The impact of cardiopulmonary exercise-derived scoring on prediction of cardio-cerebral outcome in hypertrophic cardiomyopathy.

BACKGROUND: Sudden cardiac death (SCD) and stroke-related events accompanied by atrial fibrillation (AF) can affect morbidity and mortality in hypertrophic cardiomyopathy (HCM). This study sought to evaluate a scoring system predicting cardio-cerebral events in HCM patients using cardiopulmonary exercise testing (CPET).
METHODS: We investigated the role of a previous prediction model based on CPET, the HYPertrophic Exercise-derived Risk score for Heart Failure-related events (HyperHF), which is derived from peak circulatory power ventilatory efficiency and left atrial diameter (LAD), for predicting a composite of SCD-related (SCD, serious ventricular arrhythmia, death from cardiac cause, heart failure admission) and stroke-related (new-onset AF, acute stroke) events. The Novel HyperHF risk model using left atrial volume index (LAVI) instead of LAD was proposed and compared with the previous HCM Risk-SCD model.
RESULTS: A total of 295 consecutive HCM patients (age 59.9±13.2, 71.2% male) who underwent CPET was included in the present study. During a median follow-up of 742 days (interquartile range 384-1047 days), 29 patients (9.8%) experienced an event (SCD-related event: 14 patients (4.7%); stroke-related event: 17 patients (5.8%)). The previous model for SCD risk score showed fair prediction ability (AUC of HCM Risk-SCD 0.670, p = 0.002; AUC of HyperHF 0.691, p = 0.001). However, the prediction power of Novel HyperHF showed the highest value among the models (AUC of Novel HyperHF 0.717, p<0.001).
CONCLUSIONS: Both conventional HCM Risk-SCD score and CPET-derived HyperHF score were useful for prediction of overall risk of SCD-related and stroke-related events in HCM. Novel HyperHF score using LAVI could be utilized for a better prediction power.

Introduction

The most hazardous complication of hypertrophic cardiomyopathy (HCM) is sudden cardiac death (SCD), with a prevalence of 0.2% per year [1-3]. However, atrial fibrillation (AF), which is the most common sustained arrhythmia affecting 20% of patients with HCM, can progress to serious neurologic complications resulting in deterioration of quality of life and increased mortality [4-7].

Stratification of the risk for HCM complications is crucial to the section of an appropriate therapeutic strategy. The most widely used risk model is the HCM Risk-SCD score, which was developed by the European Society of Cardiology. The model provides individualized 5-year risk estimates using major risk factors of maximal wall thickness, left atrial diameter, maximal left ventricular outflow tract pressure gradient, family history of SCD, non-sustained ventricular tachycardia (NSVT), unexplained syncope history, and age at clinical evaluation. The model predicted SCD in HCM patients with high accuracy [8,9].

Cardiopulmonary exercise testing (CPET) has been suggested as a useful approach to assess objective functional capacity as well as risk stratification in HCM patients. In the 2020 AHA HCM guideline, CPET was included in risk stratification aiding cardiologists to quantify the degree of functional limitation and select patients for heart transplantation or mechanical circulatory support [10]. Previous studies suggested a possible role of CPET assessment in stratifying overall HCM prognosis, thrombo-embolic risk, and SCD risk [11-17]. Moreover, CPET can also provide a non-invasive method for assessing the cardiovascular, pulmonary and skeletal muscle components of exercise performance [10]. Based on these advantages, a CPET-derived risk model, the HYPertrophic Exercise-derived Risk HF (HyperHF) score, which includes both CPET and echocardiographic parameters, was suggested as a useful predictor for SCD-related events [17]. However, there is no available scoring system that provides integrated risk prediction of both SCD-related and stroke-related events. Furthermore, there is adequate data reporting left atrial volume index (LAVI) to be a better measure of left atrial dilatation as compared to left atrial diameter (LAD), providing a more accurate assessment of left atrial size than conventional M-mode LAD. Hence, we created Novel HyperHF scoring system, a CPET-derived risk model that uses LAVI instead of LAD for measuring left atrial size. The aim of this study was to test the risk stratification ability of Novel HyperHF score. By comparing it with previous risk scoring models, we could propose a superior risk stratification model in predicting both SCD-related and stroke-related events.

Materials and methods

Study population

A total of 330 consecutive patients with true HCM who underwent CPET were recruited and prospectively followed in HCM centers from two tertiary University hospitals in the Republic of Korea between November 2011 and May 2018. Patients aged younger than 18 years (n = 2) or diagnosed with left ventricular hypertrophy (n = 8) were excluded to select true adult HCM patients. Additionally, patients with atrial fibrillation (n = 18), infiltrative cardiomyopathy (n = 3), pacemaker rhythm (n = 1), history of septal myectomy (n = 2) and history of alcohol septal ablation (n = 1) were excluded. A total of 295 patients were finally enrolled in this study, all with normal sinus rhythm without history of atrial fibrillation (Fig 1). The diagnosis of HCM was based on a maximal wall thickness ≥15 mm unexplained by abnormal loading conditions or in accordance with published criteria for diagnosis of disease in relatives of patients with unequivocal disease [9,10]. Sub-types of enrolled HCM patients were obstructive (n = 56), non-obstructive (n = 113) and apical (n = 126). This study conforms to the ethical guidelines of the Declaration of Helsinki and was approved by the institutional ethics board of Yonsei University Severance Hospital (no. 4-2015-0264). Written informed consent was waived because of the retrospective nature of this study.

Fig 1

Flow chart showing selection of the study population.

(HCM, hypertrophic cardiomyopathy; CPET, cardiopulmonary exercising test; LVH, left ventricular hypertrophy; CMP, cardiomyopathy).

Transthoracic echocardiography

Complete transthoracic echocardiography (TTE) was performed in all patients using commercially available scanners (GE Vivid E9, GE Healthcare, Waukesha, WI, USA; Philips IE33, Philips Medical Systems, Andover, MA, USA; Siemens Sequoia C512, Siemens Medical Solutions, Mountain View, CA, USA), including 2D, pulsed-wave, continuous-wave and color Doppler imaging. All studies were performed at rest in the left lateral position. Left ventricular (LV) dimensions and LAD were measured with M-mode in the parasternal short-axis view but the most thickened segment was evaluated throughout the examination. The LAVI and LV ejection fraction (EF) were measured using the modified Simpson’s method from images with apical two- and four-chamber views. Continuous-wave Doppler was used to assess aortic outflow peak velocity as well as peak acceleration velocity where it was present. Valsalva maneuver was additively applied when available. The E/e’ ratio was calculated based on the mitral E velocity obtained using pulsed-wave Doppler, and the mitral annular e’ velocity at the interventricular septal annulus was obtained using tissue Doppler imaging.

Cardiopulmonary exercise testing and related parameters

A CPET was performed on a treadmill according to the modified Bruce ramp protocol. Patients were strongly encouraged to achieve a peak respiratory exchange ratio (RER) >1.10. Expired gases were collected continuously throughout exercise and analyzed for ventilator volume, oxygen (O2) content, and carbon dioxide (CO2) content using a calibrated metabolic cart (Quark CPET, COSMED, Chicago, IL, USA). Expired gases were measured every 15 seconds. During the exercise test, monitoring consisted of continuous 12-lead electrocardiography, manual blood pressure (BP) measurements and heart rate recordings at every stage via the ECG. CPET was terminated based on the following criteria: patient request, ventricular tachycardia, horizontal or down-sloping ST segment depression of ≥2 mm, or a drop in systolic BP ≥20 mm Hg during exercise. A qualified exercise physiologist conducted each test, under supervision of a physician.

The following variables were derived from the CPET results: peak VO2; peak RER, defined by the ratio of CO2 production to O2 consumption at peak effort; and the minute ventilation–carbon dioxide production (VE/VCO2) slope, defined as the slope of the increase in peak ventilation/increase in CO2 production throughout exercise. Peak RER had the highest 30s average value during the last stage of the test. Heart rate reserve is defined as the difference between basal and peak heart rate.

Clinical outcomes

Clinical outcomes were evaluated during a median follow-up of 742 days (interquartile range 384–1047 years). SCD-related events comprised cardiac death, symptomatic ventricular tachycardia or ventricular fibrillation, and admission due to heart failure aggravation. Stroke-related events consisted of new-onset atrial fibrillation and acute stroke after enrollment. Overall events included both SCD-related events and stroke-related events.

Risk model verification

In HCM Risk-SCD, the risk of SCD in 5 years for an individual HCM patient can be calculated from the following equation: P^SCD at 5 years = 1 − 0.998^exp (Prognostic Index), where Prognostic Index = 0.15939858*Maximal wall thickness (mm)– 0.00294271*Maximal wall thickness2 (mm2) + 0.0259082* Left atrial diameter (mm) + 0.00446131*Maximal left ventricular outflow tract gradient (mmHg) + 0.4583082*Family history SCD + 0.82639195*NSVT + 0.71650361*Unexplained syncope—0.01799934*Age at clinical evaluation (years).

HyperHF score indicates the probability of any HF-related event over five years for a single patient and is calculated as PˆHFevents–at–5 years = 1–0.910^exp(Index) where 0.910 is the survival probability at five years and index is the sum of the products of the (centered and scaled) covariates and their coefficients estimated via the Cox model [Index = 0.045 *LAD(mm) -0.000285*pVO2 CP (% of predicted) + 0.071 * VE/VCO2 slope].

To overcome the limitations of LAD compared with LAVI, Novel HyperHF score including LAVI instead of LAD was compared with the previous two risk models (HCM Risk-SCD, HyperHF).

Statistical analysis

Statistical analysis was performed using SPSS software version 20 (IBM SPSS Statistics for Windows, IBM Corp., Armonk, NY, USA). Unless otherwise indicated, all data of continuous variables are presented as mean ± standard deviation and were compared with an independent t-test and Pearson’s correlation coefficient analysis, as appropriate. Variables that were non-normally distributed were compared using the Mann-Whitney U-test. Categorical variables were compared using the chi-square test or Fisher’s exact test, as appropriate. Simple and multivariable linear regression was applied to evaluate the significance of variables. Tests of the proportional hazards assumption for each covariate were obtained with the Kaplan-Meier estimate of survival distribution. A receiver-operating characteristic (ROC) analysis was considered to determine the predictive capability of each risk model in identifying the HF endpoint. A P-value ≤0.05 was considered statistically significant.

Results

Baseline characteristics of the study population

Baseline characteristics including patient characteristics are displayed in Table 1 according to presence and absence of overall events. There was no significant difference between the two groups.

Table 1

Baseline characteristics of overall HCM patients and patients with positive events vs. negative events (*: P value for Event (+) vs. Event (-)).

	Overall HCM (n = 295)	Event (+) (n = 29)	Event (–) (n = 266)	p value*
Age (years)	53.9 ± 13.2	53.9 ± 13.4	53.9 ± 13.2	0.996
Male sex, n (%)	210 (71.2)	23 (79.3)	187 (70.3)	0.277
Body mass index, kg/m2	24.7 ± 3.1	24.1 ± 3.5	24.8 ± 3.0	0.262
Hypertension, n (%)	134 (45.4)	8 (27.6)	126 (47.4)	0.034
Diabetes mellitus, n (%)	48 (16.3)	3 (10.3)	45 (16.9)	0.296
Dyslipidemia, n (%)	102 (34.6)	8 (27.6)	94 (35.3)	0.392
Hemoglobin, mg/dL	14.8 ± 1.6	15.2 ± 1.2	14.8 ± 1.6	0.129
Creatinine, mg/dL	0.91 ± 0.33	0.94 ± 0.24	0.90 ± 0.33	0.589
Antiplatelet, n (%)	92 (31.2)	9 (31.0)	83 (31.2)	0.985
Beta-blocker, n (%)	120 (40.7)	13 (44.8)	107 (40.2)	0.633
ACEi, n (%)	10 (3.4)	10 (100)	0 (0)	0.288
ARB, n (%)	100(31.2)	8 (27.6)	92 (34.6)	0.450
Diuretics, n (%)	20 (6.8)	4 (13.8)	16 (6.0)	0.253
Statins, n (%)	82 (27.8)	10 (34.5)	72 (27.1)	0.399

(ACEI, angiotensin-converting enzyme inhibitor; ARB, angiotensin-receptor blocker).

Echocardiographic parameters are shown in Table 2. Ejection fraction and left ventricular size were similar in the two groups. Left atrial size (LAD, LAVI) was larger in the event (+) group with thicker myocardium, p<0.001;). Compared to the event (-) group, event (+) group showed larger LA size (LAD 44.7 ± 5.1 vs. 40.8 ± 6.0 mm, p = 0.001; LAVI 46.0 ± 14.9 vs. 35.7 ± 13.5 mL/m2), thicker myocardium (maximum wall thickness 21.7 ± 5.4 vs. 19.2 ± 4.3 mm, p = 0.005), shorter deceleration time (183.3 ±63.3 vs. 205.9 ± 46.4, p = 0.017), and lower mitral annular tissue velocity (s’ 5.70 ± 1.84 vs. 6.53 ± 1.29, p<0.001; a’ 6.30 ± 1.79 vs. 7.65 ± 1.85, p = 0.006).

Table 2

Echocardiography data of overall HCM patients and patients with positive events vs. negative events (*: P value for Event (+) vs. Event (-)).

	Overall HCM (n = 295)	Event (+) (n = 29)	Event (–) (n = 266)	p value*
Ejection fraction, %	69.0 ± 7.5	67.2 ± 12.0	69.2 ± 6.8	0.382
LVEDD, mm	47.5 ± 5.0	48.0 ± 6.2	47.4 ± 4.9	0.536
LVESD, mm	29.9 ± 4.4	30.9 ± 6.4	29.8 ± 4.1	0.383
LAD, mm	41.2 ± 6.0	44.7 ± 5.1	40.8 ± 6.0	0.001
IVSd, mm	14.6 ± 5.1	16.6 ± 6.2	14.6 ± 4.9	0.105
PWDd, mm	10.5 ± 2.4	11.0 ± 3.0	10.4 ± 2.3	0.265
LVMI, mm	124.4 ± 51.8	166.9 ± 107.3	119.1 ± 38.8	0.285
LA volume index, ml/m 2	36.7 ± 13.9	46.0 ± 14.9	35.7 ± 13.5	<0.001
Maximum thickness, mm	19.4 ± 4.5	21.7 ± 5.4	19.2 ± 4.3	0.005
E velocity, m/s	0.63 ± 0.17	0.60 ± 0.19	0.62 ± 0.16	0.435
A velocity, m/s	0.64 ± 0.22	0.56 ± 0.16	0.65 ± 0.23	0.086
E over A	1.12 ± 0.60	1.15 ± 0.39	1.12 ± 0.62	0.056
Deceleration time, ms	203.7 ± 48.7	183.3 ± 63.3	205.9 ± 46.4	0.017
s’, cm/s	6.44 ± 1.37	5.70 ± 1.84	6.53 ± 1.29	0.001
e’, cm/s	4.80 ± 1.78	4.37 ± 1.64	4.85 ± 1.80	0.315
a’, cm/s	7.50 ± 1.88	6.30 ± 1.79	7.65 ± 1.85	0.006
E/e’	13.86 ± 5.5	14.03 ± 6.5	13.84 ± 5.38	0.858

(LAD, left atrial anterior-posterior dimension; LVEDD, left ventricular end-diastolic dimension; LVESD, left ventricular end systolic dimension; IVSd, interventricular septum thickness at end-diastole; PWDd, posterior wall thickness at end-diastole; LVMI, left ventricular mass index; LAVI, left atrial volume index; E, peak velocity of early diastolic trans-mitral flow; A, peak velocity of late trans-mitral flow; s’, peak velocity of systolic mitral annular motion as determined by pulsed wave Doppler; e’, peak velocity of early diastolic mitral annular motion as determined by pulse wave Doppler; a’, peak velocity of diastolic mitral annular motion as determined by pulsed wave Doppler).

Table 3 summarizes the CPET parameters. Patients with event showed significantly lower peak VO2 (23.1 ± 5.7 vs. 27.3 ± 6.4 mL/kg/min, p = 0.001) with higher VE/VCO2 slope (32.5 ± 4.7 vs. 29.7 ± 3.9 mL/kg/min, p<0.001).

Table 3

Cardiopulmonary exercise testing data of overall HCM patients and patients with positive events vs. negative events (*: P value for Event (+) vs. Event (-)).

	Overall HCM	Event (+)	Event (–)	p value*
	(n = 295)	(n = 29)	(n = 266)
Exercise duration, sec	735.2 ± 233.1	659.5 ± 202.4	743.5 ± 235.1	0.065
Peak VO2, mL/kg/min	26.9 ± 6.4	23.1 ± 5.7	27.3 ± 6.4	0.001
VE/VCO2 slope	30.0 ± 4.1	32.5 ± 4.7	29.7 ± 3.9	<0.001
LT time, sec	415.5 ± 198.6	352.1 ± 179.0	422.4 ± 199.7	0.070
METs	7.68 ± 1.84	6.60 ± 1.62	7.80 ± 1.82	0.001
Peak RER	1.15 ± 0.22	1.10 ± 0.10	1.16 ± 0.23	0.171
VD/VTp	0.28 ± 0.03	0.30 ± 0.03	0.28 ± 0.03	0.017
PETCO2, mmHg	38.6 ± 6.1	36.0 ± 5.6	38.9 ± 6.1	0.014
OUES	2235.7 ± 718.7	2008.7 ± 709.0	2259.8 ± 716.9	0.090
Baseline SBP, mmHg	123.2 ± 16.7	124.6 ± 20.6	123.1 ± 16.3	0.694
Peak SBP, mmHg	184.3 ± 35.5	171. 8 ± 38.6	185.6 ± 34.9	0.047
Baseline HR, bpm	67.8 ± 11.5	64.4 ± 10.5	68.2 ± 11.6	0.092
Peak HR, bpm	147.8 ± 25.4	132.4 ± 26.7	149.5 ± 24.7	0.001
HRR, bpm	78.4 ± 24.8	68.0 ± 23.5	79.5 ± 24.7	0.017

(VO2, oxygen consumption, VE/VCO2 slope, relation between ventilation vs. carbon dioxide production; LT time, lactate threshold time; METs, metabolic equivalent; RER, respiratory exchange ratio; VD/VTp, peak ratio of dead space to tidal volume; OUES, oxygen uptake efficiency slope; SBP, systolic blood pressure; HRR, heart rate recovery).

Study endpoints

Overall study endpoints are shown in Table 4. During a median follow-up of 742 days (interquartile range 384–1047 years), 29 patients (9.8%) experienced at least one event. There were 14 patients (4.7%) with SCD-related events, and 17 patients (5.8%) with stroke-related events.

Table 4

SCD-related and stroke-related events of the study population.

Events	Number
SCD-related events, n (%)	14 (4.7)
Cardiac death, n (%)	1 (0.3)
Admission due to HF aggravation, n (%)	2 (0.7)
Symptomatic ventricular arrhythmia, n (%)	11 (3.7)
Stroke-related events, n (%)	17 (5.8)
Acute stroke, n (%)	4 (1.4)
New onset AF, n (%)	14 (4.7)
Total	29 (9.8)

(SCD, sudden cardiac death; HF, heart failure; AF, atrial fibrillation).

Model verification

Area under the curve (AUC) values were compared for HCM Risk-SCD score, HyperHF score and Novel HyperHF score in predicting overall events using the ROC curve. HyperHF score showed numerically higher AUC value compared with HCM Risk-SCD score (0.697 vs. 0.670). However, Novel HyperHF score showed the highest value (0.717) (Fig 2).

Fig 2

Receiver operating characteristic curve of HCM Risk-SCD score, HyperHF score, and Novel HyperHF score for predicting overall events.

Prognostic ability of models for overall events was compared among HCM Risk-SCD, HyperHF score, and Novel HyperHF score. Novel HyperHF score showed the numerically highest AUC (0.670 vs. 0.697 vs. 0.717). The optimal cut off for predicting major adverse cardiac events was based on the receiver operating characteristic curve. Each number in the curve is a cut off. (ROC, receiver operating characteristic; AUC, area under the curve; HCM, hypertrophic cardiomyopathy; SCD, sudden cardiac death).

For SCD-related events, the highest AUC value was noted for HCM Risk-SCD score (AUC 0.715, p = 0.007), followed by Hyper HF score (AUC 0.695, p = 0.014) and Novel HyperHF score (AUC 0.692, p = 0.015) (S1 Fig). For stroke-related events, Novel HyperHF score showed the highest AUC value with statistical significance (AUC 0.659, p = 0.028) (S2 Fig). However, other risk prediction models did not show statistical significance (Hyper HF AUC 0.620, p = 0.098; HCM Risk-SCD AUC 0.607, p = 0.138).

Results of sensitivity, specificity, positive predictive value, and negative predictive value are displayed for overall events, SCD-related events, and stroke-related events in S1–S3 Tables, respectively. Kaplan-Meier curve for event-free survival showed significant discrimination between two groups of Novel HyperHF score < 4.5% versus ≥ 4.5% (Fig 3).

Fig 3

Kaplan-Meier curve for event-free survival by Novel HyperHF score predicting overall events.

The cumulative event-free survival was compared between Novel HyperHF score ≥4.5% and <4.5% among total patients. Patients with higher Novel HyperHF score showed significantly lower survival after overall events during the follow-up period.

To demonstrate that the novel HyperHF score is responsible for risk prediction regardless of conventional risk factors, multivariate analysis was performed. As a result, Novel HyperHF model was significantly related with the outcome. (Table 5).

Table 5

Univariate and multivariate analysis of Novel HyperHF score and major risk factors predicting outcome.

	Univariate		Multivariate
	P-value	CI	P-value	CI
Novel HyperHF	<0.001	3.024–32.191	0.017	1.469–46.580
LAVI	0.014	1.016–1.149	0.266	0.987–1.049
LAD	<0.001	1.014–1.049	0.698	0.938–1.101
Gender	0.519	0.302–1.829	0.056	0.107–1.029

(LAD, left atrial dimension; LAVI, left atrial volume index).

Discussion

The main findings of the current study are as follows: 1) both conventional HCM Risk-SCD score and CPET-derived HyperHF score were useful for prediction of overall risk of SCD-related and stroke-related events in HCM; 2) Novel HyperHF score using LAVI could be utilized for a better prediction power; 3) Novel HyperHF ≥ 4.5% showed significantly poor outcome compared to those with Novel HyperHF < 4.5%.

Even though two previous risk models (HCM Risk-SCD score and HyperHF score) were originally developed to predict SCD risk of HCM patients, components of these models are related to AF and acute stroke. Therefore, we tested whether this widely used risk model can be applied in the prediction of overall risk of both SCD-related and stroke-related events [8,9,17]. Additionally, we proposed a novel risk prediction model to improve prediction of overall events in HCM.

Prediction of SCD Riskin HCM

In HCM patients, SCD risk prediction is important to decide whether the patient should receive an implantable cardioverter defibrillator (ICD) therapy. The HCM Risk-SCD score was the most widely used method to predict the risk of SCD and guide the decision of ICD in clinical practice. ICD implantation typically is recommended for secondary prevention; it is used for the primary prevention when the 5-year risk score is higher than 4% by HCM Risk-SCD score method [9,18]. This study compared HyperHF and Novel HyperHF methods with HCM Risk-SCD. As a result, the prediction power of HCM Risk-SCD for overall events was lower than that of HyperHF and Novel HyperHF (AUC values 0.670, 0.691, and 0.717, respectively).

Benefit of using CPET in HCM for risk prediction

In recent reports, exercise parameters on CPET such as peak oxygen consumption, minute ventilation to CO2 production, and ventilatory anaerobic threshold predicted death from HF in HCM patients. Furthermore, CPET provides objective data regarding mechanisms and severity of functional limitation, which can further be applied to decision making for heart transplantation [10].

Previous HCM risk-SCD score was limited due to low sensitivity for clinically relevant decisions about ICD placement and might underestimate the number of high-risk patients who would remain unprotected and susceptible to sudden death without ICD therapy [4,19-23]. Application of CPET in patients with HCM can measure exercise function and prognosis in a variety of subsets of heart failure, including HCM. Substantial data has been collected showing that CPET is not only safe, but also a key element in comprehensive evaluation of HCM patients [24].

HyperHF score is combinational risk predicting model that integrates conventional echocardiological parameter (LAD) and CPET data such as VE/VCO2 slope and circulatory power. HyperHF score is independently related to development of heart failure complications as well [25]. However, atrial fibrillation and stroke related risks exist in HCM which can consequently deteriorate patient’s quality of life and survival. Unfortunately, there is no available scoring system that provides integrated risk prediction of both SCD-related and stroke-related events.

Novel risk prediction model for cardio-cerebral outcome in HCM

With utilization of exercise parameters and echocardiographic parameters, Novel HyperHF score could provide better prediction of overall events related to HCM. Although assessment of LA enlargement appears to provide important information regarding stroke-related events, unidimensional M-mode LA antero-posterior diameter has limitations in representing true LA size [24]. LAVI could be a more sensitive markers for detecting the risk of clinical events in patients with HCM [26]. LAVI is associated with new-onset AF and stroke recurrence in embolic stroke of undetermined source patients and may be a better surrogate of atrial cardiopathy [27]. LAVI was also superior to LAD as an independent prognostic implication in terms of ischemic cardiomyopathy [28]. The mean and standard deviation were 41.2 ± 6.0 mm for the LAD and 36.7 ± 13.9 mL/m2 for the LAVI, suggesting greater discrimination power of LAVI than LAD. Replacing LAVI in the formula of HyperHF score, Novel HyperHF score was the only statistically significant model for stroke-related event prediction.

Although LAVI alone is an important predictor for stroke in HCM, multivariate analysis suggested better prediction of both SCD and stroke-related events by Novel HyperHF score in our patient population.

Clinical applications and perspectives of Novel HyperHF score

Both SCD-related risk and stroke-related risk exist in HCM and clinicians should monitor each risk in those patients who have diagnosed with the disease. Traditional risk models are not sufficient to predict both risks and we have created Novel HyperHF score to overcome limitations. Novel HyperHF score ≥ 4.5% predicted significantly poor outcome including both cardio-cerebral outcome in HCM patients. More intensive diagnostic and follow up strategies need to be applied in those patients with higher Novel Hyper-HF score. Further validation study could broaden our perspectives.

Study limitations

This study contains several limitations. First, this study was retrospective in design, causing inherent potential limitations. Second, atrial fibrillation, which is the most prevalent arrhythmia reaching 22.5% of HCM patients, was excluded from the present study to evaluate for the true prediction ability of Novel HyperHF score in future AF risk. Third, patients in a relatively healthy condition with a capability of CPET were included, and data from only HCM patients with CPET can be assessed with HyperHF or Novel HyperHF scoring system.

Conclusions

For the overall risk prediction of cardio-cerebral outcome in HCM patients, previous HCM risk-SCD score and CPET-derived HyperHF score both provided fair prediction in this cohort from two tertiary University hospitals. Novel HyperHF substituted LAD for LAVI and showed better prediction of overall events in HCM. Novel HyperHF score might allow early identification of patients at high risk of SCD-related and stroke-related events. A future validation study using Novel HyperHF will further increase the impact of this new scoring system.

Receiver operating characteristic curve of HCM Risk-SCD score, HyperHF score, and Novel HyperHF score for predicting SCD-Related events.

Prognostic ability of models for SCD-related events was compared among HCM Risk-SCD, HyperHF score, and Novel HyperHF score. HCM Risk-SCD score showed the highest AUC, while those of HyperHF score and Novel HyperHF were similar (0.715 vs. 0.695 vs. 0.692). The optimal cut off threshold for predicting major adverse cardiac events was based on the receiver operating characteristic curve. Each number in the curve is a cut-off value. (ROC, receiver operating characteristic; AUC, area under the curve; HCM, hypertrophic cardiomyopathy; SCD, sudden cardiac death).

(TIF)

Click here for additional data file.

Receiver operating characteristic curve of HCM Risk-SCD score, HyperHF score, and Novel HyperHF score for predicting stroke-Related events.

Prognostic ability of models for stroke-related events was compared among HCM Risk-SCD, HyperHF score, and Novel HyperHF score. Novel HyperHF score showed the numerically highest AUC (0.607 vs. 0.620 vs. 0.659) and had only one p value less than 0.05 (0.138 vs. 0.098 vs. 0.028). The optimal cut off for predicting major adverse cardiac events was based on the receiver operating characteristic curve. Each number in the curve is a cut off value. (ROC, receiver operating characteristic; AUC, area under the curve; HCM, hypertrophic cardiomyopathy; SCD, sudden cardiac death).

(TIF)

Click here for additional data file.

Kaplan-Meier graph of both Novel HyperHF score and HyperHF score.

The cumulative event-free survival was compared between Novel HyperHF score and HyperHF score among total patients. Patients with higher Novel HyperHF score showed significantly lower survival after overall events during the follow-up period than patients who had higher HyperHF score. (HCM, hypertrophic cardiomyopathy; SCD, sudden cardiac death).

(TIF)

Click here for additional data file.

Sensitivity and specificity, PPV, NPV of each cut-off point for the overall events.

(SCD, Sudden cardiac death; HCM, hypertrophic cardiomyopathy; PPV = positive predictive value; NPV = negative predictive value).

(DOCX)

Click here for additional data file.

Sensitivity and specificity, PPV, NPV of each cut-off point for the SCD-related events.

(SCD, Sudden cardiac death; HCM, hypertrophic cardiomyopathy; PPV = positive predictive value; NPV = negative predictive value).

(DOCX)

Click here for additional data file.

Sensitivity and specificity, PPV, NPV of each cut-off point for the stroke-related events.

(SCD, Sudden cardiac death; HCM, hypertrophic cardiomyopathy; PPV = positive predictive value; NPV = negative predictive value).

(DOCX)

Click here for additional data file.

(TIF)

Click here for additional data file.

10 Mar 2021

PONE-D-21-01943

The Impact of Cardiopulmonary Exercise Derived Scoring on Prediction of Cardio-cerebral Outcome in Hypertrophic Cardiomyopathy

PLOS ONE

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Kind regards,

Otavio R. Coelho-Filho, M.D., Ph.D., M.P.H.

Academic Editor

PLOS ONE

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University in 2017"

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Additional Editor Comments:

The current study investigated the prognostic role of cardiopulmonary exercise testing (CPET) to predict cardio-cerebral events in HCM patients.

Although the study may add relevant data to the current literature, reviewers have identified several issues requiring careful revision.

Additional comments:

1. Replace LV and LA dimensions by indexed volumes.

2. It is not clear whether echocardiographic strain data was available.

3. Since myocardial scar by Cardiac MRI has been shown to predict Cv events in HCM patients. clarify if Cardiac MRI data is available.

4. Detail information about CV events were not provided. It is unclear how SCD and CV were defend and confirmed.

5. Statistical analysis requires improvements.

6. In order to compare models in the current study I would suggest using Harrell’s C statistics to verify discrimination of risk prediction of models. Also Continuous Net Reclassification Index (NRI) and Integrated Discrimination Index (IDI) should be considered.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Partly

Reviewer #2: Yes

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: I Don't Know

Reviewer #2: Yes

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

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Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Major Comments

In the present study, the aim was evaluating a scoring system predicting cardio-cerebral events in Hypertrophic Cardiomyopathy (HCM) patients using cardiopulmonary exercise testing (CPET). A total of 295 consecutive HCM patients (age 59.9±13.2, 71.2 % male) who underwent CPET was included in the present study. The previous model for SCD risk score showed fair prediction ability. However, the prediction power of Novel HyperHF showed the highest value among the models. So, the authors concluded that both conventional HCM Risk-SCD score and CPET-derived HyperHF score were useful for prediction of overall risk of SCD-related and stroke-related events in HCM. However, a novel HyperHF score using LAVI could be utilized for a better prediction power.

Despite the relevance of the issue, the manuscript needs to be improved. The Introduction Section, Materials and Methods and Discussion Section should be significantly improved. The central aim of the present study is the importance of CPET in the risk stratification model to predicting cardio-cerebral events in HCM. However, the major conclusion is that “HCM Risk-SCD score and CPET-derived HyperHF score were useful for prediction of overall risk of SCD-related and stroke-related events in HCM… a novel HyperHF score using LAVI could be utilized for a better prediction power”

Minor Comments

Introduction Section

1- The authors should include more details about the possible role of CPET assessment in stratifying overall HCM prognosis, as well as HYPertrophic Exercise-derived Risk HF (HyperHF).

2- The authors should include that the aim of the present study is about risk stratification model in HCM patients

Materials and Methods Section

1- The authors should include more details about the clinical characteristics of HCM patients. Were obstructive HCM patients excluded?

2- The authors should include more details about cardiopulmonary exercise testing and related parameters. Type of cycle ergometer, protocol, etc

3- The authors should include HCM Risk-SCD model in the Risk Model Verification Section

Results Section

1- The authors should describe all the significantly results presented in Table 2 and Table 3.

Discussion Section

1- What is the importance to include CPET in the risk stratification model to predicting cardio-cerebral events in HCM? This is the central aim of the present study, and the authors should improve the discuss this issue.

2- In addition, the authors should improve the discuss about the Novel HyperHF using LAVI rather than LAD.

Reviewer #2: I have read with interest your work and it surely presents new data and provide further knowledge regarding CPET and HCM.

I do have some comments and suggestions:

- Although those predictive scores are quite new and might add new prognostic information, it demands additional data. In order to investigate whether a new risk scoring model would provide complementary prognostic information to previous echocardiographic variables and/or previous risk scores, a multivariable model should be included in the result part of the paper describing the scores, differences and demonstrating this better power prediction of the new score. In this matter:

a) A kaplan-Meyer curve with both scores at the same picture with differences on prediction.

b) A table with Hazard Ratios and p values comparing both scores and some key clinical variables such as atrial diameter and volume

c) A table with the incremental value of the new score using C-Statistics

- I wonder if authors could provide additional data regarding MRI and 24-hour ECG monitoring. Some important prognostic parameters such as fibrosis and NSVT during 24-monitoring are lacking.

- It seems that LA volume index is superior to LAD and might be an explanation for the additional predictive model of the new score. It is important to demonstrate that the new score itself is the responsible for the better accuracy not LA volume index alone - another multivariable model should be enough to assess this matter.

- EOV is a strong CPET parameter. Do you have data on this ?

- Some results are included in the discussion session. It should be included in the results session and the discussion should be addressed deeply on the differences between the findings among the scores and clinical characteristics.

**********

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Reviewer #1: No

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Submitted filename: review.docx

Click here for additional data file.

13 Jun 2021

I attached Rebuttal letter to reviewers. all my answers are in there. below scripts are manuscripts of file.

#Reviewer 1

Major Comments

In the present study, the aim was evaluating a scoring system predicting cardio-cerebral events in Hypertrophic Cardiomyopathy (HCM) patients using cardiopulmonary exercise testing (CPET). A total of 295 consecutive HCM patients (age 59.9±13.2, 71.2 % male) who underwent CPET was included in the present study. The previous model for SCD risk score showed fair prediction ability. However, the prediction power of Novel HyperHF showed the highest value among the models. So, the authors concluded that both conventional HCM Risk-SCD score and CPET-derived HyperHF score were useful for prediction of overall risk of SCD-related and stroke-related events in HCM. However, a novel HyperHF score using LAVI could be utilized for a better prediction power.

Despite the relevance of the issue, the manuscript needs to be improved. The Introduction Section, Materials and Methods and Discussion Section should be significantly improved. The central aim of the present study is the importance of CPET in the risk stratification model to predicting cardio-cerebral events in HCM. However, the major conclusion is that “HCM Risk-SCD score and CPET-derived HyperHF score were useful for prediction of overall risk of SCD-related and stroke-related events in HCM… a novel HyperHF score using LAVI could be utilized for a better prediction power

#Answer

Thank you for your attentive comments. I totally agree that central aim and major aim is mismatch. Actual aim of our script is comparison of scoring models and prove of usefulness of CPET as a method of predicting prognosis.

And for improvement of scripts, by CPET included as risk stratification method in 2020 AHA HCMP Guideline, I could add possible roles of CPET and HyperHF as risk stratification. And also added more details of study population, baseline characteristics, methods of CPET and details about HCM Risk-SCD model. Moreover, improve the importance of using not only CPET data as a risk stratification models but also LAVI than LAD. And for confirmative comparison of models, We proceeded Delong’s AUC comparison methods and added the results.

Minor Comments

Introduction Section

# Comment 1

The authors should include more details about the possible role of CPET assessment in stratifying overall HCM prognosis, as well as HYPertrophic Exercise-derived Risk HF (HyperHF).

# Answer 1.

Thank you for your thorough review of the script. In recent 2020 AHA Hcmp Guideline, CPET has been inserted at risk stratification. It performed to quantify the degree of functional limitation and aid in selection of patients for heart transplantation or mechanical circulatory support. And it can be helpful in differentiating HCM from other causes of ventricular hypertrophy. And it also provides noninvasive method for assessing the cardiovascular, pulmonary and skeletal muscle components of exercise performance. (1)

And the HYPERHF score represents the first attempt of an integrated risk prediction model potentially expandable to generating individualized risk estimates for HFrelated events in a contemporary HCM population. And also CPET is useful in the evaluation of HCM patients. In this context, the HYPERHF score might allow early identification of those patients at high risk of HF progression and its complications.(2, 3)

#Comment 2

The authors should include that the aim of the present study is about risk stratification model in HCM patients

# Answer 2

Thank you for your detailed comment. As you suggest, my script has vague aim. We make amends for clarify that risk stratification and benefits of CPET are our aim.

Materials and Methods Section

# Comment 1

The authors should include more details about the clinical characteristics of HCM patients. Were obstructive HCM patients excluded?

# Answer 1

Thank you for the detailed comment. We included 56 obstructive HCM patients, 113 non-obstructive patients and 126 Apical HCM patients.

Included subtype numbers of HCM are Obstructive 56 patients, Non-obstructive 113 patients, Apical 126 patients.

# Comment 2

The authors should include more details about cardiopulmonary exercise testing and related parameters. Type of cycle ergometer, protocol, etc

# Answer 2

We totally agree with your comment. We added details about CPET and related parameters.

# Comment 3

The authors should include HCM Risk-SCD model in the Risk Model Verification Section

# Answer 3

Thank you for the advisory comment. I added HCM Risk-SCD model at Model Verification.

Results Section

# Comment 1

The authors should describe all the significantly results presented in Table 2 and Table 3.

# Answer 1

Thank you for valuable advice. I added all of siginifiant results.

Discussion Section

# Comment 1

What is the importance to include CPET in the risk stratification model in HCM? This is the central aim of the present study, and the authors should improve the discuss this issue.

# Answer 1

We appreciate for your insightful comment. In 2020 AHA Guideline, CPET data such as Peak oxygen consumption and CO2 production predict death and provides objective data on the severity and mechanism for functional limitation. By adding these data, We can makes risk stratification model more accurate.

# Comment 2

In addition, the authors should improve the discuss about the Novel HyperHF using LAVI

rather than LAD.

# Answer 2

Thank you for thorough comments. I added benefits of using LAVI than LAD.

Reviewer #2

I have read with interest your work and it surely presents new data and provide further knowledge regarding CPET and HCM.

I do have some comments and suggestions:

- Although those predictive scores are quite new and might add new prognostic information, it demands additional data. In order to investigate whether a new risk scoring model would provide complementary prognostic information to previous echocardiographic variables and/or previous risk scores, a multivariable model should be included in the result part of the paper describing the scores, differences and demonstrating this better power prediction of the new score. In this matter:

a) A kaplan-Meyer curve with both scores at the same picture with differences on prediction.

b) A table with Hazard Ratios and p values comparing both scores and some key clinical variables such as atrial diameter and volume

c) A table with the incremental value of the new score using C-Statistics

Answer A):

Thank you for thoughtful comment. We make new graph for comparison. Hyper HF score seems devided well but Novel HyperHF score devided better.

Answer B): thank you for your thoughtful comments. I added the tables as supplement for compare.

Hazard Ratio P-value

Novel Hyper HF 3.654 0.005

Hyper HF 3.035 0.011

LAVI 1.031 <0.001

LAD 1.080 0.014

PeakVO2 0.905 <0.001

VE/VCO2 1.150 <0.001

HCMRisk-SCD 1.742 0.149

Answer C)

1.- I wonder if authors could provide additional data regarding MRI and 24-hour ECG monitoring. Some important prognostic parameters such as fibrosis and NSVT during 24-monitoring are lacking.

Answer: Thank you for your thoughtful comments. We can provide NSVT, Familiar history of SCD, Unexplained syncope history as a data parts of HCM-RiskSCD score. But Unfortunately We don’t have all patients’ data of MRI. But I agree that MRI data like fibrosis could be great prognosis factor. After much of data are collected I’ll proceed the study.

2.- It seems that LA volume index is superior to LAD and might be an explanation for the additional predictive model of the new score. It is important to demonstrate that the new score itself is the responsible for the better accuracy not LA volume index alone - another multivariable model should be enough to assess this matter.

Answer:

I appreciate to the reviewer for bringing out an important issue. We performed multivariable model analysis. As result, Novel hyperHF model is the responsible for the better accuracy itself. Here is results tables.

Univariate Multivariate

P-value CI P-value CI

Novel HyperHF <0.001 3.024-32.191 0.017 1.469-46.580

LAVI 0.014 1.016-1.149 0.266 0.987-1.049

LAD <0.001 1.014-1.049 0.698 0.938-1.101

Gender 0.519 0.302-1.829 0.056 0.107-1.029

And by inspired your advice, we performed Delong(1988) AUC comparison analysis for the new score. As a result it has no better accuracy than other scores. But in storke-related events, only p-value of AUC of Novel HyperHF below 0.05 so still in Stroke-related events new parameters cloud be used (Novel HyperHF score AUC0.659, p=0.028; Hyper HF AUC 0.620, p=0.098; HCM Risk-SCD AUC 0.607, p=0.138). Results of Delong AUC comprarisons are at below graph.

Cc

HCM Risk-SCD score HyperHF score Novel HyperHF score

HCM Risk-SCD score 　 0.6814 0.4479

HyperHF score 　 　 0.2017

Novel HyperHF score

SCD related risk

HCM Risk-SCD score HyperHF score Novel HyperHF score

HCM Risk-SCD score 　 0.8335 0.7990

HyperHF score 　 　 0.7904

Novel HyperHF score

Stroke related risk

HCM Risk-SCD score HyperHF score Novel HyperHF score

HCM Risk-SCD score 　 0.9843 0.6225

HyperHF score 　 　 0.0913

Novel HyperHF score

3.- EOV is a strong CPET parameter. Do you have data on this ?

Answer: Thank you for your valuable advice. We know there is a research that EOV could be great prognostic factor for Hypertrophic cardiomyopathy like peakVO2 and VE/VCO2. (27). Unfortunately this study is retrospective and we do not checked EOV. In next time, Using EOV as a part of predicting model might be interesting and could be better choice.

4.- Some results are included in the discussion session. It should be included in the results session and the discussion should be addressed deeply on the differences between the findings among the scores and clinical characteristics.

Answer): I totally agree with your advice. We revised results session and added discussion about differences between the findings among the scores and clinical characteristics.

Submitted filename: Rebuttal letter 2021_06 01.docx

Click here for additional data file.

29 Jul 2021

PONE-D-21-01943R1

The Impact of Cardiopulmonary Exercise Derived Scoring on Prediction of Cardio-cerebral Outcome in Hypertrophic Cardiomyopathy

PLOS ONE

Dear Dr. Kim,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Please ensure that your decision is justified on PLOS ONE’s publication criteria and not, for example, on novelty or perceived impact.

For Lab, Study and Registered Report Protocols: These article types are not expected to include results but may include pilot data.

==============================

Please submit your revised manuscript by Sep 12 2021 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.

A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.

An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: http://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols.

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Kind regards,

Otavio R. Coelho-Filho, M.D., Ph.D., M.P.H.

Academic Editor

PLOS ONE

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Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

Additional Editor Comments (if provided):

Authors were able to address all comments.

The current manuscript have significantly improved and may merit publication.

I believe that the current manuscript may improve its readability if revised by a native English speaker.

I have advised the authors to have a native English speaker to proofread the manuscript.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

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Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: I Don't Know

Reviewer #2: Yes

**********

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The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

**********

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Reviewer #1: Yes

Reviewer #2: Yes

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Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: (No Response)

Reviewer #2: (No Response)

**********

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If you choose “no”, your identity will remain anonymous but your review may still be made public.

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Reviewer #1: No

Reviewer #2: No

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While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

12 Sep 2021

we include amended statements within response to reviewer rivison2. below is the script.

#Dear Otavio R. Coelho-Filho.

Thanks a lot for revising and comments to our manuscripts. Below sentences are major requirements of minor revision.

Before start, I revised authors one more time with their consents. If I need any paper, notice me please.

# Comment 1.

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.

# Answer 1.

Thank you for comments. I separated letters which for editor, named Response to Reviewers.

# Comment 2

A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.

# Answer 2.

I made Revised manuscript with track changes with yellow highlights.

# Comment 3.

An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

# Answer 3.

I attached manuscript file. Thank you.

# Comment 4.

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and include a citation and full reference for the retraction notice.

# Answer 4.

I revised Response to reviewer files and erased duplicated references.

Thank you for critical comments and I revised it. If there is retracted reference that I missed and I committed mistakes, then please give us comments.

And in addition, 19th reference is too old guidelines (2011 year) so I change it as new one (2020 guidelines)s

# Comment 5.

I believe that the current manuscript may improve its readability if revised by a native English speaker. I have advised the authors to have a native English speaker to proofread the manuscripts.

# Answer 5.

Thank you for thoughtful comments. We commissioned our manuscripts English Correction.

# Comment 6.

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

# Answer 6.

Thank you for informing us great methods for revising figures. I completed revising figures by using mentioned method.

Sincerely, Jaeman Lee, MD.

Submitted filename: Response to reviewer R2.docx

Click here for additional data file.

25 Oct 2021

The Impact of Cardiopulmonary Exercise Derived Scoring on Prediction of Cardio-cerebral Outcome in Hypertrophic Cardiomyopathy

PONE-D-21-01943R2

Dear Dr. Kim,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

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Otavio R. Coelho-Filho, M.D., Ph.D., M.P.H.

Academic Editor

PLOS ONE

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The authors satisfactory responded all questions.

Reviewers' comments:

7 Jan 2022

PONE-D-21-01943R2

The Impact of Cardiopulmonary Exercise-derived Scoring on Prediction of Cardio-cerebral Outcome in Hypertrophic Cardiomyopathy

Dear Dr. Kim:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

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on behalf of

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PLOS ONE