Prospective validation of a dynamic prognostic model for identifying COVID-19 patients at high risk of rapid deterioration.
Pharmacoepidemiol Drug Saf
; 2022 Dec 04.
Article
in English
| MEDLINE | ID: covidwho-2269128
ABSTRACT
BACKGROUND:
We sought to develop and prospectively validate a dynamic model that incorporates changes in biomarkers to predict rapid clinical deterioration in patients hospitalized for COVID-19.METHODS:
We established a retrospective cohort of hospitalized patients aged ≥18 years with laboratory-confirmed COVID-19 using electronic health records (EHR) from a large integrated care delivery network in Massachusetts including > 40 facilities from March to November 2020. A total of 71 factors, including time-varying vital signs and laboratory findings during hospitalization were screened. We used elastic net regression and tree-based scan statistics for variable selection to predict rapid deterioration, defined as progression by two levels of a published severity scale in the next 24 hours. The development cohort included the first 70% of patients identified chronologically in calendar time; the latter 30% served as the validation cohort. A cut-off point was estimated to alert clinicians of high risk of imminent clinical deterioration.RESULTS:
Overall, 3,706 patients (2,587 in the development and 1,119 in the validation cohort) met the eligibility criteria with a median of 6 days of follow-up. Twenty-four variables were selected in the final model, including 16 dynamic changes of laboratory results or vital signs. Area under the ROC curve was 0.81 (95% CI, 0.79 - 0.82) in the development set and 0.74 (95% CI, 0.71-0.78) in the validation set. The model was well calibrated (slope = 0.84 and intercept = -0.07 on the calibration plot in the validation set). The estimated cut-off point, with a positive predictive value of 83%, was 0.78.CONCLUSIONS:
Our prospectively validated dynamic prognostic model demonstrated temporal generalizability in a rapidly evolving pandemic and can be used to inform day-to-day treatment and resource allocation decisions based on dynamic changes in biophysiological factors. This article is protected by copyright. All rights reserved.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Type of study:
Cohort study
/
Observational study
/
Prognostic study
Language:
English
Journal subject:
Epidemiology
/
Drug Therapy
Year:
2022
Document Type:
Article
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