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Predictive modeling of inpatient mortality in departments of internal medicine

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Abstract

Despite overwhelming data on predictors of inpatient mortality, it is unclear which variables are the most instructive in predicting mortality of patients in departments of internal medicine. This study aims to identify the most informative predictors of inpatient mortality, and builds a prediction model on an individual level, given a constellation of patient characteristics. We use a penalized method for developing the prediction model by applying the least-absolute-shrinkage and selection-operator regression. We utilize a cohort of adult patients admitted to any of 5 departments of internal medicine during 3.5 years. We integrated data from electronic health records that included clinical, epidemiological, administrative, and laboratory variables. The prediction model was evaluated using the validation sample. Of 10,788 patients hospitalized during the study period, 874 (8.1%) died during admission. We find that the strongest predictors of inpatient mortality are prior admission within 3 months, malignant morbidity, serum creatinine levels, and hypoalbuminemia at hospital admission, and an admitting diagnosis of sepsis, pneumonia, malignant neoplastic disease, or cerebrovascular disease. The C-statistic of the risk prediction model is 89.4% (95% CI 88.4–90.4%). The predictive performance of this model is better than a multivariate stepwise logistic regression model. By utilizing the prediction model, the AUC for the independent (validation) data set is 85.7% (95% CI 84.1–87.3%). Using penalized regression, this prediction model identifies the most informative predictors of inpatient mortality. The model illustrates the potential value and feasibility of a tool that can aid physicians in decision-making.

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Author information

Authors and Affiliations

  1. Research Authority, Emek Medical Center, Clalit Health Services, Afula, Israel

    Naama Schwartz

  2. Department of Medicine D, Emek Medical Center, Clalit Health Services, 21 Rabin Avenue, 18341, Afula, Israel

    Ali Sakhnini & Naiel Bisharat

  3. Ruth and Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology, Haifa, Israel

    Naiel Bisharat

Authors
  1. Naama Schwartz
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  2. Ali Sakhnini
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  3. Naiel Bisharat
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Correspondence to Naiel Bisharat.

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Statement of human and animal rights

All procedures performed in the present study were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed consent

The present study retrospectively analyzed data abstracted from electronic health records thus informed consent was not required.

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Schwartz, N., Sakhnini, A. & Bisharat, N. Predictive modeling of inpatient mortality in departments of internal medicine. Intern Emerg Med 13, 205–211 (2018). https://doi.org/10.1007/s11739-017-1784-8

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  • DOI: https://doi.org/10.1007/s11739-017-1784-8

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