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

Author:

Caillon Antoine1,Zhao Kaiqiong1,Klein Kathleen Oros1,Greenwood Celia M T1234ORCID,Lu Zhibing5ORCID,Paradis Pierre1ORCID,Schiffrin Ernesto L16ORCID

Affiliation:

1. Lady Davis Institute for Medical Research, Sir Mortimer B. Davis-Jewish General Hospital, McGill University, Montréal, Québec, Canada

2. Department of Oncology, McGill University, Montréal, Québec, Canada

3. Department of Human Genetics, McGill University, Montréal, Québec, Canada

4. Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montréal, Québec, Canada

5. Department of Cardiology, Zhongnan Hospital of Wuhan University, Hubei, China

6. Department of Medicine, Sir Mortimer B. Davis-Jewish General Hospital, McGill University, Montréal, Québec, Canada

Abstract

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. 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. 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.

Funder

Canadian Institutes of Health Research

Special Project for Significant New Drug Research and Development in the Major National Science and Technology Projects of China

Genome Canada

Canadian Vascular Network

Fonds de recherche Québec santé

Publisher

Oxford University Press (OUP)

Subject

Internal Medicine

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