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High Systolic Blood Pressure at Hospital Admission Is an Important Risk Factor in Models Predicting Outcome of COVID-19 Patients.

Authors :
Caillon A
Zhao K
Klein KO
Greenwood CMT
Lu Z
Paradis P
Schiffrin EL
Source :
American journal of hypertension [Am J Hypertens] 2021 Apr 02; Vol. 34 (3), pp. 282-290.
Publication Year :
2021

Abstract

Background: The risk that coronavirus disease 2019 (COVID-19) patients develop critical illness that can be fatal depends on their age and immune status and may also be affected by comorbidities like hypertension. The goal of this study was to develop models that predict outcome using parameters collected at admission to the hospital.<br />Methods and Results: This is a retrospective single-center cohort study of COVID-19 patients at the Seventh Hospital of Wuhan City, China. Forty-three demographic, clinical, and laboratory parameters collected at admission plus discharge/death status, days from COVID-19 symptoms onset, and days of hospitalization were analyzed. From 157 patients, 120 were discharged and 37 died. Pearson correlations showed that hypertension and systolic blood pressure (SBP) were associated with death and respiratory distress parameters. A penalized logistic regression model efficiently predicts the probability of death with 13 of 43 variables. A regularized Cox regression model predicts the probability of survival with 7 of above 13 variables. SBP but not hypertension was a covariate in both mortality and survival prediction models. SBP was elevated in deceased compared with discharged COVID-19 patients.<br />Conclusions: Using an unbiased approach, we developed models predicting outcome of COVID-19 patients based on data available at hospital admission. This can contribute to evidence-based risk prediction and appropriate decision-making at hospital triage to provide the most appropriate care and ensure the best patient outcome. High SBP, a cause of end-organ damage and an important comorbid factor, was identified as a covariate in both mortality and survival prediction models.<br /> (© American Journal of Hypertension, Ltd 2021. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

Details

Language :
English
ISSN :
1941-7225
Volume :
34
Issue :
3
Database :
MEDLINE
Journal :
American journal of hypertension
Publication Type :
Academic Journal
Accession number :
33386395
Full Text :
https://doi.org/10.1093/ajh/hpaa225