Development and validation of a machine learning model predicting illness trajectory and hospital utilization of COVID-19 patients: A nationwide study
| Autor: | Yaron Bar-Lavie, Rom Gutman, Arnona Ziv, Sigal Liverant-Taub, Asaf Ben Arie, Michael Roimi, Malka Gorfine, Uri Shalit, Ido Calman, Jonathan Somer, Udi Gelbshtein, Danny Eytan |
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| Rok vydání: | 2021 |
| Předmět: |
Adult
Male R software medicine.medical_specialty AcademicSubjects/SCI01060 Coronavirus disease 2019 (COVID-19) Hospital bed Health Informatics Research and Applications survival analysis Machine Learning Humans Medicine Hospital utilization Registries Israel AcademicSubjects/MED00580 Survival analysis Aged Proportional Hazards Models Aged 80 and over Models Statistical Illness trajectory business.industry Proportional hazards model hospital utilization COVID-19 Length of Stay Middle Aged Prognosis Hospitals multistate model Hospitalization healthcare facilities Emergency medicine Female AcademicSubjects/SCI01530 business Resource utilization |
| Zdroj: | Journal of the American Medical Informatics Association Journal of the American Medical Informatics Association : JAMIA |
| ISSN: | 1527-974X |
| DOI: | 10.1093/jamia/ocab005 |
| Popis: | Objective The spread of coronavirus disease 2019 (COVID-19) has led to severe strain on hospital capacity in many countries. We aim to develop a model helping planners assess expected COVID-19 hospital resource utilization based on individual patient characteristics. Materials and Methods We develop a model of patient clinical course based on an advanced multistate survival model. The model predicts the patient's disease course in terms of clinical states—critical, severe, or moderate. The model also predicts hospital utilization on the level of entire hospitals or healthcare systems. We cross-validated the model using a nationwide registry following the day-by-day clinical status of all hospitalized COVID-19 patients in Israel from March 1 to May 2, 2020 (n = 2703). Results Per-day mean absolute errors for predicted total and critical care hospital bed utilization were 4.72 ± 1.07 and 1.68 ± 0.40, respectively, over cohorts of 330 hospitalized patients; areas under the curve for prediction of critical illness and in-hospital mortality were 0.88 ± 0.04 and 0.96 ± 0.04, respectively. We further present the impact of patient influx scenarios on day-by-day healthcare system utilization. We provide an accompanying R software package. Discussion The proposed model accurately predicts total and critical care hospital utilization. The model enables evaluating impacts of patient influx scenarios on utilization, accounting for the state of currently hospitalized patients and characteristics of incoming patients. We show that accurate hospital load predictions were possible using only a patient’s age, sex, and day-by-day clinical state (critical, severe, or moderate). Conclusions The multistate model we develop is a powerful tool for predicting individual-level patient outcomes and hospital-level utilization. |
| Databáze: | OpenAIRE |
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