key: cord-0698652-qm5rdtui
authors: Caillon, Antoine; Zhao, Kaiqiong; Klein, Kathleen Oros; Greenwood, Celia; Lu, Zhibing; Paradis, Pierre; Schiffrin, Ernesto L
title: High systolic blood pressure at hospital admission is an important risk factor in models predicting outcome of COVID-19 patients
date: 2021-01-02
journal: Am J Hypertens
DOI: 10.1093/ajh/hpaa225
sha: bcdd3eda3644b7ee4899c52a80ca9c4c6dce9010
doc_id: 698652
cord_uid: qm5rdtui

BACKGROUND: The risk that 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 to 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.

The coronavirus disease 2019 (COVID-2019) caused by severe acute respiratory syndrome (SARS) coronavirus-2 (SARS-CoV-2) first observed in December 2019, in Wuhan hospitals, Hubei, China, has spread since to become a worldwide pandemic. The World Health Organization (WHO) reported on November 19, 2020, 55,928 ,327 confirmed cases with 1,344,003 deaths in 235 countries (https://covid19.who.int/). The majority of the infected subjects have shown mild or even no symptoms, whereas some have presented much worse prognosis. The severe cases of COVID-19 patients developed a severe pneumonia, acute respiratory distress syndrome, a severe coagulopathy, myocardial disease, acute renal failure, encephalopathy, or multiple organ failure and death. [1] [2] [3] [4] The most frequent symptoms of COVID-19 are fever, cough, and myalgia or fatigue, and less common symptoms include sputum production, headache, loss of sense of smell and taste, hemoptysis and diarrhea. 3, 5, 6 Clinical features have included pneumonia with abnormal or severe clinical features such as acute respiratory syndrome with ground-glass opacities in subpleural regions, acute cardiac ischemia or heart failure, acute renal failure, or neurological manifestations including stroke, associated with microvascular thrombosis, all of which could lead to death. The symptoms of COVID-19 appear approximately 5 to 6 days after transmission has occurred, and the onset period to death has ranged from 6 to 40 days with a median of 14 days. 7 The risk of death and period to death are dependent on the age and status of the immune system of the patient and may also be affected by comorbidities like hypertension, cardiovascular disease, diabetes, obesity, cancer, and immune suppression from diseases or treatments. Hypertension or high blood pressure (BP) may have an important impact on the severity of COVID-19 as it is a leading risk factor for cardiovascular disease. 8 Establishing early the prognosis of COVID-19 patients using prediction models at the time of hospital admission could help relieve pressure on the healthcare system by allowing evidence-based A c c e p t e d M a n u s c r i p t 7 risk prediction and decision-making when triaging patients, and thus contribute to the ability of healthcare workers to provide the most appropriate care to the patients, which could improve outcomes. Shi et al. showed that in addition to age and sex, hypertension was identified as an important risk factor associated with severe cases of COVID-19. 9 Another study, identified markers of systemic inflammation (elevated neutrophil-to-lymphocyte ratio [NLR] , derived NLR ratio [neutrophil count divided by white cell count minus neutrophil count] and platelet-to-lymphocyte ratio) and age as predictors of poor clinical outcome in COVID-19 patients. 7 Although a high prevalence of comorbidities (88%) including hypertension, diabetes and heart disease were observed, they were not included as covariates. Yuan et al. determined an optimal cutoff value of computerized tomography (CT) scan for the prediction of COVID-19 patients with pneumonia. 10 COVID-19 patients with pneumonia who died presented a high prevalence of comorbidities (80%) including hypertension, diabetes and cardiac disease that could have contributed to death. Guo et al. demonstrated that elevated troponin T (TnT) plasma levels, a marker of cardiac injury, was associated with greater mortality rate in COVID-19 patients, which was enhanced when TnT was combined with pre-existing cardiovascular disease. 11 The above studies underscore the importance of factors such as age, respiratory disease, systemic inflammation and cardiovascular comorbidities at the time of hospital admission in determining the survival or death of COVID-19 patients.

We suggest that the combination of demographic, clinical and laboratory parameters including age, respiratory disease, systemic inflammation and comorbidities such as cardiovascular disease could be used to generate models that efficiently allow prediction of risk and development of severe disease leading to worse outcomes in COVID-19 patients at the time of admission to the hospital. To test this hypothesis, we have used the COVID-19 patient data-set from the already published study of Guo et al. from the Seventh Hospital of Wuhan, China 11 to determine the probability of death using both logistic regression and proportional hazards survival models. In both cases, an unbiased approach was used to select the covariates among parameters collected at the day of admission to the hospital, and cross-validation was used to estimate model performance.

A c c e p t e d M a n u s c r i p t 8

This is a single-center, retrospective, cohort study performed using electronic medical records of COVID-19 patients admitted from January 23 to February 23, 2020 to the Seventh Hospital of Wuhan City, China, which was s a designated hospital to treat patients with COVID-19 and was supervised by Review was required, the patient data-set coming from a study with a university hospital Ethics Board approval in China.

Data are shown as % for categorical variables and as means ± SD for continuous variables. The primary dependent variable in our analyses was patient discharge or death. We examined this outcome both as a binary variable, and looked at time to these events. All measures in Tables 1 and A c c e p t e d M a n u s c r i p t 9 2 were considered for their associations with patient outcome. Comparisons between the discharged and death patient groups were done using Chi-square tests for categorical data and Wilcoxon rank-sum tests for continuous data. P<0.05 was considered statistically significant.

Pearson correlation coefficients between variables were calculated, and visualized with a correlogram generated by the R package 'corrplot'. Correlations with P<0.01 were considered statistically significant.

The probability of death was estimated using L1 penalized logistic regression. Fivefold crossvalidation was performed with the R package 'caret', 13 a wrapper to the 'glmnet' package, to optimize the area under the receiver operator characteristic (ROC) curve in a penalized model. 14 The penalization parameter was fixed to 1.0 to force L1 penalization, and the second penalization parameter was estimated with cross-validation. After estimating the optimal number of predictors, post-selection inference was performed by fitting a standard logistic regression model, retaining only the predictors estimated to have non-zero coefficients after the first penalized regression. The variables in this post-selection inference table may not meet traditional significance thresholds since they have been selected through the cross-validation process in the penalized regression, not by their P-values. Furthermore, with these variables, we used five-fold cross-validation to estimate an ROC curve, and to estimate model performance with the area under the curve.

An L1 penalized survival time model was estimated using Cox proportional hazards in the R package glmnet. 15 First, a regularized Cox regression model, with outcome defined as time to death since onset of symptoms (right-censored model), was fit. Fivefold cross-validation was used to select the optimal subset of predictors by minimizing the deviance of the Cox model. Thereafter, postselection inference was performed with the selected predictors with an interval-censored proportional hazards model for time to death since symptom onset, matching time of hospitalization to the period when the individual was observed. This approach accounts appropriately for the fact that all variables were measured at the day of hospitalization rather than at the time of onset of A c c e p t e d M a n u s c r i p t 10 symptoms. The proportional hazards assumption was checked using the diagnostic test proposed by Grambsch and Therneau (1994) . 16 The coefficients in the formula calculating the probability of survival up to a certain time were generated with the R function 'survfit.coxph'. 17 Performance was assessed with Harrell's concordance statistic. 18

The data-set contained 58 parameters from data collected on day of admission to the hospital (including demographics, clinical characteristics and laboratory parameters), discharge/death status, days from onset of COVID-19 symptoms and days from hospitalization to discharge from the hospital or death during hospitalization for 187 patients with COVID-19. Of the 58 data parameters collected, 12 variables were removed where more than 15 individuals had missing values, leaving 43 parameters collected on admission plus discharge/death status, days from onset of COVID-19 symptoms and days of hospitalization. The cutoff for missing values in 15 individuals was based on the distribution shown in Supplemental Figure S1 . As standard prediction models do not tolerate missing values, our analysis was done using 157 patients with no missing data, a subset of the original 187 patients reported in the study of Guo et al. 11 The demographics, clinical characteristics and laboratory parameters of COVID-19 patients without and with missing data are presented in Supplemental Tables S1 and S2. Only one parameter was significantly different, neutrophil number, which was lower when patients with missing data were included.

COVID-19 patients were separated in two groups, discharged from the hospital (120 patients) or death during hospitalization (37 patients) (Tables 1 and 2 ). Deceased patients were older and presented higher systolic BP (SBP), heart rate and respiration rate (RR), and more comorbidities including diabetes, hypertension, cerebrovascular disease and atrial fibrillation compared to A c c e p t e d M a n u s c r i p t 11 discharged patients (Table 1) . As well, proportionally more deceased patients took angiotensin converting enzyme inhibitors (ACEIs) or calcium channel blockers (CCBs). Laboratory results correlated with the respiratory distress and the presence of more comorbidities in deceased compared to discharged patients ( Table 2 ). In addition, higher levels of markers of liver dysfunction (alanine aminotransferase and aspartate aminotransferase [AST]) and kidney function (creatinine) were observed in deceased patients. TnT, a marker of cardiac injury, was higher in deceased compared to discharged patients ( Table 2 ). Systemic inflammation assessed by blood cell counts and fractions and high-sensitivity C-reactive protein (hsCRP) levels was elevated in deceased versus discharged patients. All patients presented elevated lactic acid (>1 mmol/L), to a greater degree in deceased versus discharged patients. where is the baseline survival function, which can be estimated from data using a nonparametric approach. 20 Harrell's concordance statistic for this model was 0.91. Figure 3 shows the hazard ratios per standard deviation of each covariate, in contrast to this equation which shows the log hazard ratios for a single unit change on the original scale.

In this study we have generated models predicting risk of developing severe disease leading to worse outcomes. Our study includes consecutive patients recruited at the Seventh Hospital of Wuhan, and followed until death or discharge, and therefore any selection bias should be minimal, and related only to the catchment pool for the hospital. Candidate approaches has been used to identify important risk factors associated with severe cases of COVID-19. 7, [9] [10] [11] In this study, we used an unbiased approach to identify the covariates in order to generate an optimal worse outcome predicting model. This approach was successful as angiotensin II, or results from effects of systemic inflammation. However, the deceased group of patients were older, and hypertension is more prevalent in the elderly. Accordingly, significantly higher SBP in the deceased group could be the result of confounding due to the older age of the patients that died. 19 Elevated SBP could be a marker of pre-existing end-organ damage and is an A c c e p t e d M a n u s c r i p t 15 important comorbid factor. It is also unknown whether TnT was elevated in deceased compared to discharged COVID-19 patients before infection with SARS-CoV-2 or rose later. Two drugs used to control blood pressure, ACEIs and CCBs, were identified as covariates for the prediction of death of COVID-19 patients. There is no evidence that these drugs contribute to the pathophysiology of COVID-19. 19 The greater frequency of use of these drugs in deceased COVID-19 patients may be related to the higher SBP or rate of prevalence of cardiovascular disease (Table 1) .

The relatively small number of patients from one center in one country is a limitation. A larger cohort of patients from multiple centers and countries would allow validating our prediction models.

As well, confirmation that these models are applicable in other healthcare systems is important.

Secondly, the number of parameters collected on admission could be considered limited. A larger number of parameters could ensure identification of the best covariates. For example, it would have been nice to know time of infection, and to have detailed immune characterizations of the patients at admission, but these data are either unknowable or not available. Thirdly, it is unknown whether high SBP and high TnT levels in deceased patients were preexisting conditions or developed after onset of COVID-19 symptoms. Although the interval-censored survival models are more appropriate in this context, the implementation of penalized survival analysis does not allow the counting process specification. Therefore, we fit right-censored penalized models and only used interval censoring for post-selection inference. This is unlikely to materially change our results since rightcensored models starting at time of hospitalization gave similar results.

A c c e p t e d M a n u s c r i p t 16

This study generated an efficient model to predict critical disease leading to worse outcome in COVID-19 patients at admission to the hospital using 13 covariates selected among 43 demographic, clinical and laboratory parameters using an unbiased approach. A model predicting survival which included 7 of these 13 covariates was generated using a similar approach. Age, respiration rate and hsCRP1 were the 3 main covariates that predict the outcome of COVID-19 patients; both in the prediction of survival and mortality. High SBP on arrival at the hospital, which is an important comorbid factor, was identified as a covariate in both models. The prediction of critical illness and survival of COVID-19 patients at admission to the hospital could contribute to risk stratification and evidence-based decision-making at triage, which would help to provide appropriate care to COVID-19 patients, potentially contributing to improve their outcomes. These parameters predicting outcome on admission would help in both ethical crisis triage following evidence-based patient survival probability, as well as contribute to dedicating in anticipation of deterioration available resource-intensive approaches to those patients for who critical disease can be predicted. A caveat to this conclusion is that the model described here is specific to the management available at the time that this data was collected. Progress in treatment since then is reducing case-fatality rates and may eventually supersede this particular model of risk prediction and necessitate development of new models adapted to a new reality.

A c c e p t e d M a n u s c r i p t 17

No disclosure to declare.

The data and analytic methods will be/have been made available to other researchers for the purpose of reproducing the results or replicating the procedure.

M a n u s c r i p t A c c e p t e d M a n u s c r i p t 22 A c c e p t e d M a n u s c r i p t 24 A c c e p t e d M a n u s c r i p t 26 A c c e p t e d M a n u s c r i p t A c c e p t e d M a n u s c r i p t 29 Figure 3 

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