Prediction of Mortality in hospitalized COVID-19 patients in a statewide health network.

ABSTRACT:

Importance

A predictive model to automatically identify the earliest determinants of both hospital discharge and mortality in hospitalized COVID-19 patients could be of great assistance to caregivers if the predictive information is generated and made available in the immediate hours following admission.

Objective

To identify the most important predictors of hospital discharge and mortality from measurements at admission for hospitalized COVID-19 patients.

Design

Observational cohort study.

Setting

Electronic records from hospitalized patients.

Participants

Patients admitted between March 3 ^rd and August 24 ^th with COVID-19 in Johns Hopkins Health System hospitals.

Exposures

216 phenotypic variables collected within 48 hours of admission.

Main outcomes

We used age-stratified (<60 and >=60 years) random survival forests with competing risks to identify the most important predictors of death and discharge. Fine-Gray competing risk regression (FGR) models were then constructed based on the most important RSF-derived covariates.

Results

Of 2212 patients, 1913 were discharged (age 57±19, time-to-discharge 9±11 days) while 279 died (age 75±14, time to death 14±15 days). Patients >= 60 years were nearly 10 times as likely to die within 60 days of admission as those <60. As the pandemic evolved, the rate of hospital discharge increased in both older and younger patients. Incident death and hospital discharge were accurately predicted by measures of respiratory distress, inflammation, infection, renal function, red cell turn over and cardiac stress. FGR models for each of hospital discharge and mortality as outcomes based on these variables performed well in the older (AUC 0.80-0.85 at 60-days) and younger populations (AUC >0.90 at 60-days).

Conclusions and relevance

We identified markers collected within 2 days of admission that predict hospital discharge and mortality in COVID-19 patients and provide prediction models that may be used to guide patient care. Our proposed model suggests that hospital discharge and mortality can be forecasted with high accuracy based on 8-10 variables at this stage of the COVID-19 pandemic. Our findings also point to several specific pathways that could be the focus of future investigations directed at reducing mortality and expediting hospital discharge among COVID-19 patients. Probability of hospital discharge increased over the course of the pandemic.

K ey p oints

Question: Can we predict the likelihood of hospital discharge as well as mortality from data obtained in the first 48 hours from admission in hospitalized COVID-19 patients?Findings: Models based on extensive phenotyping mined directly from electronic medical records followed by variable selection, accounted for the competing events of hospital death versus discharge, predicted both death and discharge with area under the receiver operating characteristic curves of >0.80.Meaning: Hospital discharge and mortality can be forecasted with high accuracy based on just 8-10 variables, and the probability of hospital discharge increased over the course of the pandemic.

SUBMITTER: Ambale-Venkatesh B 

PROVIDER: S-EPMC7899480 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature