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

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.

Keywords: COVID-19; blood pressure; death; hypertension; prediction model; survival; troponin T.

Graphical Abstract

Figure 1.

Correlogram of the correlation coefficients of the 43 parameters collected at admission and death status. Correlations were determined using a Pearson Product Moment correlation. Correlations with P value <0.01 were considered significant. The insignificant correlation coefficient values are left blank. Correlation coefficient scale is represented on the right of the figure with the blue square showing positive correlation and red square negative correlation. Abbreviations: ACEIs, angiotensin-converting enzyme inhibitors; ARBs, angiotensin receptor blockers; CCBs, calcium channel blockers; COPD, chronic obstructive pulmonary disease; DBP, diastolic blood pressure; FiO₂, fraction of inspired oxygen; hsCRP, high-sensitivity C-reactive protein; PaO₂, partial pressure of oxygen; SBP, systolic blood pressure; SpO₂, peripheral oxygen saturation.

Figure 2.

ROC curve of the model predicting death in patients with COVID-19 using 13 selected parameters collected on admission to the hospital. The dashed line represents the reference line. The area under the ROC curve is 0.886. Abbreviation: ROC, receiver operator characteristic.

Figure 3.

The estimated hazard ratios for each predictor of the counting process Cox proportional hazard regression model predicting survival of COVID-19 patients. Hazard ratios in the figure are for a 1-SD change in the covariate. Abbreviations: FiO₂, fraction of inspired oxygen; hsCRP, high-sensitivity C-reactive protein; PaO₂, partial pressure of oxygen; SBP, systolic blood pressure.