Predicting 30-day return hospital admissions in patients with COVID-19 discharged from the emergency department: A national retrospective cohort study

Abstract

Objectives: Identification of patients with coronavirus disease 2019 (COVID-19) at risk for deterioration after discharge from the emergency department (ED) remains a clinical challenge. Our objective was to develop a prediction model that identifies patients with COVID-19 at risk for return and hospital admission within 30 days of ED discharge.

Methods: We performed a retrospective cohort study of discharged adult ED patients (n = 7529) with SARS-CoV-2 infection from 116 unique hospitals contributing to the National Registry of Suspected COVID-19 in Emergency Care. The primary outcome was return hospital admission within 30 days. Models were developed using classification and regression tree (CART), gradient boosted machine (GBM), random forest (RF), and least absolute shrinkage and selection (LASSO) approaches.

Results: Among patients with COVID-19 discharged from the ED on their index encounter, 571 (7.6%) returned for hospital admission within 30 days. The machine-learning (ML) models (GBM, RF, and LASSO) performed similarly. The RF model yielded a test area under the receiver operating characteristic curve of 0.74 (95% confidence interval [CI], 0.71-0.78), with a sensitivity of 0.46 (95% CI, 0.39-0.54) and a specificity of 0.84 (95% CI, 0.82-0.85). Predictive variables, including lowest oxygen saturation, temperature, or history of hypertension, diabetes, hyperlipidemia, or obesity, were common to all ML models.

Conclusions: A predictive model identifying adult ED patients with COVID-19 at risk for return for return hospital admission within 30 days is feasible. Ensemble/boot-strapped classification methods (eg, GBM, RF, and LASSO) outperform the single-tree CART method. Future efforts may focus on the application of ML models in the hospital setting to optimize the allocation of follow-up resources.

Keywords: COVID‐19; SARS‐CoV‐2; clinical prediction model; discharge planning; emergency department; machine learning; prognosis; readmissions.

FIGURE 1

CONSORT flow diagram identifying adult patients in the National Registry of Suspected COVID‐19 in Emergency Care data set with polymerase chain reaction (PCR)–confirmed severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) who had a return hospital admission within 30 days of discharge from their index emergency department (ED) visit. Models were designed to distinguish the characteristics of the patients in the third box from the bottom (n = 7529) that predicted (or were associated with) their appearance in the final box at the bottom (n = 517). COVID‐19, coronavirus disease 2019

FIGURE 2

Receiver operator characteristic curves for models predicting return hospital admission of patients who are positive for coronavirus disease 2019 infection and discharged from the emergency department. CART, classification and regression tree; GBM, gradient boosted machine; LASSO, least absolute shrinkage and selection; RF, random forest

FIGURE 3

Predicted probabilities of return hospital admission compared with true outcome for the 30% test set (n = 1669). CART, classification and regression tree; GBM, gradient boosted machine; LASSO, least absolute shrinkage and selection; RF, random forest

FIGURE 4

Variable importance plots for (A) gradient boosting machine and (B) random forest models. ED, emergency department