Implications of Cardiac Markers in Risk-Stratification and Management for COVID-19 Patients

Pengping Li; Wei Wu; Tingting Zhang; Ziyu Wang; Jie Li; Mengyan Zhu; Yuan Liang; Wenhua You; Kening Li; Rong Ding; Bin Huang; Lingxiang Wu; Weiwei Duan; Yi Han; Xuesong Li; Xin Tang; Xin Wang; Han Shen; Qianghu Wang; Hong Yan; Xinyi Xia; Yong Ji; Hongshan Chen


Crit Care. 2021;25(158) 

In This Article

Methods and Materials

Study Design and Participants

This single-center retrospective study included consecutive patients diagnosed with COVID-19 at Wuhan Huoshenshan Hospital in China, between February 4 and April 10, 2020. The Wuhan Huoshenshan Hospital was built in ten days due to the extent of the pandemic, which exceeded the existing health system capacity. Most of the patients admitted to the Huoshenshan Hospital were transferred from other hospitals. The study design was approved by the institutional ethics board. Written informed consent was waived due to the urgency of the COVID-19 pandemic.

The disease severity was determined according to the clinical classification criterion in the Diagnosis and Treatment Protocol for Novel Coronavirus Pneumonia released by the National Health Commission of China (7th edition, Patients who met any of the following criteria during hospitalization were diagnosed as a severe case: (1) shortness of breath defined by respiration rate ≥ 30 breaths/min, (2) oxygen saturation ≤ 93 at rest, and (3) alveolar oxygen partial pressure/fraction of inspiration O2 (PaO2/FiO2) ≤ 300 mmHg (1 mmHg = 0.133 kPa). Patients whose pulmonary imaging showed significant progression of lesions > 50% within 24–48 h were also treated as severe cases. Patients who met any of the following conditions were diagnosed as critically severe: (1) respiratory failure requiring mechanical ventilation, (2) shock, and (3) organ failure needing intensive care unit (ICU) monitoring and treatment. In addition, the severe/critical cohort was categorized into two groups based on the presence or absence of pre-existing CAD (165/1515 [10.9%] and 1350/1515 [89.1%], respectively), according to clinical diagnosis and/or medical history on admission (Figure 1a). Whether patients had pre-existing CAD or not was determined according to the description of CAD history in medical records on admission; New onset CAD was defined according to clinical diagnosis in medical records, such as "patient had undergone coronary angiography and had stenosis of non-major vessels, so the diagnosis of coronary heart disease was basically established", or "Diagnostic basis of CAD: recurrent unstable angina attack, history of hypertension and diabetes for many years". In 165 patients with CAD, 10 were new onset in which 8 were survivors; Suspected CAD in our study was based on electronic medical records with the descriptions as follows: "The recent occurrence of rapid atrial fibrillation which does not rule out the possibility of coronary artery disease", or "a previous history of suspected coronary artery disease. Ten patients in CAD group and seven patients in non-CAD group had a history of congestive heart failure. Two patients in non-survivors and 14 patients in survivors with CAD had information of Ejection fraction (EF).

Figure 1.

Flow chart of study design. a Flow chart of COVID-19 patients recruitment. b Single-cell RNA expression profile of SARS-CoV-2 receptors from 12 healthy human heart samples (c). Bulk RNA expression profile of SARS-CoV-2 receptors from 93 CAD patients and 48 healthy control

Data Collection

Clinical information was collected during hospitalization by attending physicians. The radiologic results including chest radiography or computed tomography (CT) were retrieved from the radiology information system documents. Patient data, including demographics, medical history, comorbidities, laboratory examinations, viral load such as ORF1ab and nucleocapsid (N), SARS-CoV-2-specific IgG and IgM antibodies, and outcomes were collected from electronic medical records and analyzed. The ORF1ab and nucleocapsid (N) genes were detected by performing real-time PCR assay and the number of cycles (CT value) is used to measure the viral load. A higher CT value indicates to a lower viral load. A CT value < 40 was defined as SARS-CoV-2 viral positive. Levels of IgM and IgG > 10 was defined as positive and ≤ 10 as negative.[19]

Cardiac Biomarkers and Myocardial Damage

Cardiac biomarkers are substances released into the blood when the myocardial cell is damaged or stressed and can be useful in the early prediction or diagnosis of disease.[20] We used the common approach to handle missing data which is to omit those cases with the missing data and analyzed the remaining data.[21] For each marker, patients with missing value were removed. We dichotomizing the continuous data of the specific cardiac biomarker for each patient into categories, e.g., normal or abnormal, by using the approaches to modelling continuous variable.[22–24] Abnormal cardiac biomarker results were defined as serum levels above the upper reference limit.

Transcriptional Profiles

To characterize the expression patterns of SARS-CoV-2 receptors in the heart, scRNA-seq of 9791 cells isolated from 12 normal human hearts of non-COVID-19 cohorts was performed (Figure 1b). The scRNA-seq datawere obtained from the Gene Expression Omnibus (GEO) database with the accession number GSE109816[25] and processed using the Seurat toolkit.[26]

Furthermore, to compare the gene expression difference of the SARS-CoV-2 receptor between the CAD patients and the non-CAD patient, we analyzed the bulk RNA expression profile of SARS-CoV-2 receptors in the peripheral blood mononuclear cells (PBMC). The expression profile of peripheral blood from 93 CAD patients and 48 healthy individuals of non-COVID-19 cohorts (Figure 1c) which were approved by separate Institutional Review Board (IRB) was downloaded from the GEO database with the accession number GSE113079[27] and was analyzed using the Agilent-067406 Human Microarray V4.0 platform (Aglient, Santa Clara, USA).

Statistical Analysis

Continuous and categorical variables are presented as median (interquartile range [IQR]) and number (%), respectively. Comparisons between groups were performed using the Mann–Whitney U test, χ 2 test, or Fisher's exact test where appropriate. Pearson's correlations were employed to study the association between laboratory parameters. All statistical analyses were conducted using R software. Multivariate Cox regressions, log-rank tests, and Kaplan–Meier curves to plot the cumulative rates of death were implemented using the Survival package in R software. The receiver operating characteristic (ROC) curve to assess the overall accuracy of a prognostic marker was applied by using the R package ROCR. A P-value < 0.05 was used to indicate statistical significance.