tetano
Editor, Senior Moderator
JACC Adv
. 2022 Jun;1(2):100043.
doi: 10.1016/j.jacadv.2022.100043. Epub 2022 May 8.
Real-Time Prediction of Mortality, Cardiac Arrest, and Thromboembolic Complications in Hospitalized Patients With COVID-19
Julie K Shade[SUP] 1 2 [/SUP], Ashish N Doshi[SUP] 2 3 [/SUP], Eric Sung[SUP] 1 2 [/SUP], Dan M Popescu[SUP] 2 4 [/SUP], Anum S Minhas[SUP] 5 [/SUP], Nisha A Gilotra[SUP] 5 [/SUP], Konstantinos N Aronis[SUP] 2 5 [/SUP], Allison G Hays[SUP] 5 [/SUP], Natalia A Trayanova[SUP] 1 2 5 [/SUP]
Affiliations
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.
Keywords: 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.
. 2022 Jun;1(2):100043.
doi: 10.1016/j.jacadv.2022.100043. Epub 2022 May 8.
Real-Time Prediction of Mortality, Cardiac Arrest, and Thromboembolic Complications in Hospitalized Patients With COVID-19
Julie K Shade[SUP] 1 2 [/SUP], Ashish N Doshi[SUP] 2 3 [/SUP], Eric Sung[SUP] 1 2 [/SUP], Dan M Popescu[SUP] 2 4 [/SUP], Anum S Minhas[SUP] 5 [/SUP], Nisha A Gilotra[SUP] 5 [/SUP], Konstantinos N Aronis[SUP] 2 5 [/SUP], Allison G Hays[SUP] 5 [/SUP], Natalia A Trayanova[SUP] 1 2 5 [/SUP]
Affiliations
- PMID: 35756388
- PMCID: PMC9080121
- DOI: 10.1016/j.jacadv.2022.100043
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.
Keywords: 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.