Dataset Information

Novel Machine Learning Approach to Predict and Personalize Length of Stay for Patients Admitted with Syncope from the Emergency Departmen.

ABSTRACT: Background: Syncope, a common problem encountered in the emergency department (ED), has a multitude of causes ranging from benign to life-threatening. Hospitalization may be required, but the management can vary substantially depending on specific clinical characteristics. Models predicting admission and hospitalization length of stay (LoS) are lacking. The purpose of this study was to design an effective, exploratory model using machine learning (ML) technology to predict LoS for patients presenting with syncope. Methods: This was a retrospective analysis using over 4 million patients from the National Emergency Department Sample (NEDS) database presenting to the ED with syncope between 2016-2019. A multilayer perceptron neural network with one hidden layer was trained and validated on this data set. Results: Receiver Operator Characteristics (ROC) were determined for each of the five ANN models with varying cutoffs for LoS. A fair area under the curve (AUC of 0.78) to good (AUC of 0.88) prediction performance was achieved based on sequential analysis at different cutoff points, starting from the same day discharge and ending at the longest analyzed cutoff LoS ≤7 days versus >7 days, accordingly. The ML algorithm showed significant sensitivity and specificity in predicting short (≤48 h) versus long (>48 h) LoS, with an AUC of 0.81. Conclusions: Using variables available to triaging ED clinicians, ML shows promise in predicting hospital LoS with fair to good performance for patients presenting with syncope.

SUBMITTER: Lee S

PROVIDER: S-EPMC9864075 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Novel Machine Learning Approach to Predict and Personalize Length of Stay for Patients Admitted with Syncope from the Emergency Department.

Lee Sangil S Reddy Mudireddy Avinash A Kumar Pasupula Deepak D Adhaduk Mehul M Barsotti E John EJ Sonka Milan M Statz Giselle M GM Bullis Tyler T Johnston Samuel L SL Evans Aron Z AZ Olshansky Brian B Gebska Milena A MA

Journal of personalized medicine 20221220 1

Background: Syncope, a common problem encountered in the emergency department (ED), has a multitude of causes ranging from benign to life-threatening. Hospitalization may be required, but the management can vary substantially depending on specific clinical characteristics. Models predicting admission and hospitalization length of stay (LoS) are lacking. The purpose of this study was to design an effective, exploratory model using machine learning (ML) technology to predict LoS for patients prese ...[more]

PMID: 36675668

Similar Datasets

Project description:Performance measures, particularly pay for performance, may have unintended consequences for safety-net institutions caring for disproportionate shares of Medicaid or uninsured patients.To describe emergency department (ED) compliance with proposed length-of-stay measures for admissions (8 hours or 480 minutes) and discharges, transfers, and observations (4 hours or 240 minutes) by safety-net status.The 2008 National Hospital Ambulatory Medical Care Survey (NHAMCS) ED data were stratified by safety-net status (Centers for Disease Control and Prevention definition) and disposition (admission, discharge, observation, transfer). The 2008 NHAMCS is a national probability sample of 396 hospitals (90.2% unweighted response rate) and 34 134 patient records. Visits were excluded for patients younger than 18 years, missing length-of-stay data or dispositions of missing, other, left against medical advice, or dead on arrival. Median and 90th percentile ED lengths of stay were calculated for each disposition and admission/discharge subcategories (critical care, psychiatric, routine) stratified by safety-net status. Multivariable analyses determined associations with length-of-stay measure compliance.Emergency Department length-of-stay measure compliance by disposition and safety-net status.Of the 72.1% ED visits (N = 24 719) included in the analysis, 42.3% were to safety-net EDs and 57.7% were to non-safety-net EDs. The median length of stay for safety-net was 269 minutes (interquartile range [IQR], 178-397 minutes) for admission vs 281 minutes (IQR, 178-401 minutes) for non-safety-net EDs; 156 minutes (IQR, 95-239 minutes) for discharge vs 148 minutes (IQR, 88-238 minutes); 355 minutes (IQR, 221-675 minutes) for observations vs 298 minutes (IQR, 195-440 minutes); and 235 minutes (IQR, 155-378 minutes) for transfers vs 239 minutes (IQR, 142-368 minutes). Safety-net status was not independently associated with compliance with ED length-of-stay measures; the odds ratio was 0.83 for admissions (95% CI, 0.52-1.34); 1.03 for discharges (95% CI, 0.83-1.27); 1.05 for observations (95% CI, 0.57-1.95), 1.30 for transfers (95% CI, 0.70-2.45]); or subcategories except for psychiatric discharges (1.67, [95% CI, 1.02-2.74]).Compliance with proposed ED length-of-stay measures for admissions, discharges, transfers, and observations did not differ significantly between safety-net and non-safety-net hospitals.

Project description:ObjectiveThis study aims to develop and compare different models to predict the Length of Stay (LoS) and the Prolonged Length of Stay (PLoS) of inpatients admitted through the emergency department (ED) in general patient settings. This aim is not only to promote any specific model but rather to suggest a decision-supporting tool (i.e., a prediction framework).MethodsWe analyzed a dataset of patients admitted through the ED to the "Sant"Orsola Malpighi University Hospital of Bologna, Italy, between January 1 and October 26, 2022. PLoS was defined as any hospitalization with LoS longer than 6 days. We deployed six classification algorithms for predicting PLoS: Random Forest (RF), Support Vector Machines (SVM), Gradient Boosting (GB), AdaBoost, K-Nearest Neighbors (KNN), and logistic regression (LoR). We evaluated the performance of these models with the Brier score, the area under the ROC curve (AUC), accuracy, sensitivity (recall), specificity, precision, and F1-score. We further developed eight regression models for LoS prediction: Linear Regression (LR), including the penalized linear models Least Absolute Shrinkage and Selection Operator (LASSO), Ridge and Elastic-net regression, Support vector regression, RF regression, KNN, and eXtreme Gradient Boosting (XGBoost) regression. The model performances were measured by their mean square error, mean absolute error, and mean relative error. The dataset was randomly split into a training set (70%) and a validation set (30%).ResultsA total of 12,858 eligible patients were included in our study, of whom 60.88% had a PloS. The GB classifier best predicted PloS (accuracy 75%, AUC 75.4%, Brier score 0.181), followed by LoR classifier (accuracy 75%, AUC 75.2%, Brier score 0.182). These models also showed to be adequately calibrated. Ridge and XGBoost regressions best predicted LoS, with the smallest total prediction error. The overall prediction error is between 6 and 7 days, meaning there is a 6-7 day mean difference between actual and predicted LoS.ConclusionOur results demonstrate the potential of machine learning-based methods to predict LoS and provide valuable insights into the risks behind prolonged hospitalizations. In addition to physicians' clinical expertise, the results of these models can be utilized as input to make informed decisions, such as predicting hospitalizations and enhancing the overall performance of a public healthcare system.

Project description:Background and aimsSyncope is a frequent reason for hospital emergency admissions, presenting significant challenges in determining its cause and associated risks. Despite its prevalence, research on using artificial intelligence (AI) to improve patient outcomes in this context has been limited. The main objective of current study is to predict the severity of syncope cases using machine learning (ML) algorithms based on data collected during on-site treatment and ambulance transportation.MethodsThis study analyzed 572 records from five Spanish public hospitals (2018-2021), focusing on hospitalization, ICU admission, and mortality. A three-phase strategy was used: data preprocessing, model exploration, and model selection. In the exploration phase, three data transformations techniques were applied and in each of them, models were evaluated using stratified 10-fold cross-validation, optimizing AUC, accuracy, and recall, with emphasis on minimizing false negatives (FN). The top-performing models were fine-tuned and tested. The strategy was implemented using Python libraries and a diverse set of ML classifiers were applied, including linear discriminant analysis (LDA), random forest (RF), dummy classifier (DC), and gradient boosting (GB).ResultsThe RF classifier performed best for predicting hospitalization, reducing FN to 37% and achieving a true negative rate (TN) of 78%, with a recall of 0.63 and accuracy of 0.74. For ICU, DC showed FN = 29%, TN = 57%, recall = 0.625, and accuracy = 0.58. The LDA classifier excelled in predicting hospital mortality, with FN = 40%, TN = 89%, recall = 0.6, and accuracy = 0.88. These results indicate that RF was superior for predicting hospitalization, while DC for ICU and LDA performed better for predicting mortality.ConclusionsThis study provides an experimental foundation for the application of ML techniques in managing syncope in ED. The intention is to stimulate AI research in this area, with a view to integrating these models into clinical workflows in the future.

Project description:BackgroundProlonged emergency department length of stay (EDLOS) in critically ill patients leads to increased mortality. This nationwide study investigated patient and hospital characteristics associated with prolonged EDLOS and in-hospital mortality in adult patients admitted from the emergency department (ED) to the intensive care unit (ICU).MethodsWe conducted a retrospective cohort study using data from the National Emergency Department Information System. Prolonged EDLOS was defined as an EDLOS of ≥ 6 h. We constructed multivariate logistic regression models of patient and hospital variables as predictors of prolonged EDLOS and in-hospital mortality.ResultsBetween 2016 and 2019, 657,622 adult patients were admitted to the ICU from the ED, representing 2.4% of all ED presentations. The median EDLOS of the overall study population was 3.3 h (interquartile range, 1.9-6.1 h) and 25.3% of patients had a prolonged EDLOS. Patient characteristics associated with prolonged EDLOS included night-time ED presentation and Charlson comorbidity index (CCI) score of 1 or higher. Hospital characteristics associated with prolonged EDLOS included a greater number of staffed beds and a higher ED level. Prolonged EDLOS was associated with in-hospital mortality after adjustment for selected confounders (adjusted odds ratio: 1.18, 95% confidence interval: 1.16-1.20). Patient characteristics associated with in-hospital mortality included age ≥ 65 years, transferred-in, artificially ventilated in the ED, assignment of initial triage to more urgency, and CCI score of 1 or higher. Hospital characteristics associated with in-hospital mortality included a lesser number of staffed beds and a lower ED level.ConclusionsIn this nationwide study, 25.3% of adult patients admitted to the ICU from the ED had a prolonged EDLOS, which in turn was significantly associated with an increased in-hospital mortality risk. Hospital characteristics, including the number of staffed beds and the ED level, were associated with prolonged EDLOS and in-hospital mortality.

Dataset Information

Novel Machine Learning Approach to Predict and Personalize Length of Stay for Patients Admitted with Syncope from the Emergency Departmen.

Publications

Novel Machine Learning Approach to Predict and Personalize Length of Stay for Patients Admitted with Syncope from the Emergency Department.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets