
==== Front
J Cardiovasc Magn Reson
J Cardiovasc Magn Reson
Journal of Cardiovascular Magnetic Resonance
1097-6647
1532-429X
Elsevier

S1097-6647(24)01103-7
10.1016/j.jocmr.2024.101076
101076
Original Research
Cardiovascular magnetic resonance feature tracking derived strain analysis can predict return to training following exertional heatstroke
Zhang Jun a1
Luo Song a1
Qi Li a
Xu Shutian b
Yi Dongna a
Jiang Yue a1
Kong Xiang a
Liu Tongyuan a
Dou Weiqiang c
Cai Jun cj65081491@163.com
a⁎
Zhang Long Jiang kevinzhlj@163.com
a⁎
a Department of Radiology, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, 210002 Jiangsu, China
b National Clinical Research Centre of Kidney Diseases, Jinling Hospital, Medical School of Nanjing University, Nanjing, 210002 Jiangsu, China
c MR Research, GE Healthcare, 100076, Beijing, China
⁎ Corresponding authors. Department of Radiology, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing 210002, Jiangsu, China cj65081491@163.comkevinzhlj@163.com
1 These authors contributed equally.

06 8 2024
2024
06 8 2024
26 2 10107623 3 2024
28 7 2024
30 7 2024
© 2024 The Authors
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Background

Exertional heatstroke (EHS) is increasingly common in young trained soldiers. However, prognostic markers in EHS patients remain unclear. The objective of this study was to evaluate cardiovascular magnetic resonance (CMR) feature tracking derived left ventricle (LV) strain as a biomarker for return to training (RTT) in trained soldiers with EHS.

Methods

Trained soldiers (participants) with EHS underwent CMR cine sequences between June 2020 and August 2023. Two-dimensional (2D) LV strain parameters were derived. At 3 months after index CMR, the participants with persistent cardiac symptoms including chest pain, dyspnea, palpitations, syncope, and recurrent heat-related illness were defined as non-RTT. Multivariable logistic regression analysis was used to develop a predictive RTT model. The performance of different models was compared using the area under curve (AUC).

Results

A total of 80 participants (median age, 21 years; interquartile range (IQR), 20–23 years) and 27 health controls (median age, 21 years; IQR, 20–22 years) were prospectively included. Of the 77 participants, 32 had persistent cardiac symptoms and were not able to RTT at 3 months follow-up after experiencing EHS. The 2D global longitudinal strain (GLS) was significantly impaired in EHS participants compared to the healthy control group (−15.8 ± 1.7% vs −16.9 ± 1.2%, P = 0.001), which also showed significant statistical differences between participants with RTT and non-RTT (−15.0 ± 3.5% vs −16.5 ± 1.4%, P < 0.001). 2D-GLS (≤ −15.0%) (odds ratio, 1.53; 95% confidence interval: 1.08, 2.17; P = 0.016) was an independent predictor for RTT even after adjusting known risk factors. 2D-GLS provided incremental prognostic value over the clinical model and conventional CMR parameters model (AUCs: 0.72 vs 0.88, P = 0.013; 0.79 vs 0.88, P = 0.023; respectively).

Conclusion

Two-dimensional global longitudinal strain (≤ −15.0%) is an incremental prognostic CMR biomarker to predict RTT in soldiers suffering from EHS.

Graphical abstract

ga1

Keywords

Exertional heatstroke
cardiovascular magnetic resonance
Feature tracking
Return to training
Abbreviations

2D two-dimensional

AUC area under curve

CMR-FT cardiovascular magnetic resonance feature tracking

GCS global circumferential strain

GLS global longitudinal strain

GRS global radial strain

IDI integrative discrimination index

NRI net reclassification index

ROC receiver operator characteristic

EHS exertional heat stroke

RTT return to training

LV left ventricular

IQR interquartile range

CMR cardiovascular magnetic resonance

FT feature tracking

hs-cTnT high sensitivity cardiac troponin T

cTnI cardiac troponin I

NT-pro-BNP N-terminal pro-B-type natriuretic peptide

LGE late gadolinium enhancement

FIESTA fast imaging employing steady state acquisition

LVM left ventricular mass

EDV end diastolic volume

ESV end systolic volumen

CO cardiac output

CI cardiac index

LVEF left ventricular ejection fraction

ROC receiver operating curve

BP blood pressure

BMI body mass index

ECV extracellular volume fraction

OR odds ratio
==== Body
pmc1 Background

Extreme heat can lead to heatstroke, which is the most frequent consequence of extreme exercise in high temperature environments [1], [2]. However, most deaths related to heat stroke are the result of underlying cardiovascular disorders, which are often unknown [3]. Exertional heatstroke (EHS) is a medical emergency that is directly related to strenuous physical activity in high temperature environments, which most often affects healthy young people (e.g. soldiers, athletes, etc) [1]. The cardiovascular system is an important target organ of EHS, and myocardial involvement is also an important cause of death in these patients [4]. Cardiovascular diseases can occur in hospitalized EHS patients, comprising a wide spectrum of disorders, including myocarditis, pericarditis, and multisystem inflammatory syndrome [1], [5].

Reports of myocarditis and pericarditis occurring in young soldiers or athletes generated clinical concerns regarding the risk of return to training (RTT) after EHS [6]. Current research suggests that most individuals recover completely within a few weeks; however, some individuals may suffer from prolonged sequela that may require early prognostic assessment [7]. Lingering cardiac symptoms, including exercise intolerance, tachycardia, and chest pain, are increasingly recognized as late complications following heatstroke [3]. Several screening strategies for a safe RTT of soldiers or competitive athletes have been proposed, mainly based on experts' opinions [1], [6]. The data accumulated so far, derived from cross-sectional studies, usually rely on an approach based on clinical symptoms, biomarkers, and so on [7], [8], [9]. However, the prognostic value of these indeces are highly variable. Thus, there is a need for novel biomarkers for predicting EHS patients’ RTT.

Cardiovascular magnetic resonance (CMR) imaging has been used to assess cardiac anatomy and function in EHS patients [5]. CMR feature tracking (FT) is an emerging post-processing technology based on conventional cine images that do not require prospective image acquisition and can reflect the overall and local ventricular function through myocardial strain analysis indicators [10]. CMR-FT–derived myocardial strain analysis has been shown to detect subclinical myocardial dysfunction and improve prognostic value in patients with myocarditis [11]. However, the prognostic value of integrating CMR parameters in the RTT prediction model has not been investigated in soldiers or athletes with EHS, to the best of our knowledge.

Our study hypothesis is that CMR-FT can detect myocardial strain abnormalities associated with prognosis in EHS patients. To investigate the prognostic value of CMR-FT-derived myocardial strain in trained soldiers with EHS, we first compared the difference in myocardial strain parameters between soldiers suffering from EHS and age-matched healthy controls (HCs) and between patients able and unable to RTT after index EHS, and then further explored the association between left ventricular (LV) myocardial strain and RTT.

2 Methods

2.1 Study participants

This single-center, prospective, observational study was approved by the research ethics board committee of Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University. Written informed consent was obtained from each participant.

Between June 2020 and August 2023, the soldiers with EHS (hereafter referred to as “participants,” with possible subclinical cardiac involvement but no formal clinical indication for CMR imaging) with high-level outdoor training were consecutively enrolled. The diagnostic criteria for EHS are in line with the expert consensus of heatstroke in China [12]. The inclusion criteria were defined as follows: 1) age ≥18 years, hospitalized during the acute EHS; 2) no known cardiovascular disease or complications. Exclusion criteria were 1) individuals with non-diagnostic CMR images; 2) individuals with known contraindications to CMR; 3) individuals unwilling to participate in long-term follow-up. A total of 42 participants were included in our previous publications [5], [13] which studied the CMR findings of EHS rather than their prognosis.

The HC group was similar in terms of age and gender, level of training, and cardiovascular risk factors and had no known previous heart disease or comorbidities. Other exclusion criteria were the same as for EHS patients. The HCs underwent routine blood tests and CMR exams with the same protocols at our hospital. These individuals were included as controls after they demonstrated normal CMR findings.

2.2 Blood sampling

Blood sampling was performed immediately before the CMR examination for all participants. The following blood sampling parameters were measured at baseline: high-sensitivity cardiac troponin T (hs-cTnT), cardiac troponin I (cTnI), myoglobin, N-terminal pro-B-type natriuretic peptide (NT-pro-BNP), as well as routine blood tests. Blood samples were processed at our hospital laboratory using standardized commercially available test kits.

2.3 CMR image acquisition

A multiparametric CMR scan protocol, including cine imaging, native T1 mapping, T2 mapping, and late gadolinium enhancement (LGE), was performed. The detailed CMR protocols have been previously described [5], [13], which can be seen in the Supplemental Materials. Briefly, all CMR was performed on a clinical 3T scanner (Discovery MR 750, GE Medical Systems, Waukesha, Wisconsin) with an eight-channel cardiac coil. To perform morphologic and functional analysis, a fast imaging employing steady state acquisition (FIESTA) was acquired, consisting of a stack of contiguous parallel short-axis slices covering the entire LV from base to apex, and three LV long-axis slices were used for cardiac cine imaging. With electrocardiogram gating and respiratory gating during the examination, cardiac cine imaging was performed at end-expiratory breath-hold.

2.4 CMR post-processing and imaging analysis

CMR post-processing was performed using CVI 42 (version 5.11.2, Circle Cardiovascular Imaging Inc., Calgary, Alberta, Canada ). Two cardiovascular radiologists (L.Q. and S.L., with 7 and 13 years of experience in CMR image analysis, respectively) who were blinded to clinical information contoured and evaluated all CMR images independently. The results were reviewed by one experienced cardiovascular radiologist (J.Z., with >10 years of experience in CMR imaging analysis).

The geometry and functional parameters of LV were automatically processed with manual adjustments. The papillary muscle contour was excluded from the endocardium. The obtained parameters included left ventricular mass (LVM), end-diastolic volume (EDV), end-systolic volume (ESV), cardiac output (CO), cardiac index (CI), and LV ejection fraction (EF). The endocardial and epicardial borders were automatically drawn with manual correction at the end of diastole and systole. LV volume was standardized using the body surface area (BSA). In case of disagreement, a consensus was reached through consultation.

Myocardial strain analysis was performed by FT by using cine imaging. The endocardial and epicardial borders were automatically drawn with manual correction at the end of the diastole. The LV two-dimensional (2D) global strain in radial, circumferential, and longitudinal modes was automatically derived. Short-axis images were analyzed for global circumferential strain (GCS) and global radial strain (GRS). The long-axis images were used for global longitudinal strain (GLS) measurement. Global T1, ECV, and T2 values were averaged over the whole LV myocardium short-axis slices.

To assess intraobserver reproducibility, strain data measurements were repeated in 30 individuals by one cardiovascular radiologist (L.Q.), who randomly selected the 30 cases from 107 participants and HCs, at least 3 months after the initial analysis; another cardiovascular radiologist (X.K., with 8-year experience in CMR image analysis), who was blinded to the first radiologist’s measurements, repeated analysis of those 30 patients to assess interobserver reproducibility. In addition, assessment of LGE was performed by two cardiovascular radiologists (L.Q. and S.L.) blinded to the participants’ clinical data. If there were disagreements, the third observer (J.Z.) adjudicated any discrepancies between these two observers. LGE patterns were categorized into linear mid-wall, subepicardial, focal, and multiple patterns.

2.5 Clinical follow-up

The clinical team from the Heat Stroke Center of Jinling Hospital followed the participants for 3 months following the index EHS event. Electronic and medical records were reviewed for the collection of follow-up data and/or the patient was contacted by telephone. The primary outcome measurement was non-RTT, defined as a composite of intolerance to training due to persistent symptoms (chest pain, dyspnea, palpitations, and syncope) [14], [15] or recurrent heat-related illness during 3 months follow-up after index CMR. Reports of the clinical symptoms or events were reviewed and judged by a clinical events committee [16], which consists of three independent heat stroke experts (nonauthors).

2.6 Statistical analysis

All analyses were performed using R software (version 4.1.3, the R Foundation, Vienna, Austria ), GraphPad Prism (version 9, Graph-Pad Software, San Diego, California ) software, and MedCalc (version 20.100, MedCalc Software, Ostend, Belgium) software. Categorical data were presented as counts (percentages) and continuous variables as mean with standard deviation or median with interquartile range (IQR), as appropriate for the type of data. The normality of distribution was tested by using the Shapiro-Wilk test. Comparisons between the two groups were performed with the non-paired Student test (for normal distribution) or Mann-Whitney U test (for abnormal distribution) with continuous variables or χ2 tests with categorical variables. Comparisons between groups were conducted using one-way analysis of variance for normally distributed data and the Kruskal-Wallis rank-sum test for non-normally distributed data. In the case of P < 0.05 on the global level, pairwise comparisons were conducted and adjusted using Bonferroni correction. Readers’ reproducibility for LV strain parameters was performed by using Bland-Altman analysis and coefficient of variation.

By using receiver operating characteristic (ROC) curve analysis on participants' predicted capability of RTT, statistically optimal cutoffs for classifying participants as high or low as possible for RTT were determined based on the Youden J statistic. Univariable and multivariable logistic regressions were used to build prediction models. The variables associated with outcome with P < 0.10 in univariate logistic regression models were included in the multivariable analysis. Multivariable logistic regression analyses using enter mode were used to assess the independent predictor of outcome. To assess the incremental predictive value of 2D-GLS, different multivariable models were used for adjustment: 1) model 1 (traditional clinical risk factors model) involved age, body mass index (BMI), gender, systolic blood pressure (BP), diastolic BP, chest pain, present exertional dyspnea, present syncope, present hs-cTnT, cTnI, and NT-pro-BNP; 2) model 2 (CMR-derived parameters model) involved model 1 + LVEF, EDV/BSA, SV/BSA, CO, CI, T1 mapping, extraceullar volume fraction (ECV), T2 mapping, LGE; 3) model 3 (2D-GLS model) involved model 2 + 2D-GLS value. The incremental predictive values for predicting RTT were assessed by using area under curve (AUC) before and after the addition of 2D-GLS. The additional predictive value of the 2D-GLS was calculated by AUC increment, continuous net reclassification improvement (NRI), categorical NRI, and integrated discrimination improvement (IDI). A two-tailed value of P < 0.05 was considered statistically significant.

3 Results

3.1 Baseline characteristics

A total of 84 hospitalized participants with EHS were enrolled, 4 patients were excluded due to claustrophobia (n = 1), refusal to undergo MRI examination (n = 1), known cardiac conditions (n = 1), and non-diagnostic CMR images (n = 1). Eighty participants were enrolled in the baseline CMR study. After further exclusion of participants without contact information (n = 2) and refusal to participate follow-up (n = 1), a total of 77 participants (median age, 21 years; IQR, 20–23 years) were included in our final analysis. HCs (n = 27) with a similar training level and age distribution (median age, 21 years; IQR, 20–22 years) were also enrolled. Fig. 1 provides the flowchart of this study.Fig. 1 Study population flowchart. The flowchart shows the selection process (participants (A) and healthy control (B)) based on eligibility. EHS exertional heatstroke, MRI magnetic resonance imaging imaging, CMR cardiovascular magnetic resonance, RTT return to training.

Fig. 1

Baseline demographic characteristics, blood results, and CMR findings of the study cohort are reported in Table 1. The median (IQR) time interval between the EHS diagnosis and CMR examination was 3 (2–5) days. Exertional syncope (75.3%, 58/77) and dizziness were the most common cardiac symptoms (63.6%, 49/77), followed by atypical chest pain (26.0%, 20/77) and exertional dyspnea (14.3%, 11/77). In addition, 4 participants (5.2%, 4/77) suffered from recurrent heat illness after discharge.Table 1 Baseline characteristics of the study cohort.

Table 1Characteristic	Controls, n = 27	EHS, n = 80	P value	RTT (Y), n = 45	RTT (N), n = 32	P value	
Days from diagnosis to CMR	-	3 (2, 5)	-	3 (2, 5)	5 (3, 6)	0.019	
Days from diagnosis to follow-up	-	95 (82, 103)	-	88 (85, 102)	101 (91, 109)	0.212	
Training duration (hours/week)	23.1 ± 4.3	22.7 ± 3.6	0.423	22.5 ± 3.8	23.1 ± 3.5	0.472	
Age (years)	21 (20, 22)	21 (20, 23)	0.262	21 (20, 22)	21 (19, 23)	0.669	
Gender			0.648			0.308	
 Male	25 (92.6%)	76 (95%)		44 (97.8%)	29 (90.6%)		
 Female	2 (7.4%)	4 (5.0%)		1 (2.2%)	3 (9.4%)		
BMI (kg/m2)	22.9 ± 2.7	22.5 ± 1.9	0.353	22.1 ± 2.1	22.7 ± 1.6	0.471	
Symptoms	
 Chest pain, n (%)	-	20 (26.0%)	-	10 (22.1%)	10 (31.3%)	0.424	
 Exertional dyspnea, n (%)	-	11 (14.3%)	-	6 (13.3%)	5 (15.6%)	1.000	
 Syncope, n (%)	-	58 (75.3%)	-	35 (77.8%)	23 (71.9%)	0.554	
 Dizziness, n (%)	-	49 (63.6%)	-	27 (60.0%)	22 (68.8%)	0.479	
Systolic BP (mmHg)	-	118 (111, 126)	-	118 (111, 124)	121 (109, 133)	0.463	
Diastolic BP (mmHg)	-	70 ± 10	-	69 ± 9	71 ± 11	0.693	
hs-cTnT (ng/mL)	-	0.015 (0.007, 0.040)	-	0.015 (0.005, 0.025)	0.015 (0.008, 0.047)	0.475	
hs-cTnT (by category)						0.773	
 Normal, n (%)	-	38 (47.5%)		21 (46.7%)	16 (50.0%)		
 Elevate, n (%)	-	42 (52.5%)		24 (53.3%)	16 (50.0%)		
cTnI (ng/mL)	-	0.03 (0.02, 0.12)	-	0.91 ± 0.15	0.11 ± 0.14	0.685	
cTnI (by category)						0.007	
 Normal, n (%)	-	46 (56.2%)		32 (71.1%)	13 (40.6%)		
 Elevate, n (%)	-	34 (43.8%)		13 (28.9%)	19 (59.4%)		
NT-pro-BNP (pmol/mL)	-	7.2 (2.78, 21.9)	-	7.2 (2.41, 21.9)	7.82 (3.77, 24.15)	0.623	
NT-pro-BNP (by category)						0.008	
 Normal, n (%)	-	70 (87.5%)		44 (97.8%)	25 (78.1%)		
 Elevate, n (%)	-	10 (12.5%)		1 (2.2%)	7 (21.9%)		
Myoglobin (ng/mL)	-	106.0 (45.4, 302.3)	-	89.6 (45.6, 248.3)	122.2 (40.0, 394.0)	0.336	
Myoglobin (by category)						0.375	
 Normal, n (%)	-	31 (39.7%)	-	20 (44.4%)	11 (34.4%)		
 Elevate, n (%)	-	49 (61.3%)		25 (55.6%)	21 (65.6%)		
Heart rate (bpm)	61 ± 10	56 ± 8	<0.001	56 ± 8	55 ± 7	0.102	
ECG abnormal, n (%)	-	35 (43.8%)	-	18 (40.0%)	17 (53.1%)	0.254	
LVEF (%)	58.4 ± 5.6	58.4 ± 5.7	0.821	58.3 ± 5.4	59.2 ± 6.9	0.592	
LVM/BSA (g/m2)	48.0 (45.3, 53.4)	51.5 (48.4, 56.3)	0.003	52.1 (47.2, 55.3)	52.2 (49.3, 57.9)	0.185	
EDV/BSA (mL/m2)	78.8 ± 9.6	88.5 ± 10.6	<0.001	85.1 ± 10.5	93.4 ± 8.9	0.001	
ESV/BSA (mL/m2)	32.8 ± 5.5	36.7 ± 7.8	0.020	36.7 ± 8.4	36.9 ± 7.1	0.865	
SV/BSA (mL/m2)	46.0 ± 6.6	52.0 ± 8.2	0.001	50.6 ± 7.7	53.6 ± 8.6	0.180	
CO (L/min)	5.6 ± 0.9	5.2 ± 0.9	0.178	5.1 ± 0.9	5.4 ± 1.0	0.387	
C&I (L/min/m2)	3.1 ± 0.6	2.9 ± 0.5	0.106	2.8 ± 0.5	3.0 ± 0.5	0.427	
T1 mapping (ms)	1456 ± 26	1514 ± 25	<0.001	1509 ± 51	1517 ± 59	0.527	
ECV (%)	20.6 ± 1.6	24.3 ± 2.6	<0.001	24.3 ± 2.7	24.5 ± 2.6	0.816	
T2 mapping (ms)	44.6 ± 2.1	46.5 ± 2.6	0.012	46.6 ± 2.5	46.3 ± 2.6	0.607	
LGE present, n (%)	0 (0.0%)	54 (67.5%)	<0.001	31 (68.9%)	23 (71.9%)	0.778	
2D-GCS (%)	−19.21 ± 1.36	−18.07 ± 1.36	0.002	−18.26 ± 1.16	−17.78 ± 1.10	0.324	
2D-GLS (%)	−16.93 ± 1.22	−15.81 ± 1.67	0.001	−16.53 ± 1.43	−14.99 ± 3.54	<0.001	
2D-GRS (%)	32.49 ± 3.75	30.16 ± 4.29	0.004	30.88 ± 3.54	29.14 ± 4.01	0.223	
EHS exertional heat stroke, RTT return to training, CMR cardiovascular magnetic resonance , BMI body mass index, BP blood pressure, hs-cTnT high-sensitivity cardiac troponin T, cTnI cardiac troponin I, NT-pro-BNP N-terminal pro-B-type natriuretic peptide, ECG electrocardiogram, LVEF left ventricular ejection fraction, LVM left ventricular mass, BSA body surface area, EDV end-diastolic volume, ESV end-systolic volume, SV stroke volume, CO cardiac output, C&I cardiac index, ECV extracellular volume, LGE late gadolinium enhancement, 2D two-dimensional, GCS global circumferential strain, GLS global longitudinal strain, GRS global radial strain.

Values are n (%), mean ± standard deviation, or median (25th to 75th percentile). The categorical classifications of cTnI (<0.06 ng/mL), hs-cTnT (<0.014 ng/mL), NT-pro-BNP (≤53.1 pmol/L), and myoglobin (<73 ng/mL) are based on the threshold. BMI was calculated as weight in kilograms divided by height in meters squared.

Bold values represent the statistical significance of the data.

3.2 CMR parameters between EHS patients and healthy controls

Subjects suffering from EHS exhibited a significant increase in cardiac functional indicators, including LVM/BSA, EDV/BSA, ESV/BSA, and SV/BSA (P = 0.003, P < 0.001, P = 0.020, and P = 0.001, respectively), compared to HCs. The LVEF, CO, and CI showed no significant differences between participants and HCs (P = 0.821, P = 0.178, and P = 0.106, respectively).

Compared to the HC groups, the participants showed significantly increased global native T1, ECV, and T2 values (P < 0.001, P < 0.001, and P = 0.012). Global native T1, ECV, and T2 values showed no significant difference between RTT and non-RTT groups (all P > 0.05).

For LV myocardial strain, compared to HCs, those suffering from EHS had significantly impaired LV 2D-GCS, 2D-GLS, and 2D-GRS (−19.2 ± 1.4% vs −18.1 ± 1.4%, P = 0.002; −16.9 ± 1.2% vs −15.8 ± 1.7%, P = 0.001; 32.5 ± 3.75% vs 30.2 ± 4.3%, P = 0.004; respectively) (Table 1, Fig. 2). The 2D-GCS, 2D-GLS, and 2D-GRS showed significantly statistical differences among these groups (HC, participants with RTT, and participants with non-RTT) (P = 0.007, P < 0.001, and P = 0.003, respectively), however, only 2D-GLS showed significantly statistical differences between participants with RTT group and participants with non-RTT groups (P < 0.001) (Figs. 2 and 3). Both intra- and interobserver reproducibilities were good to excellent for LV strain parameters (see Supplemental Material for details). Good interobserver and intraobserver agreement for CMR parameters, including T1, ECV, and T2, has been demonstrated in our previous publications [5], [13].Fig. 2 Examples of global strain values (2D-GCS in the left panel, 2D-GRS in the middle panel, and 2D-GLS in the right panel). Top row: images of a healthy control with normal 2D-GCS (−20.3%), 2D-GRS of 33.6%, and 2D-GLS of −17.2%. Middle row: images of a participant with RTT, decreased 2D-GCS of −16.7%, reduced 2D-GRS of 26.4%, and 2D-GLS (cutoffs: −15.00%) of −14.4%. Bottom row: images of a participant without RTT, compared to the middle row, the 2D-GCS (−12.1%) and 2D-GRS (23.8%) were decreased, and 2D-GLS (−10.1%, cutoffs: −15.0%) was lower. 2D two-dimensional, GCS global circumferential strain, GLS global longitudinal strain, GRS global radial strain, HC healthy control, RTT return to training.

Fig. 2

Fig. 3 Dot plots show LV strain by group (HC, participants with RTT, and participants without RTT). P value is for comparison of all three groups performed by using analysis of variance with Bonferroni-corrected post hoc comparisons. (A) 2D-GCS, (B) 2D-GLS, (C) 2D-GRS in LV of HC and the participants. Dots indicate HC and participants' data points, with horizontal bars representing the mean and whiskers representing ±standard deviation. LV left ventricular, HC healthy control, RTT return to training, 2D two-dimensional, GCS global circumferential strain, GLS global longitudinal strain, GRS global radial strain.

Fig. 3

3.3 Predictors of RTT

Follow-up assessments were performed after 3 months from index EHS diagnosis. At the 3 months follow-up, 45 participants (58.4%, 45/77) had fully returned to training. Cardiac symptoms and recurrent heat-related illnesses were observed in 32 participants (41.6%, 32/77) who did not RTT. Exertional dyspnea (78.1%, 25/32) was the most common cardiac symptom in these patients.

Among the LV strain indices for predicting RTT, 2D-GLS exhibited the highest integrated areas under the ROC curve (cutoff values: −15.0%) (Fig. S2) and was therefore included as a candidate predictor in subsequent analyses. The cTnI, EDV/BSA, and 2D-GLS were independent predictors of RTT in the predictive model (Table 2). Variables selected by the enter algorithm from baseline and standard clinical variables were cTnI (odds ratios [OR] = 2.60, P = 0.022) (model 1) and hence composed a statistically identified small subset of independent predictors of RTT. The results of the multivariable logistic regression analyses indicated that EDV/BSA emerged as an independent predictor for RTT in model 2. After adjustment for traditional factors (age, gender, and BMI) in model 2, EDV/BSA (OR = 1.91, P = 0.019) still provided independent predictive capability. We then aimed to determine whether 2D-GLS was associated with RTT even after adjustment for systolic BP, cTnI, EDV/BSA, and other clinically important variables (age, gender, and BMI). In the final multivariable analysis (model 3), the variable associated with RTT was 2D-GLS (OR = 1.53, P = 0.016).Table 2 Univariable and multivariable binary logistic regression analyses of baseline characteristics for prediction of the RTT.

Table 2Characteristic	Univariable analysis		Multivariable analysis	
OR
(95% CI)	P value		OR
(95% CI)	P value	Adjusted OR*(95% CI)	P value*	
Model 1								
Age (years)	1.03 (0.83–1.26)	0.779						
BMI (kg/m2)	1.32 (0.95–1.83)	0.090		1.22 (0.94–1.60)	0.130			
Gender (male)	0.93 (0.05–16.72)	0.961						
Systolic BP (mmHg)	0.98 (0.95–1.01)	0.332						
Diastolic BP (mmHg)	0.99 (0.94–1.03)	0.688						
Chest pain, present	1.59 (0.57–4.43)	0.375						
Exertional dyspnea, present	1.83 (0.23–3.00)	0.777						
Syncope, present	1.73 (0.25–2.07)	0.555						
hs-cTnT (by category)	1.87 (0.35–2.16)	0.773						
cTnI (by category)	3.59 (1.38–6.35)	0.009		2.60 (1.19–6.81)	0.022			
NT-pro-BNP (by category)	2.32 (1.43–5.98)	0.022		0.12 (0.01–1.27)	0.079			


	

	

	

	

	

	

	

	
Model 2								
LVEF (%)	1.04 (0.96–1.13)	0.196						
EDV/BSA (mL/m2)	1.90 (1.85–1.96)	0.002		1.48 (1.07–2.05)	0.017	1.91 (1.82–2.01)	0.019	
SV/BSA (mL/m2)	0.95 (0.89–1.01)	0.103						
CO (L/min)	0.75 (0.47–1.19)	0.227						
C&I (L/min/m2)	0.49 (0.20–1.22)	0.127						
T1 mapping (ms)	1.01 (0.99–1.02)	0.424						
T2 mapping (ms)	1.05 (0.88–1.26)	0.599						
ECV (%)	0.95 (0.78–1.15)	0.580						
LGE present	1.15 (0.42–3.21)	0.778						
cTnI (by category)	3.59 (1.38–9.35)	0.009		0.37 (0.12–1.10)	0.073			
NT-pro-BNP (by category)	2.32 (1.43–5.98)	0.022		0.13 (0.01–1.60)	0.079			


	

	

	

	

	

	

	

	
Model 3								
cTnI (by category)	3.59 (1.38–9.35)	0.009		1.31 (0.36–4.67)	0.677			
EDV/BSA (mL/m2)	1.90 (1.85–1.96)	0.002		0.98 (0.88–1.01)	0.056			
NT-pro-BNP (by category)	3.32 (1.43–5.98)	0.022		0.18 (0.02–1.81)	0.146			
2D-GLS (< −15.00%)	1.52 (1.11–2.10)	<0.001		2.30 (1.43–3.69)	0.001	1.53 (1.08–2.17)	0.016	
RTT return to training, Model 1 traditional clinical risk factors model, Model 2 CMR functional parameters model, Model 3 two-dimensional global longitudinal strain model, OR odds ratio, CI confidence interval, BP blood pressure, BMI body mass index, hs-cTnT high-sensitivity cardiac troponin T, cTnI cardiac troponin I, NT-pro-BNP N-terminal pro-B-type natriuretic peptide, EDV diastolic volume, BSA body surface area, CO cardiac output, CI cardiac index, ECV extracellular volume fraction, LGE late gadolinium enhancement, 2D two-dimensional, GLS global longitudinal strain, CMR cardiovascular magnetic resonance, SV stroke volume.

Data in parentheses are 95% CIs. Bold values represent the statistical significance of the data.

* Indicate that the adjusted parameters include age, gender, and BMI.

3.4 Incremental value of the model adding 2D-GLS

In the study, three models were created with RTT as the criterion: model 1 (clinical parameters index), model 2 (CMR-derived functional parameters model), and model 3 (2D-GLS parameters model). In the subjects suffering from EHS for the prediction of the RTT, AUC value was 0.72 (95% confidence interval [CI]: 0.62–0.82) for model 1 with traditional clinical risk factors. The addition of CMR functional parameters findings improved the AUC value to 0.79 (95% CI: 0.67–0.87; AUC improvement for model 1: 0.07; categorical NRI = 0.09, P = 0.221; continuous NRI = 0.28, P = 0.281; IDI = 0.06, P = 0.034). Beyond CMR functional parameters findings, the addition of 2D-GLS further improved the AUC value to 0.88 (95% CI: 0.79–0.95; AUC improvement for model 2: 0.09). Analysis of NRI and IDI for comparing competing risk prediction models showed for model 3 compared with model 2 a categorical NRI of 0.54 (95% CI: 0.34–0.96; P = 0.030), continuous NRI of 0.24 (95% CI: 0.03–0.46; P < 0.001), and IDI of 0.16 (95% CI: 0.07–0.25; P < 0.001) (Table 3 and Fig. 4).Table 3 Model discrimination and reclassification for the comparison of models to predict the RTT.

Table 3Measure	Model 1	Model 2	P value*	Model 3	P value*	
AUCs (95% CI)	0.72 (0.62–0.82)	0.79 (0.67, 0.87)	0.136	0.88 (0.79, 0.95)	0.023	
Continuous NRI, (95% CI)	-	0.28 (−0.16, 0.72)	0.281	0.24 (0.03, 0.46)	<0.001	
Categorical NRI, (95% CI)	-	0.09 (−0.07, 0.26)	0.221	0.54 (0.34, 0.96)	0.030	
IDI, (95% CI)	-	0.06 (0.01, 0.11)	0.034	0.16 (0.07, 0.25)	<0.001	
RTT return to training, Model 1 traditional clinical risk factors model, Model 2 CMR functional parameters model, Model 3 two-dimensional global longitudinal strain model, AUC area under curve, CI confidence interval, NRI net reclassification improvement, IDI integrated discrimination improvement.

Data in parentheses are 95% CIs. Bold values represent the significance of the data.

* Indicate that the current model is compared to the previous model.

Fig. 4 Receiver operating characteristic curve analysis for prediction of RTT after 3 months. The areas under curve (AUC) are for model 3 significantly greater than the AUC for model 1 and Model 2. RTT return to training, CI confidence interval, PPV positive predictive value, NPV negative predictive value.

Fig. 4

Similarly, compared to model 1, model 3 significantly improves its predictive capability as reflected by the larger AUC (AUCs: 0.88 (95% CI: 0.79–0.95) vs 0.72 (95% CI: 0.62–0.82); P = 0.013) (Fig. 4). Furthermore, a substantially improved reclassification was demonstrated for model 3 compared with the model 1: categorical NRI of 0.23 (95% CI: 0.01–0.46; P = 0.030), continuous NRI of 0.54 (95% CI: 0.14–0.75; P < 0.001), and IDI of 0.22 (95% CI: 0.12–0.31; P < 0.001).

4 Discussion

The prognostic value of LV strain remains unclear in predicting return to training in participants with heat stroke. Our study showed that the prognostic utility of LV strain parameters assessed by CMR-FT analysis in a prospective cohort of participants following EHS. At baseline, participants had significantly reduced LV strain values compared to the HC group with similar age and level of training. The higher baseline 2D-GLS predicted RTT status at follow-up. Finally, the addition of 2D-GLS to traditional clinical risk factors and CMR functional parameters provided greater prognostic performance in predicting RTT.

Echocardiography is a widely available imaging modality that provides useful information in sports cardiology, particularly in areas of pre-participation screening and to evaluate exercise-induced cardiac remodeling [17]. Several studies have shown GLS by speckle tracking echocardiography to offer a high predictive value in a variety of myocardial conditions, such as ischemic and no ischemic LV dysfunction [18], [19]. Myocardial strain provides prognostic information that is independent of and incremental to standard parameters in a range of clinical scenarios [20]. However, LV strain measurement by speckle tracking echocardiography is challenging because of the low signal-to-noise, limited acoustic windows, and operator dependency [21], [22]. Our study focused on the use of CMR-FT for fast and reliable quantification of LV myocardial strain analysis with shorter measurement time and increased reproducibility compared to echocardiography.

Increasing evidence supports the role of strain parameters assessed by CMR-FT analysis in assessing different categories of heart disease [23]. Buss et al. [24] reported on patients with dilated cardiomyopathy who underwent conventional CMR cine imaging with subsequent assessment of radial, circumferential, and longitudinal strain measurements. They observed significant correlations between all strain parameters and mortality. However, GLS was found to be an independent and superior predictor of outcome when compared with radial and circumferential strain, which was consistent with our findings. In our previous studies, we have confirmed the CMR manifestations of myocardial involvement and suspected myocarditis in patients suffering from heat illness [5], [14]. Lee et al. [25] reported the potential prognostic role of strain parameters assessed by CMR-FT analysis in a small population of 37 patients with myocarditis. In a larger population of patients with suspected myocarditis, Fischer et al. [12] found that GLS by CMR-FT was significantly associated with major adverse cardiovascular events and was an independent predictor. Similarly, our study showed that 2D-GLS was an independent predictor of RTT in trained soldiers with EHS. The 2D-GLS is an indicator of LV function; decreased longitudinal strain indicates impaired LV function [26]. In this study, the 2D-GLS of EHS patients in the RTT group was higher than that in the non-RTT group, indicating that LV dysfunction was consistent with persistent cardiac symptoms.

Cardiac troponins (such as hs-cTnT, cTnI) reflect the laboratory markers of myocardial injury and emerging as the gold standard for biochemical detection of myocardial injury, which is a universal marker for routine risk stratification and selection of appropriate treatment strategies [27]. Previous studies have indicated that a significant elevation of cTnI (>1.5 ng/mL) is an independent risk factor among heat-related illnesses patients [28]. Consistent with this study, our study provided evidence that cTnI is an independent predictor of RTT in patients with experiencing EHS. The increase of cardiac troponin in the early stage of the disease indicates myocardial injury, which has a certain clinical value in predicting the prognosis of the disease.

The incremental prognostic value of 2D-GLS has been demonstrated across a range of cardiovascular diseases, including myocarditis, myocardial infarction, and non-ischemic cardiomyopathy [11], [26], [29]. In the current study, we found that a model that combined the 2D-GLS strain with clinical and conventional CMR variables can best predict the RTT ability of EHS patients. The presence of LGE has been acknowledged as a robust prognostic determinant for heart failure hospitalization and mortality in patients with cardiovascular diseases [30]. However, the results of this study were different, and the existence of LGE is not an independent predictor of failure to RTT. The possible reason is that LGE does not mean certain fibrosis during acute attack, and it may disappear after several months [31]. We found that patients with 2D-GLS better than 15.0% were more likely to RTT regardless of the presence of LGE in CMR. These findings suggested that 2D-GLS can provide incremental prognostic value of RTT for patients with heatstroke. This study emphasized the importance of 2D-GLS function in the prognosis of RTT in patients with EHS, and provides a basis for guiding treatment to improve the clinical outcome of patients, but its real clinical significance needs further prospective research in a large sample patients.

5 Limitations

Our study has limitations. First, this is a prospective study with a limited sample size from one center, which failed to fully reflect the overall population and may have reduced the reliability of the statistical analysis, so the reliability of the results needs to be confirmed by further research. Second, in this study, global strain analysis of LV rather than regional or segmental strain analysis of LV was performed because strain assessment at the global level is more robust than those at the sectional or segmental level [32]. Despite considering many potential confounders, there is likely to be residual bias due to unmeasured confounding. Finally, the 3 months follow-up duration may be short. Current research suggests that most individuals recover completely within a few weeks, if the EHS event is promptly identified and cooled aggressively [33]. Further prospective multicenter studies are needed to demonstrate whether strain parameters assessed by CMR-FT analysis can be used to predict long-term RTT in EHS patients.

6 Conclusion

In conclusion, LV strain parameters assessed by CMR-FT analysis provided important prognostic information for RTT in patients with EHS. The 2D-GLS provided an incremental prognostic value to predict RTT in trained soldiers with EHS.

Funding

None.

Author contributions

Weiqiang Dou: Software, Methodology, Formal analysis. Jun Cai: Project administration, Methodology, Investigation. Long Jiang Zhang: Writing – review and editing, Validation, Supervision, Project administration, Methodology, Investigation, Conceptualization. Jun Zhang: Writing – review and editing, Writing – original draft, Visualization, Validation, Resources, Project administration, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Song Luo: Software, Methodology, Formal analysis, Data curation. Li Qi: Validation, Project administration, Methodology, Data curation, Conceptualization. Shutian Xu: Software, Methodology, Investigation, Formal analysis. Dongna Yi: Visualization, Investigation, Data curation, Conceptualization. Yue Jiang: Supervision, Software, Formal analysis, Conceptualization. Xiang Kong: Validation, Software, Formal analysis, Conceptualization. Tongyuan Liu: Visualization, Software, Formal analysis, Conceptualization.

Funding

None.

Declaration of competing interests

Weiqiang Dou is an employee of MR Research of GE Healthcare who did not control and analyze the data, all other authors declared no conflict of interest.

Appendix A Supplementary material

Supplementary material

Supplementary material

.

Availability of data and materials

Raw data are available upon reasonable request to the corresponding author.

Appendix A Supplementary data associated with this article can be found in the online version at doi:10.1016/j.jocmr.2024.101076.
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