a b s t r a c t

Background and objectives: Despite a growing understanding of Exertional heatstroke (EHS), there is a paucity of clinical evidence for risk-stratification of patients with EHS. The objective of this study was to identify an appropriate scoring system for prognostic assessment of EHS.

Methods: This was a retrospective cohort study of all patients with EHS admitted to intensive care unit (ICU) of the General Hospital of Southern Theatre Command of PLA between October 2008 and May 2019. Inflammatory indices and Organ function parameters at admission, the Acute Physiology and Chronic Health Evaluation II (APACHE II) scores, Sequential Organ Failure Assessment scores, and Glasgow Coma Scale score were collected. Risk factors for 90-day mortality were identified using multivariate Cox proportional hazard risk regression model.

Results: 189 patients (all male) were finally included, with a median age of 21.0 years (IQR 19.0-27.0), median APACHE II score of 11.0 (IQR 8.0-16.0), median SOFA score of 3.0 (IQR 2.0-6.0), and median GCS score of 12.0 (IQR 7.0-14.0). There were 166 survivors (87.8%) and 23 non-survivors (12.2%). Compared with survivor group, non-survivors had higher incidence of severe organ damage, including rhabdomyolysis (46.1% vs 63.6%), disseminated intravascular coagulation (25.6% vs 90.0%), acute liver injury (69.4% vs 95.7%), and acute kidney injury (36.6% vs 95.7%). Multivariate Cox risk regression model showed that SOFA score was an independent risk factor for 90-day mortality, with an optimal cutoff score of 7.5.

Conclusions: SOFA score may be a clinically useful predictor of death in EHS. Prospective studies are required to confirm the effectiveness of SOFA score and the optimal cutoff level.

(C) 2022

1. Introduction

Exertional heatstroke (EHS) is associated with excessive physical ac- tivity and most commonly occurs among athletes, manual laborers, and military personnel [1,2]. Despite a growing understanding of the condi- tion, the incidence of heatstroke has increased dramatically over the past few decades [3]. In June 13, 2022, from California to Virginia, pop- ulations were hit by a heat wave that peaked at nearly 50 ?C, according

* Corresponding author at: Department of Critical Care Medicine, General Hospital of Southern Theatre Command of Peoples Liberation Army, Guangzhou 510010, China.

E-mail address: Zhifengliu7797@163.com (Z. Liu).

¹ These authors contributed equally to this work.

to the US National Weather Service. In 2020, the Lancet published a report on population health and climate change in China. The report pointed out that in the past 20 years, the number of heat-related deaths increased by 4 times, reaching 26,800 in 2019, compared with the period 1986-2005. The average number of hot days increased by 13 in 2019, while the risk of death in older adults due to extreme heat increased by 10.4% [4-6]. Therefore, identification of effective methods for assessment of patients with this condition is a key imperative.

A variety of scoring systems are used for evaluation and prognostic assessment of patients admitted to the intensive care unit (ICU). The Acute Physiology and Chronic Health Evaluation (APACHE), SAPS (Simplified Acute Physiology Score), and the Mortality Probability Model (MPM) are frequently used to assess critically ill patients in the

https://doi.org/10.1016/j.ajem.2022.08.042

0735-6757/(C) 2022

ICU [7]. The scores have been continually refined based on research, and the third- or fourth-generation versions of these scores are currently in use, namely the APACHE IV, MPM0-III, and SAPS 3. APACHE score is a commonly used tool to evaluate the severity of patients in clinical prac- tice. The higher the score, the greater is the possibility of in-hospital mortality [8]. SAPS is used to measure the risk of death and to choose the best therapy for ICU patients [9]. In recent years, MPM score has been shown to be ineffective in postoperative evaluation and for assess- ment of patients with acute coronary syndrome; therefore, it is not used frequently [10,11]. There are also other related scoring systems, includ- ing the modified early warning score and National Early Warning Score , which have a good ability to discriminate pa- tients at risk of cardiac arrest, unanticipated ICU admission, or death [12,13]. In recent years, Sequential organ failure assessment and qSOFA scores have been widely used for prognostic assessment of patients with various critical diseases, including sepsis, Multiple organ dysfunction syndrome, pancreatitis, tumor, and for perioperative assessment [14,15].

At present, there is no scoring system specifically for prognostic as- sessment of patients with heatstroke. Therefore, identification of a suit- able evaluation system for organ function and prognosis of patients with heatstroke is a key imperative. Previous studies have used 12 clin- ical parameters to score heatstroke patients to predict the severity of pa- tients [16]; however, this study had an insufficient sample size and inclusion of different hospital levels and inter-observer variability may have affected the results. The main purpose of the present study was to identify an appropriate scoring system for prognostic assessment of patients with EHS.

1. Methods
   1. Study design and participants

This was a retrospective cohort study of patients with EHS who were admitted to the ICU of the General Hospital of Southern Theatre Com- mand of Peoples Liberation Army (PLA) between October 2008 and May 2019. The inclusion criteria were: 1) patients aged >=18 years who met the following diagnostic criteria of EHS [4]: history of exposure to hot and humid weather or strenuous activity, concurrent hyperthermia (central temperature > 40 ?C), and neurological dysfunction (including delirium, cognitive disorder, and disturbed consciousness); 2) availabil- ity of complete data pertaining to clinical manifestations and cardiac markers. The exclusion criteria were: 1) presence of coexisting irrevers- ible underlying diseases affecting mortality, and 2) pregnant or lactating women.

The study was approved by the Research Ethics Commission of the General Hospital of the Southern Theatre Command of PLA (HE-2020- 09). The requirement for informed consent of patients was waived off by the Ethics Commission.

• 1. Research procedures

Comprehensive treatment was provided to all patients, including body cooling, intravenous infusion, and anti-inflammatory drugs. Organ function supports were provided as per clinical guidelines, in- cluding appropriate hydration, alkalization of urine, and blood purifica- tion with polymer interception [17].

The baseline data of patients were collected, including inflammatory and organ function parameters at admission, APACHE II score, SOFA score, and the Glasgow Coma Scale score. Patients were divided into survivor group and non-survivor group. The baseline clinical char- acteristics at admission were compared between the two groups, and the risk factors for 90-day mortality were analyzed. Finally, receiver op- erating characteristic (ROC) curve analysis was performed to determine the predictive value of SOFA score for 90-day mortality.

• 1. Definitions

The diagnosis of disseminated intravascular coagulation was based on the diagnostic criteria established by the International Society on Thrombosis and Haemostasis (ISTH), with a total score >= 5 points indicating the presence of DIC [18]. acute liver injury was defined as plasma levels of total bilirubin (TBIL) >=34.2 umol/L and an International normalized ratio >=1.5, or presence of Hepatic encephalopathy (any grade) [19]. Acute kidney injury was defined according to the Kidney Disease: Improving Global Outcomes (KDIGO) standard [20]. Rhabdomyolysis was defined as serum creatine kinase (CK) level >= 1000 U/L. [21]

• 1. Statistical analysis

Categorical variables are expressed as frequencies and percentages, and intergroup differences were assessed using the Mann-Whitney U test, Chi-squared test, or Fisher’s exact-test. Continuous variables are expressed as median (interquartile range). Continuous variables exhibiting a Gaussian distribution were compared using Student’s t- test or One-way ANOVA, and those with a non-Gaussian distribution were compared using the Wilcoxon rank-sum test. The endpoint event was death within 90 days after onset. Risk factors for 90-day mor- tality were identified using univariate analysis, and variables associated with P values <0.1 were included in the multivariate Cox regression model. Forward selection was used for step-wise elimination of variables. The prognostic impact of each indicator was analyzed, and the Prognostic risk factors were identified. ROC curve analysis was performed to assess the ability of the independent risk factors to predict 90-day mortality of patients with EHS. Statistical analyses were performed using the SPSS Windows version 23.0 (IBM Inc., Chicago, IL), Empower (R) (http://www.empowerstats.com, X&Y solutions, Inc., Boston, MA, USA), and R (http://www.R-project.org) software. P values (two-tailed) <0.05 were considered indicative of statistical significance.

1. Results
   1. Demographics and baseline characteristics

Out of a total of 208 patients, 19 patients were excluded because of missing data. Thus, 189 patients were included in this study (Fig. 1). All 189 patients were male, with a median age of 21.0 (19.0-27.0) years, median APACHE II score of 11.0 (8.0-16.0), median SOFA score of 3.0 (2.0-6.0), and median GCS score of 12.0 (7.0-14.0). There were

166 survivors (87.8%) and 23 non-survivors (12.2%). Compared with survivor group, non-survivor group had a higher proportion of patients with severe organ damage, including rhabdomyolysis (46.1% vs 63.6%),

208 PatientswithHeat stroke were screened between Jan 2008 and Jun 2019

Non-survial group N=23

Survial group N=166

Include in analyses 189 patients

Lost to follow-up 19 patients

Fig. 1. Flowchart of the clinical study.

DIC (25.6% vs 90.0%), acute liver injury (69.4% vs 95.7%), and acute kid- ney injury (36.6% vs 95.7%). In addition, the ICU time and hospitalization

Table 2

Risk factors for 90-day mortality with exertional heatstroke patients.

costs in the non-survivor group were significantly higher than those in the survivor group (P < 0.05) (Table 1).

3.2

. Risk factor analysis	GCS	0.6 (0.4, 0.8) <0.001	1.4 (0.7, 2.5) 0.333
	MB >= 1000 ng/mL	8.6 (3.0, 25.0) <0.001	0.4 (0.0, 6.7) 0.539
In the multivariate Cox risk regression model, SOFA score was iden-	Acute Kidney Injury	19.7 (4.4, 87.5) <0.001	12.4 (0.1, 2642.5) 0.358

Variables Univariate OR (95% CI)

P-value

APACHE II	1.5 (1.3, 1.9) <0.001	1.4 (0.9, 2.3) 0.172
SOFA	2.2 (1.5, 3.1) <0.001	1.8 (1.1, 3.0) 0.036

Multivariate? OR (95% CI)

P-value

tified as an independent predictor of 90-day mortality of patients with EHS patients after adjusting for age (Table 2). On further analysis of

Disseminated Intravascular Coagulation

18.4 (5.0, 67.2) <0.001 8.3 (0.1, 535.1) 0.320

the effects of organ injury on 90-day mortality, combination of RM, DIC, and acute kidney injury was a factor affecting the 90-day mortality prior to adjusting (P < 0.05). The combination of organ injury had an ef- fect on mortality even after adjusting for age, APACHE II, SOFA score, and GCS score in the model (Table 3). Analysis of the relationship between RM and other organ injury revealed an association of RM with acute kid- ney injury (P < 0.05) (Table 4).

3.3. Sensitivity analysis

The area under the ROC curve of SOFA score for prediction of mortal- ity was 96.3% (95% CI 0.923-1.000, P < 0.001); the optimal cutoff SOFA score level was 7.5, the sensitivity was 92.3%, and the specificity was 91.2% (Fig. 2).

D-D 1.1 (1.0, 1.2) <0.001 1.1 (0.9, 1.2) 0.286

Cystatin C 2.8 (1.7, 4.7) <0.001 NA

INR 2.1 (1.5, 3.1) <0.001 NA

FIB 0.2 (0.1, 0.4) <0.001 NA

Acute Liver Injury 10.1 (1.3, 77.3) 0.027 NA

* Adjust model adjust for: Age.

1. Discussion

The role of various critical illness scores in EHS remains unclear. In our cohort, patients who died within 90 days of onset of EHS had more severe organ function impairment at admission. High SOFA score was an independent risk factor for death in patients with EHS; SOFA score > 7.5 had 92.3% sensitivity and 91.2% specificity for predicting mortality in EHS.

Table 1

Baseline and clinical characteristics between survivors and non-survivors with exertional heatstroke.

Variables	All cohort (n = 189)	Survivor (N = 166)	Non-survivor (N = 23)	P-value
APACHE IIscore, median (IQR)	11.0 (8.0-16.0)	10.0 (7.2-14.0)	23.0 (20.0-24.0)	<0.001
SOFAscore, median (IQR)	3.0 (2.0-6.0)	3.0 (2.0-5.0)	12.0 (10.0-15.0)	<0.001
GCSscore, median (IQR)	12.0 (7.0-14.0)	12.0 (9.0-14.0)	5.0 (3.0-7.0)	<0.001
Age (years), median (IQR)	21.0 (19.0-27.0)	21.0 (19.0-27.0)	21.0 (18.5-24.0)	0.515
WBC(1 x 109/L), median (IQR)	11.4 (8.7-14.6)	11.4 (8.9-14.5)	10.4 (8.1-15.5)	0.928
Neutrophil (1 x 109/L), median (IQR)	8.9 (6.5-12.4)	9.0 (6.6-12.3)	8.7 (6.3-13.2)	0.873
Lymphocyte (1 x 109/L), median (IQR)	1.1 (0.6-1.9)	1.1 (0.7-1.9)	0.6 (0.3-2.5))	0.942
Monocytes (1 x 109/L), median (IQR)	0.7 (0.4-1.0)	0.7 (0.4-1.0)	0.7(0.2-0.8)	0.757
Platelets (1 x 109/L), median (IQR)	165.0 (82.2-218.2)	170.0 (105.5-223.5)	76.0 (30.0-89.0)	<0.001
mean platelet volume (%)median (IQR)	10.7 (10.2-11.4)	10.7 (10.2-11.4)	11.1 (10.5-11.8)	0.153
Platelet distribution width (%)median (IQR)	12.5 (11.4-13.8)	12.4 (11.3-13.6)	13.1(12.1-15.7)	0.011
TBIL (umol/L), median (IQR)	15.9 (10.1-29.4)	14.8 (9.8-25.8)	29.9 (15.0-118.1)	<0.001
ALT(U/L), median (IQR)	35.0 (20.0-222.5)	32.0 (19.0-149.5)	170.5 (61.5-1648.2)	<0.001
AST(U/L), median (IQR)	67.0 (35.0-228.0)	62.0 (33.5-163.5)	323.0 (99.5-1511.8)	<0.001
BUN (mmol/L), median (IQR)	5.8 (4.5-7.6)	5.6 (4.4-7.0)	8.0 (6.2-9.4)	0.004
CR (umol/L), median (IQR)	128.0 (92.0-163.0)	122.5 (87.8-149.0)	222.0 (183.5-268.5)	<0.001
Cystatin C(mg/L)median (IQR)	1.0 (0.8-1.2)	1.0 (0.8-1.2)	1.5 (1.1-2.9)	<0.001
CK (U/L), median (IQR)	920.0 (358.2-2555.0)	854.5 (323.2-2223.2)	1672.0 (817.8-6993.0)	0.010
CK-MB (ng/mL), median (IQR)	38.0 (26.0-72.0)	37.0 (26.0-66.2)	98.0 (26.5-326.0)	0.026
MB (ng/mL), median (IQR)	468.9 (127.0-1000.0)	359.6 (81.5-1000.0)	1000.0 (935.1-1000.0)	<0.001
CTNI (pg/mL), median (IQR)	110.0 (21.4-420.6)	70.0(11.5-260.0)	1530.0 (895.6-3020.0)	<0.001
PT (s), median (IQR)	15.9 (14.1-20.6)	15.4 (14.1-18.0)	31.9 (21.4-43.5)	<0.001
INR median (IQR)	1.3 (1.1-1.8)	1.2 (1.1-1.5)	3.2 (1.9-4.8)	<0.001
APTT (s), median (IQR)	39.0 (33.5-49.4)	37.9 (33.3-45.1)	85.7 (55.5-120.6)	<0.001
TT(s), median (IQR)	17.6 (16.6-21.1)	17.3 (16.5-19.1)	32.8 (23.1-58.7)	0.014
FIB(g/L), median (IQR)	2.5 (2.0-2.9)	2.5 (2.1-2.9)	1.6 (0.9-2.1)	<0.001
D-D(mg/L), median (IQR)	1.7 (0.5-6.8)	1.1 (0.5-4.2)	10.0 (9.5-20.0)	<0.001
CRP (mg/dL), median (IQR)	3.3 (1.4-6.5)	3.3 (1.0-6.7)	3.4 (3.3-3.6)	0.482
PCT (ng/mL), median (IQR)	1.8 (0.8-4.3)	1.6 (0.8-4.1)	3.6 (1.3-6.6)	0.044
Transfusion N (%)	51/183 (27.9%)	36/165(21.8%)	15/18(83.3%)	0.015
HB(U)median (IQR)	10.8 (7.0-19.5)	11.5 (5.5-13.0)	10.0 (7.0-20.8)	0.490
PLT(U)median (IQR)	45.0 (30.0-105.0)	37.5 (18.8-45.0)	108.0 (82.5-142.5)	<0.001
Plasma(mL)median (IQR)	3610.0 (1020.0-8800.0)	1910.0 (540.0-4100.0)	9400.0 (5772.5-15,090.0)	<0.001
Cryoprecipitate(U)median (IQR)	78.5 (37.0-144.5)	42.0 (20.0-74.5)	146.0 (101.0-190.5)	<0.001
Rhabdomyolysis, CK >= 1000 U/L, N (%)	84/175 (48.0%)	71/154 (46.1%)	14/22 (63.6%)	0.124
Rhabdomyolysis,MB >= 1000 ng/mL, N (%)	52/161 (32.3%)	37/140 (26.4%)	16/22 (72.7%)	<0.001
Lymphocytopenia<0.8 x 109/L N (%)	72/187 (38.5%)	59/164 (35.8%)	13/21 (59.1%)	0.035
Disseminated Intravascular Coagulation N (%)	50/145 (34.5%)	32/125 (25.6%)	18/20 (90.0%)	<0.001
Acute Liver Injury N (%)	131/180 (72.8%)	109/157 (69.4%)	22/23 (95.7%)	0.008
Acute Kidney Injury N (%)	82/187 (43.9%)	60/164 (36.6%)	22/23 (95.7%)	<0.001
ICU time (d)median (IQR)	5.0 (3.0-9.0)	5.0 (3.0-9.0)	7.0 (5.0-12.0)	0.404
survival time (d)median (IQR)		90.0 (90.0-90.0)	7.0 (5.0-12.0)	<0.001
Hospitalization costs (RMB yuan)median (IQR)	40,665.4 (21,989.3-103,378.0)	37,103.9 (21,307.0-72,135.9)	167,955.3 (130,519.3-341,704.3)	<0.001

Table 3

Effects of organ injury on 90-day mortality in exertional heatstroke patients.

	Multivariate		Multivariate		Multivariate		Multivariate
	OR (95%CI) P-value		OR? (95%CI) P-value		OR?? (95%CI) P-value		OR# (95%CI) P-value
RM(MB >= 1000 ng/mL)	6.0 (1.0, 34.7) 0.047		2.5 (0.2, 34.9) 0.484		7.0 (0.1, 561.5) 0.383		4.7 (0.4, 57.4) 0.223
RM(CK >= 1000 U/L)	0.1 (0.0, 0.8) 0.029		0.1 (0.0, 1.9) 0.122		0.0 (0.0, 2.0) 0.083		0.1 (0.0, 1.9) 0.132
DIC	62.3 (4.3, 895.7) 0.002		24.0 (0.4, 1468.3) 0.130		37.4 (0.0, 69,604.6) 0.346		9.1 (0.4, 197.2) 0.160
Acute Liver Injury Acute Kidney Injury	9,000,971.7 (0.0, Inf) 0.992 18.3 (1.8, 188.7) 0.014		inf. (0.0, Inf) 0.996 10.9 (0.4, 290.7) 0.155		67.8 (0.1, 47,624.5) 0.207 7,915,040.9 (0.0, Inf) 0.996		inf. (0.0, Inf) 0.996 5.8 (0.5, 68.1) 0.162

Non-adjusted model adjust for: None.

* Adjust model adjust for: Age; APACHII.

?? Adjust model adjust for: Age; SOFA.

^# Adjust model adjust for: Age; GCS.

Table 4

The relationship between RM and other organ injury in EHS.

Variables RM(MB >= 1000 ng/ml)

	Correlation	95%CI	P-value
Acute Kidney Injury	0.423	0.287, 0.543	<0.001
Acute Liver Injury	0.302	0.151, 0.440	<0.001
DIC	0.255	0.092, 0.405	0.003
GCS	-0.249	-0.431, -0.048	0.016
Lymphocytopenia	0.069	-0.087, 0.222	0.383
90-day mortality	0.338	0.194,0.468	<0.001

SOFA and APACHE II scores have been shown to be strong predictors of in-hospital mortality [22]. However, some studies found that APACHE II score is not a good predictor of in-hospital mortality of patients with tumor and epilepsy in ICU [23,24]. In the past, SOFA score was mainly used for the evaluation and prognostic assessment of patients with sep- sis [25]. Recent studies have found that SOFA can also accurately assess the prognosis of patients with other diseases, including cancer, acute pancreatitis, acute Liver failure, Acute respiratory distress syndrome , and COVID-19 [26,27]. SOFA describes multiple organ dysfunc- tion using a number of parameters, including oxygenation index (arte- rial oxygen tension [PaO₂]/aspirated oxygen fraction [FiO₂]), mean

Fig. 2. ROC curve of SOFA score for predicting 90-day mortality after EHS.

arterial pressure, GCS score, creatinine or urine volume, bilirubin, and platelets. The patients’ systemic functions are evaluated from six differ- ent aspects: respiration, cardiovascular, liver, coagulation, kidney, and nerve [28]. Each organ system is rated on a functional scale of 0 to 4, and SOFA score of each item is added up to an overall score of 0 to 24, with higher scores indicating more severe disease [29]. A cytokine storm intensity curve was also associated with SOFA score in patients with septic shock, which was earlier than that of SOFA score in the shock group [29]. Monitoring of SOFA score is helpful in monitoring the dynamic changes of cytokine storm in the patient’s condition. Treat- ment strategies aimed at decreasing the SOFA score at admission may be pivotal to reduce the 90-day mortality of patients with EHS.

In addition, the advantage of SOFA score over APACHE II score is that the variables required for SOFA score are simple and easy to understand, and are more applicable to clinical practice. EHS is often associated with multi-system injury. The potential complications include persistent changes in consciousness, DIC, ARDS, acute kidney, heart and liver dam- age, and ultimately multiple organ dysfunction, or even death [30]. Our findings are consistent with those of previous studies in which 90-day mortality was associated with more severe organ function impairment at admission. Our results showed that high SOFA score was an indepen- dent risk factor for death, which also confirmed that SOFA score was a good predictor of organ function in EHS.

However, SOFA score does not take into account the condition of striated muscles. Rhabdomyolysis, while not a symptom, is a typical finding in EHS [31,32]. Besides SOFA score, the effect of myoglobin con- centration on the prognosis of EHS patients should also be considered [33]. In our cohort, rhabdomyolysis with significantly elevated myoglo- bin was associated with acute kidney injury and DIC, while rhabdomy- olysis alone was associated with acute kidney injury and 90-day mortality. In patients with rhabdomyolysis, damage to muscle mem- brane causes significantly increased serum levels of myoglobin and CK. After renal metabolism, myoglobin blocks the renal tubules and causes renal ischemia, leading to acute kidney injury [34,35]. Therefore, comprehensive evaluation of the prognostic value of SOFA score com- bined with myoglobin concentration in patients with EHS is required.

Some limitations of our study should be considered. First, this was a single-center retrospective cohort study which may limit the generaliz- ability of the findings. Second, other indicators of statistical variables have not been included, and the predictive value of SOFA score may have been overestimated owing to the small sample size or lack of inclu- sion of some other indicators. A larger study is required to assess the prognostic value of SOFA score in combination with myoglobin for more in-depth risk stratification of patients with EHS.

1. Conclusions

In this study, high SOFA score at admission was found to be an inde- pendent risk factor for death in patients with exertional heatstroke. SOFA score > 7.5 had a 92.3% sensitivity and 91.2% specificity for predicting mortality. Besides SOFA score, the effect of myoglobin

concentration on the prognosis of exertional heatstroke patients should also be considered.

Ethics approval and consent to participate

The study was approved by the Research Ethics Commission of the General Hospital of the Southern Theatre Command of PLA (HE-2020- 09). The requirement for informed consent of patients was waived off by the Ethics Commission.

Consent for publication

Not applicable.

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Funding

This work was supported by grants from the National Natural Science Foundation of China [NO.82072143], grants from the Nature Science Foundation of Guangdong Province of China [2021A1515010170], the grants from the PLA Logistics Research Project of China [18CXZ030, BLJ20J006, CLB20J032], the grants from the Guizhou Science and Tech- nology Planning Project [Guizhou Science and Technology Cooperation Support (2021) General 413], the grants from the National Natural Sci- ence Foundation of China (NSFC) supported research innovation and ex- ploration [2018YFC170810523].

Author contributions

All authors had full access to all the data in the study and take re- sponsibility for the integrity of the data and the accuracy of the data analysis. MW and ZFL were responsible for the study concept and de- sign. LZ and JJJ were responsible for collecting the data. MW was respon- sible for statistical analysis. LZ were responsible for drafting the manuscript. ZFL and MW were responsible for critical reading of a final version of the manuscript.

CRediT authorship contribution statement

Ming Wu: Data curation. Li Zhong: Formal analysis, Funding acqui- sition, Writing – original draft. Jingjing Ji: Resources, Software. Zhifeng Liu: Conceptualization.

Declaration of Competing Interest

The authors declare that they have no competing interests.

Acknowledgements

None.

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