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Personalized Prediction of Hospital Mortality in COVID-19-Positive Patients

Daniel Rozenbaum1, Jacob Shreve1, Nathan Radakovich2

  • 1Department of Hematology and Medical Oncology, Cleveland Clinic, Cleveland, OH.

Mayo Clinic Proceedings. Innovations, Quality & Outcomes
|May 18, 2021
PubMed

Insights

Researchers developed a machine learning model to predict in-hospital mortality and length of stay for patients with coronavirus disease 2019 (COVID-19). This tool aids healthcare systems in resource allocation for COVID-19 patients.

Area of Science:

  • * Medical Informatics
  • * Machine Learning in Healthcare
  • * Public Health

Background:

  • * Coronavirus disease 2019 (COVID-19) poses a significant challenge to healthcare systems globally.
  • * Accurate prediction of patient outcomes is crucial for effective resource management.

Purpose of the Study:

  • * To develop and validate predictive models for in-hospital mortality.
  • * To predict length of stay (LOS) for hospitalized COVID-19 patients.
  • * To create a decision tool for healthcare systems.

Main Methods:

  • * Multicenter retrospective cohort study of 764 COVID-19-positive patients.
  • * Utilized LightGBM, a machine learning algorithm, for prediction.
  • * Models were developed to predict mortality at 7, 14, and 30 days, and in-hospital LOS.

Main Results:

  • * The model achieved an area under the receiver operating characteristics curve of 0.86 for 7-day, 0.88 for 14-day, and 0.85 for 30-day mortality.
  • * Identified key predictors including age, initial ICU admission, race, and organ dysfunction.
  • * Median LOS was 5 days for the general floor and 10 days for the ICU.

Conclusions:

  • * A validated decision tool for predicting COVID-19 patient mortality and LOS was developed.
  • * The tool provides explainable and patient-specific predictions.
  • * Aids healthcare systems in optimizing bed allocation and resource distribution.
Abstract

Keywords:
ANC, absolute neutrophil countAST, aspartate aminotransferaseBMI, body mass indexCK, creatinine kinaseCOVID-19, coronavirus disease 2019CRP, C-reactive proteinCXR, chest radiographD1, day 1ICU, intensive care unitINR, international normalized ratioLDH, lactate dehydrogenaseLOS, length of stayLightGBM, Light Gradient Boosting MachineNC, nasal cannulaNan, missing valuePTT, partial thromboplastin timeQ, quartileROC AUC, area under the receiver operating characteristics curveSHAP, SHapley Additive exPlanationsSUN, serum urea nitrogen

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