PPH and propose an ML-based predictive algorithm. From inception to December 2022, a systematic search of the following electronic databases of peer-reviewed journal articles and online search records will be conducted: Cochrane Central Register, PubMed, EMBASE (via Ovid), Scopus, WOS, IEEE Xplore and the Google Scholar search engine. All studies that meet the following criteria will be considered: (1) they include the general population with a clear definition of the diagnosis of PPH; (2) they include ML models for predicting PPH with a clear description of the ML models; and (3) they demonstrate the performance of the ML models with metrics, including area under the receiver operating characteristic curve, accuracy, precision, sensitivity and specificity. Non-English language papers will be excluded. Data extraction will be performed independently by two investigators. The PROBAST, which includes a total of 20 signallings, will be used as a tool to assess the risk of bias and applicability of each included study.Ethics and disseminationEthical approval is not required, as our review will include published and publicly accessible data. Findings from this review will be disseminated via publication in a peer-review journal.PROSPERO registration numberThe protocol for this review was submitted at PROSPERO with ID number CRD42022354896.
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Jiang, Mengyao, Yuxia Ma, Siyi Guo, Liuqi Jin, Lin Lv, Lin Han, and Ning An. "Using Machine Learning Technologies in Pressure Injury Management: Systematic Review." JMIR Medical Informatics 9, no. 3 (March 10, 2021): e25704. http://dx.doi.org/10.2196/25704.
Повний текст джерелаАнотація:
Background Pressure injury (PI) is a common and preventable problem, yet it is a challenge for at least two reasons. First, the nurse shortage is a worldwide phenomenon. Second, the majority of nurses have insufficient PI-related knowledge. Machine learning (ML) technologies can contribute to lessening the burden on medical staff by improving the prognosis and diagnostic accuracy of PI. To the best of our knowledge, there is no existing systematic review that evaluates how the current ML technologies are being used in PI management. Objective The objective of this review was to synthesize and evaluate the literature regarding the use of ML technologies in PI management, and identify their strengths and weaknesses, as well as to identify improvement opportunities for future research and practice. Methods We conducted an extensive search on PubMed, EMBASE, Web of Science, Cumulative Index to Nursing and Allied Health Literature (CINAHL), Cochrane Library, China National Knowledge Infrastructure (CNKI), the Wanfang database, the VIP database, and the China Biomedical Literature Database (CBM) to identify relevant articles. Searches were performed in June 2020. Two independent investigators conducted study selection, data extraction, and quality appraisal. Risk of bias was assessed using the Prediction model Risk Of Bias ASsessment Tool (PROBAST). Results A total of 32 articles met the inclusion criteria. Twelve of those articles (38%) reported using ML technologies to develop predictive models to identify risk factors, 11 (34%) reported using them in posture detection and recognition, and 9 (28%) reported using them in image analysis for tissue classification and measurement of PI wounds. These articles presented various algorithms and measured outcomes. The overall risk of bias was judged as high. Conclusions There is an array of emerging ML technologies being used in PI management, and their results in the laboratory show great promise. Future research should apply these technologies on a large scale with clinical data to further verify and improve their effectiveness, as well as to improve the methodological quality.
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Zhang, Zheqing, Luqian Yang, Wentao Han, Yaoyu Wu, Linhui Zhang, Chun Gao, Kui Jiang, Yun Liu, and Huiqun Wu. "Machine Learning Prediction Models for Gestational Diabetes Mellitus: Meta-analysis." Journal of Medical Internet Research 24, no. 3 (March 16, 2022): e26634. http://dx.doi.org/10.2196/26634.
Повний текст джерелаАнотація:
Background Gestational diabetes mellitus (GDM) is a common endocrine metabolic disease, involving a carbohydrate intolerance of variable severity during pregnancy. The incidence of GDM-related complications and adverse pregnancy outcomes has declined, in part, due to early screening. Machine learning (ML) models are increasingly used to identify risk factors and enable the early prediction of GDM. Objective The aim of this study was to perform a meta-analysis and comparison of published prognostic models for predicting the risk of GDM and identify predictors applicable to the models. Methods Four reliable electronic databases were searched for studies that developed ML prediction models for GDM in the general population instead of among high-risk groups only. The novel Prediction Model Risk of Bias Assessment Tool (PROBAST) was used to assess the risk of bias of the ML models. The Meta-DiSc software program (version 1.4) was used to perform the meta-analysis and determination of heterogeneity. To limit the influence of heterogeneity, we also performed sensitivity analyses, a meta-regression, and subgroup analysis. Results A total of 25 studies that included women older than 18 years without a history of vital disease were analyzed. The pooled area under the receiver operating characteristic curve (AUROC) for ML models predicting GDM was 0.8492; the pooled sensitivity was 0.69 (95% CI 0.68-0.69; P<.001; I2=99.6%) and the pooled specificity was 0.75 (95% CI 0.75-0.75; P<.001; I2=100%). As one of the most commonly employed ML methods, logistic regression achieved an overall pooled AUROC of 0.8151, while non–logistic regression models performed better, with an overall pooled AUROC of 0.8891. Additionally, maternal age, family history of diabetes, BMI, and fasting blood glucose were the four most commonly used features of models established by the various feature selection methods. Conclusions Compared to current screening strategies, ML methods are attractive for predicting GDM. To expand their use, the importance of quality assessments and unified diagnostic criteria should be further emphasized.
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Pandor, Abdullah, Michael Tonkins, Steve Goodacre, Katie Sworn, Mark Clowes, Xavier L. Griffin, Mark Holland, Beverley J. Hunt, Kerstin de Wit, and Daniel Horner. "Risk assessment models for venous thromboembolism in hospitalised adult patients: a systematic review." BMJ Open 11, no. 7 (July 2021): e045672. http://dx.doi.org/10.1136/bmjopen-2020-045672.
Повний текст джерелаАнотація:
IntroductionHospital-acquired thrombosis accounts for a large proportion of all venous thromboembolism (VTE), with significant morbidity and mortality. This subset of VTE can be reduced through accurate risk assessment and tailored pharmacological thromboprophylaxis. This systematic review aimed to determine the comparative accuracy of risk assessment models (RAMs) for predicting VTE in patients admitted to hospital.MethodsA systematic search was performed across five electronic databases (including MEDLINE, EMBASE and the Cochrane Library) from inception to February 2021. All primary validation studies were eligible if they examined the accuracy of a multivariable RAM (or scoring system) for predicting the risk of developing VTE in hospitalised inpatients. Two or more reviewers independently undertook study selection, data extraction and risk of bias assessments using the PROBAST (Prediction model Risk Of Bias ASsessment Tool) tool. We used narrative synthesis to summarise the findings.ResultsAmong 6355 records, we included 51 studies, comprising 24 unique validated RAMs. The majority of studies included hospital inpatients who required medical care (21 studies), were undergoing surgery (15 studies) or receiving care for trauma (4 studies). The most widely evaluated RAMs were the Caprini RAM (22 studies), Padua prediction score (16 studies), IMPROVE models (8 studies), the Geneva risk score (4 studies) and the Kucher score (4 studies). C-statistics varied markedly between studies and between models, with no one RAM performing obviously better than other models. Across all models, C-statistics were often weak (<0.7), sometimes good (0.7–0.8) and a few were excellent (>0.8). Similarly, estimates for sensitivity and specificity were highly variable. Sensitivity estimates ranged from 12.0% to 100% and specificity estimates ranged from 7.2% to 100%.ConclusionAvailable data suggest that RAMs have generally weak predictive accuracy for VTE. There is insufficient evidence and too much heterogeneity to recommend the use of any particular RAM.PROSPERO registration numberSteve Goodacre, Abdullah Pandor, Katie Sworn, Daniel Horner, Mark Clowes. A systematic review of venous thromboembolism RAMs for hospital inpatients. PROSPERO 2020 CRD42020165778. Available from https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=165778https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=165778
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Wu, Yi, Ruxue Li, Yating Zhang, Tianxue Long, Qi Zhang, and Mingzi Li. "Prediction Models for Prognosis of Hypoglycemia in Patients with Diabetes: A Systematic Review and Meta-Analysis." Biological Research For Nursing, July 15, 2022, 109980042211158. http://dx.doi.org/10.1177/10998004221115856.
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Objective To systematically summarize the reported prediction models for hypoglycemia in patients with diabetes, compare their performance, and evaluate their applicability in clinical practice. Methods We selected studies according to the PRISMA, appraised studies according to the Prediction model Risk of Bias Assessment Tool (PROBAST), and extracted and synthesized the data according to the CHARMS. The databases of PubMed, Web of Science, Embase, and Cochrane Library were searched from inception to 31 October 2021 using a systematic review approach to capture all eligible studies developing and/or validating a prognostic prediction model for hypoglycemia in patients with diabetes. The risk bias and clinical applicability were assessed using the PROBAST. The meta-analysis of the performance of the prediction models were also conducted. The protocol of this study was recorded in PROSPERO (CRD42022309852). Results Sixteen studies with 22 models met the eligible criteria. The predictors with the high frequency of occurrence among all models were age, HbA1c, history of hypoglycemia, and insulin use. A meta-analysis of C-statistic was performed for 21 prediction models, and the summary C-statistic and its 95% confidence interval and prediction interval were 0.7699 (0.7299–0.8098), 0.7699 (0.5862–0.9536), respectively. Heterogeneity exists between different hypoglycemia prediction models (τ2 was 0.00734≠0). Conclusions The existing predictive models are not recommended for widespread clinical use. A high-quality hypoglycemia screening tool should be developed in future studies.
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Huang, Hongbiao, Jinfeng Dong, Shuhui Wang, Yueping Shen, Yiming Zheng, Jiaqi Jiang, Bihe Zeng, et al. "Prediction Model Risk-of-Bias Assessment Tool for coronary artery lesions in Kawasaki disease." Frontiers in Cardiovascular Medicine 9 (October 13, 2022). http://dx.doi.org/10.3389/fcvm.2022.1014067.
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ObjectiveTo review and critically appraise articles on prediction models for coronary artery lesions (CALs) in Kawasaki disease included in PubMed, Embase, and Web of Science databases from January 1, 1980, to December 23, 2021.Materials and methodsStudy screening, data extraction, and quality assessment were performed by two independent reviewers, with a statistics expert resolving discrepancies. Articles that developed or validated a prediction model for CALs in Kawasaki disease were included. The Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies checklist was used to extract data from different articles, and Prediction Model Risk-of-Bias Assessment Tool (PROBAST) was used to assess the bias risk in different prediction models. We screened 19 studies from a pool of 881 articles.ResultsThe studies included 73–5,151 patients. In most studies, univariable logistic regression was used to develop prediction models. In two studies, external data were used to validate the developing model. The most commonly included predictors were C-reactive protein (CRP) level, male sex, and fever duration. All studies had a high bias risk, mostly because of small sample size, improper handling of missing data, and inappropriate descriptions of model performance and the evaluation model.ConclusionThe prediction models were suitable for the subjects included in the studies, but were poorly effective in other populations. The phenomenon may partly be due to the bias risk in prediction models. Future models should address these problems and PROBAST should be used to guide study design.
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Li, Ben, Tiam Feridooni, Cesar Cuen-Ojeda, Teruko Kishibe, Charles de Mestral, Muhammad Mamdani, and Mohammed Al-Omran. "Machine learning in vascular surgery: a systematic review and critical appraisal." npj Digital Medicine 5, no. 1 (January 19, 2022). http://dx.doi.org/10.1038/s41746-021-00552-y.
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AbstractMachine learning (ML) is a rapidly advancing field with increasing utility in health care. We conducted a systematic review and critical appraisal of ML applications in vascular surgery. MEDLINE, Embase, and Cochrane CENTRAL were searched from inception to March 1, 2021. Study screening, data extraction, and quality assessment were performed by two independent reviewers, with a third author resolving discrepancies. All original studies reporting ML applications in vascular surgery were included. Publication trends, disease conditions, methodologies, and outcomes were summarized. Critical appraisal was conducted using the PROBAST risk-of-bias and TRIPOD reporting adherence tools. We included 212 studies from a pool of 2235 unique articles. ML techniques were used for diagnosis, prognosis, and image segmentation in carotid stenosis, aortic aneurysm/dissection, peripheral artery disease, diabetic foot ulcer, venous disease, and renal artery stenosis. The number of publications on ML in vascular surgery increased from 1 (1991–1996) to 118 (2016–2021). Most studies were retrospective and single center, with no randomized controlled trials. The median area under the receiver operating characteristic curve (AUROC) was 0.88 (range 0.61–1.00), with 79.5% [62/78] studies reporting AUROC ≥ 0.80. Out of 22 studies comparing ML techniques to existing prediction tools, clinicians, or traditional regression models, 20 performed better and 2 performed similarly. Overall, 94.8% (201/212) studies had high risk-of-bias and adherence to reporting standards was poor with a rate of 41.4%. Despite improvements over time, study quality and reporting remain inadequate. Future studies should consider standardized tools such as PROBAST and TRIPOD to improve study quality and clinical applicability.