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Additional insights on the modelling of the COVID-19 clinical progression using multi-state methodology

Authors :
Cristina Vazquez Guillamet
Aaloke Mody
Andrew P. Michelson
Angella Sandra Namwase
Patrick G. Lyons
Pratik Sinha
William G. Powderly
Elvin Geng
Sean Yu
Keith F. Woeltje
Source :
American Journal of Epidemiology
Publication Year :
2021
Publisher :
Oxford University Press, 2021.

Abstract

There are limited data on longitudinal outcomes for coronavirus disease 2019 (COVID-19) hospitalizations that account for transitions between clinical states over time. Using electronic health record data from a hospital network in the St. Louis, Missouri, region, we performed multistate analyses to examine longitudinal transitions and outcomes among hospitalized adults with laboratory-confirmed COVID-19 with respect to 15 mutually exclusive clinical states. Between March 15 and July 25, 2020, a total of 1,577 patients in the network were hospitalized with COVID-19 (49.9% male; median age, 63 years (interquartile range, 50–75); 58.8% Black). Overall, 34.1% (95% confidence interval (CI): 26.4, 41.8) had an intensive care unit admission and 12.3% (95% CI: 8.5, 16.1) received invasive mechanical ventilation (IMV). The risk of decompensation peaked immediately after admission; discharges peaked around days 3–5, and deaths plateaued between days 7 and 16. At 28 days, 12.6% (95% CI: 9.6, 15.6) of patients had died (4.2% (95% CI: 3.2, 5.2) had received IMV) and 80.8% (95% CI: 75.4, 86.1) had been discharged. Among those receiving IMV, 35.1% (95% CI: 28.2, 42.0) remained intubated after 14 days; after 28 days, 37.6% (95% CI: 30.4, 44.7) had died and only 37.7% (95% CI: 30.6, 44.7) had been discharged. Multistate methods offer granular characterizations of the clinical course of COVID-19 and provide essential information for guiding both clinical decision-making and public health planning.

Details

Language :
English
ISSN :
14766256 and 00029262
Database :
OpenAIRE
Journal :
American Journal of Epidemiology
Accession number :
edsair.doi.dedup.....2ac12ea1a81a699423a815f22fe99ef6