Real-Time Prediction of Mortality, Cardiac Arrest, and Thromboembolic Complications in Hospitalized Patients With COVID-19.
JACC Adv
; 1(2): 100043, 2022 Jun.
Article
in English
| MEDLINE | ID: covidwho-1821317
ABSTRACT
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.
AM/CA, all-cause mortality/cardiac arrest; AUROC, area under the receiver operating characteristic curve; CV, cardiovascular; ICU, intensive care unit; ML, machine learning; SARS-CoV-2; SARS-CoV-2, severe acute respiratory syndrome-coronavirus-2; TE, thromboembolic events; big data; cardiac arrest; machine learning; thromboembolism
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Type of study:
Observational study
/
Prognostic study
Language:
English
Journal:
JACC Adv
Year:
2022
Document Type:
Article
Affiliation country:
J.jacadv.2022.100043
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