Identifying Risk Factors for Prolonged Length of Stay in Hospital and Developing Prediction Models for Patients with Cardiac Arrest Receiving Targeted Temperature Management

Chiu, W. Henry; Chan, Lung; Masud, Jakir Hossain Bhuiyan; Hong, Chien Tai; Chien, Yu-San; Hsu, Chih‐Hsin; Wu, Cheng‐Hsueh; Wang, Chen‐Hsu; Tan, Shennie; Chung, Chen-Chih

doi:10.31083/j.rcm2402055

Cited by 3 publications

(1 citation statement)

References 63 publications

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“…However, as LoS increases beyond level 1, there are other 'derived' factors that become more important; irregular diastolic pressure due to arrhythmia becomes more prevalent in patients with LoS between 20 and 30 days, or abnormal breathing signifies a higher New York Heart Association class of heart failure resulting in level 3 LoS. Additionally, the days required to stabilize and prep the patient before CABG are significantly increased due to the high-risk nature of the patient, coupled with any cardiac complications at the time of admission, resulting in a prolonged LoS (level 3) [70].…”

Section: Discussionmentioning

confidence: 99%

Predicting the Length of Stay of Cardiac Patients Based on Pre-Operative Variables—Bayesian Models vs. Machine Learning Models

Abdurrab,

Mahmood,

Sheikh

et al. 2024

Healthcare

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Length of stay (LoS) prediction is deemed important for a medical institution’s operational and logistical efficiency. Sound estimates of a patient’s stay increase clinical preparedness and reduce aberrations. Various statistical methods and techniques are used to quantify and predict the LoS of a patient based on pre-operative clinical features. This study evaluates and compares the results of Bayesian (simple Bayesian regression and hierarchical Bayesian regression) models and machine learning (ML) regression models against multiple evaluation metrics for the problem of LoS prediction of cardiac patients admitted to Tabba Heart Institute, Karachi, Pakistan (THI) between 2015 and 2020. In addition, the study also presents the use of hierarchical Bayesian regression to account for data variability and skewness without homogenizing the data (by removing outliers). LoS estimates from the hierarchical Bayesian regression model resulted in a root mean squared error (RMSE) and mean absolute error (MAE) of 1.49 and 1.16, respectively. Simple Bayesian regression (without hierarchy) achieved an RMSE and MAE of 3.36 and 2.05, respectively. The average RMSE and MAE of ML models remained at 3.36 and 1.98, respectively.

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Section: Discussionmentioning

confidence: 99%

Predicting the Length of Stay of Cardiac Patients Based on Pre-Operative Variables—Bayesian Models vs. Machine Learning Models

Abdurrab,

Mahmood,

Sheikh

et al. 2024

Healthcare

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Hospital Length of Stay Prediction for Planned Admissions Using Observational Medical Outcomes Partnership Common Data Model: Retrospective Study

Lee,

Kim,

Moon

et al. 2024

J Med Internet Res

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Background Accurate hospital length of stay (LoS) prediction enables efficient resource management. Conventional LoS prediction models with limited covariates and nonstandardized data have limited reproducibility when applied to the general population. Objective In this study, we developed and validated a machine learning (ML)–based LoS prediction model for planned admissions using the Observational Medical Outcomes Partnership Common Data Model (OMOP CDM). Methods Retrospective patient-level prediction models used electronic health record (EHR) data converted to the OMOP CDM (version 5.3) from Seoul National University Bundang Hospital (SNUBH) in South Korea. The study included 137,437 hospital admission episodes between January 2016 and December 2020. Covariates from the patient, condition occurrence, medication, observation, measurement, procedure, and visit occurrence tables were included in the analysis. To perform feature selection, we applied Lasso regularization in the logistic regression. The primary outcome was an LoS of 7 days or longer, while the secondary outcome was an LoS of 3 days or longer. The prediction models were developed using 6 ML algorithms, with the training and test set split in a 7:3 ratio. The performance of each model was evaluated based on the area under the receiver operating characteristic curve (AUROC) and the area under the precision-recall curve (AUPRC). Shapley Additive Explanations (SHAP) analysis measured feature importance, while calibration plots assessed the reliability of the prediction models. External validation of the developed models occurred at an independent institution, the Seoul National University Hospital. Results The final sample included 129,938 patient entry events in the planned admissions. The Extreme Gradient Boosting (XGB) model achieved the best performance in binary classification for predicting an LoS of 7 days or longer, with an AUROC of 0.891 (95% CI 0.887-0.894) and an AUPRC of 0.819 (95% CI 0.813-0.826) on the internal test set. The Light Gradient Boosting (LGB) model performed the best in the multiclassification for predicting an LoS of 3 days or more, with an AUROC of 0.901 (95% CI 0.898-0.904) and an AUPRC of 0.770 (95% CI 0.762-0.779). The most important features contributing to the models were the operation performed, frequency of previous outpatient visits, patient admission department, age, and day of admission. The RF model showed robust performance in the external validation set, achieving an AUROC of 0.804 (95% CI 0.802-0.807). Conclusions The use of the OMOP CDM in predicting hospital LoS for planned admissions demonstrates promising predictive capabilities for stays of varying durations. It underscores the advantage of standardized data in achieving reproducible results. This approach should serve as a model for enhancing operational efficiency and patient care coordination across health care settings.

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Hospital Length of Stay Prediction for Planned Admissions Using Observational Medical Outcomes Partnership Common Data Model: Retrospective Study (Preprint)

Lee,

Kim,

Moon

et al. 2024

Preprint

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BACKGROUND Accurate prediction of hospital length of stay (LoS) is essential for efficient resource management. However, conventional LoS prediction models with limited covariates and non-standardized data tend to experience limited reproducibility and generalizability when applied to the general population. OBJECTIVE In this study, we aimed to develop and validate machine learning (ML)-based LoS prediction models for planned admissions using the Observational Medical Outcomes Partnership Common Data Model (OMOP CDM). METHODS Retrospective patient-level prediction models were developed using electronic health record (EHR) data converted to the OMOP CDM (version 5.3) from Seoul National University Bundang Hospital (SNUBH) in South Korea. The study included 137,437 hospital admission episodes that occurred between January 2016 and December 2020. Covariates from the patient, condition occurrence, medication, observation, measurement, procedure, and visit occurrence tables were included in the analysis. Lasso regularization was applied in logistic regression to perform feature selection. The primary outcome was an LoS of seven days or longer, and the secondary outcome was an LoS of 3 days or longer. The prediction models were developed using six ML algorithms, with the training and test set split in a 7:3 ratio. The performance of each model was evaluated based on the area under the receiver operating characteristic curve (AUROC) and the area under Precision-Recall curve (AUPRC). SHapley Additive exPlanations (SHAP) analysis was performed to measure feature importance, while calibration plots were used to assess the reliability of the prediction models. The developed models were externally validated at an independent institution, Seoul National University Hospital (SNUH). RESULTS A total of 129,938 patient entry events were included in the planned admissions. The XGB model achieved the best performance in binary classification for predicting an LoS of 7 days or longer, with an AUROC of 0.89 and an AUPRC of 0.82 on the internal test set. The LGB model performed the best in multi-classification for predicting an LoS of 3 days or more, with a mean AUROC of 0.83 and a macro-average AUPRC of 0.56. The most important features contributing to the models were the operation performed, frequency of previous outpatient visits, patient admission department, age, and day of week admission. The RF model showed robust performance in the external validation set, achieving an AUROC of 0.80. CONCLUSIONS The use of the OMOP CDM in predicting hospital LoS for planned admissions not only demonstrates promising predictive capabilities for stays of varying durations but also underscores the advantage of standardized data in achieving reproducible results. This approach could serve as a model for enhancing operational efficiency and patient care coordination across healthcare settings.

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Identifying Risk Factors for Prolonged Length of Stay in Hospital and Developing Prediction Models for Patients with Cardiac Arrest Receiving Targeted Temperature Management

Cited by 3 publications

References 63 publications

Predicting the Length of Stay of Cardiac Patients Based on Pre-Operative Variables—Bayesian Models vs. Machine Learning Models

Predicting the Length of Stay of Cardiac Patients Based on Pre-Operative Variables—Bayesian Models vs. Machine Learning Models

Hospital Length of Stay Prediction for Planned Admissions Using Observational Medical Outcomes Partnership Common Data Model: Retrospective Study

Hospital Length of Stay Prediction for Planned Admissions Using Observational Medical Outcomes Partnership Common Data Model: Retrospective Study (Preprint)

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