Article, Cardiology

Elevated cardiac biomarkers may be effective prognostic predictors for patients with COVID-19: A multicenter, observational study

Journal logoUnlabelled imageelevated cardiac biomarkers may be effec”>American Journal of Emergency Medicine 39 (2021) 34-41

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American Journal of Emergency Medicine

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Elevated cardiac biomarkers may be effective prognostic predictors for patients with COVID-19: A multicenter, observational study

Jie Yang, MD a, Xuelian Liao, MD a, Wanhong Yin, MD a,b, Bo Wang, MD a, Jirong Yue, MD b,c, Lang Bai, MD b,d, Dan Liu, MD b,e, Ting Zhu, MD f, Zhixin Huang, MD g, Yan Kang, MD a,b,?,

Study of 2019 Novel Coronavirus Pneumonia Infected Critically Ill Patients in Sichuan Province (SUNRISE)

Group h

a Department of Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan Province 610041, China

b COVID19 Medical Team (Hubei) of West China Hospital, Sichuan University, China

c Department of Geriatrics and National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, Sichuan Province 610041, China

d Center of Infectious Disease, West China Hospital, Sichuan University, Chengdu, Sichuan Province 610041, China

e Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan Province 610041, China

f Department of Otolaryngology-Head and Neck Surgery, Renmin Hospital of Wuhan University, Wuhan, Hubei Province 430060, China

g Department of Obstetrics and Gynecology, Renmin Hospital of Wuhan University, Wuhan, Hubei Province 430060, China

h The SUNRISE Group, China

a r t i c l e i n f o

Article history:

Received 10 August 2020

Received in revised form 29 September 2020 Accepted 5 October 2020

Keywords:

COVID-19

Risk predictors

Creatine kinase isoenzyme Myoglobin

Prognosis

a b s t r a c t

Purpose: The coronavirus disease 19 (COVID-19) has become a global health event. Cardiac biomarkers like cre- atine kinase isoenzyme (CK-MB), myoglobin, and High-sensitivity troponin T were usually elevated in early stages. This study aimed to investigate whether the elevated cardiac biomarkers could become effective prognos- tic predictors for COVID-19 patients.

Methods: The present study involved 357 COVID-19 patients. The potential predictors for two study outcomes (in-hospital death and recovery status) in 28 days were selected by LASSO regression analysis. prognostic values of cardiac biomarkers selected were evaluated using the receiver operating characteristic curve (ROC) and the area under ROC (AUC).

Results: After 28-day follow-up, overall 357 patients were divided into death group (n = 25) and survival group (n = 332), or non-recovery group (n = 43) and recovery group (n = 314). The LASSO regression analysis showed elevated CK-MB and myoglobin were independent risk predictors for in-hospital death, and CK-MB and myoglobin were also independent risk predictors for non-recovery. The AUC of CK-MB and myoglobin for in-hospital death were 0.862 (95%CL: 0.804-0.920, p < 0.001) and 0.838 respectively (95%CL: 0.729-0.947, p < 0.001). The AUC of CK-MB and myoglobin for non-recovery were 0.839 (95%CL: 0.786-0.892, p < 0.001) and 0.841 (95%CL: 0.765-0.918, p < 0.001) respectively. We also found AUC of combined use of CK-MB and myo- globin for in-hospital death and non-recovery were 0.883 (95CL: 0.813-0.952, p < 0.001), and 0.873 (95%CL: 0.817-0.930, p < 0.001) respectively.

Conclusions: In patients with COVID-19, elevated CK-MB and myoglobin on admission may be effective predictors for adverse outcomes, and combined use of CK-MB and myoglobin had a better performance for prediction.

(C) 2020 The Authors. This is an open access article under the CC BY-NC-ND license (http://

creativecommons.org/licenses/by-nc-nd/4.0/).

  1. Introduction

Abbreviations: COVID-19, coronavirus disease 19; CK-MB, creatine kinase isoenzyme; ROC, receiver operating characteristic curve; AUC, the area under ROC; RT-PCR, real-time reverse transcriptase polymerase chain reaction; CRP, C-reactive protein; SD, standard de- viation; LASSO, least absolute shrinkage and selection operator.

* Corresponding author at: Department of Critical Care Medicine, West China Hospital, Sichuan University, NO.37 Guo Xue Xiang St, Chengdu 610041, China.

E-mail address: [email protected] (Y. Kang).

The coronavirus disease 2019 due to SARS-CoV-2 infection broke out initially in Wuhan city, China, from December 2019 [1]. And COVID-19 has become a public health event of international concern and spread rapidly. Since now, there are no useful and specific medicines and treat- ment for COVID-19 [2].

With increasing confirmed number of COVID-19 patients, many studies were conducted to reveal patient clinical characteristics. Abnor- mal change of characteristics and laboratory tests was more frequent in

https://doi.org/10.1016/j.ajem.2020.10.013

0735-6757/(C) 2020 The Authors. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Image of Fig. 1

Fig. 1. Study population.

severe cases or deaths, such as complete blood count, plasma biochem- ical parameters, and inflammatory marks [3,4]. As the related re- searches progressing, the researchers gradually discovered infection with SARS-CoV-2 may cause acute myocardial injury. Chaolin Huang and his colleagues reported that part of ICU patients with COVID-19 existed acute myocardial injury, which mainly showed an increase of high-sensitivity troponin I [5]. Some cardiac biomarkers including creatine kinase isoenzyme (CK-MB), myoglobin, and troponin I signifi- cantly elevated in long-term hospitalization than short-term hospitali- zation [6]. Dr. Chirag Bavishi reviewed related studies about COVID-19 and found overall prevalence of acute myocardial injury ranged from 5% to 38% in COVID-19 patients [7]. Therefore, myocardial injury and el- evation of relevant cardiac biomarkers were common in patients with SARS-CoV-2 infection [8].

Thus, how about the association between acute myocardial injury or abnormal change of cardiac biomarkers and patient prognosis. Some studies have investigated the relationship between acute myocardial in- jury and adverse prognosis. Dr. Alvaro Lorente-Ros and his (her) col- leagues found myocardial injury was independently associated with adverse outcomes [9]. Dr. Eman A. Toraih also reported assessment of cardiac injury biomarkers may improve the identification of patients at the highest risk [10]. However, the studies to investigate prognostic

Table 1

Demographics and baseline clinical characteristics of the COVID-19 patients (Death vs Survivals)

Total (n = 357)

Death (n = 25)

Survivals (n = 332)

P value

Demographics and characteristics

Age, years, median (Q1, Q3)

56.0 (43.0, 68.0)

75.0 (61.0, 79.0)

55.0 (41.0, 67.0)

<0.001

Age >= 60, no. (%)

164 (45.9)

20 (80.0)

144 (43.4)

<0.001

Male, no. (%)

185 (51.8)

15 (60.0)

170 (51.2)

0.396

Time from symptoms onset to designated hospital, days, median (Q1, Q3)

9.0 (5.0, 15.0)

7.0 (4.0, 11.0)

10.0 (5.0, 15.0)

0.086

Coexisting disordersa, no. (%)

154 (43.1)

15 (60.0)

139 (41.9)

0.078

Signs and symptoms

Respiratory rate, breaths per min, median (Q1, Q3)

20.0 (19.0, 21.0)

24.0 (22.0, 30.0)

20.0 (19.0, 21.0)

<0.001

Systolic pressure, mmHg, mean (SD)

131.0 (18.6)

139.8 (21.8)

130.4 (18.2)

0.014

Diastolic pressure, mmHg, mean (SD)

79.2 (11.8)

81.5 (15.8)

79.0 (11.5)

0.452

Fever, no. (%)

277 (77.6)

20 (80.0)

257 (77.4)

0.765

Fatigue, no. (%)

138 (38.7)

12 (48.0)

126 (38.0)

0.320

Muscle soreness, no. (%)

33 (9.2)

1 (4.0)

32 (9.6)

0.493

Headache or dizziness, no. (%)

35 (9.8)

1 (4.0)

34 (10.2)

0.491

Dyspnea, no. (%)

112 (31.4)

16 (64.0)

96 (28.9)

<0.001

Cough, no. (%)

226 (63.3)

17 (68.0)

209 (63.0)

0.614

Laboratory findings

White blood cell count, x109/L, median (Q1, Q3)

5.7 (4.3, 7.7)

9.9 (7.1, 11.1)

5.6 (4.2, 7.2)

<0.001

Neutrophil count, x109/L, median (Q1, Q3)

3.8 (2.7, 5.8)

9.1 (5.5, 10.8)

3.7 (2.6, 5.4)

<0.001

lymphocyte count, x109/L, median (Q1, Q3)

1.0 (0.7, 1.6)

0.5 (0.3, 0.5)

1.1 (0.7, 1.6)

<0.001

Monocyte count, x109/L, median (Q1, Q3)

0.4 (0.3, 0.6)

0.4 (0.2, 0.5)

0.4 (0.3, 0.6)

0.121

Platelet count, x109/L, median (Q1, Q3)

195.0 (142.0, 246.0)

142.0 (104.0, 239.0)

196.5 (145.5, 246.0)

0.046

Hemoglobin, g/L, median (Q1, Q3)

129.0 (116.0, 141.0)

122.0 (103.0, 137.0)

129.0 (117.0, 141.0)

0.103

Alanine aminotransferase, U/L, median (Q1, Q3)

25.0 (16.0, 41.0)

28.0 (20.6, 45.0)

25.0 (16.0, 41.0)

0.285

Aspartate aminotransferase, U/L, median (Q1, Q3)

27.0 (20.0, 39.2)

42.9 (34.0, 57.0)

27.0 (20.0, 38.0)

<0.001

Albumin, g/L, median (Q1, Q3)

38.5 (34.5, 42.1)

33.2 (29.7, 37.1)

39.2 (34.9, 42.3)

<0.001

Total bilirubin, umol/L, median (Q1, Q3)

10.1 (7.3, 14.4)

12.6 (8.1, 17.6)

9.9 (7.1, 14.1)

0.058

Creatinine, umol/L, median (Q1, Q3)

64.0 (51.0, 75.6)

76.0 (65.0, 112.0)

63.4 (50.7, 74.0)

0.001

Procalcitonin, mmol/L, median (Q1, Q3)

0.05 (0.03, 0.12)

0.25 (0.17, 0.54)

0.05 (0.03, 0.09)

<0.001

C-reactive protein, mg/L, median (Q1, Q3)

27.0 (6.17, 39.4)

59.5 (39.4, 93.1)

24.8 (5.2, 39.4)

<0.001

Prothrombin time, s, median (Q1, Q3)

12.5 (11.7, 13.3)

13.7 (12.9, 14.5)

12.4 (11.7, 13.2)

<0.001

D-dimer, mg/L, median (Q1, Q3)

0.8 (0.5, 1.8)

5.1 (2.0, 10.9)

0.7 (0.4, 1.5)

<0.001

CK-MB, ng/mL, median (Q1, Q3)

1.1 (0.7, 2.1)

6.9 (2.5, 11.6)

1.1 (0.6, 1.9)

<0.001

Myoglobin, ng/mL, median (Q1, Q3)

38.1 (23.0, 63.7)

194.7 (114.9, 369.3)

34.9 (21.9, 59.8)

<0.001

High-sensitivity troponin T, ng/mL, median (Q1, Q3)

0.0 (0.0, 3.0)

0.1 (0.1, 2.2)

0.0 (0.0, 3.0)

<0.001

Treatment

Lopinavir or ritonavir, no. (%)

123 (34.5)

5 (20.0)

118 (35.5)

0.115

Ribavirin, no. (%)

38 (10.6)

4 (16.0)

34 (10.2)

0.323

Abidol, no. (%)

141 (39.5)

10 (40.0)

131 (39.5)

0.975

Chloroquine phosphate, no. (%)

14 (3.9)

0 (0.0)

14 (4.2)

0.611

Glucocorticoid, no. (%)

103 (28.9)

15 (60.0)

88 (26.5)

<0.001

Immunoglobulin, no. (%)

65 (18.2)

12 (48.0)

53 (16.0)

<0.001

CK-MB, creatine kinase isoenzyme.

a Including hypertension, diabetes, chronic pulmonary disease, cardiovascular or cerebrovascular disease, Congestive heart failure, renal disease, AIDS, metastatic malignancy, hepatic disease.

value of elevated cardiac biomarkers for COVID-19 patient poor out- comes were not many. Therefore, the purpose of the present study was to describe the change of cardiac biomarkers including CK-MB, myoglobin, and high-sensitivity troponin T in patients with COVID-19 and explore the association between elevated cardiac biomarkers and poor outcomes, as well as to investigate whether elevated cardiac bio- markers were effective and valuable Prognostic predictors for COVID- 19 patient adverse prognosis.

  1. Materials and methods
    1. Study design and participants

This multicenter, observational study was conducted at Sichuan province and Wuhan city from January to March, including 22 tertiary hospitals designated for COVID-19 patients in the local area, and ap- proved by Ethics Committees of West China Hospital of Sichuan Univer- sity and Renmin Hospital East Campus of Wuhan University. Informed consent was achieved from the patient or the patient’s legally autho- rized representative. Patients confirmed by real-time reverse transcrip- tase polymerase chain reaction (RT-PCR) assay and diagnosed as COVID-19 according to guidelines for COVID-19 issued by National

Health Commission of China were recruited in the present study. En- rolled patients were followed up maximum of 28 days after admission in the designate hospital, or until in-hospital death or recovery, which- ever occurred first. Researchers recorded the patient outcomes via med- ical records. There were two outcomes reflecting prognosis in this study: the primary outcome was in-hospital death in 28 days after ad- mission and the second outcome was non-recovery in 28 days after ad- mission. Non-recovery was defined as the patient died or the patient condition did not recover and still required advanced respiratory sup- port (high-flow nasal oxygen or mechanical ventilation) at the end of 28-day follow-up. Patients receiving mechanical ventilation or high- flow nasal oxygen at the end of 28-day follow-up still received treat- ment in designate hospital until recovery or death.

Data collection

Patients confirmed by COVID-19 were transported to designated hospitals in the local areas, and their condition were evaluated on hos- pital admission. The designated hospitals were set up to only treat COVID-19 patients. Patient clinical information was collected using elec- tronic data capture and analysis system (EDC). Data entry was com- pleted by physicians and nurses who were trained on the use of EDC

Table 2

Demographics and baseline clinical characteristics of the COVID-19 patients (Non-recovery vs Recovery)

Total (n = 357)

Non-recovery (n = 43)

Recovery (n = 314)

P value

Demographics and characteristics

Age, years, median (Q1, Q3) 56.0 (43.0, 68.0)

75.0 (62.0, 79.5)

54.0 (40.0, 66.0)

<0.001

Age >= 60, no. (%) 164 (45.9)

35 (81.4)

129 (41.1)

<0.001

Male, no. (%) 185 (51.8)

28 (65.1)

157 (50.0)

0.063

Time from symptoms onset to designated hospital, days, median (Q1, Q3) 9.0 (5.0, 15.0)

9.0 (4.5, 15.5)

9.0 (5.0, 15.0)

0.658

Coexisting disordersa, no. (%) 154 (43.1)

27 (62.8)

127 (40.4)

0.006

Signs and symptoms

Respiratory rate, breaths per min, median (Q1, Q3)

20.0 (19.0, 21.0)

22.0 (19.0, 26.5)

20.0 (19.0, 21.0)

0.025

Systolic pressure, mmHg, mean (SD)

131.0 (18.6)

137.4 (21.4)

130.1 (18.0)

0.016

Diastolic pressure, mmHg, mean (SD)

79.2 (11.8)

80.9 (14.4)

79.0 (11.5)

0.398

Fever, no. (%)

277 (77.6)

34 (79.1)

243 (77.4)

0.804

Fatigue, no. (%)

138 (38.7)

19 (44.2)

119 (37.9)

0.427

Muscle soreness, no. (%)

33 (9.2)

3 (7.0)

30 (9.6)

0.781

Headache or dizziness, no. (%)

35 (9.8)

2 (4.7)

33 (10.5)

0.285

Dyspnea, no. (%)

112 (31.4)

25 (58.1)

87 (27.7)

<0.001

Cough, no. (%)

226 (63.3)

27 (62.8)

199 (63.4)

0.940

Laboratory findings

White blood cell count, x109 /L, median (Q1, Q3)

5.7 (4.3, 7.7)

9.7 (7.1, 10.4)

5.5 (4.1, 6.9)

<0.001

Neutrophil count, x109/L, median (Q1, Q3)

3.8 (2.7, 5.8)

8.1 (5.5, 9.5)

3.6 (2.6, 5.2)

<0.001

Lymphocyte count, x109/L, median (Q1, Q3)

1.0 (0.7, 1.6)

0.5 (0.4, 0.9)

1.1 (0.8, 1.6)

<0.001

Monocyte count, x109/L, median (Q1, Q3)

0.4 (0.3, 0.6)

0.4 (0.2, 0.5)

0.4 (0.3, 0.6)

0.193

Platelet count, x109/L, median (Q1, Q3)

195.0 (142.0, 246.0)

155.0 (111.5, 239.0)

197 (147.0, 246.0)

0.034

Hemoglobin, g/L, median (Q1, Q3)

129.0 (116.0, 141.0)

124.0 (104.0, 137.0)

129.0 (117.0, 141.0)

0.047

Alanine aminotransferase, U/L, median (Q1, Q3)

25.0 (16.0, 41.0)

30.0 (21.3, 45.7)

24.5 (16.0, 40.0)

0.029

Aspartate aminotransferase, U/L, median (Q1, Q3)

27.0 (20.0, 39.2)

44.0 (33.0, 56.5)

26.0 (20.0, 36.0)

<0.001

Albumin, g/L, median (Q1, Q3)

38.5 (34.5, 42.1)

33.0 (29.8, 36.1)

39.4 (35.6, 42.8)

<0.001

Total bilirubin, umol/L, median (Q1, Q3)

10.1 (7.3, 14.4)

12.6 (7.9, 19.7)

9.7 (6.9, 13.7)

0.003

Creatinine, umol/L, median (Q1, Q3)

64.0 (51.0, 75.6)

74.0 (56.7, 105.8)

63.0 (51.0, 74.0)

0.003

Procalcitonin, mmol/L, median (Q1, Q3)

0.05 (0.03, 0.12)

0.19 (0.12, 0.45)

0.05 (0.03, 0.09)

<0.001

C-reactive protein, mg/L, median (Q1, Q3)

27.0 (6.17, 39.4)

55.7 (39.4, 108.0)

22.8 (5.0, 39.4)

<0.001

Prothrombin time, s, median (Q1, Q3)

12.5 (11.7, 13.3)

13.4 (12.7, 14.5)

12.3 (11.7, 13.1)

<0.001

D-dimer, mg/L, median (Q1, Q3)

0.8 (0.5, 1.8)

3.8 (1.3, 11.3)

0.7 (0.4, 1.4)

<0.001

CK-MB, ng/mL, median (Q1, Q3)

1.1 (0.7, 2.1)

4.9 (1.9, 8.2)

1.0 (0.6, 1.8)

<0.001

Myoglobin, ng/mL, median (Q1, Q3)

38.1 (23.0, 63.7)

130.9 (65.8, 343.5)

33.0 (21.7, 57.0)

<0.001

High-sensitivity troponin T, ng/mL, median (Q1, Q3)

0.0 (0.0, 3.0)

0.1 (0.0, 2.1)

0.0 (0.0, 3.0)

<0.001

Treatment

Lopinavir or ritonavir, no. (%)

123 (34.5)

9 (20.9)

114 (36.3)

0.047

Ribavirin, no. (%)

38 (10.6)

7 (16.3)

31 (9.9)

0.194

Abidol, no. (%)

141 (39.5)

21 (48.8)

120 (38.2)

0.182

Chloroquine phosphate, no. (%)

14 (3.9)

1 (2.3)

13 (4.1)

1.000

Glucocorticoid, no. (%)

103 (28.9)

25 (58.1)

78 (24.8)

<0.001

Immunoglobulin, no. (%)

65 (18.2)

20 (46.5)

45 (14.3)

<0.001

CK-MB, creatine kinase isoenzyme.

a Including hypertension, diabetes, chronic pulmonary disease, cardiovascular or cerebrovascular disease, Congestive heart failure, renal disease, AIDS, metastatic malignancy, hepatic disease.

and were working in the designated hospitals. Data quality was over- seen by a team of senior ICU physicians and statisticians. Demographics and characteristics included age, gender, time from symptoms onset to designated hospital, and coexisting disorders. Respiratory rate, blood pressure, and more consisted of signs and symptoms. Laboratory find- ings including white blood cell count, neutrophil count, lymphocyte count, monocyte count, platelet count, hemoglobin, alanine aminotransferase, aspartate aminotransferase, albumin, total bilirubin, creatinine, procalcitonin, C-reactive protein (CRP), prothrombin time, D-dimer, CK-MB, myoglobin and high-sensitivity troponin T, were ex- amined on admission. Other clinical information about patient treat- ment was also collected.

Statistical analyses

Classification variables were presented as number and percentage (%), and compared by Chi-square or Fisher’s exact probability test. Normally distributed continuous variables were presented as mean +- standard deviation (SD), and compared by independent t-test. Non- normally distributed continuous variables were presented as medians and interquartile ranges, and compared using Mann-Whitney U test. P < 0.05 was considered statistically significant in comparing differ- ences between survival and death groups, or recovery and non- recovery groups. spearman correlation analysis was used to investigate the correlation between myoglobin and either CK-MB or high- sensitivity troponin T.

Potential risk predictors for in-hospital death or non-recovery were identified using least absolute shrinkage and selection operator (LASSO) regression analysis, and LASSO regression was a method of machine learning regression, which was used to choose independent risk factors affecting outcomes and applied to minimize the potential collinearity of the variables measured and overfitting of the variables. It is a logistic re- gression model penalizing the absolute size of the coefficients of a re- gression model based on the value of ?. With larger penalties, the estimates of weaker factors shrink toward zero, so that only the stron- gest predictors remain in the predictive model [11]. The ROC and AUC were built to assess the prognostic performance of predictors selected. All related statistics analyses were performed using R software 4.0.1.

  1. Results

In the present study, a total of 357 patients confirmed by COVID-19 and receiving laboratory test of cardiac biomarkers were enrolled be- tween January 20 and March 15, 2020. According to patient survival sta- tus, 357 patients were divided into death group (n = 25) and survival group (n = 332). On the other hand, 43 patients did not recover in 28 days, and 314 patients recovered according to patient recovery status after 28-day follow-up (Fig. 1). The median age of all patients was 56.0 (43.0, 68.0) years old, and 164 (45.9%) patients were older than 60 years old. 185 (51.8%) patients were male, and 154 (43.1%) patients were with coexisting disorders. The median time from symptoms onset to designated hospital was 9.0 (5.0, 15.0) days (Tables 1 and 2).

Image of Fig. 2

Fig. 2. The level of creatine kinase isoenzyme (CK-MB), myoglobin and high-sensitivity troponin T were compared and analyzed. According to two outcomes in this study, patients were divided into survival group and death group, or recovery group and non-recovery group.

The respiratory rates in death and non-recovery group were both significantly faster than survival and recovery group (24.0 (22.0, 30.0) versus 20.0 (19.0, 21.0), and 22.0 (19.0, 26.5) versus 20.0 (19.0, 21.0)).

Dyspnea in death and non-recovery group occurred more frequently than survival and recovery group (64% versus 28.9% and 58.1% versus 27.7%). Laboratory findings like aspartate aminotransferase, total biliru- bin, creatinine, procalcitonin, CRP, D-dimer were also significantly higher in death and non-recovery group. However, other laboratory findings including platelet count, hemoglobin, and albumin significantly decreased in these two groups. Cardiac biomarkers including CK-MB, myoglobin, and high-sensitivity troponin T significantly elevated in death and non-recovery group in Fig. 2. Moreover, admission myoglobin was positively correlated with both CKMB and high-sensitivity troponin T (Fig. 3). Died patients or non-recovery patients used glucocorticoid and immunoglobulin more frequently than survival or recovery pa- tients. The other clinical characteristics and their differences between the two groups were also presented in Tables 1 and 2.

19 variables were included into the LASSO regression analysis to se- lect potential predictors for in-hospital death or non-recovery in 28 days. The included variables were demographics (age >= 60 and coexisting disorders), signs and symptoms (respiratory rate and dys- pnea), laboratory findings (lymphocyte count, platelet count, hemoglo- bin, alanine aminotransferase, aspartate aminotransferase, albumin,

Image of Fig. 3

Fig. 3. The correlation between myoglobin and either creatine kinase isoenzyme (CK-MB) or high-sensitivity troponin T, Shadows in curves indicated the 95% confidence intervals of the corresponding estimates. A Myoglobin was positively correlated with CKMB (r = 0.430, p < 0.001). B Myoglobin was positively correlated with high-sensitivity troponin T (r = 0.437, p < 0.001).

total bilirubin, creatinine, procalcitonin, CRP, prothrombin time, D-dimer, CK-MB, myoglobin and high-sensitivity troponin T. The LASSO regression results showed age >= 60, dyspnea, respiratory rate, as- partate aminotransferase, albumin, total bilirubin, CRP, D-dimer, CK-MB and myoglobin were prognostic factors for in-hospital death when the binomial deviance was the smallest; and age >= 60, dyspnea, hemoglobin, albumin, total bilirubin, CRP, D-dimer, CK-MB and myoglobin were prognostic factors for non-recovery when the binomial deviance was the smallest (Fig. 4). Therefore, CK-MB and myoglobin were considered as potential predictors for adverse prognosis (in-hospital death or non- recovery) in 28 days. However, high-sensitivity troponin T was not a predictor for adverse prognosis according to LASSO regression analysis in this study.

The ROC and AUC of CK-MB and myoglobin for in-hospital death were 0.862 (95%CL: 0.804-0.920, p < 0.001) and 0.838 (95%CL:

0.729-0.947, p < 0.001), and the AUC of CK-MB and myoglobin for non-recovery were 0.839 (95%CL: 0.786-0.892, p < 0.001) and 0.841 (95%CL: 0.765-0.918, p < 0.001), respectively (Fig. 5A, B). Be-

cause previous studies as well as the present study showed higher CRP, D-dimer, total bilirubin, and high-sensitivity troponin T might be associated to COVID-19 patient adverse prognosis, we calculated the AUC of these variables and compared the prognostic perfor- mance among predictors selected [12-14]. As seen the Fig. 5C, D, the highest AUC for in-hospital death was CK-MB, and the AUC of myoglobin was higher than high-sensitivity troponin T, CRP, and total bilirubin. For non-recovery, the AUC of CK-MB and myoglobin were both higher than high-sensitivity troponin T, D-dimer, CRP, and total bilirubin. With a cut-off value of 2.2, CK-MB exhibited sen- sitivity 80.0%, specificity 79.5% for in-hospital death. As well as for in- hospital death, with a cut-off value of 90.9, myoglobin exhibited sen- sitivity 80.0% and specificity 88.3%. CK-MB exhibited sensitivity 95.4% and specificity 63.1% for non-recovery with a cut-off value of

1.4. And myoglobin exhibited sensitivity 74.4% and specificity 85.4%

for non-recovery with a cut-off value of 68.9. We also found that combined use of CK-MB and myoglobin showed a better Predictive performance for prognosis (AUC for in-hospital death = 0.883 and AUC for non-recovery = 0.873, respectively). Therefore, CK-MB and myoglobin could be combined to early predict patient prognosis better.

  1. Discussion

From the outbreak of COVID-19 in Wuhan city, the number of con- firmed patients increased to more than 80,000 and spread all over the world. Early studies revealed the clinical features and characteristics of COVID-19 patients [15-17]. Risk factors affecting adverse prognosis were also identified, such as comorbidities, older age, dyspnea, and ab- normal laboratory findings [18,19]. More than that, some studies grad- ually found COVID-19 patients with cardiovascular disease appeared to be severe illness commonly, and COVID-19 may lead to acute myo- cardial injury with elevated cardiac biomarkers including CK-MB, high-sensitivity troponin T and myoglobin [20,21]. In the present study, these three biomarkers were all elevated on hospital admission. However, the LASSO regression analyses indicated elevated CK-MB and myoglobin were potential risk predictors for adverse prognosis, and high-sensitivity troponin T could not be considered as a significant predictor in this study. The AUC of CK-MB and myoglobin selected for in-hospital death or non-recovery in 28 days were both more than 0.8. Therefore, CK-MB and myoglobin may be effective prognostic predictors for adverse prognosis of COVID-19 patients.

In hospitalized patients with COVID-19, the prevalence of myocar- dial injury is high, and usually with significant elevation of related car- diac biomarkers [22,23]. Of them, an increase level of CK-MB is a useful cardiac biomarker for acute myocardial injury [24]. Some studies have also revealed the association between CKMB and patient severity or prognosis. CK-MB in ICU patients was higher than non-ICU patients,

Image of Fig. 4

Fig. 4. The risk predictors creatine kinase isoenzyme (CK-MB) and myoglobin for in-hospital death or non-recovery in 28 days were selected using LASSO regression analysis. A Binomial deviance plot of the lowest point of red curve (left dash line), which correspond to a ten-variable model for in-hospital death. B Binomial deviance plot of the lowest point of red curve (left dash line), which correspond to a nine-variable model for non-recovery in 28 days.

and the level of CK-MB in non-survivors was significantly higher than survivors [4,25]. The present study showed the level of CK-MB significantly elevated in adverse prognosis groups, and this finding was according with previous studies [26,27]. The AUC of CK-MB for in- hospital death and non-recovery were 0.862 and 0.839, respectively, and both higher than D-dimer, CRP, total bilirubin and high-sensitivity troponin T. These results indicated CK-MB could be an effective and

valuable predictive biomarker to early recognize patients with adverse prognosis.

Myoglobin is a type of cytoplasmic protein existing in cardiac and skel- etal muscle, and myoglobin in circulation increases rapidly after myocytes damage [28,29]. In severe and critically ill COVID-19 patients, the level of myoglobin was significantly higher than mild patients [30]. And in COVID-19 patients requiring ICU treatment, elevation of myoglobin

Image of Fig. 5

Fig. 5. Receiver operating characteristic curve (ROC) and the area under the curve (AUC) for death or non-recovery in 28 days prediction. A The ROC and AUC for in-hospital death of creatine kinase isoenzyme (CK-MB) and myoglobin were 0.862 and 0.838, respectively. B The ROC and AUC for non-recovery in 28 days of CK-MB and myoglobin were 0.839 and 0.84, respectively. C The ROC and AUC for in-hospital death of CKMB and myoglobin comparing with D-dimer, C-reactive protein (CRP), total bilirubin and high-sensitivity troponin T. D The ROC and AUC for non-recovery in 28 days of CK-MB and myoglobin comparing with D-dimer, CRP, total bilirubin and high-sensitivity troponin T.

occurred in 35.2% of patients [31]. Elevated myoglobin might be associ- ated with disease severity and adverse prognosis. Even though myoglobin was not as cardiac specific as troponin measurements were, myoglobin was positively correlative with CK-MB and high-sensitivity troponin T. This study demonstrated the level of myoglobin significantly elevated in death group or non-recovery group, showed in Fig. 2. The AUC of myoglo- bin for in-hospital death was higher than CRP, total bilirubin and high- sensitivity troponin T, and the AUC of myoglobin for non-recovery was also higher than D-dimer, CRP, total bilirubin and high-sensitivity tropo- nin T. Therefore, these results indicated myoglobin also could be a valu- able predictive biomarker for adverse prognosis in COVID-19 patients and might be used as a supplementary biomarker besides CK-MB for diag- nosis and prediction. Even though CK-MB or myoglobin showed a good performance for prediction when they used alone, combined use of CK- MB and myoglobin had a better predictive performance for patient prog- nosis. Therefore, when COVID-19 patients on admission, we could evalu- ate patient prognosis using combined biomarkers.

High-sensitivity troponin T is a gold-standard biomarker for reflecting the severity of ongoing myocardial damage and previous studies have proved this finding [27,32]. In COVID-19 patients, some of them exhibited elevation of high-sensitivity troponin T and high mor- tality [33]. Therefore, high-sensitivity troponin T might have a good po- tential in predicting COVID-19 patient prognosis. However, in the present study, high-sensitivity troponin T could not be considered as a predictor for prognosis. We thought several reasons might be associated with this result. Firstly, only univariate analysis was used to explore

high-sensitivity troponin predicting prognosis in previous studies, and multivariate analysis was not used to adjust confounders [5,33]. Sec- ondly, this result might be associated with the data characteristics in this study and the LASSO regression analysis. Thirdly, Dr. Aimo reported high-sensitivity troponin T seemed less predictive than other cardiac biomarker when considering absolute values [34]. Therefore, these three reasons could explain this result to some extent.

This study also has some inevitable limitations due to data collected in the early stage of Covid-19 outbreak. Because of the condition at that time, completed laboratory test including cardiac biomarkers was not available for every patient, and the sample size is limited for this study. Therefore, the present study finally enrolled 357 patients who re- ceived laboratory test of cardiac biomarkers on admission to be ana- lyzed. Because the number of events was limited, we did not develop a validation set. Further related large-sample studies are needed to ex- plore and prove our opinion.

  1. Conclusion

In patients with COVID-19, CKMB and myoglobin may be considered as effective and valuable Predictive biomarkers for patient adverse prognosis, and combined use of CK-MB and myoglobin had a better per- formance for prediction. Early determination of CK-MB and myoglobin could help clinicians increase awareness of the possibility of adverse outcomes and may improve patient prognosis.

Ethics approval

The present study was initiated by researchers in West China Hospi- tal and approved by Ethics Committees of West China Hospital of Si- chuan University and Renmin Hospital of Wuhan University.

Funding

The present work was supported by the Project of Novel Coronavirus Pneumonia in West China Hospital (HX2019nCoV027).

Authors’ contributions

Jie Yang and Yan Kang designed the study. Jie yang drafted the man- uscript, and Yan Kang revised it. Jie Yang, Xuelian Liao, Wanhong Ying, Bo Wang, Jirong Yue, Lang Bai, Dan Liu, Ting Zhu and Zhixin Huang par- ticipated in the data collection and analysis.

CRediT authorship contribution statement

Jie Yang: Conceptualization, Methodology, Validation, Formal analysis, Investigation, Writing – original draft, Writing – review & editing. Xuelian Liao: Validation, Investigation, Supervision, Project administration. Wanhong Yin: Investigation, Supervision. Bo Wang: Investigation, Supervision. Jirong Yue: Investigation. Lang Bai: Investi- gation. Dan Liu: Investigation. Ting Zhu: Investigation. Zhixin Huang: Investigation. Yan Kang: Investigation, Validation, Writing – review & editing, Supervision.

Declaration of Competing Interest

The authors declare that they have no conflict of interest.

Acknowledgements

The present study proceeding was supported by involved staff and patients. We would like to thank Desong Qiu from Sichuan Zhikang Technology CO., Chengdu, for his help of establishing the electronic data capture and analysis system. We thanked Ruoran Wang from Depart- ment of Critical Care Medicine, West China Hospital of Sichuan Univer- sity, Chengdu, China, for his useful help and positive advices. And last, we thanked all patients and their families involved in the study.

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