Relevant bibliographies by topics / Delirium prediction

Academic literature on the topic 'Delirium prediction'

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Published: 11 March 2023

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Journal articles on the topic "Delirium prediction"

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Linkaitė, Gabrielė, Mantas Riauka, Ignė Bunevičiūtė, and Saulius Vosylius. "Evaluation of PRE-DELIRIC (PREdiction of DELIRium in ICu patients) delirium prediction model for the patients in the intensive care unit." Acta medica Lituanica 25, no. 1 (May 14, 2018): 14–22. http://dx.doi.org/10.6001/actamedica.v25i1.3699.

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Introduction. Delirium not only compromises patient care, but is also associated with poorer outcomes: increased duration of mechanical ventilation, higher mortality, and greater long-term cognitive dysfunction. The PRE-DELIRIC model is a tool used to calculate the risk of the development of delirium. The classification of the patients into groups by risk allows efficient initiation of preventive measures. The goal of this study was to validate the PRE-DELIRIC model using the CAM-ICU (The Confusion Assessment Method for the Intensive Care Unit) method for the diagnosis of delirium. Materials and methods. Patients admitted to the University Hospital of Vilnius during February 2015 were enrolled. Every day, data were collected for APACHE-II and PRE-DELIRIC scores. Out of 167 patients, 38 (23%) were included and screened using the CAM-ICU method within 24 hours of admission to the ICU. We defined patients as having delirium when they had at least one positive CAM-ICU screening or haloperidol administration due to sedation. To validate the PRE-DELIRIC model, we calculated the area under receiver operating characteristic curve. Results. The mean age of the patients was 69.2 ± 17.2 years, 19 (50%) were male, APACHE-II mean score 18.0 ± 7.4 points. Delirium was diagnosed in 22 (58%) of 38 patients. Data used for validation of the PRE-DELIRIC model resulted in an area under the curve of 0.713 (p < 0.05, 95% CI 0.539–0.887); sensitivity and specificity for the patients with 20% risk were, accordingly, 77.3% and 50%; 40% risk – 45.5% and 81.3%, 60% – 36.4%, and 87.5%. Conclusions. The PRE-DELIRIC model predicted delirium in the patients within 24 hours of admission to the ICU. Preventive therapy could be efficiently targeted at high-risk patients if both of the methods are to be implemented.
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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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Amerongen, Hilde van Nieuw, Sandra Stapel, Jan Jaap Spijkstra, Dagmar Ouweneel, and Jimmy Schenk. "Comparison of Prognostic Accuracy of 3 Delirium Prediction Models." American Journal of Critical Care 32, no. 1 (January 1, 2023): 43–50. http://dx.doi.org/10.4037/ajcc2023213.

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Background Delirium is a severe complication in critical care patients. Accurate prediction could facilitate determination of which patients are at risk. In the past decade, several delirium prediction models have been developed. Objectives To compare the prognostic accuracy of the PRE-DELIRIC, E-PRE-DELIRIC, and Lanzhou models, and to investigate the difference in prognostic accuracy of the PRE-DELIRIC model between patients receiving and patients not receiving mechanical ventilation. Methods This retrospective study involved adult patients admitted to the intensive care unit during a 2-year period. Delirium was assessed by using the Confusion Assessment Method for the Intensive Care Unit or any administered dose of haloperidol or quetiapine. Model discrimination was assessed by calculating the area under the receiver operating characteristic curve (AUC); values were compared using the DeLong test. Results The study enrolled 1353 patients. The AUC values were calculated as 0.716 (95% CI, 0.688–0.745), 0.681 (95% CI, 0.650–0.712), and 0.660 (95% CI, 0.629–0.691) for the PRE-DELIRIC, E-PRE-DELIRIC, and Lanzhou models, respectively. The difference in model discrimination was statistically significant for comparison of the PRE-DELIRIC with the E-PRE-DELIRIC (AUC difference, 0.035; P = .02) and Lanzhou models (AUC difference, 0.056; P &lt; .001). In the PRE-DELIRIC model, the AUC was 0.711 (95% CI, 0.680–0.743) for patients receiving mechanical ventilation and 0.664 (95% CI, 0.586–0.742) for those not receiving it (difference, 0.047; P = .27). Conclusion Statistically significant differences in prognostic accuracy were found between delirium prediction models. The PRE-DELIRIC model was the best-performing model and can be used in patients receiving or not receiving mechanical ventilation.
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Pagali, Sandeep R., Donna Miller, Karen Fischer, Darrell Schroeder, Norman Egger, Dennis M. Manning, Maria I. Lapid, Robert J. Pignolo, and M. Caroline Burton. "Predicting Delirium Risk Using an Automated Mayo Delirium Prediction Tool." Mayo Clinic Proceedings 96, no. 5 (May 2021): 1229–35. http://dx.doi.org/10.1016/j.mayocp.2020.08.049.

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Matsumoto, Koutarou, Yasunobu Nohara, Mikako Sakaguchi, Yohei Takayama, Shota Fukushige, Hidehisa Soejima, and Naoki Nakashima. "Delirium Prediction Using Machine Learning Interpretation Method and Its Incorporation into a Clinical Workflow." Applied Sciences 13, no. 3 (January 25, 2023): 1564. http://dx.doi.org/10.3390/app13031564.

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Delirium in hospitalized patients is a worldwide problem, causing a burden on healthcare professionals and impacting patient prognosis. A machine learning interpretation method (ML interpretation method) presents the results of machine learning predictions and promotes guided decisions. This study focuses on visualizing the predictors of delirium using a ML interpretation method and implementing the analysis results in clinical practice. Retrospective data of 55,389 patients hospitalized in a single acute care center in Japan between December 2017 and February 2022 were collected. Patients were categorized into three analysis populations, according to inclusion and exclusion criteria, to develop delirium prediction models. The predictors were then visualized using Shapley additive explanation (SHAP) and fed back to clinical practice. The machine learning-based prediction of delirium in each population exhibited excellent predictive performance. SHAP was used to visualize the body mass index and albumin levels as critical contributors to delirium prediction. In addition, the cutoff value for age, which was previously unknown, was visualized, and the risk threshold for age was raised. By using the SHAP method, we demonstrated that data-driven decision support is possible using electronic medical record data.
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Matsuoka, Ayaka, Toru Miike, Mariko Miyazaki, Taku Goto, Akira Sasaki, Hirotaka Yamazaki, Moe Komaki, et al. "Development of a delirium predictive model for adult trauma patients in an emergency and critical care center: a retrospective study." Trauma Surgery & Acute Care Open 6, no. 1 (November 2021): e000827. http://dx.doi.org/10.1136/tsaco-2021-000827.

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BackgroundDelirium has been shown to prolong the length of intensive care unit stay, hospitalization, and duration of ventilatory control, in addition to increasing the use of sedatives and increasing the medical costs. Although there have been a number of reports referring to risk factors for the development of delirium, no model has been developed to predict delirium in trauma patients at the time of admission. This study aimed to create a scoring system that predicts delirium in trauma patients.MethodsIn this single-center, retrospective, observational study, trauma patients aged 18 years and older requiring hospitalization more than 48 hours were included and divided into the development and validation cohorts. Univariate analysis was performed in the development cohort to identify factors significantly associated with prediction of delirium. The final scoring system for predicting delirium was developed using multivariate analysis and internal validation was performed.ResultsOf the 308 patients in the development cohort, 91 developed delirium. Clinical Frailty Score, fibrin/fibrinogen degradation products, low body mass index, lactate level, and Glasgow Coma Scale score were independently associated with the development of delirium. We developed a scoring system using these factors and calculated the delirium predictive score, which had an area under the curve of 0.85. In the validation cohort, 46 of 206 patients developed delirium. The area under the curve for the validation cohort was 0.86, and the calibration plot analysis revealed the scoring system was well calibrated in the validation cohort.DiscussionThis scoring system for predicting delirium in trauma patients consists of only five risk factors. Delirium prediction at the time of admission may be useful in clinical practice.Level of evidencePrognostic and epidemiological, level III.
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Boettger, Soenke, Rafael Meyer, André Richter, Susana Franco Fernandez, Alain Rudiger, Maria Schubert, Josef Jenewein, and David Garcia Nuñez. "Screening for delirium with the Intensive Care Delirium Screening Checklist (ICDSC): Symptom profile and utility of individual items in the identification of delirium dependent on the level of sedation." Palliative and Supportive Care 17, no. 1 (May 24, 2018): 74–81. http://dx.doi.org/10.1017/s1478951518000202.

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AbstractObjectiveThe importance of the proper identification of delirium, with its high incidence and adversities in the intensive care setting, has been widely recognized. One common screening instrument is the Intensive Care Delirium Screening Checklist (ICDSC); however, the symptom profile and key features of delirium dependent on the level of sedation have not yet been evaluated.MethodIn this prospective cohort study, the ICDSC was evaluated versus the Diagnostic and Statistical Manual, 4th edition, text revision, diagnosis of delirium set as standard with respect to the symptom profile, and correct identification of delirium. The aim of this study was to identify key features of delirium in the intensive care setting dependent on the Richmond Agitation and Sedation Scale levels of sedation: drowsiness versus alert and calmness.ResultThe 88 delirious patients of 225 were older, had more severe disease, and prolonged hospitalization. Irrespective of the level of sedation, delirium was correctly classified by items related to inattention, disorientation, psychomotor alterations, inappropriate speech or mood, and symptom fluctuation. In the drowsy patients, inattention reached substantial sensitivity and specificity, whereas psychomotor alterations and sleep-wake cycle disturbances were sensitive lacked specificity. The positive prediction was substantial across items, whereas the negative prediction was only moderate. In the alert and calm patient, the sensitivities were substantial for psychomotor alterations, sleep-wake cycle disturbances, and symptom fluctuations; however, these fluctuations were not specific. The positive prediction was moderate and the negative prediction substantial. Between the nondelirious drowsy and alert, the symptom profile was similar; however, drowsiness was associated with alterations in consciousness.Significance of resultsIn the clinical routine, irrespective of the level of sedation, delirium was characterized by the ICDSC items for inattention, disorientation, psychomotor alterations, inappropriate speech or mood and symptom fluctuation. Further, drowsiness caused altered levels of consciousness.
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Green, Cameron, William Bonavia, Candice Toh, and Ravindranath Tiruvoipati. "Prediction of ICU Delirium." Critical Care Medicine 47, no. 3 (March 2019): 428–35. http://dx.doi.org/10.1097/ccm.0000000000003577.

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Kotfis, Katarzyna, Marta Bott-Olejnik, Aleksandra Szylińska, and Iwona Rotter. "Could Neutrophil-to-Lymphocyte Ratio (NLR) Serve as a Potential Marker for Delirium Prediction in Patients with Acute Ischemic Stroke? A Prospective Observational Study." Journal of Clinical Medicine 8, no. 7 (July 22, 2019): 1075. http://dx.doi.org/10.3390/jcm8071075.

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Delirium is an acute brain disorder that commonly occurs in patients with acute ischemic stroke (AIS). Pathomechanism of delirium is related to the neuroinflammatory process and oxidative stress. Search for readily available diagnostic marker that will aid clinicians in early identification of delirium is ongoing. The aim of this study was to investigate whether neutrophil-to-lymphocyte ratio (NLR) could serve as a potential marker for delirium prediction in patients with AIS and to find an easy diagnostic tool using laboratory and clinical parameters to predict delirium. Prospective observational study (NCT03944694) included patients with AIS admitted to the neurology department of a district general hospital. All patients were screened for delirium using CAM-ICU (Confusion Assessment Method for Intensive Care Unit). Demographic and medical history data and admission lab results, including differential white blood cell analysis, were collected from all patients. We included 1001 patients in the final analysis. The mean age of the sample was 71 years, and 52% of patients were males. The incidence of early-onset delirium was 17.2%. The NLR was elevated in delirious patients (6.39 ± 8.60 vs. 4.61 ± 5.61, p < 0.001). The best cut-off value of NLR to predict delirium using the receiver operating characteristics (ROC) was determined at 4.86. Multivariable logistic regression analysis showed that the odds ratio (OR) for developing delirium with NLR > 4.86 (adjusted for age, sex, body mass index (BMI), comorbidities, and baseline neurology) was 1.875 (95% CI 1.314–2.675, p = 0.001). As a result of different combinations of markers and clinical parameters based on logistic regression, a formula—DELirium in Acute Ischemic Stroke (DELIAS score)—was obtained with the area under the ROC curve of 0.801 (p < 0.001). After regression of the cut-off points of the obtained curve, a significant correlation of the DELIAS score was observed with the occurrence of early-onset delirium (OR = 8.976, p < 0.001) and with delirium until the fifth day after AIS (OR = 7.744, p < 0.001). In conclusion, NLR can be regarded as a potential marker for prediction of early-onset delirium after AIS. On the basis of combined laboratory and clinical parameters, the DELIAS score was calculated, which gave the highest predictive value for delirium in the analyzed group of patients after ischemic stroke. However, further studies are needed to validate these findings.
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Oosterhoff, Jacobien H. F., Aditya V. Karhade, Tarandeep Oberai, Esteban Franco-Garcia, Job N. Doornberg, and Joseph H. Schwab. "Prediction of Postoperative Delirium in Geriatric Hip Fracture Patients: A Clinical Prediction Model Using Machine Learning Algorithms." Geriatric Orthopaedic Surgery & Rehabilitation 12 (January 2021): 215145932110622. http://dx.doi.org/10.1177/21514593211062277.

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Introduction Postoperative delirium in geriatric hip fracture patients adversely affects clinical and functional outcomes and increases costs. A preoperative prediction tool to identify high-risk patients may facilitate optimal use of preventive interventions. The purpose of this study was to develop a clinical prediction model using machine learning algorithms for preoperative prediction of postoperative delirium in geriatric hip fracture patients. Materials & Methods Geriatric patients undergoing operative hip fracture fixation were queried in the American College of Surgeons National Surgical Quality Improvement Program database (ACS NSQIP) from 2016 through 2019. A total of 28 207 patients were included, of which 8030 (28.5%) developed a postoperative delirium. First, the dataset was randomly split 80:20 into a training and testing subset. Then, a random forest (RF) algorithm was used to identify the variables predictive for a postoperative delirium. The machine learning-model was developed on the training set and the performance was assessed in the testing set. Performance was assessed by discrimination (c-statistic), calibration (slope and intercept), overall performance (Brier-score), and decision curve analysis. Results The included variables identified using RF algorithms were (1) age, (2) ASA class, (3) functional status, (4) preoperative dementia, (5) preoperative delirium, and (6) preoperative need for mobility-aid. The clinical prediction model reached good discrimination (c-statistic = .79), almost perfect calibration (intercept = −.01, slope = 1.02), and excellent overall model performance (Brier score = .15). The clinical prediction model was deployed as an open-access web-application: https://sorg-apps.shinyapps.io/hipfxdelirium/ . Discussion & Conclusions We developed a clinical prediction model that shows promise in estimating the risk of postoperative delirium in geriatric hip fracture patients. The clinical prediction model can play a beneficial role in decision-making for preventative measures for patients at risk of developing a delirium. If found to be externally valid, clinicians might use the available web-based application to help incorporate the model into clinical practice to aid decision-making and optimize preoperative prevention efforts.
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Dissertations / Theses on the topic "Delirium prediction"

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Sheikhalishahi, Seyedmostafa. "Machine learning applications in Intensive Care Unit." Doctoral thesis, Università degli studi di Trento, 2022. http://hdl.handle.net/11572/339274.

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The rapid digitalization of the healthcare domain in recent years highlighted the need for advanced predictive methods particularly based upon deep learning methods. Deep learning methods which are capable of dealing with time- series data have recently emerged in various fields such as natural language processing, machine translation, and the Intensive Care Unit (ICU). The recent applications of deep learning in ICU have increasingly received attention, and it has shown promising results for different clinical tasks; however, there is still a need for the benchmark models as far as a handful of public datasets are available in ICU. In this thesis, a novel benchmark model of four clinical tasks on a multi-center publicly available dataset is presented; we employed deep learning models to predict clinical studies. We believe this benchmark model can facilitate and accelerate the research in ICU by allowing other researchers to build on top of it. Moreover, we investigated the effectiveness of the proposed method to predict the risk of delirium in the varying observation and prediction windows, the variable ranking is provided to ease the implementation of a screening tool for helping caregivers at the bedside. Ultimately, an attention-based interpretable neural network is proposed to predict the outcome and rank the most influential variables in the model predictions’ outcome. Our experimental findings show the effectiveness of the proposed approaches in improving the application of deep learning models in daily ICU practice.
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Ha, Albert Sangji. "A Contemporary, Population-Based Analysis of the Incidence, Cost, Outcomes, and Preoperative Risk Prediction of Postoperative Delirium Following Major Urologic Cancer Surgeries." Thesis, Harvard University, 2017. http://nrs.harvard.edu/urn-3:HUL.InstRepos:32676128.

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Introduction Postoperative delirium is associated with poor outcomes and increased healthcare costs across numerous surgical and medical disciplines. Although characterized in other surgical fields, the population-based incidence, outcomes, and cost of delirium have not been assessed in major urologic cancer surgeries. We sought to evaluate the incidence, outcomes, and cost of postoperative delirium after major urologic cancer surgeries, specifically after radical prostatectomy (RP), radical nephrectomy (RN), partial nephrectomy (PN), and radical cystectomy (RC) in the USA. We have also developed a preoperative risk prediction model specific to major urologic cancer surgeries to identify patients at high risk for postoperative delirium. Methods Using the Premier Hospital Database, we retrospectively identified patients who underwent radical prostatectomy (RP), radical nephrectomy (RN), partial nephrectomy (PN), and radical cystectomy (RC) from 2003 to 2013. Postoperative delirium was identified using ICD-9 codes, as well as postoperative use of antipsychotics, sitters, and restraints. We constructed regression models to assess for mortality, discharge disposition, length of stay (LOS), and direct hospital costs. A preoperative risk stratification scoring system was also developed using known risk factors of delirium. The entire cohort was randomly divided into training (70%) and validation (30%) cohorts. Preoperative patient, hospital, and surgical characteristics associated with delirium were analyzed using multivariate regression, and a risk prediction score was developed using the training cohort. Its performance was quantified using Receiver Operating Characteristic (ROC) analysis in both cohorts. Results We identified 165,387 patients representing a weighted total of 1,097,355 patients. 30,063 (2.7%) experienced postoperative delirium. The greatest incidence of delirium occurred after RC, with 6,268 cases (11%). Delirious patients had greater adjusted odds of in-hospital mortality (OR 3.65; p <0.001), 90-day mortality (OR 1.47; p = 0.013), discharge with home health services (OR 2.25; p <0.001), discharge to skilled nursing facilities (OR 4.64; p <0.001), and 0.9-day increase in median LOS (p <0.001). Delirious patients also experienced a $2,697 increase in direct admission costs (p <0.001), with the greatest costs in RC patients ($30,859 vs. $26,607; p<0.001). The largest driver of costs was in room and board across all surgeries (p<0.001). Our training and validation cohorts consisted of a weighted total of 767,408 and 329,926 patients, respectively. Our final model revealed many factors that increase risk for delirium, which were used to create a preoperative risk score. The additive score was predictive of delirium in both the training (OR: 1.35, 95% CI, 1.32-1.37, p<0.001) and validation cohorts (OR: 1.34, 95% CI 1.31-1.36, p<0.001). The score also demonstrated good discrimination in predicting delirium in the training (AUC: 0.74, 95% CI, 0.74-0.76) and validation (AUC: 0.75, 95% CI, 0.73-0.76) cohorts. Conclusion Patients with postoperative delirium experienced worse outcomes, prolonged LOS, and increased admission costs following major urologic cancer surgeries. In particular, the largest incidence and costs occurred in delirious patients after RC. Moreover, the results of the pre-operative risk prediction tool for delirium following major urologic cancer surgeries are promising given their consistency with published delirium risk factors and ease of use. Further testing will shed light on its clinical utility.
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Cunningham, Emma Louise. "Predicting the risk of post-operative delirium : use of neuropsychology, serum and CSF biomarkers and genetics to predict risk of post-operative delirium." Thesis, Queen's University Belfast, 2015. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.695315.

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Delirium following surgery is common and is associated with negative outcomes. Across surgical populations pre-operative cognitive impairment is a consistent risk factor for post-operative delirium. This study tests the hypothesis that the quantification of brain vulnerability, using neuropsychological tests, plasma and cerebrospinal fluid (CSF) biomarkers, and Apolipoprotein E status, can quantify the risk of post-operative delirium following elective primary arthroplasty surgery. An observational cohort study of patients over 65 years of age, admitted for elective primary hip or knee arthroplasty, under spinal anaesthetic, was undertaken with participants recruited between 23rd March 2012 and 21st October 2014. Of the 315 participants completing the study 40 (12.7%) developed delirium post-operatively. On univariate analyses several baseline characteristics, pre-operative performance on several neurocognitive tests, pre-operative plasma albumin concentration and CSF matrix metalloproteinase (MMP) 3 concentration were associated with post-oper:ative delirium. Only one pre-operative neuropsychological test - 3-item recall - and CSF MMP-3 concentration remained significant following multivariate analyses involving the entire cohort. The findings of this study support the hypothesis that quantification of brain vulnerability can predict the risk of delirium following elective arthroplasty surgery.
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Book chapters on the topic "Delirium prediction"

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van den Boogaard, Mark, and John W. Devlin. "Prediction Models for Delirium in Critically Ill Adults." In Delirium, 57–72. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-25751-4_5.

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Amadin, Frank Iwebuke, and Moses Eromosele Bello. "A Neuro Fuzzy Approach for Predicting Delirium." In Advances in Intelligent Systems and Computing, 692–99. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01054-6_50.

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Mehrotra, Anchit, and Natasha Keric. "Delirium as a Predictor of Mortality in Mechanically Ventilated Patients in the Intensive Care Unit." In 50 Landmark Papers, 276–77. Boca : CRC Press, [2020]: CRC Press, 2019. http://dx.doi.org/10.1201/9780429316944-88.

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Dostovic, Zikrija, Dzevdet Smajlovic, Ernestina Dostovic, and Omer C. "Risk Factors for Delirium in the Acute Stroke." In Mental Illnesses - Understanding, Prediction and Control. InTech, 2012. http://dx.doi.org/10.5772/29885.

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Jauk, Stefanie, Sai Pavan Kumar Veeranki, Diether Kramer, Stefan Högler, David Mühlecker, Erwin Eberhartl, Arthur Schueler, et al. "External Validation of a Machine Learning Based Delirium Prediction Software in Clinical Routine." In Studies in Health Technology and Informatics. IOS Press, 2022. http://dx.doi.org/10.3233/shti220353.

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Background: Various machine learning (ML) models have been developed for the prediction of clinical outcomes, but there is missing evidence on their performance in clinical routine and external validation. Objectives: Our aim was to deploy and prospectively evaluate an already developed delirium prediction software in clinical routine of an external hospital. Methods: We compared updated ML models of the software and models re-trained with the external hospital’s data. The best models were deployed in clinical routine for one month, and risk predictions for all admitted patients were compared to the risk ratings of a senior physician. After using the software, clinicians completed a questionnaire assessing technology acceptance. Results: Re-trained models achieved a high discriminative performance (AUROC > 0.92). Compared to clinical risk ratings, the software achieved a sensitivity of 100.0% and a specificity of 90.6%. Usefulness, ease of use and output quality were rated positively by the users. Conclusion: A ML based delirium prediction software achieved a high discriminative performance and high technology acceptance at an external hospital using re-trained ML models.
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Rossi Varallo, Fabiana, Alan Maicon de Oliveira, Ariane Cristina Barboza Zanetti, Helaine Carneiro Capucho, Leonardo Régis Leira Pereira, Lucas Borges Pereira, Maria Olívia Barboza Zanetti, Thalita Zago Oliveira, and Vinícius Detoni Lopes. "Drug-Induced Delirium among Older People." In New Insights into the Future of Pharmacoepidemiology and Drug Safety [Working Title]. IntechOpen, 2021. http://dx.doi.org/10.5772/intechopen.95470.

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Although underdiagnosed, delirium is a common and potentially preventable problem in older patients, being associated with morbimortality. Drugs have been associated with the development of delirium in the geriatric population and may be considered the most easily reversible trigger. Polypharmacy, prescription of deliriogenic, anticholinergic and potentially inappropriate drugs are contributing factors for the occurrence of the disturb. Furthermore, changes in pharmacokinetic and pharmacodynamic parameters, which are intrinsic of the aged process, may contribute for cognitive impairment. Identification and reversal of clinical conditions associated with delirium are the first step to treat the disturbance, as well as mitigation of environmental factors and the exposition to deliriogenic drugs. Current evidence does not support the prescription of antipsychotics and benzodiazepines for the treatment of delirium. However, the judicious use of first- or second-generation antipsychotics can be considered in severe cases. Multi-component non-pharmacological, software-based intervention to identify medications that could contribute to delirium, predictive models, tools, training of health professionals and active actions of pharmacovigilance may contribute to the screening, prevention, and management of delirium in older people. Besides, it is also important to improve the report of drug-induced delirium in medical records, to develop properly risk management plans and avoid cascade iatrogenesis.
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Cheng, Yan, Yijun Shao, James Rudolph, Charlene R. Weir, Beth Sahlmann, and Qing Zeng-Treitler. "Accuracies of Training Labels and Machine Learning Models: Experiments on Delirium and Simulated Data." In MEDINFO 2021: One World, One Health – Global Partnership for Digital Innovation. IOS Press, 2022. http://dx.doi.org/10.3233/shti220161.

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Supervised predictive models require labeled data for training purposes. Complete and accurate labeled data is not always available, and imperfectly labeled data may need to serve as an alternative. An important question is if the accuracy of the labeled data creates a performance ceiling for the trained model. In this study, we trained several models to recognize the presence of delirium in clinical documents using data with annotations that are not completely accurate. In the external evaluation, the support vector machine model with a linear kernel performed best, achieving an area under the curve of 89.3% and accuracy of 88%, surpassing the 80% accuracy of the training sample. We then generated a set of simulated data and carried out a series of experiments which demonstrated that models trained on imperfect data can (but do not always) outperform the accuracy of the training data.
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Conference papers on the topic "Delirium prediction"

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Azevedo, Wylson, Eduardo Augusto Schutz, Mayara Menezes Attuy, Thamara Graziela Flores, and Melissa Agostini Lampert. "Prediction model to delirium in hospitalized elderly people." In XIII Congresso Paulista de Neurologia. Zeppelini Editorial e Comunicação, 2021. http://dx.doi.org/10.5327/1516-3180.478.

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Introduction: Delirium has a high prevalence in hospitalized elderly patients. This is due to low hospital detection and the absence of a screening instrument. Objective: evaluate predictive variables in the development of delirium in na in-hospital environment. Methods: Cross-sectional study. Data collection was carried out between 2015-2016, with a sample of 493 elderly people. The variables used were age, sex, the reason for hospitalization, Identification of Elderly at Risk (ISAR), delirium during hospitalization using the Confusion Assessment Method, frailty using the Edmonton Scale, the impact of comorbidities by the Charlson Index and hospital immobility. Predictive variables were identified through logistic regression. Results: 469 elderly people were taken. The presence of delirium during hospitalization was mostly observed between 80 and 89 years old (n = 12), female (n = 16), with the most common reasons for hospitalization due to fractures (n = 6) and accident brain vascular (n = 11), 79% chance of surviving in one year using the Charlson Index (n = 11) and with ISAR> 2 (n = 26). There are important associations for the development of delirium for patients who have a 98% chance of surviving in one year (p = 0.05) and with ISAR <2 (p = 0.027), with a 34% increased chance and 38%, respectively. Conclusion: It is observed that, by the results, the predictive variables of inhospital delirium are patients with a 98% chance of survival and with ISAR <2.
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Davoudi, Anis, Tezcan Ozrazgat-Baslanti, Ashkan Ebadi, Alberto C. Bursian, Azra Bihorac, and Parisa Rashidi. "Delirium Prediction using Machine Learning Models on Predictive Electronic Health Records Data." In 2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE). IEEE, 2017. http://dx.doi.org/10.1109/bibe.2017.00014.

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Lucini, Filipe R., Kirsten M. Fiest, Henry T. Stelfox, and Joon Lee. "Delirium prediction in the intensive care unit: a temporal approach." In 2020 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) in conjunction with the 43rd Annual Conference of the Canadian Medical and Biological Engineering Society. IEEE, 2020. http://dx.doi.org/10.1109/embc44109.2020.9176042.

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Kohistani, Z., S. Repschläger, W. Kai, W. Schiller, A. Welz, H. Treede, and S. Kebir. "Postoperative Delirium Prediction through Machine Learning in Patients Undergoing Aortocoronary Bypass Surgery." In 50th Annual Meeting of the German Society for Thoracic and Cardiovascular Surgery (DGTHG). Georg Thieme Verlag KG, 2021. http://dx.doi.org/10.1055/s-0041-1725694.

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Prendergast, N., P. Tiberio, K. Rengel, C. Boncyk, E. W. Ely, P. Pandharipande, and T. D. Girard. "Derivation of a Clinical Prediction Rule for Sedative-Associated Delirium During Acute Respiratory Failure." In American Thoracic Society 2021 International Conference, May 14-19, 2021 - San Diego, CA. American Thoracic Society, 2021. http://dx.doi.org/10.1164/ajrccm-conference.2021.203.1_meetingabstracts.a2862.

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Kohistani, Z., S. Kebir, S. Repschläger, M. Hamiko, and F. Bakhtiary. "Comparison of Machine Learning Models for Delirium Prediction in Patients Undergoing Aortocoronary Bypass Surgery." In 51st Annual Meeting of the German Society for Thoracic and Cardiovascular Surgery (DGTHG). Georg Thieme Verlag KG, 2022. http://dx.doi.org/10.1055/s-0042-1742792.

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Prendergast, N., P. J. Tiberio, C. A. Onyemekwu, K. M. Potter, S. M. Nouriae, K. F. Rengel, C. Boncyk, E. W. Ely, P. P. Pandharipande, and T. D. Girard. "Multiple Statistical Approaches to a Clinical Prediction Rule for Sedative-Associated Delirium During Acute Respiratory Failure." In American Thoracic Society 2022 International Conference, May 13-18, 2022 - San Francisco, CA. American Thoracic Society, 2022. http://dx.doi.org/10.1164/ajrccm-conference.2022.205.1_meetingabstracts.a2284.

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Gergen, D. J., and E. L. Burnham. "Evaluating the Alcohol Use Disorders Identification Test (AUDIT-C) as a Delirium Prediction Tool in the Critically Ill." In American Thoracic Society 2020 International Conference, May 15-20, 2020 - Philadelphia, PA. American Thoracic Society, 2020. http://dx.doi.org/10.1164/ajrccm-conference.2020.201.1_meetingabstracts.a3574.

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Ma, Owen, Arindam Dutta, Daniel W. Bliss, and Amy Z. Crepeau. "Predicting postoperative delirium in patients undergoing deep hypothermia circulatory arrest." In 2017 51st Asilomar Conference on Signals, Systems, and Computers. IEEE, 2017. http://dx.doi.org/10.1109/acssc.2017.8335566.

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Harvey, J., R. Shaw, J. Soar, L. Neffgen, M. Morris, R. Gunary, and H. Steiner. "Delirium as a Predictor of Post Traumatic Stress Symptoms Following Ventilation for Respiratory Failure." In American Thoracic Society 2009 International Conference, May 15-20, 2009 • San Diego, California. American Thoracic Society, 2009. http://dx.doi.org/10.1164/ajrccm-conference.2009.179.1_meetingabstracts.a5837.

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