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Real-Time Prediction of Mortality, Cardiac Arrest, and Thromboembolic Complications in Hospitalized Patients With
Julie K Shade1,2, Ashish N Doshi2,3, Eric Sung1,2
1Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, USA.
Insights
The novel COVID-HEART predictor accurately forecasts mortality and thromboembolic events in hospitalized COVID-19 patients. This continuously updating tool offers real-time risk scores to improve patient triage and resource allocation.
Area of Science:
- Medical Informatics
- Cardiology
- Pulmonology
Background:
- COVID-19 infection presents significant morbidity and mortality risks.
- Current prediction models for COVID-19 complications are limited and do not account for disease dynamics.
- There is a need for dynamic risk assessment in hospitalized COVID-19 patients.
Purpose of the Study:
- To develop and validate the COVID-HEART predictor, a novel risk-prediction technology.
- To forecast adverse events, specifically all-cause mortality/cardiac arrest (AM/CA) and thromboembolic events (TEs), in hospitalized COVID-19 patients.
- To create a continuously updating risk-prediction tool for dynamic disease management.
Main Methods:
- Retrospective registry data from 2,550 (AM/CA) and 1,854 (TEs) severe acute respiratory syndrome coronavirus 2 infected patients were used for training.
- An additional 1,100 (AM/CA) and 796 (TEs) patients were used for testing to evaluate performance under changing clinical guidelines.
- Leave-hospital-out validation was performed to ensure generalizability.
Main Results:
- The COVID-HEART predictor achieved a mean area under the receiver operating characteristic curve of 0.917 for AM/CA and 0.757 for TE during temporally divided testing.
- Early warning times were substantial, with interquartile ranges of 14-21 hours for AM/CA and 12-60 hours for TE.
- Validation on left-out hospitals showed strong performance with AUCs of 0.956 for AM/CA and 0.781 for TE.
Conclusions:
- The COVID-HEART predictor is a continuously updating and interpretable tool that accurately forecasts AM/CA and TE in hospitalized COVID-19 patients.
- The predictor provides real-time risk scores, enabling practical changes in patient triage and resource allocation.
- Potential applications extend to post-hospitalization COVID-19 care and potentially other conditions.
Background:
COVID-19 infection carries significant morbidity and mortality. Current risk prediction for complications in COVID-19 is limited, and existing approaches fail to account for the dynamic course of the disease.
Objectives:
The purpose of this study was to develop and validate the COVID-HEART predictor, a novel continuously updating risk-prediction technology to forecast adverse events in hospitalized patients with COVID-19.
Methods:
Retrospective registry data from patients with severe acute respiratory syndrome coronavirus 2 infection admitted to 5 hospitals were used to train COVID-HEART to predict all-cause mortality/cardiac arrest (AM/CA) and imaging-confirmed thromboembolic events (TEs) (n = 2,550 and n = 1,854, respectively). To assess COVID-HEART's performance in the face of rapidly changing clinical treatment guidelines, an additional 1,100 and 796 patients, admitted after the completion of development data collection, were used for testing. Leave-hospital-out validation was performed.
Results:
Over 20 iterations of temporally divided testing, the mean area under the receiver operating characteristic curve were 0.917 (95% confidence interval [CI]: 0.916-0.919) and 0.757 (95% CI: 0.751-0.763) for prediction of AM/CA and TE, respectively. The interquartile ranges of median early warning times were 14 to 21 hours for AM/CA and 12 to 60 hours for TE. The mean area under the receiver operating characteristic curve for the left-out hospitals were 0.956 (95% CI: 0.936-0.976) and 0.781 (95% CI: 0.642-0.919) for prediction of AM/CA and TE, respectively.
Conclusions:
The continuously updating, fully interpretable COVID-HEART predictor accurately predicts AM/CA and TE within multiple time windows in hospitalized COVID-19 patients. In its current implementation, the predictor can facilitate practical, meaningful changes in patient triage and resource allocation by providing real-time risk scores for these outcomes. The potential utility of the predictor extends to COVID-19 patients after hospitalization and beyond COVID-19.
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