Journal articles on the topic 'Clinical risk prediction'
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Dolan, M., and M. Doyle. "Violence risk prediction." British Journal of Psychiatry 177, no. 4 (October 2000): 303–11. http://dx.doi.org/10.1192/bjp.177.4.303.
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BackgroundViolence risk prediction is a priority issue for clinicians working with mentally disordered offenders.AimsTo review the current status of violence risk prediction research.MethodLiterature search (Medline). Key words: violence, risk prediction, mental disorder.ResultsSystematic/structured risk assessment approaches may enhance the accuracy of clinical prediction of violent outcomes. Data on the predictive validity of available clinical risk assessment tools are based largely on American and North American studies and further validation is required in British samples. The Psychopathy Checklist appears to be a key predictor of violent recidivism in a variety of settings.ConclusionsViolence risk prediction is an inexact science and as such will continue to provoke debate. Clinicians clearly need to be able to demonstrate the rationale behind their decisions on violence risk and much can be learned from recent developments in research on violence risk prediction.
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Halabi, Susan, Cai Li, and Sheng Luo. "Developing and Validating Risk Assessment Models of Clinical Outcomes in Modern Oncology." JCO Precision Oncology, no. 3 (December 2019): 1–12. http://dx.doi.org/10.1200/po.19.00068.
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The identification of prognostic factors and building of risk assessment prognostic models will continue to play a major role in 21st century medicine in patient management and decision making. Investigators often are interested in examining the relationship among host, tumor-related, and environmental variables in predicting clinical outcomes. We distinguish between static and dynamic prediction models. In static prediction modeling, variables collected at baseline typically are used in building models. On the other hand, dynamic predictive models leverage the longitudinal data of covariates collected during treatment or follow-up and hence provide accurate predictions of patients’ prognoses. To date, most risk assessment models in oncology have been based on static models. In this article, we cover topics related to the analysis of prognostic factors, centering on factors that are both relevant at the time of diagnosis or initial treatment and during treatment. We describe the types of risk prediction and then provide a brief description of the penalized regression methods. We then review the state-of-the art methods for dynamic prediction and compare the strengths and limitations of these methods. Although static models will continue to play an important role in oncology, developing and validating dynamic models of clinical outcomes need to take a higher priority. A framework for developing and validating dynamic tools in oncology seems to still be needed. One of the limitations in oncology that may constrain modelers is the lack of access to longitudinal biomarker data. It is highly recommended that the next generation of risk assessments consider longitudinal biomarker data and outcomes so that prediction can be continually updated.
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Lawrie, Stephen M. "Clinical risk prediction in schizophrenia." Lancet Psychiatry 1, no. 6 (November 2014): 406–8. http://dx.doi.org/10.1016/s2215-0366(14)70310-4.
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Fonarow, Gregg C., Deborah B. Diercks, and W. Franklin Peacock. "Assessing Clinical Risk Prediction Tools." Annals of Emergency Medicine 50, no. 6 (December 2007): 741–42. http://dx.doi.org/10.1016/j.annemergmed.2007.05.028.
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Nguyen, A. Tuan, Hyewon Jeong, Eunho Yang, and Sung Ju Hwang. "Clinical Risk Prediction with Temporal Probabilistic Asymmetric Multi-Task Learning." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 10 (May 18, 2021): 9081–91. http://dx.doi.org/10.1609/aaai.v35i10.17097.
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Although recent multi-task learning methods have shown to be effective in improving the generalization of deep neural networks, they should be used with caution for safety-critical applications, such as clinical risk prediction. This is because even if they achieve improved task-average performance, they may still yield degraded performance on individual tasks, which may be critical (e.g., prediction of mortality risk). Existing asymmetric multi-task learning methods tackle this negative transfer problem by performing knowledge transfer from tasks with low loss to tasks with high loss. However, using loss as a measure of reliability is risky since low loss could result from overfitting. In the case of time-series prediction tasks, knowledge learned for one task (e.g., predicting the sepsis onset) at a specific timestep may be useful for learning another task (e.g., prediction of mortality) at a later timestep, but lack of loss at each timestep makes it difficult to measure the reliability at each timestep. To capture such dynamically changing asymmetric relationships between tasks in time-series data, we propose a novel temporal asymmetric multi-task learning model that performs knowledge transfer from certain tasks/timesteps to relevant uncertain tasks, based on the feature-level uncertainty. We validate our model on multiple clinical risk prediction tasks against various deep learning models for time-series prediction, which our model significantly outperforms without any sign of negative transfer. Further qualitative analysis of learned knowledge graphs by clinicians shows that they are helpful in analyzing the predictions of the model.
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Lambert, Samuel A., Gad Abraham, and Michael Inouye. "Towards clinical utility of polygenic risk scores." Human Molecular Genetics 28, R2 (July 31, 2019): R133—R142. http://dx.doi.org/10.1093/hmg/ddz187.
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Abstract Prediction of disease risk is an essential part of preventative medicine, often guiding clinical management. Risk prediction typically includes risk factors such as age, sex, family history of disease and lifestyle (e.g. smoking status); however, in recent years, there has been increasing interest to include genomic information into risk models. Polygenic risk scores (PRS) aggregate the effects of many genetic variants across the human genome into a single score and have recently been shown to have predictive value for multiple common diseases. In this review, we summarize the potential use cases for seven common diseases (breast cancer, prostate cancer, coronary artery disease, obesity, type 1 diabetes, type 2 diabetes and Alzheimer’s disease) where PRS has or could have clinical utility. PRS analysis for these diseases frequently revolved around (i) risk prediction performance of a PRS alone and in combination with other non-genetic risk factors, (ii) estimation of lifetime risk trajectories, (iii) the independent information of PRS and family history of disease or monogenic mutations and (iv) estimation of the value of adding a PRS to specific clinical risk prediction scenarios. We summarize open questions regarding PRS usability, ancestry bias and transferability, emphasizing the need for the next wave of studies to focus on the implementation and health-economic value of PRS testing. In conclusion, it is becoming clear that PRS have value in disease risk prediction and there are multiple areas where this may have clinical utility.
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Ky, Bonnie, Carla L. Warneke, Daniel John Lenihan, Puneet S. Cheema, Dennis Frederic Moore, Mark G. Campbell, Chilakamarri Yeshwant, et al. "Clinical risk prediction in anthracycline cardiotoxicity." Journal of Clinical Oncology 32, no. 15_suppl (May 20, 2014): 9624. http://dx.doi.org/10.1200/jco.2014.32.15_suppl.9624.
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van Geel, Tineke, Geert-Jan Dinant, Piet Geusens, and Joop van den Bergh. "Fracture risk prediction in clinical practice." Maturitas 81, no. 1 (May 2015): 112. http://dx.doi.org/10.1016/j.maturitas.2015.02.035.
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Magee, L. A., and P. v. Dadelszen. "Clinical risk prediction of pre-eclampsia." BMJ 342, apr07 4 (April 7, 2011): d1863. http://dx.doi.org/10.1136/bmj.d1863.
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Li, Juan, Mingyao Lai, and Linbo Cai. "MEDB-58. Risk factors and risk prediction models for medulloblastoma recurrence." Neuro-Oncology 24, Supplement_1 (June 1, 2022): i119—i120. http://dx.doi.org/10.1093/neuonc/noac079.432.
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Abstract BACKGROUND: There is a clear need for systematic appraisal of models/factors predicting medulloblastoma recurrence because clinical decisions about adjuvant treatment are taken on the basis of such variables. METHODS： A total of 273 patients diagnosed with medulloblastoma were retrospectively analyzed. The pre-rediotherapy neutrophile-lymphocyte ratio (NLR) was calculated, and other clinical characteristics were collected such as genetic type , whether with dissemination, degree with excision. The Kaplan-Meier method was used for survival analysis. Cox regression models was used to identify independent prognostic factors. R software was used to develop a nomogram with all the independent prognostic factors included. The prognostic predictive ability of the nomogram was evaluated by Concordance-index (C-index), area under the curve (AUC), and calibration curve. RESULTS: The median median progression-free survival time was 63.8 months in overall cohort. Univariate and multivariate cox hazards regression analysis identified independent prognostic factors associated with the PFS of patients with medulloblastoma to include age, residual tumor volume ＞1.5cm3 after excision, NLR ＞4.5, whether with dissemination before RT, and whether the genetic type is group 3,which were integrated to establish a nomogram. The C-indexes of nomogram were 0.696 and 0.676 in the training and validation cohort, respectively. The AUC of predicting 3-years PFS showed satisfactory accuracy as well (Training cohort: AUC=0.696; Validation cohort: AUC=0.676). The calibration curve showed agreement between the ideal and predicted values. Kaplan-Meier curves based on the PFS showed significant differences between nomogram predictive low-, and high groups (P < 0.001). CONCLUSIONS: We found that pre-treatment NLR was an independent prognostic factor for recurrence or metastasis of medulloblastoma after treatment. In combination with NRL and clinical factors, nomogram has a good prediction of PFS in patients with medulloblastoma after radiotherapy. It has the potential to facilitate more precise risk stratification to guide personalized treatment of medulloblastoma.
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Zhou, Shu-Ping, Su-Ding Fei, Hui-Hui Han, Jing-Jing Li, Shuang Yang, and Chun-Yang Zhao. "A Prediction Model for Cognitive Impairment Risk in Colorectal Cancer after Chemotherapy Treatment." BioMed Research International 2021 (February 20, 2021): 1–13. http://dx.doi.org/10.1155/2021/6666453.
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Background. A prediction model can be developed to predict the risk of cancer-related cognitive impairment in colorectal cancer patients after chemotherapy. Methods. A regression analysis was performed on 386 colorectal cancer patients who had undergone chemotherapy. Three prediction models (random forest, logistic regression, and support vector machine models) were constructed using collected clinical and pathological data of the patients. Calibration and ROC curves and C -indexes were used to evaluate the selected models. A decision curve analysis (DCA) was used to determine the clinical utility of the line graph. Results. Three prediction models including a random forest, a logistic regression, and a support vector machine were constructed. The logistic regression model had the strongest predictive power with an area under the curve (AUC) of 0.799. Age, BMI, colostomy, complications, CRA, depression, diabetes, QLQ-C30 score, exercise, hypercholesterolemia, diet, marital status, education level, and pathological stage were included in the nomogram. The C -index (0.826) and calibration curve showed that the nomogram had good predictive ability and the DCA curves indicated that the model had strong clinical utility. Conclusions. A prediction model with good predictive ability and practical clinical value can be developed for predicting the risk of cognitive impairment in colorectal cancer after chemotherapy.
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Jahan, Sharmin, and Mohammad Ali. "Risk of Gestational Diabetes Mellitus (GDM) Using Clinical Prediction Model Based on Maternal Characteristics." Journal of Armed Forces Medical College, Bangladesh 11, no. 1 (December 15, 2016): 64–68. http://dx.doi.org/10.3329/jafmc.v11i1.30675.
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Introduction: The healthcare delivery challenges in Bangladesh are phenomenal. Improving maternal and child health, reducing the high maternal and infant mortality & morbidity are challenging. Arrangement of additional expenditure for GDM screening is again challenging. The efficiency of screening could be enhanced by considering womens risks of gestational diabetes on the basis of their clinical characteristics.Objectives: To find out the use of the clinical prediction model of gestational diabetes mellitus (GDM) is valid for Bangladeshi pregnant women and to assess the risk of gestational diabetes by using clinical prediction model based on maternal characteristics.Materials and Methods: A cross sectional study was carried out from July 2011 to June 2012 among purposively selected 217 pregnant women of ?24 weeks of gestation in the Gynae and Obstetric outpatient department of Combined Military Hospital, Dhaka. Data were collected by face to face interview, anthropometric measurement and record review. Two step oral glucose tests were done for diagnosis of GDM.Results: According to Chadakaran clinical prediction model 84 (38.7%) respondents were at high risk, 92 (42.4%) were at intermediate risk and 41(18.9%) found at low risk of gestational diabetes but only 24(11.05%) developed gestational diabetes. Highest occurrence of gestational diabetes was found in high risk group 17 (20.2%) with zero occurrence in low risk group. Risk score performance at the level of ?380, sensitivity was 100% and specificity 21.8%, 13.6% positive predictive value, 100% negative predictive value and area under curve was 0.385. At the level of 460 score the sensitivity and specificity was found closest (70.8% and 65.3%, respectively) and area under curve was highest 0.657. The receiver operating characteristics curve of the risk score in the study sample for predicting women with glucose tolerance test demonstrated an area 0.763 (95%, 0.682 0.845).Conclusion: The use of clinical prediction model is a simple, non invasive, cost effective useful method to identify women at increased risk of gestational diabetes mellitus and could be short listed for further testing.Journal of Armed Forces Medical College Bangladesh Vol.11(1) 2015: 64-68
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Jauk, Stefanie, Diether Kramer, Birgit Großauer, Susanne Rienmüller, Alexander Avian, Andrea Berghold, Werner Leodolter, and Stefan Schulz. "Risk prediction of delirium in hospitalized patients using machine learning: An implementation and prospective evaluation study." Journal of the American Medical Informatics Association 27, no. 9 (September 1, 2020): 1383–92. http://dx.doi.org/10.1093/jamia/ocaa113.
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Abstract Objective Machine learning models trained on electronic health records have achieved high prognostic accuracy in test datasets, but little is known about their embedding into clinical workflows. We implemented a random forest–based algorithm to identify hospitalized patients at high risk for delirium, and evaluated its performance in a clinical setting. Materials and Methods Delirium was predicted at admission and recalculated on the evening of admission. The defined prediction outcome was a delirium coded for the recent hospital stay. During 7 months of prospective evaluation, 5530 predictions were analyzed. In addition, 119 predictions for internal medicine patients were compared with ratings of clinical experts in a blinded and nonblinded setting. Results During clinical application, the algorithm achieved a sensitivity of 74.1% and a specificity of 82.2%. Discrimination on prospective data (area under the receiver-operating characteristic curve = 0.86) was as good as in the test dataset, but calibration was poor. The predictions correlated strongly with delirium risk perceived by experts in the blinded (r = 0.81) and nonblinded (r = 0.62) settings. A major advantage of our setting was the timely prediction without additional data entry. Discussion The implemented machine learning algorithm achieved a stable performance predicting delirium in high agreement with expert ratings, but improvement of calibration is needed. Future research should evaluate the acceptance of implemented machine learning algorithms by health professionals. Conclusions Our study provides new insights into the implementation process of a machine learning algorithm into a clinical workflow and demonstrates its predictive power for delirium.
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Qasim, Sadia, Sehar Zahid, Ashbah Islam, Muhammad Anwar, Shahzia Siddique, and Arshad Rafique. "Clinical Risk Index Score (CRIB II) as a Predictor of Neonatal Mortality among Premature Babies." Pakistan Journal of Medical and Health Sciences 16, no. 8 (August 31, 2022): 70–71. http://dx.doi.org/10.53350/pjmhs2216870.
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Aim: To determine predictive accuracy of CRIB II score in predicting mortality among premature with low birth weight neonates. Study design: Cross-sectional study. Place and duration of study: Paediatric NICU, Services Hospital Lahore from 2nd June 2017 to 2nd December 2017. Methodology: Two hundred and forty preterm new-born born before 32 weeks and birth weight from 600-1500grams were enrolled. Temperature was measured using rectal thermometer. Arterial blood gas sample was taken using standard sampling technique for base excess. Premature infants were evaluated for CRIB-II Score within 1 hour of arrival. Neonates were followed for 28 days to assess for their survival status. Results: The mean CRIB-II score was 10.95±2.79, mean gestational age was 29.16±1.58 weeks and mean weight was 1111.13±202.28 grams. There were 123(51.25%) males and 117(48.75%) were females. Actual neonatal mortality was 82(34.17%). Predictive accuracy of CRIB II score in predicting mortality among premature and very low birth weight neonates admitted to NICU was 92.68% for sensitivity, 94.94% specificity, 90.48% positive predictive value and 96.15% had negative predictive value. Conclusion: The predictive accuracy of CRIB II score is higher in prediction of mortality among premature and very low birth weight neonates. Keywords: Neonates, Premature, Very low birth weight, Mortality, CRIB II score, Prediction
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Effraimidis, Grigoris. "MANAGEMENT OF ENDOCRINE DISEASE: Predictive scores in autoimmune thyroid disease: are they useful?" European Journal of Endocrinology 181, no. 3 (September 2019): R119—R131. http://dx.doi.org/10.1530/eje-19-0234.
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Prediction models are of a great assistance for predicting the development of a disease, detecting or screening undiagnosed patients, predicting the effectiveness of a treatment and helping toward better decision-making. Recently, three predictive scores in the field of autoimmune thyroid disease (AITD) have been introduced: The Thyroid Hormones Event Amsterdam (THEA) score: a predictive score of the development of overt AITD, the Graves’ Events After Therapy (GREAT) score: a prediction score for the risk of recurrence after antithyroid drugs withdrawal and the Prediction Graves’ Orbitopathy (PREDIGO) score: a prediction score for the development of Graves’ orbitopathy in newly diagnosed patients with Graves’ hyperthyroidism. Their construction, clinical applicability, the possible preventative measurements which can be taken to diminish the risks and the potential future developments which can improve the accuracy of the predictive scores are discussed in this review.
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Engel, Christoph, and Christine Fischer. "Breast Cancer Risks and Risk Prediction Models." Breast Care 10, no. 1 (2015): 7–12. http://dx.doi.org/10.1159/000376600.
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BRCA1/2 mutation carriers have a considerably increased risk to develop breast and ovarian cancer. The personalized clinical management of carriers and other at-risk individuals depends on precise knowledge of the cancer risks. In this report, we give an overview of the present literature on empirical cancer risks, and we describe risk prediction models that are currently used for individual risk assessment in clinical practice. Cancer risks show large variability between studies. Breast cancer risks are at 40-87% for BRCA1 mutation carriers and 18-88% for BRCA2 mutation carriers. For ovarian cancer, the risk estimates are in the range of 22-65% for BRCA1 and 10-35% for BRCA2. The contralateral breast cancer risk is high (10-year risk after first cancer 27% for BRCA1 and 19% for BRCA2). Risk prediction models have been proposed to provide more individualized risk prediction, using additional knowledge on family history, mode of inheritance of major genes, and other genetic and non-genetic risk factors. User-friendly software tools have been developed that serve as basis for decision-making in family counseling units. In conclusion, further assessment of cancer risks and model validation is needed, ideally based on prospective cohort studies. To obtain such data, clinical management of carriers and other at-risk individuals should always be accompanied by standardized scientific documentation.
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Kim, Jae Kwon, and Sanggil Kang. "Neural Network-Based Coronary Heart Disease Risk Prediction Using Feature Correlation Analysis." Journal of Healthcare Engineering 2017 (2017): 1–13. http://dx.doi.org/10.1155/2017/2780501.
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Background. Of the machine learning techniques used in predicting coronary heart disease (CHD), neural network (NN) is popularly used to improve performance accuracy. Objective. Even though NN-based systems provide meaningful results based on clinical experiments, medical experts are not satisfied with their predictive performances because NN is trained in a “black-box” style. Method. We sought to devise an NN-based prediction of CHD risk using feature correlation analysis (NN-FCA) using two stages. First, the feature selection stage, which makes features acceding to the importance in predicting CHD risk, is ranked, and second, the feature correlation analysis stage, during which one learns about the existence of correlations between feature relations and the data of each NN predictor output, is determined. Result. Of the 4146 individuals in the Korean dataset evaluated, 3031 had low CHD risk and 1115 had CHD high risk. The area under the receiver operating characteristic (ROC) curve of the proposed model (0.749 ± 0.010) was larger than the Framingham risk score (FRS) (0.393 ± 0.010). Conclusions. The proposed NN-FCA, which utilizes feature correlation analysis, was found to be better than FRS in terms of CHD risk prediction. Furthermore, the proposed model resulted in a larger ROC curve and more accurate predictions of CHD risk in the Korean population than the FRS.
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Teo, Koon K., Norman J. Davies, and Terrence J. Montague. "The Clinical Utility of Postinfarction Risk Prediction." Chest 101, no. 2 (February 1992): 534–40. http://dx.doi.org/10.1378/chest.101.2.534.
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Clemente-Callejo, Carlota, Agustín Julián-Jiménez, Francisco Javier Candel, and Juan González del Castillo. "Models for bacteraemia risk prediction. Clinical implications." Revista Española de Quimioterapia 35, Suppl3 (October 24, 2022): 89–93. http://dx.doi.org/10.37201/req/s03.19.2022.
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Bacteraemia has important consequences for the patient, as it is associated with worse clinical outcomes. On the other hand, unnecessarily obtaining samples for blood cultures increases costs and the workload in the microbiology laboratory. Its diagnosis implies a time delay, but decisions about start antibiotic treatment, discharge, or admits the patient must be taken during the first attention and, therefore, before known the blood cultures results. This manuscript reviews the different strategies based on clinical scores and biomarkers that are useful for predicting bacteraemia and improving initial decision-making.
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McEvoy, John W., George A. Diamond, Robert C. Detrano, Sanjay Kaul, Michael J. Blaha, Roger S. Blumenthal, and Steven R. Jones. "Risk and the Physics of Clinical Prediction." American Journal of Cardiology 113, no. 8 (April 2014): 1429–35. http://dx.doi.org/10.1016/j.amjcard.2014.01.418.
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Wang, W. C. "Personalized Prediction Model for Hepatocellular Carcinoma With a Bayesian Clinical Reasoning Approach." Journal of Global Oncology 4, Supplement 2 (October 1, 2018): 210s. http://dx.doi.org/10.1200/jgo.18.84600.
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Background: Predictive models for the risk of hepatocellular carcinoma (HCC) are often appropriate for average-risk population but not tailored for a personalized prediction model for individual risk of hepatocellular carcinoma (HCC), namely personalized prediction model. Aim: The objective of this study is to build up an individually tailored predictive model for HCC by using a Bayesian clinical reasoning algorithm to stratify risk groups of the underlying population. Methods: Data were derived from a community-based screening cohort consisting of 98,552 subjects between 1999 and 2007. Information on HBV and HCV infection status, liver function test, AFT, family history of liver cancer, demographic characteristics, lifestyle variables and relevant biomarkers were collected. The occurrence of HCC was ascertained by the linkage of the nationwide cancer registry till the end of 2007. Bayesian clinical reasoning model was adopted by constructing the basic model taken as the prior model for average-risk subject. We then updated the basic model by sequentially incorporating other risk factors for HCC encrypted in the likelihood ratio to form posterior probability that was used for predicting individual risk of HCC. Results: By dint of Bayesian clinical reasoning model with a step-by-step update of the risk of HCC for the sequentially obtained information, a 57-year-old man was predicted to yield 0.69% of HCC risk with the prior model. After history-taking of having hepatitis B carrier (likelihood ratio [LR]: 3.65), family history (LR: 1.43), and no alcohol drinking (LR: 0.89), the posterior risk for HCC was enhanced up to 3.13%. After further biochemical examination, the updated risk of HCC for a man [the following biomarkers [ALT = 30 IU/L (LR: 0.78), AST = 56 IU/L (LR: 8.99), platelets = (203 × /μL) (unit cube of ten) (LR: 0.55)] was increase to 11.07%. Conclusion: We proposed a individually tailored prediction model for HCC by incorporating routine information with a sequential Bayesian clinical reasoning approach.
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Kao, Hao-Yun, Chi-Chang Chang, Chin-Fang Chang, Ying-Chen Chen, Chalong Cheewakriangkrai, and Ya-Ling Tu. "Associations between Sex and Risk Factors for Predicting Chronic Kidney Disease." International Journal of Environmental Research and Public Health 19, no. 3 (January 22, 2022): 1219. http://dx.doi.org/10.3390/ijerph19031219.
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Gender is an important risk factor in predicting chronic kidney disease (CKD); however, it is under-researched. The purpose of this study was to examine whether gender differences affect the risk factors of early CKD prediction. This study used data from 19,270 adult health screenings, including 5101 with CKD, to screen for 11 independent variables selected as risk factors and to test for the significant effects of statistical Chi-square test variables, using seven machine learning techniques to train the predictive models. Performance indicators included classification accuracy, sensitivity, specificity, and precision. Unbalanced category issues were addressed using three extraction methods: manual sampling, the synthetic minority oversampling technique, and SpreadSubsample. The Chi-square test revealed statistically significant results (p < 0.001) for gender, age, red blood cell count in urine, urine protein (PRO) content, and the PRO-to-urinary creatinine ratio. In terms of classifier prediction performance, the manual extraction method, logistic regression, exhibited the highest average prediction accuracy rate (0.8053) for men, whereas the manual extraction method, linear discriminant analysis, demonstrated the highest average prediction accuracy rate (0.8485) for women. The clinical features of a normal or abnormal PRO-to-urinary creatinine ratio indicated that PRO ratio, age, and urine red blood cell count are the most important risk factors with which to predict CKD in both genders. As a result, this study proposes a prediction model with acceptable prediction accuracy. The model supports doctors in diagnosis and treatment and achieves the goal of early detection and treatment. Based on the evidence-based medicine, machine learning methods are used to develop predictive model in this study. The model has proven to support the prediction of early clinical risk of CKD as much as possible to improve the efficacy and quality of clinical decision making.
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Seena, F., A. Wolf, and T. Fanshawe. "Assessing Violence in Psychosis – A Clinical Prediction Rule." European Psychiatry 41, S1 (April 2017): S154. http://dx.doi.org/10.1016/j.eurpsy.2017.01.2015.
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BackgroundCurrent approaches to stratify patients with psychosis into risk groups are limited by inconsistency, variable accuracy, and unscalability.MethodsThis paper will present an overview of current approaches based on a systematic review. It will also present a novel scalable approach based on a total national cohort of 75 158 Swedish individuals aged 15–65 with a diagnosis of severe mental illness (schizophrenia, schizophrenic-spectrum, bipolar disorder, and other psychotic illnesses). We developed predictive models for violent offending through linkage of population-based registers and tested them in external validation. We measured discrimination and calibration for prediction of violent offending at 1 year using specified risk cut-offs.Findings: A 16-item model was developed from pre-specified routinely collected criminal history, socio-demographic and clinical risk factors. In external validation, the model showed good measures of discrimination (c-index 0.89) and calibration. For risk of violent offending at 1 year, using a 5% cut off, sensitivity was 64% and specificity was 94%. Positive and negative predictive values were 11% and 99%, respectively. The model was used to generate a simple web-based risk calculator (OxMIV).InterpretationWe have developed a prediction score in a national cohort of all patients with psychosis that can be used as an adjunct to decision-making in clinical practice by identifying those who are at low risk of future violent offending and higher risk individuals who may benefit from additional risk management. Further evaluation in other populations and countries is needed.
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Middleton, M., MJ Stechman, S. Moug, K. McCarthy, and J. Hewitt. "Risk prediction tools for older general surgical patients." Reviews in Clinical Gerontology 25, no. 1 (February 2015): 12–21. http://dx.doi.org/10.1017/s0959259815000027.
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SummaryThis review identifies and examines the clinical application of risk prediction tools, including the American Society of Anaesthesiology (ASA) Classification System and POSSUM-based scores, in the older general surgical patient. Predicting outcomes in this patient group remains difficult; it is challenging to design a risk prediction tool that will apply to both emergency and elective surgery and that remains valid across the wide age range that this patient group encompasses. Risk prediction tools can have benefit but should be used in conjunction with the clinical assessment of those experienced in the care of this challenging patient group.
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Fistouris, Johan, Christina Bergh, and Annika Strandell. "Pregnancy of unknown location: external validation of the hCG-based M6NP and M4 prediction models in an emergency gynaecology unit." BMJ Open 12, no. 11 (November 2022): e058454. http://dx.doi.org/10.1136/bmjopen-2021-058454.
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ObjectiveTo investigate if M6NP predicting ectopic pregnancy (EP) among women with pregnancy of unknown location (PUL) is valid in an emergency gynaecology setting and comparing it with its predecessor M4.DesignRetrospective cohort study.SettingUniversity Hospital.ParticipantsWomen with PUL.MethodsAll consecutive women with a PUL during a study period of 3 years were screened for inclusion. Risk prediction of an EP was based on two serum human chorionic gonadotropin (hCG) levels taken at least 24 hours and no longer than 72 hours apart.Main outcome measuresThe area under the ROC curve (AUC) expressed the ability of a model to distinguish an EP from a non-EP (discrimination). Calibration assessed the agreement between the predicted risk of an EP and the true risk (proportion) of EP. The proportion of EPs and non-EPs classified as high risk assessed the model’s sensitivity and false positive rate (FPR). The proportion of non-EPs among women classified as low risk was the model’s negative predictive value (NPV). The clinical utility of a model was evaluated with decision curve analysis.Results1061 women were included in the study, of which 238 (22%) had a final diagnosis of EP. The AUC for EP was 0.85 for M6NP and 0.81 for M4. M6NP made accurate risk predictions of EP up to predictions of 20% but thereafter risks were underestimated. M4 was poorly calibrated up to risk predictions of 40%. With a 5% threshold for high risk classification the sensitivity for EP was 95% for M6NP, the FPR 50% and NPV 97%. M6NP had higher sensitivity and NPV than M4 but also a higher FPR. M6NP had utility at all thresholds as opposed to M4 that had no utility at thresholds≤5%.ConclusionsM6NP had better predictive performance than M4 and is valid in women with PUL attending an emergency gynaecology unit. Our results can encourage implementation of M6NP in related yet untested clinical settings to effectively support clinical decision-making.
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Antonucci, Linda A., Nora Penzel, Rachele Sanfelici, Alessandro Pigoni, Lana Kambeitz-Ilankovic, Dominic Dwyer, Anne Ruef, et al. "Using combined environmental–clinical classification models to predict role functioning outcome in clinical high-risk states for psychosis and recent-onset depression." British Journal of Psychiatry 220, no. 4 (February 14, 2022): 229–45. http://dx.doi.org/10.1192/bjp.2022.16.
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BackgroundClinical high-risk states for psychosis (CHR) are associated with functional impairments and depressive disorders. A previous PRONIA study predicted social functioning in CHR and recent-onset depression (ROD) based on structural magnetic resonance imaging (sMRI) and clinical data. However, the combination of these domains did not lead to accurate role functioning prediction, calling for the investigation of additional risk dimensions. Role functioning may be more strongly associated with environmental adverse events than social functioning.AimsWe aimed to predict role functioning in CHR, ROD and transdiagnostically, by adding environmental adverse events-related variables to clinical and sMRI data domains within the PRONIA sample.MethodBaseline clinical, environmental and sMRI data collected in 92 CHR and 95 ROD samples were trained to predict lower versus higher follow-up role functioning, using support vector classification and mixed k-fold/leave-site-out cross-validation. We built separate predictions for each domain, created multimodal predictions and validated them in independent cohorts (74 CHR, 66 ROD).ResultsModels combining clinical and environmental data predicted role outcome in discovery and replication samples of CHR (balanced accuracies: 65.4% and 67.7%, respectively), ROD (balanced accuracies: 58.9% and 62.5%, respectively), and transdiagnostically (balanced accuracies: 62.4% and 68.2%, respectively). The most reliable environmental features for role outcome prediction were adult environmental adjustment, childhood trauma in CHR and childhood environmental adjustment in ROD.ConclusionsFindings support the hypothesis that environmental variables inform role outcome prediction, highlight the existence of both transdiagnostic and syndrome-specific predictive environmental adverse events, and emphasise the importance of implementing real-world models by measuring multiple risk dimensions.
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Biccard, B. M., and R. N. Rodseth. "Utility of clinical risk predictors for preoperative cardiovascular risk prediction." British Journal of Anaesthesia 107, no. 2 (August 2011): 133–43. http://dx.doi.org/10.1093/bja/aer194.
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Biccard, B. M., and R. N. Rodseth. "Utility of Clinical Risk Predictors for Preoperative Cardiovascular Risk Prediction." Survey of Anesthesiology 56, no. 1 (February 2012): 36–38. http://dx.doi.org/10.1097/01.sa.0000410678.63543.5c.
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Hoogeveen, Renate M., João P. Belo Pereira, Nick S. Nurmohamed, Veronica Zampoleri, Michiel J. Bom, Andrea Baragetti, S. Matthijs Boekholdt, et al. "Improved cardiovascular risk prediction using targeted plasma proteomics in primary prevention." European Heart Journal 41, no. 41 (August 18, 2020): 3998–4007. http://dx.doi.org/10.1093/eurheartj/ehaa648.
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Abstract Aims In the era of personalized medicine, it is of utmost importance to be able to identify subjects at the highest cardiovascular (CV) risk. To date, single biomarkers have failed to markedly improve the estimation of CV risk. Using novel technology, simultaneous assessment of large numbers of biomarkers may hold promise to improve prediction. In the present study, we compared a protein-based risk model with a model using traditional risk factors in predicting CV events in the primary prevention setting of the European Prospective Investigation (EPIC)-Norfolk study, followed by validation in the Progressione della Lesione Intimale Carotidea (PLIC) cohort. Methods and results Using the proximity extension assay, 368 proteins were measured in a nested case–control sample of 822 individuals from the EPIC-Norfolk prospective cohort study and 702 individuals from the PLIC cohort. Using tree-based ensemble and boosting methods, we constructed a protein-based prediction model, an optimized clinical risk model, and a model combining both. In the derivation cohort (EPIC-Norfolk), we defined a panel of 50 proteins, which outperformed the clinical risk model in the prediction of myocardial infarction [area under the curve (AUC) 0.754 vs. 0.730; P < 0.001] during a median follow-up of 20 years. The clinically more relevant prediction of events occurring within 3 years showed an AUC of 0.732 using the clinical risk model and an AUC of 0.803 for the protein model (P < 0.001). The predictive value of the protein panel was confirmed to be superior to the clinical risk model in the validation cohort (AUC 0.705 vs. 0.609; P < 0.001). Conclusion In a primary prevention setting, a proteome-based model outperforms a model comprising clinical risk factors in predicting the risk of CV events. Validation in a large prospective primary prevention cohort is required to address the value for future clinical implementation in CV prevention.
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Lithovius, Raija, Anni A. Antikainen, Stefan Mutter, Erkka Valo, Carol Forsblom, Valma Harjutsalo, Niina Sandholm, and Per-Henrik Groop. "Genetic Risk Score Enhances Coronary Artery Disease Risk Prediction in Individuals With Type 1 Diabetes." Diabetes Care 45, no. 3 (January 12, 2022): 734–41. http://dx.doi.org/10.2337/dc21-0974.
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OBJECTIVE Individuals with type 1 diabetes are at a high lifetime risk of coronary artery disease (CAD), calling for early interventions. This study explores the use of a genetic risk score (GRS) for CAD risk prediction, compares it to established clinical markers, and investigates its performance according to the age and pharmacological treatment. RESEARCH DESIGN AND METHODS This study in 3,295 individuals with type 1 diabetes from the Finnish Diabetic Nephropathy Study (467 incident CAD, 14.8 years follow-up) used three risk scores: a GRS, a validated clinical score, and their combined score. Hazard ratios (HR) were calculated with Cox regression, and model performances were compared with the Harrell C-index (C-index). RESULTS A HR of 6.7 for CAD was observed between the highest and the lowest 5th percentile of the GRS (P = 1.8 × 10−6). The performance of GRS (C-index = 0.562) was similar to HbA1c (C-index = 0.563, P = 0.96 for difference), HDL (C-index = 0.571, P = 0.6), and total cholesterol (C-index = 0.594, P = 0.1). The GRS was not correlated with the clinical score (r = −0.013, P = 0.5). The combined score outperformed the clinical score (C-index = 0.813 vs. C-index = 0.820, P = 0.003). The GRS performed better in individuals below the median age (38.6 years) compared with those above (C-index = 0.637 vs. C-index = 0.546). CONCLUSIONS A GRS identified individuals at high risk of CAD and worked better in younger individuals. GRS was also an independent risk factor for CAD, with a predictive power comparable to that of HbA1c and HDL and total cholesterol, and when incorporated into a clinical model, modestly improved the predictions. The GRS promises early risk stratification in clinical practice by enhancing the prediction of CAD.
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VAN DEN MUNCKHOF, SVEN, ALI ASADI NIKOOYAN, and AMIR ABBAS ZADPOOR. "ASSESSMENT OF OSTEOPOROTIC FEMORAL FRACTURE RISK: FINITE ELEMENT METHOD AS A POTENTIAL REPLACEMENT FOR CURRENT CLINICAL TECHNIQUES." Journal of Mechanics in Medicine and Biology 15, no. 03 (June 2015): 1530003. http://dx.doi.org/10.1142/s0219519415300033.
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Femoral fracture risk prediction is a necessary step preceding effective pharmacological intervention or pre-operative planning. Current clinical methods for fracture risk prediction rely on 2D imaging methods and have limited predictive value. Researchers are therefore trying to find improved methods for fracture prediction. During last few decades, many studies have focused on integration of 3D imaging techniques and the finite element (FE) method to improve the accuracy of fracture assessment techniques. In this paper, we review the recent advances in FE and other techniques for predicting the risk of femoral fractures. Based on a number of selected studies, the different steps that are involved in generation of patient-specific FE models are reviewed with particular emphasis on the fracture criteria. The inaccuracies that might arise due to the imperfections of the involved steps are also discussed. It is concluded that compared to image- and geometry-based techniques, FE is a more promising approach for prediction of fracture loads. However, certain technological advancements in FE modeling protocols are required before FE modeling can be recruited in clinical settings.
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Pain, Oliver, Kylie P. Glanville, Saskia Hagenaars, Saskia Selzam, Anna Fürtjes, Jonathan R. I. Coleman, Kaili Rimfeld, Gerome Breen, Lasse Folkersen, and Cathryn M. Lewis. "Imputed gene expression risk scores: a functionally informed component of polygenic risk." Human Molecular Genetics 30, no. 8 (February 22, 2021): 727–38. http://dx.doi.org/10.1093/hmg/ddab053.
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Abstract Integration of functional genomic annotations when estimating polygenic risk scores (PRS) can provide insight into aetiology and improve risk prediction. This study explores the predictive utility of gene expression risk scores (GeRS), calculated using imputed gene expression and transcriptome-wide association study (TWAS) results. The predictive utility of GeRS was evaluated using 12 neuropsychiatric and anthropometric outcomes measured in two target samples: UK Biobank and the Twins Early Development Study. GeRS were calculated based on imputed gene expression levels and TWAS results, using 53 gene expression–genotype panels, termed single nucleotide polymorphism (SNP)-weight sets, capturing expression across a range of tissues. We compare the predictive utility of elastic net models containing GeRS within and across SNP-weight sets, and models containing both GeRS and PRS. We estimate the proportion of SNP-based heritability attributable to cis-regulated gene expression. GeRS significantly predicted a range of outcomes, with elastic net models combining GeRS across SNP-weight sets improving prediction. GeRS were less predictive than PRS, but models combining GeRS and PRS improved prediction for several outcomes, with relative improvements ranging from 0.3% for height (P = 0.023) to 4% for rheumatoid arthritis (P = 5.9 × 10−8). The proportion of SNP-based heritability attributable to cis-regulated expression was modest for most outcomes, even when restricting GeRS to colocalized genes. GeRS represent a component of PRS and could be useful for functional stratification of genetic risk. Only in specific circumstances can GeRS substantially improve prediction over PRS alone. Future research considering functional genomic annotations when estimating genetic risk is warranted.
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Wang, Yi, Yanyan Xiao, Qidi Yang, Fang Wang, Ying Wang, and Cui Yuan. "Clinical prediction models for multidrug-resistant organism colonisation or infection in critically ill patients: a systematic review protocol." BMJ Open 12, no. 9 (September 2022): e064566. http://dx.doi.org/10.1136/bmjopen-2022-064566.
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IntroductionMultidrug-resistant organisms (MDROs) are pathogenic bacteria that are the leading cause of hospital-acquired infection which is associated with high morbidity and mortality rates in intensive care units, increasing hospitalisation duration and cost. Predicting the risk of MDRO colonisation or infection for critically ill patients supports clinical decision-making. Several models predicting MDRO colonisation or infection have been developed; however, owing to different disease scenarios, bacterial species and few externally validated cohorts in different prediction models; the stability and applicability of these models for MDRO colonisation or infection in critically ill patients are controversial. In addition, there are currently no standardised risk scoring systems to predict MDRO colonisation or infection in critically ill patients. The aim of this systematic review is to summarise and assess models predicting MDRO colonisation or infection in critically ill patients and to compare their predictive performance.Methods and analysisWe will perform a systematic search of PubMed, Cochrane Library, CINAHL, Embase, Web of science, China National Knowledge Infrastructure and Wanfang databases to identify all studies describing the development and/or external validation of models predicting MDRO colonisation or infection in critically ill patients. Two reviewers will independently extract and review the data using the Data Extraction for Systematic Reviews of Prediction Modelling Studies checklist; they will also assess the risk of bias using the Prediction Model Risk of Bias Assessment Tool. Quantitative data on model predictive performance will be synthesised in meta-analyses, as applicable.Ethics and disseminationEthical permissions will not be required because all data will be extracted from published studies. We intend to publish our results in peer-reviewed scientific journals and to present them at international conferences on critical care.PROSPERO registration numberCRD42022274175.
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Borre, Ethan, Adam Goode, Giselle Raitz, Bimal Shah, Angela Lowenstern, Ranee Chatterjee, Lauren Sharan, et al. "Predicting Thromboembolic and Bleeding Event Risk in Patients with Non-Valvular Atrial Fibrillation: A Systematic Review." Thrombosis and Haemostasis 118, no. 12 (October 30, 2018): 2171–87. http://dx.doi.org/10.1055/s-0038-1675400.
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Background Atrial fibrillation (AF) is a common cardiac arrhythmia that increases the risk of stroke. Medical therapy for decreasing stroke risk involves anticoagulation, which may increase bleeding risk for certain patients. In determining the optimal therapy for stroke prevention for patients with AF, clinicians use tools with various clinical, imaging and patient characteristics to weigh stroke risk against therapy-associated bleeding risk. Aim This article reviews published literature and summarizes available risk stratification tools for stroke and bleeding prediction in patients with AF. Methods We searched for English-language studies in PubMed, Embase and the Cochrane Database of Systematic Reviews published between 1 January 2000 and 14 February 2018. Two reviewers screened citations for studies that examined tools for predicting thromboembolic and bleeding risks in patients with AF. Data regarding study design, patient characteristics, interventions, outcomes, quality, and applicability were extracted. Results Sixty-one studies were relevant to predicting thromboembolic risk and 38 to predicting bleeding risk. Data suggest that CHADS2, CHA2DS2-VASc and the age, biomarkers, and clinical history (ABC) risk scores have the best evidence for predicting thromboembolic risk (moderate strength of evidence for limited prediction ability of each score) and that HAS-BLED has the best evidence for predicting bleeding risk (moderate strength of evidence). Limitations Studies were heterogeneous in methodology and populations of interest, setting, interventions and outcomes analysed. Conclusion CHADS2, CHA2DS2-VASc and ABC scores have the best prediction for stroke events, and HAS-BLED provides the best prediction for bleeding risk. Future studies should define the role of imaging tools and biomarkers in enhancing the accuracy of risk prediction tools. Primary Funding Source Patient-Centered Outcomes Research Institute (PROSPERO #CRD42017069999)
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Billheimer, Dean, Eugene W. Gerner, Christine E. McLaren, and Bonnie LaFleur. "Combined Benefit of Prediction and Treatment: A Criterion for Evaluating Clinical Prediction Models." Cancer Informatics 13s2 (January 2014): CIN.S13780. http://dx.doi.org/10.4137/cin.s13780.
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Clinical treatment decisions rely on prognostic evaluation of a patient's future health outcomes. Thus, predictive models under different treatment options are key factors for making good decisions. While many criteria exist for judging the statistical quality of a prediction model, few are available to measure its clinical utility. As a consequence, we may find that the addition of a clinical covariate or biomarker improves the statistical quality of the model, but has little effect on its clinical usefulness. We focus on the setting where a treatment decision may reduce a patient's risk of a poor outcome, but also comes at a cost; this may be monetary, inconvenience, or the potential side effects. This setting is exemplified by cancer chemoprevention, or the use of statins to reduce the risk of cardiovascular disease. We propose a novel approach to assessing a prediction model using a formal decision analytic framework. We combine the predictive model's ability to discriminate good from poor outcome with the net benefit afforded by treatment. In this framework, reduced risk is balanced against the cost of treatment. The relative cost–benefit of treatment provides a useful index to assist patient decisions. This index also identifies the relevant clinical risk regions where predictive improvement is needed. Our approach is illustrated using data from a colorectal adenoma chemoprevention trial.
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Wong, Vincent Wai-Sun, Stephen Lam Chan, Frankie Mo, Tung-Ching Chan, Herbert Ho-Fung Loong, Grace Lai-Hung Wong, Yanni Yan-Ni Lui, et al. "Clinical Scoring System to Predict Hepatocellular Carcinoma in Chronic Hepatitis B Carriers." Journal of Clinical Oncology 28, no. 10 (April 1, 2010): 1660–65. http://dx.doi.org/10.1200/jco.2009.26.2675.
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Purpose Hepatitis B virus (HBV) infection is an important etiology for hepatocellular carcinoma (HCC). We aim to develop a simple clinical score in predicting the risk of HCC among HBV carriers. Patients and Methods We first evaluated 1,005 patients and found that the following five factors independently predicted HCC development: age, albumin, bilirubin, HBV DNA, and cirrhosis. These variables were used to construct a prediction score ranging from 0 to 44.5. The score was validated in another prospective cohort of 424 patients. Results During a median follow-up of 10 years, 105 patients (10.%) in the training cohort and 45 patients (10.6%) in the validation cohort developed HCC. Cutoff values of 5 and 20 best discriminated HCC risk. By applying the cutoff value of 5, the score excluded future HCC development with high accuracy (negative predictive value = 97.8% and 97.3% in the training and validation cohorts, respectively). In the validation cohort, the 5-year HCC-free survival rates were 98.3%, 90.5%, and 78.9% in the low-, medium-, and high-risk groups, respectively. The hazard ratios for HCC in the medium- and high-risk groups were 12.8 and 14.6, respectively. Conclusion A simple prediction score constructed from routine clinical and laboratory parameters is accurate in predicting HCC development in HBV carriers. Future prospective validation is warranted.
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Whiting, Daniel, and Seena Fazel. "How accurate are suicide risk prediction models? Asking the right questions for clinical practice." Evidence Based Mental Health 22, no. 3 (June 27, 2019): 125–28. http://dx.doi.org/10.1136/ebmental-2019-300102.
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Prediction models assist in stratifying and quantifying an individual’s risk of developing a particular adverse outcome, and are widely used in cardiovascular and cancer medicine. Whether these approaches are accurate in predicting self-harm and suicide has been questioned. We searched for systematic reviews in the suicide risk assessment field, and identified three recent reviews that have examined current tools and models derived using machine learning approaches. In this clinical review, we present a critical appraisal of these reviews, and highlight three major limitations that are shared between them. First, structured tools are not compared with unstructured assessments routine in clinical practice. Second, they do not sufficiently consider a range of performance measures, including negative predictive value and calibration. Third, the potential role of these models as clinical adjuncts is not taken into consideration. We conclude by presenting the view that the current role of prediction models for self-harm and suicide is currently not known, and discuss some methodological issues and implications of some machine learning and other analytic techniques for clinical utility.
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Degnan, Andrew J. "Risk Prediction With Carotid MRI." Neurosurgery 69, no. 4 (October 2011): E1033. http://dx.doi.org/10.1227/neu.0b013e31822999a3.
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Woodfield, John C., Peter M. Sagar, Dinesh K. Thekkinkattil, Praveen Gogu, Lindsay D. Plank, and Dermot Burke. "Accuracy of the Surgeons’ Clinical Prediction of Postoperative Major Complications Using a Visual Analog Scale." Medical Decision Making 37, no. 1 (July 10, 2016): 101–12. http://dx.doi.org/10.1177/0272989x16651875.
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Background. Although the risk factors that contribute to postoperative complications are well recognized, prediction in the context of a particular patient is more difficult. We were interested in using a visual analog scale (VAS) to capture surgeons’ prediction of the risk of a major complication and to examine whether this could be improved. Methods. The study was performed in 3 stages. In phase I, the surgeon assessed the risk of a major complication on a 100-mm VAS immediately before and after surgery. A quality control questionnaire was designed to check if the VAS was being scored as a linear scale. In phase II, a VAS with 6 subscales for different areas of clinical risk was introduced. In phase III, predictions were completed following the presentation of detailed feedback on the accuracy of prediction of complications. Results. In total, 1295 predictions were made by 58 surgeons in 859 patients. Eight surgeons did not use a linear scale (6 logarithmic, 2 used 4 categories of risk). Surgeons made a meaningful prediction of major complications (preoperative median score 40 mm for complications v. 22 mm for no complication, P < 0.001; postoperative 46 mm v. 21 mm, P < 0.001). In phase I, the discrimination of prediction for preoperative (0.778), postoperative (0.810), and POSSUM (Physiological and Operative Severity Score for the Enumeration of Mortality and Morbidity) morbidity (0.750) prediction was similar. Although there was no improvement in prediction with a multidimensional VAS, there was a significant improvement in the discrimination of prediction after feedback (preoperative, 0.895; postoperative, 0.918). Conclusion. Awareness of different ways a VAS is scored is important when designing and interpreting studies. Clinical assessment of major complications by the surgeon was initially comparable to the prediction of the POSSUM morbidity score and improved significantly following the presentation of clinically relevant feedback.
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Cherkasov, M. F., O. L. Degtyarev, A. B. Lageza, and K. A. Demin. "PREDICTION METHOD FOR ACUTE TRAUMATIC PANCREATITIS IN SHOCK-INDUCING POLYTRAUMA." Grekov's Bulletin of Surgery 178, no. 5 (December 26, 2019): 52–56. http://dx.doi.org/10.24884/0042-4625-2019-178-5-52-56.
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INTRODUCTION. Analysis of consideration of risk factors (RF) in the prediction and treatment of acute traumatic pancreatitis (ATP) based on studied sources has revealed that classic isolated RF cannot completely account for the dynamics and onset of ATP.The OBJECTIVE was to develop a system for early prediction of the probability of developing ATP in case of shockinducing polytrauma.MATERIAL AND METHODS. Our prediction model was based on retrospective analysis of case records of patients had injured in car crashes and slip-and-fall accidents. Clinical and mathematical simulation were employed to describe real cause-effect relations. The study left out isolated minor injuries, which did not imply any pancreatopathy RF. We compiled the selection of 469 shock-inducing polytrauma case records. Stepwise research was carried out to create the prediction system. RESULTS. We identified 15 RF affecting the probability of ATP development. Mathematical processing of obtained quantitative characteristics determined the predictive score (PS) of every RF (PS RF). The resulting cumulative PS RF were used as the basis to build a mathematical prediction model for the probability of ATP development. A lookup table was suggested for practical application in polytrauma surgery. The study carried out with reference on available sources and research works of the authors was used to stipulate basic principles for clinical and mathematical simulation of risk factors causing development of pancreatopathy in shock-inducing polytrauma situation within a traumatic disease case. The issues of pathology predicting were also highlighted. The paper offers a scientifically justified and elaborated predictive evaluation based on the system of risk factors affecting the ATP development.CONCLUSION. Implementation of the scoring evaluation method to identify the risk of pathology onset based on combinations of risk factors considerably increased informative value of predictions and improved the efficiency of individually tailored preventive measures corresponding to the risk of pancreatopathy in shock-inducing polytrauma cases.The authors declare no conflict of interest.The authors confirm that they respect the rights of the people participated in the study, including obtaining informed consent when it is necessary, and the rules of treatment of animals when they are used in the study. Author Guidelines contains the detailed information.
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Westerlund, Annie M., Johann S. Hawe, Matthias Heinig, and Heribert Schunkert. "Risk Prediction of Cardiovascular Events by Exploration of Molecular Data with Explainable Artificial Intelligence." International Journal of Molecular Sciences 22, no. 19 (September 24, 2021): 10291. http://dx.doi.org/10.3390/ijms221910291.
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Cardiovascular diseases (CVD) annually take almost 18 million lives worldwide. Most lethal events occur months or years after the initial presentation. Indeed, many patients experience repeated complications or require multiple interventions (recurrent events). Apart from affecting the individual, this leads to high medical costs for society. Personalized treatment strategies aiming at prediction and prevention of recurrent events rely on early diagnosis and precise prognosis. Complementing the traditional environmental and clinical risk factors, multi-omics data provide a holistic view of the patient and disease progression, enabling studies to probe novel angles in risk stratification. Specifically, predictive molecular markers allow insights into regulatory networks, pathways, and mechanisms underlying disease. Moreover, artificial intelligence (AI) represents a powerful, yet adaptive, framework able to recognize complex patterns in large-scale clinical and molecular data with the potential to improve risk prediction. Here, we review the most recent advances in risk prediction of recurrent cardiovascular events, and discuss the value of molecular data and biomarkers for understanding patient risk in a systems biology context. Finally, we introduce explainable AI which may improve clinical decision systems by making predictions transparent to the medical practitioner.
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Kubota, Naoto, Naoto Fujiwara, and Yujin Hoshida. "Clinical and Molecular Prediction of Hepatocellular Carcinoma Risk." Journal of Clinical Medicine 9, no. 12 (November 26, 2020): 3843. http://dx.doi.org/10.3390/jcm9123843.
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Prediction of hepatocellular carcinoma (HCC) risk becomes increasingly important with recently emerging HCC-predisposing conditions, namely non-alcoholic fatty liver disease and cured hepatitis C virus infection. These etiologies are accompanied with a relatively low HCC incidence rate (~1% per year or less), while affecting a large patient population. Hepatitis B virus infection remains a major HCC risk factor, but a majority of the patients are now on antiviral therapy, which substantially lowers, but does not eliminate, HCC risk. Thus, it is critically important to identify a small subset of patients who have elevated likelihood of developing HCC, to optimize the allocation of limited HCC screening resources to those who need it most and enable cost-effective early HCC diagnosis to prolong patient survival. To date, numerous clinical-variable-based HCC risk scores have been developed for specific clinical contexts defined by liver disease etiology, severity, and other factors. In parallel, various molecular features have been reported as potential HCC risk biomarkers, utilizing both tissue and body-fluid specimens. Deep-learning-based risk modeling is an emerging strategy. Although none of them has been widely incorporated in clinical care of liver disease patients yet, some have been undergoing the process of validation and clinical development. In this review, these risk scores and biomarker candidates are overviewed, and strategic issues in their validation and clinical translation are discussed.
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North, R. A., L. M. McCowan, G. A. Dekker, L. Poston, E. H. Chan, A. W. Stewart, M. A. Black, et al. "Clinical Risk Prediction for Preeclampsia in Nulliparous Women." Obstetric Anesthesia Digest 32, no. 1 (March 2012): 46–47. http://dx.doi.org/10.1097/01.aoa.0000410809.69501.7a.
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Thrift, Aaron P., Bradley J. Kendall, Nirmala Pandeya, Thomas L. Vaughan, and David C. Whiteman. "A Clinical Risk Prediction Model for Barrett Esophagus." Cancer Prevention Research 5, no. 9 (July 11, 2012): 1115–23. http://dx.doi.org/10.1158/1940-6207.capr-12-0010.
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Salisbury, Adam C., and John A. Spertus. "Realizing the Potential of Clinical Risk Prediction Models." Circulation: Cardiovascular Quality and Outcomes 8, no. 4 (July 2015): 332–34. http://dx.doi.org/10.1161/circoutcomes.115.002038.
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Sterckx, Lucas, Gilles Vandewiele, Isabelle Dehaene, Olivier Janssens, Femke Ongenae, Femke De Backere, Filip De Turck, et al. "Clinical information extraction for preterm birth risk prediction." Journal of Biomedical Informatics 110 (October 2020): 103544. http://dx.doi.org/10.1016/j.jbi.2020.103544.
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Abd, Thura T., Michael J. Blaha, Roger S. Blumenthal, and Parag H. Joshi. "Cardiovascular Disease Risk Prediction - Integration into Clinical Practice." Current Cardiovascular Risk Reports 7, no. 5 (August 29, 2013): 346–53. http://dx.doi.org/10.1007/s12170-013-0332-y.
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Vickers, Andrew J., and Melissa Assel. "Biomarker Evaluation and Clinical Development." Société Internationale d’Urologie Journal 1, no. 1 (October 13, 2020): 16–22. http://dx.doi.org/10.48083/zcjs3811.
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Most candidate biomarkers are never adopted into clinical practice. The likelihood that a biomarker with good predictive properties will be incorporated into urologic decision-making and will improve patient care can be enhanced by following established principles of biomarker development. Studies should follow the REMARK guidelines, should have clinically relevant outcomes, and should evaluate the biomarker on the same patients to whom the biomarker would be applied in practice. It is also important to recognize that biomarker research is comparative: the question is not whether a biomarker provides information, but whether it provides better information than is already available. Continuous biomarkers should not be categorized above or below a fixed cutpoint: risk prediction allows for individualization of care. The risk predictions must be calibrated, that is, close to a patient’s true risk, and decision analysis is required to determine whether using the biomarker in clinical practice would change decisions and improve outcomes. Finally, impact studies are needed to evaluate how use of the biomarker in the real world affects outcomes.
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Clifton, Daniel R., Dustin R. Grooms, Jay Hertel, and James A. Onate. "Predicting Injury: Challenges in Prospective Injury Risk Factor Identification." Journal of Athletic Training 51, no. 8 (August 1, 2016): 658–61. http://dx.doi.org/10.4085/1062-6050-51.11.03.
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Context: Musculoskeletal injury-prediction methods vary and may have limitations that affect the accuracy of results and clinical meaningfulness. Background: Research examining injury risk factors is meaningful, but attempting to extrapolate injury risk from studies that do not prospectively assess injury occurrence may limit clinical applications. Injury incidence is a vital outcome measure, which allows for the appropriate interpretation of injury-prediction analyses; a lack of injury-incidence data may decrease the accuracy and increase the uncertainty of injury-risk estimates. Extrapolating results that predict an injury risk factor to predicting actual injuries may lead to inappropriate clinical decision-making models. Conclusions: Improved understanding of the limitations of injury-prediction methods, specifically those that do not prospectively assess injuries, will allow clinicians to better assess the clinical meaningfulness of the results.
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Chen, Wenyue, and Sufang Sun. "Clinical Application of a Multiparameter-Based Nomogram Model in Predicting Preeclampsia." Evidence-Based Complementary and Alternative Medicine 2022 (June 13, 2022): 1–7. http://dx.doi.org/10.1155/2022/7484112.
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Based on single-center data, the related predictive factors of preeclampsia (PE) were investigated, and a nomogram prediction model was established and validated. A retrospective collection of 93 PE patients admitted to our hospital from January 2019 to January 2021 were included in the PE group. In addition, non-PE pregnant women were selected for physical examination during the same period for matching, and 170 normal pregnant women who met the matching conditions were found as the normal pregnancy group. Clinical data of the selected candidates were collected. The risk factors of PE were screened by logistic regression analysis, and the lipopograph prediction model was constructed and verified. Logistic analysis results showed that age (OR = 3.069, 95% CI = 1.233–7.638), prepregnancy BMI (OR = 2.896, 95% CI = 1.193–7.029), vitamin E deficiency (OR = 2.803, 95% CI = 1.134–6.928), 25-(OH)D (OR = 0.944, 95% CI = 0.903∼9.988), PLGF (OR = 0.887, 95% CI = 0.851∼0.924), PAPP-A (OR = 1.240, 95% CI = 1.131∼1.360), and PI (OR = 6.376, 95% CI = 1.163∼34.967) were the independent risk factors for PE prediction ( P < 0.05 ). The ROC curve showed that the AUC of the model for predicting the risk of PE was 0.957 (95% CI: 0.935–0.979), and the specificity and sensitivity were 0.912 and 0.892, respectively. H-L goodness of the fit test showed that there was no statistical significance in the deviation between the actual observed value and the predicted value of the risk in the line graph model (χ2 = 7.001, P = 0.536 ). The bootstrap test was used for internal verification, and the original data were repeatedly sampled 1000 times. The average absolute error of the calibration curve is 0.014, and the fitting degree between the calibration curve and the ideal curve is good. Age, prepregnancy BMI, lack of vitamin E, 25-(OH)D, PLGF, PAPP-A, and PI are independent risk factors for predicting PE. The establishment of a nomogram prediction model based on the above parameters can help identify PE high-risk groups in the early clinical stage and provide a reference for individualized clinical diagnosis and treatment.