Nonexercise prediction equation for peak oxygen uptake in Chinese patients with cardiovascular disease

Nonexercise prediction equation for peak oxygen uptake in Chinese patients with cardiovascular disease | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Nonexercise prediction equation for peak oxygen uptake in Chinese patients with cardiovascular disease Xiaojun Wu, Liyan Ran, Haoning Cui, Hongyan Shi, Lixia Yuan, Tianwei Luan, and 11 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7561891/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Background Peak oxygen uptake (VO₂ peak) is a key indicator for evaluating cardiopulmonary function and cardiovascular prognosis. The use of the gold standard test method, the cardiopulmonary exercise test (CPET), is limited in clinical practice. Most existing nonexercise prediction equations are based on healthy or North American populations, and their applicability in Chinese patients with cardiovascular disease (CVD) is unknown. This study aimed to establish a VO₂ peak prediction equation applicable to Chinese CVD patients. Methods This retrospective multicentre study included CPET data from 21,402 CVD patients from 20 medical centres from January 2018–December 2024. Patients were randomly divided into a development group (n = 14,981) and a verification group (n = 6421) at a ratio of 7:3. The regression analysis method was used to establish a Chinese CVD prediction equation to predict the VO₂ peak value of CVD patients, and this equation was compared with prediction equations established in other cohorts. Results Age, sex, height, weight, and CVD diagnosis were included as factors in the predictive equation. The regression equation was as follows: VO 2 peak (mL/min) = 246.206 – (9.858 × age [years]) + (187.643*sex [male = 1; female = 0]) + (4.500 × height [cm]) + (10.250 × weight [kg]) + (94.969 × SA [yes = 1, no = 0]) - (39.293 × PCI [yes = 1, no = 0]) - (110.124× MI [yes = 1, no = 0]) - (208.447 × HF [yes = 1, no = 0]), adjusted R2 = 0.51, SEE = 264 mL/min. The Wasserman (152%), Friend health (197%), and Friend CVD equations (117%) overestimated the VO₂ peak of Chinese CVD patients, whereas the Chinese CVD Eq. (104%) predicted values closer to the measured values. Conclusions In this study, a VO₂ peak prediction equation for Chinese CVD patients was developed. Compared with existing equations, this equation has a prediction smaller error and is more suitable for the Chinese population. Cardiopulmonary exercise testing Cardiac rehabilitation Prediction equation Peak oxygen uptake Figures Figure 1 Figure 2 Introduction Cardiovascular disease (CVD) seriously threatens the quality of life and life expectancy of patients. 1 2 Previous studies have applied the measurement of peak oxygen uptake (VO₂ peak) to assess the risk of CVD. 3 VO₂ peak is an independent predictor of cardiovascular events and all-cause mortality 4 5 and therefore is recommended as a clinical vital sign. 3 In CVD patients, lower VO₂ peak levels were significantly associated with early death and a greater medical burden. 6 Compared with traditional risk factors such as obesity, diabetes, or dyslipidaemia, VO₂ peak shows greater predictive value in predicting mortality and CVD risk. 7 The cardiopulmonary exercise test (CPET) can be used to directly and accurately evaluate an individual's maximum oxygen uptake capacity during extreme exercise. 8 VO₂ peak, as a key indicator of the efficacy of cardiac rehabilitation, 7 not only provides a basis for formulating personalized rehabilitation plans and nursing plans but can also guide clinical treatment through risk stratification. However, VO₂ peak can be directly measured by the CPET. However, direct measurement of VO₂ peak has limitations such as a lack of professional equipment and insufficient technical personnel. 9 The advantage of the nonexercise prediction equation is that there is no need for exercise tests, and the required information can be conveniently obtained from electronic medical records. 6 This equation is commonly used for predicting VO₂ peak in clinical practice. 10 However, most of the existing equations used to predict VO₂ peak were developed on the basis of cohort studies of healthy individuals or North American CVD patients, and their applicability in Chinese CVD patients has not yet been verified. In clinical practice, it is necessary to use predictive equations developed for populations with similar demographic and biopsychosocial characteristics to prevent deviations in clinical decision-making. The use of VO₂ peak prediction equations developed on the basis of different populations may lead to deviations for Chinese clinicians when evaluating the VO₂ peak level of CVD patients, affecting the accuracy of the formulation of personalized rehabilitation plans. Therefore, this study aimed to construct a nonexercise prediction equation (Chinese CVD equation) for VO₂ peak using the CPET data of Chinese CVD patients, thereby providing simple and accurate prediction tools. Materials and Methods Study population A total of 21,402 patients with CVD and CPET data from 20 medical centres in China from January 2018 to December 2024 were included in this study. These hospitals are widely distributed in major regions of China, including 9 in Northeast China, 3 in Northwest China, 2 in East China, 2 in South China, 2 in North China, 1 in Central China and 1 in Southwest China. This was a retrospective observational study. All the data were anonymized historical data and did not involve patient intervention or additional biological sample collection. All personal information was deidentified and reviewed by the Ethics Review Committee of the Second Affiliated Hospital of Harbin Medical University, which approved exemption from the informed consent requirement (KY2025-031). Rigorous review of the data from each participating medical centre was performed to ensure that VO 2 peak values were within the expected normal range before being included in this study. The inclusion criteria were as follows: (1) current or previous occurrence of myocardial infarction (MI), percutaneous coronary intervention (PCI), heart failure (HF), stable angina (SA), or other CVDs; (2) completion of a symptom-limiting CPET on a cycle ergometer; (3) age between 30 and 79 years; and (4) a peak respiratory exchange ratio (RER) ≥ 1.0. The included CVD categories included MI, PCI, HF, SA and other CVDs. Specifically, the MI category included patients who had reported PCI but not HF, and the PCI category excluded individuals who had reported MI or HF. The HF category did not exclude patients who reported events in other CVD categories. The SA category excluded patients with PCI, HF and MI. The other category excluded patients with PCI, HF, MI and SA. Ultimately, a total of 21,402 patients with CPET data were included in this study. By using the random sampling method, 70% (n = 14,981) of the participants were used to establish the development equation, and the remaining 30% (n = 6421) of the participants were used to validate the equation. Cardiopulmonary exercise test In this study, a symptom-limiting CPET was performed using a cycle ergometer. The CPET was carried out by experienced personnel at each centre, and all operation procedures strictly followed the "Standards for Testing and Training Exercises" guidelines issued by the American Heart Association (AHA). 11 12 Throughout the experiment, indicators such as VO 2 were collected using CPET equipment. With the Ramp protocol, the increasing load per minute was the value of the maximum power divided by 8 to 12. The target test time was 8 to 12 minutes. The test was continued until the participants showed restrictive symptoms, upon which the test was terminated. The CPET consists of four stages: the resting period, the warm-up period, the loading period, and the recovery period: (1) Resting period: The patient sat still for 3 minutes, and the heart rate and blood pressure were measured at rest; (2) Warm-up period: The patient pedalled for 3 minutes without load; (3) Loading period: The load was acutely increased at a predetermined power until the patient showed symptom limitations; and (4) Recovery period: The patient performed 3 minutes of no-load cycling and sat still for 3 minutes. Establishment of the Chinese CVD cohort equation and its comparison with other predictive equations The VO 2 peak prediction equation was constructed by using a multivariate forward stepwise regression analysis method in the development group. The included variables were age, sex, height, weight, and CVD diagnosis. In the validation group, we calculated the percentage of the predicted value to the measured value, the standard error of the estimate (SEE), the correlation coefficient, and the adjusted coefficient of determination, and the intragroup correlation coefficient (ICC) was used to compare the predicted VO 2 peak and the measured VO 2 peak by the equations constructed in the study, the Wasserman equation, the Friend health equation and the Friend CVD equation. In addition to conducting overall analyses in the validation group, we also performed subgroup analyses on the basis of sex and CVD diagnosis category. Statistical analysis The analysis was carried out in R version 4.3.2. Continuous variables with a normal distribution are expressed as the means and standard deviations [means (standard deviations)], and t tests were used for comparisons. Continuous variables with a nonnormal distribution are expressed as the median and quartiles [median (first quartile, third quartile)] and were analysed with the Mann‒Whitney U test. The comparison of categorical variables was conducted using the chi-square test, and these variables are expressed as the number of cases and their constituent ratios [number of cases (%)]. Results The CPET data of 21,402 patients with CVD were analysed in this study. The results in Table 1 show that both male and female patients showed statistically significant (p < 0.001) differences in all baseline indices, including age, height, weight, BMI, CVD type, and CPET results. VO 2 peak was significantly greater in male patients than in female patients (1386.00 vs. 1050.00 mL/min; p < 0.001). Table 1 Descriptive characteristics of the Chinese CVD cohort Characteristics Overall, N = 21402 Male, N = 14562 Female, N = 6840 p Age,year 57.06 ± 10.24 57.00 (50.00–64.00) 59.00 (52.00–66.00) < .001 Height, cm 167.92 ± 7.78 172.00 (168.00-175.00) 160.00 (157.00-164.00) < .001 Weight, kg 72.22 ± 12.56 75.00 (70.00–84.00) 62.00 (56.00–69.00) < .001 BMI, kg/m 2 25.51 ± 3.64 25.80 (23.80–28.00) 24.20 (22.20–26.60) < .001 PCI 4643 (21.69%) 3618 (24.8%) 1025 (15.0%) < .001 Angina 10123 (47.30%) 5889 (40.4%) 4234 (61.9%) < .001 HF 1670 (7.80%) 1256 (8.6%) 414 (6.1%) < .001 MI 3597 (16.81%) 3017 (20.7%) 580 (8.5%) < .001 Other 1369 (6.40%) 782 (5.4%) 587 (8.6%) < .001 CPET RER peak 1.130 ± 0.09 1.12 (1.07–1.19) 1.11 (1.05–1.17) < .001 HR peak 131.29 ± 22.16 129.00 (116.00-143.000) 134.00 (120.00-149.00) < .001 Measured VO 2 peak, mL/min 1310.11 ± 376.87 1386.00 (1160.00-1650.00) 1050.00 (876.75–1242.00) < .001 Measured VO 2 peak, mL/kg/min 18.17 ± 4.30 18.20 (15.50-21.48) 16.67 (14.200–19.60) < .001 Predicted VO 2 peak from Chinese CVD equation, mL/min 1308.91 ± 269.66 1414.42 (1265.64-1571.33) 1076.52 (942.18-1202.21) < .001 Predicted VO 2 peak from Chinese CVD equation, mL/kg/min 18.16 ± 2.43 18.54 (17.21–19.99) 17.08 (15.31–18.76) < .001 Predicted VO 2 peak from Wasserman equation, mL/min 1934.03 ± 521.19 2182.93(1926.13-2460.80) 136.45(1211.23-1480.47) < .001 Predicted VO 2 peak from Wasserman equation, mL/kg/min 26.68 ± 5.19 28.67 (26.12–31.40) 21.29 (19.25–23.74) < .001 Predicted VO 2 peak from Friend CVD equation, mL/min 1492.71 ± 382.82 1646.40 (1447.87-1864.90) 1113.60 (986.68-1256.19) < .001 Predicted VO 2 peak from Friend CVD equation, mL/kg/min 20.56 ± 3.33 21.91 (20.17–23.550) 17.90(16.31–19.62) < .001 Predicted VO 2 peak from Friend health equation, mL/min 2503.26 ± 672.91 2849.58 (2574.78-3155.47) 1662.25 (1492.03-1859.09) < .001 Predicted VO 2 peak from Friend health equation, mL/kg/min 34.50 ± 6.60 37.79 (35.06–40.65) 26.82(24.09–29.81) < .001 Abbreviations: BMI, body mass index; CVD, cardiovascular disease; CPET, cardiopulmonary exercise testing; HF, heart failure; HR, heart rate; MI, myocardial infarction; PCI, percutaneous coronary intervention; RER, respiratory exchange ratio; SA, Stable Angina; VO₂ peak, peak oxygen uptake. The results in Table 2 show that there were no significant differences between the development group (n = 14981) and the validation group (n = 6421) in terms of baseline characteristics such as age, sex, height, weight, BMI, or other CVDs (p > 0.05). The measured values of VO₂ peak were 1269.20 mL/min and 1270.00 mL/min in the development and validation groups, respectively. Table 2 Descriptive characteristics from the development and validation groups Characteristics Development, N = 14981 Validation, N = 6421 p Age,year 58.00 (51.00–65.00) 58.00 (50.00–65.00) .064 Sex Female 4776 (31.9%) 2064 (32.1%) .716 Male 10205 (68.1%) 4357 (67.9%) Height, cm 169.00 (162.00-173.00) 169.00 (162.00-174.00) .750 Weight, kg 71.00 (63.00–80.00) 72.00 (63.50–80.00) .760 BMI, kg/m 2 25.30 (23.20-27.66) 25.30 (23.20-27.51) .803 PCI 3262 (21.8%) 1381 (21.5%) .678 Angina 7058 (47.1%) 3065 (47.7%) .413 HF 1147 (7.7%) 523 (8.1%) .233 MI 2547 (17%) 1050 (16.4%) .253 Other 967 (6.5%) 402 (6.3%) .616 CPET RER peak 1.12 (1.06–1.19) 1.12 (1.06–1.19) .301 HR peak 130.00 (117.00-145.00) 131.00 (117.00-145.00) .414 Measured VO 2 peak, mL/min 1269.20 (1032.50-1530.80) 1270.00 (1040.00-1550.00) .160 Measured VO 2 peak, mL/kg/min 17.68 (15.00-20.90) 17.80 (15.10–21.00) .130 Predicted VO 2 peak from Chinese CVD equation, mL/min 1305.21 (1119.69-1492.51) 1307.47 (1125.20-1500.89) .429 Predicted VO 2 peak from Chinese CVD equation, mL/kg/min 18.12 (16.650-19.695) 18.16 (16.66–19.68) .389 Predicted VO 2 peak from Wasserman equation, mL/min 1939.30 (1471.21-2315.80) 1952.83 (1478.28-2325.76) .355 Predicted VO 2 peak from Wasserman equation, mL/kg/min 26.74 (22.87–30.10) 26.82 (22.86–30.17) .404 Predicted VO 2 peak from Friend CVD equation, mL/min 1481.06 (1197.44-1757.70) 1486.26 (1197.30-1763.30) .480 Predicted VO 2 peak from Friend CVD equation, mL/kg/min 20.78 (18.30-22.84) 20.85 (18.34–22.88) .409 Predicted VO 2 peak from Friend health equation, mL/min 2596.96 (1885.57-2991.17) 2601.53 (1886.39-3004.01) .439 Predicted VO 2 peak from Friend health equation, mL/kg/min 35.58 (29.69–39.22) 35.58 (29.60-39.35) .507 Abbreviations: BMI, body mass index; CVD, cardiovascular disease; CPET, cardiopulmonary exercise testing; HF, heart failure; HR, heart rate; MI, myocardial infarction; PCI, percutaneous coronary intervention; RER, respiratory exchange ratio; SA, Stable Angina; VO₂ peak, peak oxygen uptake. The results of stepwise multiple forward regression analysis revealed that age, sex, height, weight and CVD diagnosis all significantly contributed to the regression equation for predicting VO₂ peak (Table 3 ). With the increasing number of included variables, the root mean square error (RMSE) decreased from 310.9 to 264.0 mL/min. The equation for predicting VO₂ peak in patients with CVD in China is as follows: VO 2 peak(mL/min) = 246.206 – (9.858 × age [years]) + (187.643*sex [male = 1; female = 0]) + (4.500 × height [cm]) + (10.250 × weight [kg]) + (94.969 × SA [yes = 1, no = 0]) - (39.293 × PCI [yes = 1, no = 0]) - (110.124× MI [yes = 1, no = 0]) - (208.447 × HF [yes = 1, no = 0]). SEE = 264 mL/min; R = 0.71; adjusted R 2 = 0.51. Table 3 Forward stepwise multiple regression results for prediction of absolute VO2 peak (mL/min) from sex, weight, age, height, and CVD category RMSE R 2 P value Weight, kg 310.9 0.321 < 0.001 Age, yr 291.5 0.403 < 0.001 SA 282.4 0.440 < 0.001 Sex 269.1 0.491 < 0.001 HF 266.4 0.501 < 0.001 MI 265.1 0.506 < 0.001 Height, cm 264.2 0.509 < 0.001 PCI 264.0 0.510 < 0.001 Abbreviations: CVD, cardiovascular disease; HF, heart failure; MI, myocardial infarction; PCI, percutaneous coronary intervention; SA, Stable Angina; VO 2 peak, peak oxygen uptake. The research results in Table 4 show that in the validation group, the value predicted by the Chinese CVD Eq. (1311.63 mL/min) was the closest to the measured value (1315.64 mL/min). The values predicted by the Wasserman equation, Friend health equation and Friend CVD Eq. (1939.87 vs. 2508.71 vs. 1496.06 mL/min) were all higher than the measured values (1311.63 mL/min). The Pearson correlation coefficient of the Chinese CVD equation was 0.71, and the ICC was 0.68. The Pearson correlation coefficients of the Wasserman equation, the Friend health equation and the Friend CVD equation were 0.64, 0.63 and 0.68, respectively; the ICCs were 0.31, 0.09 and 0.61, respectively. The Pearson correlation coefficient and ICC values predicted by the Chinese CVD equation were both greater than those predicted by the other three equations. The percentage range of VO₂ peak predicted by the Chinese CVD equation was the closest to the measured value (100%-102%). The percentage ranges predicted by the Wasserman equation, Friend health equation and Friend CVD equation were all higher than the measured values (128%–176% vs. 173%–231% vs. 105%–128%, respectively). Table 4 Measured and predicted VO2peak values from the validation group according to CVD category and sex Any CVD PCI MI HF SA All (N = 6421) Males (N = 4357) Females (N = 2064) Category (n = 1381) Category (n = 1050) Category (n = 523) Category (n = 3065) Measured VO 2 peak, mL/min 1315.64 ± 376.02 1390.00 (1169.00-1660.00) 1054.00 (880.00-1251.00) 1263.50 (1031.40–1520.00) 1231.85 (1015.00-1452.30) 1050.00 (840.00-1280.00) 1330.00 (1098.00-1630.00) Measured VO 2 peak, mL/kg/min 18.25 ± 4.30 18.30 (15.50–21.60) 16.70 (14.30-19.55) 17.30 (14.80-20.22) 16.58 (14.20-19.11) 14.77 (12.00-17.50) 19.00 (16.30-22.42) Predicted VO 2 peak from Chinese CVD equation, mL/min 1311.63 ± 270.09 1419.86 (1266.67-1568.126) 1079.58 (946.73-1200.25) 1304.46 (1135.77-1448.10) 1270.25 (1122.13-1424.85) 1098.30 (903.49-1245.49) 1376.64 (1168.55-1572.56) Predicted VO 2 peak from Chinese CVD equation, mL/kg/min 18.19 ± 2.44 18.59 (17.22–20.01) 17.14 (15.39–18.77) 17.67 (16.50-18.75) 17.12 (15.98–18.02) 15.04 (13.70-16.13) 19.35 (18.05–20.76) Percent predicted Vo2peak from Chinese CVD equation, % 104% 101% 101% 101% 102% 100% 101% Predicted VO 2 peak from Wasserman equation, mL/min 1939.87 ± 524.15 a 2184.00 (1935.98-2471.36) a 1340.97 (1212.47-1486-25) a 2005.20 (1632.84-2311.04) a 2109.61 (1778.30-2424.32) a 1897.00 (1481.11-2225.20) a 1836.40 (1407.68-2296.76) a Predicted VO 2 peak from Wasserman equation, mL/kg/min 26.75 ± 5.24 a 28.70 (26.17–31.43) a 21.26 (19.22–23.72) a 27.04 (23.89–30.04) a 28.27 (25.11–31.05) a 26.02 (22.58–29.14) a 26.24 (22.20-29.98) a Wasserman equation %Predicted 152% 157% 128% 151% 166% 176% 134% Predicted VO 2 peak from Friend health equation, mL/min 2508.71 ± 676.29 2850.01 (2587.27-3158.15) 1665.39 (1492.49-1861.46) 2669.57 (2261.87-2985.07) 2768.07 (2429.75-3106.37) 2555.88 (2055.38-2893.97) 2484.01 (1761.75-2963.93) Predicted VO 2 peak from Friend health equation, mL/kg/min 34.55 ± 6.63 37.92 (35.19–40.65) 26.82 (24.09–29.81) 36.23 (31.94–39.48) 37.63 (33.50-40.26) 35.06 (29.90-38.44) 34.67 (28.38–38.96) Friend health cohort equation %Predicted 197% 206% 160% 199% 216% 231% 173% Predicted VO 2 peak from Friend CVD equation, mL/min 1496.06 ± 385.36 a 1647.20 (1456.68-1868.80) a 1114.22 (986.33-1254.12) a 1576.20 (1337.12–1800.00) a 1585.37 (1369.06-1828.06) a 1164.80 (948.98-1373.55) a 1453.43 (1173.84-1753.65) a Predicted VO 2 peak from Friend CVD equation, mL/kg/min 20.59 ± 3.36 a 21.96 (20.22–23.61) a 17.89 (16.32–19.60) a 21.50 (19.56–23.28) a 21.54 (19.66–23.22) a 16.10 (14.03–17.92) a 20.83 (18.50-22.92) a Friend CVD cohort equation %Predicted 117% 118% 105% 121% 128% 108% 107% RMSE for Chinese CVD equation equation 263.60 288.4 200.6 257.2 254.5 236.9 269.2 RMSE for Wasserman equation 288.8 310.3 228.3 259.6 255.7 242.0 279.1 RMSE for Friend health equation 291.8 309.4 229.3 260.1 257.2 243.9 279.4 RMSE for Friend CVD equation 277.40 302.1 216.0 259.6 254.1 242.9 271.7 Pearson correlation for Chinese CVD equation equation 0.71 b 0.62 b 0.66 b 0.68 b 0.64 b 0.67 b 0.72 b Pearson correlation for Wasserman equation 0.64 b,c 0.53 b,c 0.52 b,c 0.68 b 0.64 b 0.65 b 0.69 b Pearson correlation for Friend health equation equation 0.63 b,c 0.54 b,c 0.51 b,c 0.68 b 0.63 b 0.65 b 0.69 b Pearson correlation for Friend CVD equation equation 0.68 b,c 0.57 b,c 0.59 b,c 0.68 b 0.65 b 0.65 b 0.71 b ICC for Chinese CVD equation Eq. (95% CI) 0.68 (0.66–0.69) d 0.55 (0.53–0.57) d 0.63 (0.60–0.65) d 0.64 (0.61–0.67) d 0.61 (0.57–0.65) d 0.66 (0.60–0.70) d 0.67 (0.65–0.69) d ICC for Wasserman Eq. (95% CI) 0.31 (-0.10-0.62) d 0.10 (-0.04-0.32) d 0.29 (-0.07-0.55) d 0.28 (-0.09-0.61) d 0.20 (-0.07-0.51) d 0.21 (-0.07-0.52) d 0.42 (-0.08-0.70) d ICC for Friend health equation Eq. (95% CI) 0.09 (-0.04-0.31) d 0.07 (-0.03-0.25) d 0.14 (-0.06-0.40) d 0.14 (-0.05-0.41) d 0.10 (-0.04-0.32) d 0.11 (-0.05-0.34) d 0.22 (-0.08-0.53) d ICC for Friend CVD equation Eq. (95% CI) 0.61 (0.38–0.74) d 0.45 (0.13–0.64) d 0.56 (0.52–0.61) d 0.53 (0.06–0.74) d 0.43 (-0.06-0.70) d 0.63 (0.54–0.70) d 0.69 (0.62–0.74) d Abbreviations: CVD, cardiovascular disease; HF, heart failure; MI, myocardial infarction; PCI, percutaneous coronary intervention; SA, Stable Angina; VO 2 peak, peak oxygen uptake. a Significantly different from measured VO 2 peak (P < .05). b Significant correlation (P < .05). c Significantly different correlation compared with the Chinese CVD cohort equation (P < .05). d Significant ICC (P < .05). Figure 1 shows the Bland‒Altman plots of the measured and predicted VO 2 peaks in the validation group. In the validation group, the prediction deviation of the Chinese CVD equation was − 4 mL/min. The prediction deviation of the Wasserman equation was + 624.2 mL/min, that of the Friend health equation was + 1193.1 mL/min, and that of the Friend CVD equation was + 180.4 mL/min. Figure 2 shows the comparison of the measured and predicted VO₂ peak values in the validation group. When the Chinese CVD cohort equation was used, the distributions were significantly different. The number of individuals with a deviation of < 0.5 METs between the measured and predicted VO₂ peaks was 2,411 (37.5%), whereas the number of individuals with a deviation of ≥ 2 METs was 350 (5.5%). The r value was 0.71, and the R² value was 0.51. When the Wasserman cohort equation was used, the number of individuals with a deviation of < 0.5 METs between the measured and predicted VO₂ peaks was 483 (7.5%), whereas the number of individuals with a deviation of ≥ 2 METs was 3861 (60.1%). The r value was 0.64, and the R² value was 0.41. When using the Friend health cohort equation was used, the number of individuals with a deviation of < 0.5 METs between the measured and predicted VO₂ peaks was 53 (0.8%), whereas the number of individuals with a deviation of ≥ 2 METs was 5901 (91.9%). The r value was 0.63, and the R² value was 0.40. When using the Friend CVD cohort equation was used, the number of individuals with a deviation of < 0.5 METs between the measured and predicted VO₂ peaks was 1754 (27.3%), whereas the number of individuals with a deviation of ≥ 2 METs was 899 (14%). The r value was 0.68, and the R² value was 0.46. Discussion VO₂ peak, an important indicator for evaluating the prognosis of CVD patients, is significantly negatively correlated with the risk of death and the incidence of cardiovascular events. The American Heart Association (AHA) has adopted VO₂ peak as a vital sign assessment tool. However, the widely used predictive equation in clinical practice was developed on the basis of data from healthy individuals or North American CVD patients, 6 13 and the peak VO₂ value in Chinese patients may be overestimated. 14 Studies have shown that there are significant differences between Chinese and Western populations in terms of physiological functions (cardiopulmonary adaptability, muscle oxygen metabolism), genetic background and environmental factors, 15–17 further highlighting the limitations of the existing equation in predicting VO₂ peak in Chinese CVD patients. Therefore, on the basis of a group of CVD patients in China, a new nonexercise prediction equation, the Chinese CVD equation, was developed in this study. This equation was constructed with age, sex, height, weight and CVD diagnosis as the core variables, although other factors are known to have an impact on VO 2 peak; 3 18 however, on the premise of ensuring prediction accuracy, prioritizing indicators that can be quickly obtained from electronic medical records can significantly improve clinical practicability and work efficiency. The establishment of this model not only fills the gap in race-specific prediction tools but also provides an evidence-based basis for accurately assessing the cardiopulmonary function of Chinese patients and optimizing individualized treatment decisions. Compared to prediction equations developed based on Western populations, the Chinese CVD equation aligns with the physiological characteristics of Chinese patients in terms of physical fitness and metabolism, underscoring the importance of developing and applying population-specific prediction equations. Compared with the Wasserman equation (Bland‒Altman deviation + 624.2 mL/min, ICC 0.10‒0.42) and the Friend health equation (Bland‒Altman deviation, + 1193.1 mL/min; ICC, 0.07–0.22), the Bland‒Altman deviation from the Chinese CVD equation was only − 4 mL/min, and the ICC value increased to 0.55–0.68. Furthermore, compared with the Friend CVD equation, which was also developed for the CVD population, the Chinese CVD equation has better accuracy (Bland‒Altman deviation of -4 mL/min vs. +180.4 mL/min; ICCs of 0.55‒0.68 vs. 0.29‒0.49). This result demonstrates that the Chinese CVD equation significantly improves predictive performance and enhances its clinical applicability in the Chinese population, highlighting the importance of utilizing localized prediction tools across different populations to achieve more precise individualized health management. A VO₂ peak study in CVD patients in North America revealed that the difference in VO₂ peak is significantly correlated with disease type, 19 which is consistent with the results of this study. The reasons for these differences may be the degree of myocardial injury, coronary artery stenosis or occlusion, and skeletal muscle function under different disease states. 10 19–21 In the Chinese CVD prediction equation, the VO₂ peak values in the SA, PCI, MI, and HF groups showed a downward trend (1376.64 vs. 1304.46 vs. 1270.25 vs. 1098.30 mL/min, respectively). Further analysis revealed that the predicted values (1304.46, 1270.25, 1098.30, and 1376.64 mL/min, respectively) for the PCI, MI, HF, and SA patients obtained by the Chinese CVD prediction equation were very close to the measured values (1263.50, 1231.85, 1050.00, and 1330.00 mL/min, respectively). The predicted values as a percentage of the measured values were 101%, 102%, 100%, and 101%, respectively, indicating that the equation has high accuracy in predicting VO 2 peak in patients with different CVD types. In contrast, the Wasserman equation, Friend health equation and Friend CVD equation all overestimated the values to varying degrees: the predicted values obtained with the Wasserman equation were 2005.20, 2109.61, 1897.00 and 1836.40 mL/min, respectively, and the predicted percentages were 151%, 166%, 176% and 134%, respectively; the predicted values obtain with the Friend health equation were 2669.57, 2768.07, 2555.88 and 2484.01 mL/min, respectively, and the predicted percentages were 199%, 216%, 231% and 173%, respectively; the predicted values obtained with the Friend CVD equation were 1576.20, 1585.37, 1164.80 and 1453.43 mL/min, respectively, and the predicted percentages were 121%, 128%, 108% and 107%, respectively. The overestimation of VO₂ peak with these equations can lead to inaccurate assessment of exercise tolerance, interfere with the formulation of rehabilitation programs, and may increase the risk of exercise safety due to improper exercise intensity. The percentage of the measured VO₂ peak value to the predicted value is a key indicator for evaluating the prognosis of CVD and has important clinical value. Studies have shown that this indicator is significantly correlated with all-cause mortality. For every 1% increase in the predicted percentage, the risk of death can be reduced by 3%. 22 When the percentage of the predicted value to the measured value is less than 50%, the cardiovascular function of the patient is severely impaired, and the prognosis deteriorates significantly. 5 The existing prediction equations were primarily established on the basis of data from healthy individuals and North American CVD patients, but there are significant differences in the physiological characteristics of the CVD population in China. Therefore, establishing a VO₂ peak prediction equation applicable to the Chinese population 23–25 will help achieve more accurate risk stratification, optimize individualized treatment plans, and provide a race-specific evidence-based basis for clinical decision-making. Cardiac rehabilitation is an important part of CVD management and has been carried out in 54.7% of countries worldwide; 26 however, there are still challenges in conducting cardiac rehabilitation in China. 27 28 Only 22% of hospitals across the country have implemented cardiac rehabilitation programmes. 27 Research shows that China needs to build over 3 million cardiac rehabilitation sites. 29 The development of cardiac rehabilitation is limited by various factors, such as scarce hospital resources, a shortage of professionals and low public awareness, 30 especially the lack of key equipment such as CPET equipment, 3 making it difficult for primary medical institutions to assess the VO₂ peak level of patients accurately. Therefore, the VO₂ peak prediction equation for Chinese CVD patients developed in this study can provide an alternative solution to compensate for resource shortages in resource-limited settings. Clinicians can use this prediction equation to formulate personalized cardiac rehabilitation prescriptions for patients. Notably, the clinical evaluation of VO₂ peak needs to be comprehensively performed in combination with the scientific nature of the detection method and the applicable scenarios. Although the Chinese CVD equation developed in this study can provide relatively accurate predicted values of VO₂ peak for Chinese CVD patients, there may be deviations due to individual physiological differences. When the values of VO₂ peak are predicted using the prediction equation, patients’ actual physical condition and clinical symptoms should be considered. Therefore, in clinical practice, it is still recommended that the CPET be used promptly and preferentially for VO₂ peak assessment, especially when precise exercise prescriptions need to be formulated, surgical risks need to be evaluated or prognosis needs to be judged. Locally validated predictive equations should be used only as alternative tools in situations where CPET equipment is insufficient and patients cannot tolerate exercise testing (e.g., severe heart failure, bone and joint disease) or in large-scale screening scenarios. In this study, the first VO₂ peak prediction equation for Chinese patients with CVD was established. The strengths of this study include its large sample size, multicentre design, and provision of a substantial amount of data from female patients. This study also has several limitations. First, this study included only cycle ergometer CPET data; future research is needed to further explore CPET data in a treadmill setting. Second, the predictive equations were not designed to fully account for the potential effects of comorbidities such as hypertension, diabetes, pulmonary diseases, or orthopaedic conditions on VO₂ peak. Third, although all tests were conducted by experienced laboratory personnel, differences in the equipment used and the test personnel may have influenced the VO₂ peak measurement results. Fourth, this study was retrospective and may be affected by potential selection bias. Conclusions In conclusion, this study established a nonexercise VO₂ peak prediction equation applicable to patients with CVD in China for the first time. Compared with the existing equations, the Chinese CVD equation has smaller prediction errors and higher accuracy, providing a more reliable prediction tool for the assessment of VO₂ peak in Chinese CVD patients. Abbreviations VO₂ peak Peak oxygen uptake CPET Cardiopulmonary exercise test CVD Cardiovascular disease MI Myocardial infarction PCI Percutaneous coronary intervention HF Heart failure SA Stable angina RER Respiratory exchange ratio Declarations Acknowledgments Thanks to the cardiac rehabilitation practitioners in the 20 centers. Authors’ contributions Xiaojun Wu and Haoning Cui contributed to the conception or design of the work. Liyan Ran, Xiaojun Wu, and Haoning Cui worked on performing the analysis, and preparing the figures. Liyan Ran, Xiaojun Wu, and Haoning Cui writing the manuscript. Shiyu Wang, Xianghui Zheng, Qifeng Li, Tianhui Cao, and Xinyu Hou worked on reviewing the initial manuscript and editing the manuscript. Xiaojun Wu, Hongyan Shi, Lixia Yuan, Tianwei Luan, Dajun Li, Haixia Liu, and Fangfang Bai collected and curated the data. Chao Fang, Jian Wu, and Bo Yu supervised the project and reviewed the final draft. All authors have read and approved the manuscript. Funding The authors report no involvement in the research by the sponsor that could have influenced the outcome of this work. This study was supported by the Key Research and Development Program of Heilongjiang (Grant no. 2022ZX01A28), Heilongjiang Provincial Medical and Health Scientific Research Project (Grant no. 20240303010021), National Natural Science Foundation of China (Grant nos. 82172537, 82372565), and Natural Science Foundation of Heilongjiang Province (Grant no. ZL2024H007). Availability of data and materials The dataset used during the current study is available from the corresponding author on reasonable request. Conflicts of interest The authors declare that they have no competing interests Ethics approval and consent to participate All personal information was deidentified and reviewed by the Ethics Review Committee of the Second Affiliated Hospital of Harbin Medical University, which approved exemption from the informed consent requirement (KY2025-031). Consent for publication Not applicable References Mehta LS, Velarde GP, Lewey J, et al. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7561891","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":540136388,"identity":"3dfa5410-9561-4991-8b72-857f393d23d9","order_by":0,"name":"Xiaojun Wu","email":"","orcid":"","institution":"The Second Affiliated Hospital of Harbin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiaojun","middleName":"","lastName":"Wu","suffix":""},{"id":540136391,"identity":"c6ae061a-d3a6-4ca4-8e01-e6eda4729fb2","order_by":1,"name":"Liyan Ran","email":"","orcid":"","institution":"The Second Affiliated Hospital of Harbin 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1","display":"","copyAsset":false,"role":"figure","size":5123635,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of the measured and predicted VO₂ peak values in the validation group\u003c/p\u003e","description":"","filename":"Figure1BlandAltman.png","url":"https://assets-eu.researchsquare.com/files/rs-7561891/v1/678974286cb2d7af5a402918.png"},{"id":95524125,"identity":"d458c591-7094-49dc-bd6b-fa1e76ffde10","added_by":"auto","created_at":"2025-11-10 10:02:18","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":8583560,"visible":true,"origin":"","legend":"\u003cp\u003eBland‒Altman plots of the measured and predicted VO2 peaks in the validation group\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-7561891/v1/d5c382a6184ac911ff83d0ed.png"},{"id":95530742,"identity":"20750d9f-e4d8-454e-bf4a-b8d5596425b7","added_by":"auto","created_at":"2025-11-10 10:21:41","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":15364505,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7561891/v1/8c29135b-f2b8-46d5-ad21-9b52811ea0f6.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Nonexercise prediction equation for peak oxygen uptake in Chinese patients with cardiovascular disease","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCardiovascular disease (CVD) seriously threatens the quality of life and life expectancy of patients.\u003csup\u003e1 2\u003c/sup\u003e Previous studies have applied the measurement of peak oxygen uptake (VO₂ peak) to assess the risk of CVD.\u003csup\u003e3\u003c/sup\u003e VO₂ peak is an independent predictor of cardiovascular events and all-cause mortality\u003csup\u003e4 5\u003c/sup\u003e and therefore is recommended as a clinical vital sign.\u003csup\u003e3\u003c/sup\u003e In CVD patients, lower VO₂ peak levels were significantly associated with early death and a greater medical burden.\u003csup\u003e6\u003c/sup\u003e Compared with traditional risk factors such as obesity, diabetes, or dyslipidaemia, VO₂ peak shows greater predictive value in predicting mortality and CVD risk.\u003csup\u003e7\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eThe cardiopulmonary exercise test (CPET) can be used to directly and accurately evaluate an individual's maximum oxygen uptake capacity during extreme exercise.\u003csup\u003e8\u003c/sup\u003e VO₂ peak, as a key indicator of the efficacy of cardiac rehabilitation,\u003csup\u003e7\u003c/sup\u003e not only provides a basis for formulating personalized rehabilitation plans and nursing plans but can also guide clinical treatment through risk stratification. However, VO₂ peak can be directly measured by the CPET. However, direct measurement of VO₂ peak has limitations such as a lack of professional equipment and insufficient technical personnel.\u003csup\u003e9\u003c/sup\u003e The advantage of the nonexercise prediction equation is that there is no need for exercise tests, and the required information can be conveniently obtained from electronic medical records.\u003csup\u003e6\u003c/sup\u003e This equation is commonly used for predicting VO₂ peak in clinical practice.\u003csup\u003e10\u003c/sup\u003e However, most of the existing equations used to predict VO₂ peak were developed on the basis of cohort studies of healthy individuals or North American CVD patients, and their applicability in Chinese CVD patients has not yet been verified.\u003c/p\u003e\u003cp\u003eIn clinical practice, it is necessary to use predictive equations developed for populations with similar demographic and biopsychosocial characteristics to prevent deviations in clinical decision-making. The use of VO₂ peak prediction equations developed on the basis of different populations may lead to deviations for Chinese clinicians when evaluating the VO₂ peak level of CVD patients, affecting the accuracy of the formulation of personalized rehabilitation plans. Therefore, this study aimed to construct a nonexercise prediction equation (Chinese CVD equation) for VO₂ peak using the CPET data of Chinese CVD patients, thereby providing simple and accurate prediction tools.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy population\u003c/h2\u003e\u003cp\u003eA total of 21,402 patients with CVD and CPET data from 20 medical centres in China from January 2018 to December 2024 were included in this study. These hospitals are widely distributed in major regions of China, including 9 in Northeast China, 3 in Northwest China, 2 in East China, 2 in South China, 2 in North China, 1 in Central China and 1 in Southwest China. This was a retrospective observational study. All the data were anonymized historical data and did not involve patient intervention or additional biological sample collection. All personal information was deidentified and reviewed by the Ethics Review Committee of the Second Affiliated Hospital of Harbin Medical University, which approved exemption from the informed consent requirement (KY2025-031). Rigorous review of the data from each participating medical centre was performed to ensure that VO\u003csub\u003e2\u003c/sub\u003e peak values were within the expected normal range before being included in this study. The inclusion criteria were as follows: (1) current or previous occurrence of myocardial infarction (MI), percutaneous coronary intervention (PCI), heart failure (HF), stable angina (SA), or other CVDs; (2) completion of a symptom-limiting CPET on a cycle ergometer; (3) age between 30 and 79 years; and (4) a peak respiratory exchange ratio (RER)\u0026thinsp;\u0026ge;\u0026thinsp;1.0. The included CVD categories included MI, PCI, HF, SA and other CVDs. Specifically, the MI category included patients who had reported PCI but not HF, and the PCI category excluded individuals who had reported MI or HF. The HF category did not exclude patients who reported events in other CVD categories. The SA category excluded patients with PCI, HF and MI. The other category excluded patients with PCI, HF, MI and SA. Ultimately, a total of 21,402 patients with CPET data were included in this study. By using the random sampling method, 70% (n\u0026thinsp;=\u0026thinsp;14,981) of the participants were used to establish the development equation, and the remaining 30% (n\u0026thinsp;=\u0026thinsp;6421) of the participants were used to validate the equation.\u003c/p\u003e\u003c/div\u003e"},{"header":"Cardiopulmonary exercise test","content":"\u003cp\u003eIn this study, a symptom-limiting CPET was performed using a cycle ergometer. The CPET was carried out by experienced personnel at each centre, and all operation procedures strictly followed the \"Standards for Testing and Training Exercises\" guidelines issued by the American Heart Association (AHA).\u003csup\u003e11 12\u003c/sup\u003e Throughout the experiment, indicators such as VO\u003csub\u003e2\u003c/sub\u003e were collected using CPET equipment. With the Ramp protocol, the increasing load per minute was the value of the maximum power divided by 8 to 12. The target test time was 8 to 12 minutes. The test was continued until the participants showed restrictive symptoms, upon which the test was terminated.\u003c/p\u003e\u003cp\u003eThe CPET consists of four stages: the resting period, the warm-up period, the loading period, and the recovery period: (1) Resting period: The patient sat still for 3 minutes, and the heart rate and blood pressure were measured at rest; (2) Warm-up period: The patient pedalled for 3 minutes without load; (3) Loading period: The load was acutely increased at a predetermined power until the patient showed symptom limitations; and (4) Recovery period: The patient performed 3 minutes of no-load cycling and sat still for 3 minutes.\u003c/p\u003e"},{"header":"Establishment of the Chinese CVD cohort equation and its comparison with other predictive equations","content":"\u003cp\u003eThe VO\u003csub\u003e2\u003c/sub\u003e peak prediction equation was constructed by using a multivariate forward stepwise regression analysis method in the development group. The included variables were age, sex, height, weight, and CVD diagnosis. In the validation group, we calculated the percentage of the predicted value to the measured value, the standard error of the estimate (SEE), the correlation coefficient, and the adjusted coefficient of determination, and the intragroup correlation coefficient (ICC) was used to compare the predicted VO\u003csub\u003e2\u003c/sub\u003e peak and the measured VO\u003csub\u003e2\u003c/sub\u003e peak by the equations constructed in the study, the Wasserman equation, the Friend health equation and the Friend CVD equation. In addition to conducting overall analyses in the validation group, we also performed subgroup analyses on the basis of sex and CVD diagnosis category.\u003c/p\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eThe analysis was carried out in R version 4.3.2. Continuous variables with a normal distribution are expressed as the means and standard deviations [means (standard deviations)], and t tests were used for comparisons. Continuous variables with a nonnormal distribution are expressed as the median and quartiles [median (first quartile, third quartile)] and were analysed with the Mann‒Whitney U test. The comparison of categorical variables was conducted using the chi-square test, and these variables are expressed as the number of cases and their constituent ratios [number of cases (%)].\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eThe CPET data of 21,402 patients with CVD were analysed in this study. The results in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e show that both male and female patients showed statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) differences in all baseline indices, including age, height, weight, BMI, CVD type, and CPET results. VO\u003csub\u003e2\u003c/sub\u003e peak was significantly greater in male patients than in female patients (1386.00 vs. 1050.00 mL/min; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDescriptive characteristics of the Chinese CVD cohort\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOverall,\u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;21402\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMale,\u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;14562\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eFemale,\u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;6840\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge,year\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e57.06\u0026thinsp;\u0026plusmn;\u0026thinsp;10.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e57.00 (50.00\u0026ndash;64.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e59.00 (52.00\u0026ndash;66.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHeight, cm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e167.92\u0026thinsp;\u0026plusmn;\u0026thinsp;7.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e172.00\u003c/p\u003e\u003cp\u003e(168.00-175.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e160.00 (157.00-164.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWeight, kg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e72.22\u0026thinsp;\u0026plusmn;\u0026thinsp;12.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e75.00 (70.00\u0026ndash;84.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e62.00 (56.00\u0026ndash;69.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI, kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e25.51\u0026thinsp;\u0026plusmn;\u0026thinsp;3.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e25.80 (23.80\u0026ndash;28.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e24.20 (22.20\u0026ndash;26.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePCI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4643 (21.69%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3618 (24.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1025 (15.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAngina\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10123 (47.30%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5889 (40.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4234 (61.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1670 (7.80%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1256 (8.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e414 (6.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3597 (16.81%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3017 (20.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e580 (8.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1369 (6.40%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e782 (5.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e587 (8.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCPET\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRER peak\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.130\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.12 (1.07\u0026ndash;1.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.11 (1.05\u0026ndash;1.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHR peak\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e131.29\u0026thinsp;\u0026plusmn;\u0026thinsp;22.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e129.00 (116.00-143.000)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e134.00 (120.00-149.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMeasured VO\u003csub\u003e2\u003c/sub\u003epeak, mL/min\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1310.11\u0026thinsp;\u0026plusmn;\u0026thinsp;376.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1386.00 (1160.00-1650.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1050.00 (876.75\u0026ndash;1242.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMeasured VO\u003csub\u003e2\u003c/sub\u003epeak, mL/kg/min\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18.17\u0026thinsp;\u0026plusmn;\u0026thinsp;4.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18.20 (15.50-21.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16.67 (14.200\u0026ndash;19.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePredicted VO\u003csub\u003e2\u003c/sub\u003epeak from Chinese CVD equation, mL/min\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1308.91\u0026thinsp;\u0026plusmn;\u0026thinsp;269.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1414.42 (1265.64-1571.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1076.52 (942.18-1202.21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePredicted VO\u003csub\u003e2\u003c/sub\u003epeak from Chinese CVD equation, mL/kg/min\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18.16\u0026thinsp;\u0026plusmn;\u0026thinsp;2.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18.54 (17.21\u0026ndash;19.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e17.08 (15.31\u0026ndash;18.76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePredicted VO\u003csub\u003e2\u003c/sub\u003epeak from Wasserman equation, mL/min\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1934.03\u0026thinsp;\u0026plusmn;\u0026thinsp;521.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2182.93(1926.13-2460.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e136.45(1211.23-1480.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePredicted VO\u003csub\u003e2\u003c/sub\u003epeak from Wasserman equation, mL/kg/min\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e26.68\u0026thinsp;\u0026plusmn;\u0026thinsp;5.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e28.67 (26.12\u0026ndash;31.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e21.29 (19.25\u0026ndash;23.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePredicted VO\u003csub\u003e2\u003c/sub\u003epeak from Friend CVD equation, mL/min\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1492.71\u0026thinsp;\u0026plusmn;\u0026thinsp;382.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1646.40 (1447.87-1864.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1113.60 (986.68-1256.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePredicted VO\u003csub\u003e2\u003c/sub\u003epeak from Friend CVD equation, mL/kg/min\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20.56\u0026thinsp;\u0026plusmn;\u0026thinsp;3.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21.91 (20.17\u0026ndash;23.550)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e17.90(16.31\u0026ndash;19.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePredicted VO\u003csub\u003e2\u003c/sub\u003epeak from Friend health equation, mL/min\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2503.26\u0026thinsp;\u0026plusmn;\u0026thinsp;672.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2849.58 (2574.78-3155.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1662.25 (1492.03-1859.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePredicted VO\u003csub\u003e2\u003c/sub\u003epeak from Friend health equation, mL/kg/min\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e34.50\u0026thinsp;\u0026plusmn;\u0026thinsp;6.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e37.79 (35.06\u0026ndash;40.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e26.82(24.09\u0026ndash;29.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003eAbbreviations: BMI, body mass index; CVD, cardiovascular disease; CPET, cardiopulmonary exercise testing; HF, heart failure; HR, heart rate; MI, myocardial infarction; PCI, percutaneous coronary intervention; RER, respiratory exchange ratio; SA, Stable Angina; VO₂ peak, peak oxygen uptake.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe results in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e show that there were no significant differences between the development group (n\u0026thinsp;=\u0026thinsp;14981) and the validation group (n\u0026thinsp;=\u0026thinsp;6421) in terms of baseline characteristics such as age, sex, height, weight, BMI, or other CVDs (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). The measured values of VO₂ peak were 1269.20 mL/min and 1270.00 mL/min in the development and validation groups, respectively.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDescriptive characteristics from the development and validation groups\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDevelopment,\u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;14981\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eValidation,\u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;6421\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge,year\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e58.00 (51.00\u0026ndash;65.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e58.00 (50.00\u0026ndash;65.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.064\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4776 (31.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2064 (32.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.716\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10205 (68.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4357 (67.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHeight, cm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e169.00 (162.00-173.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e169.00 (162.00-174.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.750\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWeight, kg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e71.00 (63.00\u0026ndash;80.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e72.00 (63.50\u0026ndash;80.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.760\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI, kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e25.30 (23.20-27.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e25.30 (23.20-27.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.803\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePCI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3262 (21.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1381 (21.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.678\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAngina\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7058 (47.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3065 (47.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.413\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1147 (7.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e523 (8.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.233\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2547 (17%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1050 (16.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.253\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e967 (6.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e402 (6.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.616\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCPET\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRER peak\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.12 (1.06\u0026ndash;1.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.12 (1.06\u0026ndash;1.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.301\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHR peak\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e130.00 (117.00-145.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e131.00 (117.00-145.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.414\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMeasured VO\u003csub\u003e2\u003c/sub\u003epeak, mL/min\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1269.20 (1032.50-1530.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1270.00 (1040.00-1550.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.160\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMeasured VO\u003csub\u003e2\u003c/sub\u003epeak, mL/kg/min\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17.68 (15.00-20.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17.80 (15.10\u0026ndash;21.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.130\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePredicted VO\u003csub\u003e2\u003c/sub\u003epeak from Chinese CVD equation, mL/min\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1305.21 (1119.69-1492.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1307.47 (1125.20-1500.89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.429\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePredicted VO\u003csub\u003e2\u003c/sub\u003epeak from Chinese CVD equation, mL/kg/min\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18.12 (16.650-19.695)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18.16 (16.66\u0026ndash;19.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.389\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePredicted VO\u003csub\u003e2\u003c/sub\u003epeak from Wasserman equation, mL/min\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1939.30 (1471.21-2315.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1952.83 (1478.28-2325.76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.355\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePredicted VO\u003csub\u003e2\u003c/sub\u003epeak from Wasserman equation, mL/kg/min\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e26.74 (22.87\u0026ndash;30.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26.82 (22.86\u0026ndash;30.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.404\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePredicted VO\u003csub\u003e2\u003c/sub\u003epeak from Friend CVD equation, mL/min\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1481.06 (1197.44-1757.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1486.26 (1197.30-1763.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.480\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePredicted VO\u003csub\u003e2\u003c/sub\u003epeak from Friend CVD equation, mL/kg/min\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20.78 (18.30-22.84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20.85 (18.34\u0026ndash;22.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.409\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePredicted VO\u003csub\u003e2\u003c/sub\u003epeak from Friend health equation, mL/min\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2596.96 (1885.57-2991.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2601.53 (1886.39-3004.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.439\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePredicted VO\u003csub\u003e2\u003c/sub\u003epeak from Friend health equation, mL/kg/min\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e35.58 (29.69\u0026ndash;39.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e35.58 (29.60-39.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.507\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eAbbreviations: BMI, body mass index; CVD, cardiovascular disease; CPET, cardiopulmonary exercise testing; HF, heart failure; HR, heart rate; MI, myocardial infarction; PCI, percutaneous coronary intervention; RER, respiratory exchange ratio; SA, Stable Angina; VO₂ peak, peak oxygen uptake.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe results of stepwise multiple forward regression analysis revealed that age, sex, height, weight and CVD diagnosis all significantly contributed to the regression equation for predicting VO₂ peak (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). With the increasing number of included variables, the root mean square error (RMSE) decreased from 310.9 to 264.0 mL/min. The equation for predicting VO₂ peak in patients with CVD in China is as follows: VO\u003csub\u003e2\u003c/sub\u003e peak(mL/min)\u0026thinsp;=\u0026thinsp;246.206 \u0026ndash; (9.858 \u0026times; age [years]) + (187.643*sex [male\u0026thinsp;=\u0026thinsp;1; female\u0026thinsp;=\u0026thinsp;0]) + (4.500 \u0026times; height [cm]) + (10.250 \u0026times; weight [kg]) + (94.969 \u0026times; SA [yes\u0026thinsp;=\u0026thinsp;1, no\u0026thinsp;=\u0026thinsp;0]) - (39.293 \u0026times; PCI [yes\u0026thinsp;=\u0026thinsp;1, no\u0026thinsp;=\u0026thinsp;0]) - (110.124\u0026times; MI [yes\u0026thinsp;=\u0026thinsp;1, no\u0026thinsp;=\u0026thinsp;0]) - (208.447 \u0026times; HF [yes\u0026thinsp;=\u0026thinsp;1, no\u0026thinsp;=\u0026thinsp;0]). SEE\u0026thinsp;=\u0026thinsp;264 mL/min; R\u0026thinsp;=\u0026thinsp;0.71; adjusted R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.51.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eForward stepwise multiple regression results for prediction of absolute VO2 peak (mL/min) from sex, weight, age, height, and CVD category\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRMSE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWeight, kg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e310.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.321\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge, yr\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e291.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.403\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e282.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.440\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e269.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.491\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e266.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.501\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e265.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.506\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHeight, cm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e264.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.509\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePCI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e264.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.510\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eAbbreviations: CVD, cardiovascular disease; HF, heart failure; MI, myocardial infarction; PCI, percutaneous coronary intervention; SA, Stable Angina; VO\u003csub\u003e2\u003c/sub\u003e peak, peak oxygen uptake.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe research results in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e show that in the validation group, the value predicted by the Chinese CVD Eq.\u0026nbsp;(1311.63 mL/min) was the closest to the measured value (1315.64 mL/min). The values predicted by the Wasserman equation, Friend health equation and Friend CVD Eq.\u0026nbsp;(1939.87 vs. 2508.71 vs. 1496.06 mL/min) were all higher than the measured values (1311.63 mL/min). The Pearson correlation coefficient of the Chinese CVD equation was 0.71, and the ICC was 0.68. The Pearson correlation coefficients of the Wasserman equation, the Friend health equation and the Friend CVD equation were 0.64, 0.63 and 0.68, respectively; the ICCs were 0.31, 0.09 and 0.61, respectively. The Pearson correlation coefficient and ICC values predicted by the Chinese CVD equation were both greater than those predicted by the other three equations. The percentage range of VO₂ peak predicted by the Chinese CVD equation was the closest to the measured value (100%-102%). The percentage ranges predicted by the Wasserman equation, Friend health equation and Friend CVD equation were all higher than the measured values (128%\u0026ndash;176% vs. 173%\u0026ndash;231% vs. 105%\u0026ndash;128%, respectively).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMeasured and predicted VO2peak values from the validation group according to CVD category and sex\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eAny CVD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePCI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eHF\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eSA\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAll\u003c/p\u003e\u003cp\u003e(N\u0026thinsp;=\u0026thinsp;6421)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMales\u003c/p\u003e\u003cp\u003e(N\u0026thinsp;=\u0026thinsp;4357)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eFemales\u003c/p\u003e\u003cp\u003e(N\u0026thinsp;=\u0026thinsp;2064)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCategory\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;1381)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCategory\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;1050)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eCategory\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;523)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eCategory\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;3065)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMeasured VO\u003csub\u003e2\u003c/sub\u003epeak, mL/min\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1315.64\u0026thinsp;\u0026plusmn;\u0026thinsp;376.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1390.00 (1169.00-1660.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1054.00 (880.00-1251.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1263.50\u003c/p\u003e\u003cp\u003e(1031.40\u0026ndash;1520.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1231.85 (1015.00-1452.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1050.00 (840.00-1280.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1330.00 (1098.00-1630.00)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMeasured VO\u003csub\u003e2\u003c/sub\u003epeak, mL/kg/min\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18.25\u0026thinsp;\u0026plusmn;\u0026thinsp;4.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18.30 (15.50\u0026ndash;21.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16.70 (14.30-19.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e17.30 (14.80-20.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e16.58 (14.20-19.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e14.77 (12.00-17.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e19.00 (16.30-22.42)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePredicted VO\u003csub\u003e2\u003c/sub\u003epeak from Chinese CVD equation, mL/min\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1311.63\u0026thinsp;\u0026plusmn;\u0026thinsp;270.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1419.86\u003c/p\u003e\u003cp\u003e(1266.67-1568.126)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1079.58\u003c/p\u003e\u003cp\u003e(946.73-1200.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1304.46\u003c/p\u003e\u003cp\u003e(1135.77-1448.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1270.25 (1122.13-1424.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1098.30 (903.49-1245.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1376.64 (1168.55-1572.56)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePredicted VO\u003csub\u003e2\u003c/sub\u003epeak from Chinese CVD equation, mL/kg/min\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18.19\u0026thinsp;\u0026plusmn;\u0026thinsp;2.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18.59 (17.22\u0026ndash;20.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e17.14 (15.39\u0026ndash;18.77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e17.67 (16.50-18.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e17.12 (15.98\u0026ndash;18.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e15.04 (13.70-16.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e19.35 (18.05\u0026ndash;20.76)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePercent predicted Vo2peak from Chinese CVD equation, %\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e104%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e101%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e101%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e101%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e102%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e100%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e101%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePredicted VO\u003csub\u003e2\u003c/sub\u003epeak from Wasserman equation, mL/min\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1939.87\u0026thinsp;\u0026plusmn;\u0026thinsp;524.15\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2184.00\u003c/p\u003e\u003cp\u003e(1935.98-2471.36)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1340.97\u003c/p\u003e\u003cp\u003e(1212.47-1486-25)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2005.20\u003c/p\u003e\u003cp\u003e(1632.84-2311.04)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2109.61\u003c/p\u003e\u003cp\u003e(1778.30-2424.32)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1897.00\u003c/p\u003e\u003cp\u003e(1481.11-2225.20)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1836.40\u003c/p\u003e\u003cp\u003e(1407.68-2296.76)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePredicted VO\u003csub\u003e2\u003c/sub\u003epeak from Wasserman equation, mL/kg/min\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e26.75\u0026thinsp;\u0026plusmn;\u0026thinsp;5.24\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e28.70 (26.17\u0026ndash;31.43)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e21.26 (19.22\u0026ndash;23.72)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e27.04 (23.89\u0026ndash;30.04)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e28.27 (25.11\u0026ndash;31.05)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e26.02 (22.58\u0026ndash;29.14)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e26.24 (22.20-29.98)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWasserman equation %Predicted\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e152%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e157%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e128%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e151%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e166%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e176%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e134%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePredicted VO\u003csub\u003e2\u003c/sub\u003epeak from Friend health equation, mL/min\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2508.71\u0026thinsp;\u0026plusmn;\u0026thinsp;676.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2850.01 (2587.27-3158.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1665.39 (1492.49-1861.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2669.57 (2261.87-2985.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2768.07 (2429.75-3106.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2555.88 (2055.38-2893.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2484.01 (1761.75-2963.93)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePredicted VO\u003csub\u003e2\u003c/sub\u003epeak from Friend health equation, mL/kg/min\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e34.55\u0026thinsp;\u0026plusmn;\u0026thinsp;6.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e37.92 (35.19\u0026ndash;40.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e26.82 (24.09\u0026ndash;29.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e36.23 (31.94\u0026ndash;39.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e37.63 (33.50-40.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e35.06 (29.90-38.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e34.67 (28.38\u0026ndash;38.96)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFriend health cohort equation %Predicted\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e197%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e206%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e160%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e199%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e216%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e231%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e173%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePredicted VO\u003csub\u003e2\u003c/sub\u003epeak from Friend CVD equation, mL/min\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1496.06\u0026thinsp;\u0026plusmn;\u0026thinsp;385.36\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1647.20 (1456.68-1868.80)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1114.22 (986.33-1254.12)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1576.20 (1337.12\u0026ndash;1800.00)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1585.37 (1369.06-1828.06)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1164.80 (948.98-1373.55)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1453.43 (1173.84-1753.65)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePredicted VO\u003csub\u003e2\u003c/sub\u003epeak from Friend CVD equation, mL/kg/min\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20.59\u0026thinsp;\u0026plusmn;\u0026thinsp;3.36\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21.96 (20.22\u0026ndash;23.61)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e17.89 (16.32\u0026ndash;19.60)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e21.50 (19.56\u0026ndash;23.28)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e21.54 (19.66\u0026ndash;23.22)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e16.10 (14.03\u0026ndash;17.92)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e20.83 (18.50-22.92)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFriend CVD cohort equation %Predicted\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e117%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e118%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e105%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e121%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e128%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e108%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e107%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRMSE for Chinese CVD equation equation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e263.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e288.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e200.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e257.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e254.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e236.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e269.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRMSE for Wasserman equation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e288.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e310.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e228.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e259.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e255.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e242.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e279.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRMSE for Friend health equation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e291.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e309.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e229.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e260.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e257.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e243.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e279.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRMSE for Friend CVD equation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e277.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e302.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e216.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e259.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e254.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e242.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e271.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePearson correlation for Chinese CVD equation equation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.71\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.62\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.66\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.68\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.64\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.67\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.72\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePearson correlation for Wasserman equation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.64\u003csup\u003eb,c\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.53\u003csup\u003eb,c\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.52\u003csup\u003eb,c\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.68\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.64\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.65\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.69\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePearson correlation for Friend health equation equation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.63\u003csup\u003eb,c\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.54\u003csup\u003eb,c\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.51\u003csup\u003eb,c\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.68\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.63\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.65\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.69\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePearson correlation for Friend CVD equation equation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.68\u003csup\u003eb,c\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.57\u003csup\u003eb,c\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.59\u003csup\u003eb,c\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.68\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.65\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.65\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.71\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eICC for Chinese CVD equation Eq.\u0026nbsp;(95% CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.68 (0.66\u0026ndash;0.69)\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.55 (0.53\u0026ndash;0.57)\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.63 (0.60\u0026ndash;0.65)\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.64 (0.61\u0026ndash;0.67)\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.61 (0.57\u0026ndash;0.65)\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.66 (0.60\u0026ndash;0.70)\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.67 (0.65\u0026ndash;0.69)\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eICC for Wasserman Eq.\u0026nbsp;(95% CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.31 (-0.10-0.62)\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.10 (-0.04-0.32)\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.29 (-0.07-0.55)\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.28 (-0.09-0.61)\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.20 (-0.07-0.51)\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.21 (-0.07-0.52)\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.42 (-0.08-0.70)\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eICC for Friend health equation Eq.\u0026nbsp;(95% CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.09 (-0.04-0.31)\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.07 (-0.03-0.25)\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.14 (-0.06-0.40)\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.14 (-0.05-0.41)\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.10 (-0.04-0.32)\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.11 (-0.05-0.34)\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.22 (-0.08-0.53)\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eICC for Friend CVD equation Eq.\u0026nbsp;(95% CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.61 (0.38\u0026ndash;0.74)\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.45 (0.13\u0026ndash;0.64)\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.56 (0.52\u0026ndash;0.61)\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.53 (0.06\u0026ndash;0.74)\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.43 (-0.06-0.70)\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.63 (0.54\u0026ndash;0.70)\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.69 (0.62\u0026ndash;0.74)\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e\u003cp\u003eAbbreviations: CVD, cardiovascular disease; HF, heart failure; MI, myocardial infarction; PCI, percutaneous coronary intervention; SA, Stable Angina; VO\u003csub\u003e2\u003c/sub\u003e peak, peak oxygen uptake.\u003c/p\u003e\u003cp\u003e\u003csup\u003ea\u003c/sup\u003eSignificantly different from measured VO\u003csub\u003e2\u003c/sub\u003e peak (P\u0026thinsp;\u0026lt;\u0026thinsp;.05).\u003c/p\u003e\u003cp\u003e\u003csup\u003eb\u003c/sup\u003eSignificant correlation (P\u0026thinsp;\u0026lt;\u0026thinsp;.05).\u003c/p\u003e\u003cp\u003e\u003csup\u003ec\u003c/sup\u003eSignificantly different correlation compared with the Chinese CVD cohort equation (P\u0026thinsp;\u0026lt;\u0026thinsp;.05).\u003c/p\u003e\u003cp\u003e\u003csup\u003ed\u003c/sup\u003eSignificant ICC (P\u0026thinsp;\u0026lt;\u0026thinsp;.05).\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the Bland‒Altman plots of the measured and predicted VO\u003csub\u003e2\u003c/sub\u003e peaks in the validation group. In the validation group, the prediction deviation of the Chinese CVD equation was \u0026minus;\u0026thinsp;4 mL/min. The prediction deviation of the Wasserman equation was +\u0026thinsp;624.2 mL/min, that of the Friend health equation was +\u0026thinsp;1193.1 mL/min, and that of the Friend CVD equation was +\u0026thinsp;180.4 mL/min.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the comparison of the measured and predicted VO₂ peak values in the validation group. When the Chinese CVD cohort equation was used, the distributions were significantly different. The number of individuals with a deviation of \u0026lt;\u0026thinsp;0.5 METs between the measured and predicted VO₂ peaks was 2,411 (37.5%), whereas the number of individuals with a deviation of \u0026ge;\u0026thinsp;2 METs was 350 (5.5%). The r value was 0.71, and the R\u0026sup2; value was 0.51. When the Wasserman cohort equation was used, the number of individuals with a deviation of \u0026lt;\u0026thinsp;0.5 METs between the measured and predicted VO₂ peaks was 483 (7.5%), whereas the number of individuals with a deviation of \u0026ge;\u0026thinsp;2 METs was 3861 (60.1%). The r value was 0.64, and the R\u0026sup2; value was 0.41. When using the Friend health cohort equation was used, the number of individuals with a deviation of \u0026lt;\u0026thinsp;0.5 METs between the measured and predicted VO₂ peaks was 53 (0.8%), whereas the number of individuals with a deviation of \u0026ge;\u0026thinsp;2 METs was 5901 (91.9%). The r value was 0.63, and the R\u0026sup2; value was 0.40. When using the Friend CVD cohort equation was used, the number of individuals with a deviation of \u0026lt;\u0026thinsp;0.5 METs between the measured and predicted VO₂ peaks was 1754 (27.3%), whereas the number of individuals with a deviation of \u0026ge;\u0026thinsp;2 METs was 899 (14%). The r value was 0.68, and the R\u0026sup2; value was 0.46.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eVO₂ peak, an important indicator for evaluating the prognosis of CVD patients, is significantly negatively correlated with the risk of death and the incidence of cardiovascular events. The American Heart Association (AHA) has adopted VO₂ peak as a vital sign assessment tool. However, the widely used predictive equation in clinical practice was developed on the basis of data from healthy individuals or North American CVD patients,\u003csup\u003e6 13\u003c/sup\u003e and the peak VO₂ value in Chinese patients may be overestimated.\u003csup\u003e14\u003c/sup\u003e Studies have shown that there are significant differences between Chinese and Western populations in terms of physiological functions (cardiopulmonary adaptability, muscle oxygen metabolism), genetic background and environmental factors,\u003csup\u003e15\u0026ndash;17\u003c/sup\u003e further highlighting the limitations of the existing equation in predicting VO₂ peak in Chinese CVD patients. Therefore, on the basis of a group of CVD patients in China, a new nonexercise prediction equation, the Chinese CVD equation, was developed in this study. This equation was constructed with age, sex, height, weight and CVD diagnosis as the core variables, although other factors are known to have an impact on VO\u003csub\u003e2\u003c/sub\u003e peak;\u003csup\u003e3 18\u003c/sup\u003e however, on the premise of ensuring prediction accuracy, prioritizing indicators that can be quickly obtained from electronic medical records can significantly improve clinical practicability and work efficiency. The establishment of this model not only fills the gap in race-specific prediction tools but also provides an evidence-based basis for accurately assessing the cardiopulmonary function of Chinese patients and optimizing individualized treatment decisions.\u003c/p\u003e\u003cp\u003eCompared to prediction equations developed based on Western populations, the Chinese CVD equation aligns with the physiological characteristics of Chinese patients in terms of physical fitness and metabolism, underscoring the importance of developing and applying population-specific prediction equations. Compared with the Wasserman equation (Bland‒Altman deviation\u0026thinsp;+\u0026thinsp;624.2 mL/min, ICC 0.10‒0.42) and the Friend health equation (Bland‒Altman deviation, +\u0026thinsp;1193.1 mL/min; ICC, 0.07\u0026ndash;0.22), the Bland‒Altman deviation from the Chinese CVD equation was only \u0026minus;\u0026thinsp;4 mL/min, and the ICC value increased to 0.55\u0026ndash;0.68. Furthermore, compared with the Friend CVD equation, which was also developed for the CVD population, the Chinese CVD equation has better accuracy (Bland‒Altman deviation of -4 mL/min vs. +180.4 mL/min; ICCs of 0.55‒0.68 vs. 0.29‒0.49). This result demonstrates that the Chinese CVD equation significantly improves predictive performance and enhances its clinical applicability in the Chinese population, highlighting the importance of utilizing localized prediction tools across different populations to achieve more precise individualized health management.\u003c/p\u003e\u003cp\u003eA VO₂ peak study in CVD patients in North America revealed that the difference in VO₂ peak is significantly correlated with disease type,\u003csup\u003e19\u003c/sup\u003e which is consistent with the results of this study. The reasons for these differences may be the degree of myocardial injury, coronary artery stenosis or occlusion, and skeletal muscle function under different disease states.\u003csup\u003e10 19\u0026ndash;21\u003c/sup\u003e In the Chinese CVD prediction equation, the VO₂ peak values in the SA, PCI, MI, and HF groups showed a downward trend (1376.64 vs. 1304.46 vs. 1270.25 vs. 1098.30 mL/min, respectively). Further analysis revealed that the predicted values (1304.46, 1270.25, 1098.30, and 1376.64 mL/min, respectively) for the PCI, MI, HF, and SA patients obtained by the Chinese CVD prediction equation were very close to the measured values (1263.50, 1231.85, 1050.00, and 1330.00 mL/min, respectively). The predicted values as a percentage of the measured values were 101%, 102%, 100%, and 101%, respectively, indicating that the equation has high accuracy in predicting VO\u003csub\u003e2\u003c/sub\u003e peak in patients with different CVD types. In contrast, the Wasserman equation, Friend health equation and Friend CVD equation all overestimated the values to varying degrees: the predicted values obtained with the Wasserman equation were 2005.20, 2109.61, 1897.00 and 1836.40 mL/min, respectively, and the predicted percentages were 151%, 166%, 176% and 134%, respectively; the predicted values obtain with the Friend health equation were 2669.57, 2768.07, 2555.88 and 2484.01 mL/min, respectively, and the predicted percentages were 199%, 216%, 231% and 173%, respectively; the predicted values obtained with the Friend CVD equation were 1576.20, 1585.37, 1164.80 and 1453.43 mL/min, respectively, and the predicted percentages were 121%, 128%, 108% and 107%, respectively. The overestimation of VO₂ peak with these equations can lead to inaccurate assessment of exercise tolerance, interfere with the formulation of rehabilitation programs, and may increase the risk of exercise safety due to improper exercise intensity. The percentage of the measured VO₂ peak value to the predicted value is a key indicator for evaluating the prognosis of CVD and has important clinical value. Studies have shown that this indicator is significantly correlated with all-cause mortality. For every 1% increase in the predicted percentage, the risk of death can be reduced by 3%.\u003csup\u003e22\u003c/sup\u003e When the percentage of the predicted value to the measured value is less than 50%, the cardiovascular function of the patient is severely impaired, and the prognosis deteriorates significantly.\u003csup\u003e5\u003c/sup\u003e The existing prediction equations were primarily established on the basis of data from healthy individuals and North American CVD patients, but there are significant differences in the physiological characteristics of the CVD population in China. Therefore, establishing a VO₂ peak prediction equation applicable to the Chinese population\u003csup\u003e23\u0026ndash;25\u003c/sup\u003e will help achieve more accurate risk stratification, optimize individualized treatment plans, and provide a race-specific evidence-based basis for clinical decision-making.\u003c/p\u003e\u003cp\u003eCardiac rehabilitation is an important part of CVD management and has been carried out in 54.7% of countries worldwide;\u003csup\u003e26\u003c/sup\u003e however, there are still challenges in conducting cardiac rehabilitation in China.\u003csup\u003e27 28\u003c/sup\u003e Only 22% of hospitals across the country have implemented cardiac rehabilitation programmes.\u003csup\u003e27\u003c/sup\u003e Research shows that China needs to build over 3\u0026nbsp;million cardiac rehabilitation sites.\u003csup\u003e29\u003c/sup\u003e The development of cardiac rehabilitation is limited by various factors, such as scarce hospital resources, a shortage of professionals and low public awareness,\u003csup\u003e30\u003c/sup\u003e especially the lack of key equipment such as CPET equipment,\u003csup\u003e3\u003c/sup\u003e making it difficult for primary medical institutions to assess the VO₂ peak level of patients accurately. Therefore, the VO₂ peak prediction equation for Chinese CVD patients developed in this study can provide an alternative solution to compensate for resource shortages in resource-limited settings. Clinicians can use this prediction equation to formulate personalized cardiac rehabilitation prescriptions for patients. Notably, the clinical evaluation of VO₂ peak needs to be comprehensively performed in combination with the scientific nature of the detection method and the applicable scenarios. Although the Chinese CVD equation developed in this study can provide relatively accurate predicted values of VO₂ peak for Chinese CVD patients, there may be deviations due to individual physiological differences. When the values of VO₂ peak are predicted using the prediction equation, patients\u0026rsquo; actual physical condition and clinical symptoms should be considered. Therefore, in clinical practice, it is still recommended that the CPET be used promptly and preferentially for VO₂ peak assessment, especially when precise exercise prescriptions need to be formulated, surgical risks need to be evaluated or prognosis needs to be judged. Locally validated predictive equations should be used only as alternative tools in situations where CPET equipment is insufficient and patients cannot tolerate exercise testing (e.g., severe heart failure, bone and joint disease) or in large-scale screening scenarios.\u003c/p\u003e\u003cp\u003eIn this study, the first VO₂ peak prediction equation for Chinese patients with CVD was established. The strengths of this study include its large sample size, multicentre design, and provision of a substantial amount of data from female patients. This study also has several limitations. First, this study included only cycle ergometer CPET data; future research is needed to further explore CPET data in a treadmill setting. Second, the predictive equations were not designed to fully account for the potential effects of comorbidities such as hypertension, diabetes, pulmonary diseases, or orthopaedic conditions on VO₂ peak. Third, although all tests were conducted by experienced laboratory personnel, differences in the equipment used and the test personnel may have influenced the VO₂ peak measurement results. Fourth, this study was retrospective and may be affected by potential selection bias.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003e In conclusion, this study established a nonexercise VO₂ peak prediction equation applicable to patients with CVD in China for the first time. Compared with the existing equations, the Chinese CVD equation has smaller prediction errors and higher accuracy, providing a more reliable prediction tool for the assessment of VO₂ peak in Chinese CVD patients.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eVO₂ peak\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003ePeak oxygen uptake\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eCPET\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eCardiopulmonary exercise test\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eCVD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eCardiovascular disease\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eMyocardial infarction\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003ePCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003ePercutaneous coronary intervention\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eHF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eHeart failure\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eStable angina\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eRER\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eRespiratory exchange ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003eAcknowledgments\u003c/p\u003e\n\u003cp\u003eThanks to the cardiac rehabilitation practitioners in the 20 centers.\u003c/p\u003e\n\u003cp\u003eAuthors\u0026rsquo; contributions\u003c/p\u003e\n\u003cp\u003eXiaojun Wu and Haoning Cui contributed to the conception or design of the work. Liyan Ran, Xiaojun Wu, and Haoning Cui worked on performing the analysis, and preparing the figures. Liyan Ran, Xiaojun Wu, and Haoning Cui writing the manuscript. Shiyu Wang, Xianghui Zheng, Qifeng Li, Tianhui Cao, and Xinyu Hou worked on reviewing the initial manuscript and editing the manuscript. Xiaojun Wu, Hongyan Shi, Lixia Yuan, Tianwei Luan, Dajun Li, Haixia Liu, and Fangfang Bai collected and curated the data. Chao Fang, Jian Wu, and Bo Yu supervised the project and reviewed the final draft. All authors have read and approved the manuscript.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThe authors report no involvement in the research by the sponsor that could have influenced the outcome of this work. This study was supported by the Key Research and Development Program of Heilongjiang (Grant no. 2022ZX01A28), Heilongjiang Provincial Medical and Health Scientific Research Project (Grant no. 20240303010021), National Natural Science Foundation of China (Grant nos. 82172537, 82372565), and \u0026nbsp;Natural Science Foundation of Heilongjiang Province (Grant no. ZL2024H007).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe dataset \u0026nbsp;used during the current study is available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003eConflicts of interest\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eAll personal information was deidentified and reviewed by the Ethics Review Committee of the Second Affiliated Hospital of Harbin Medical University, which approved exemption from the informed consent requirement (KY2025-031).\u003c/p\u003e\n\u003cp\u003eConsent for publication\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMehta LS, Velarde GP, Lewey J, et al. 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Global perspectives on heart disease rehabilitation and secondary prevention: a scientific statement from the Association of Cardiovascular Nursing and Allied Professions, European Association of Preventive Cardiology, and International Council of Cardiovascular Prevention and Rehabilitation. \u003cem\u003eEur Heart J\u003c/em\u003e 2023;44(28):2515-25. doi: 10.1093/eurheartj/ehad225\u003c/li\u003e\n\u003cli\u003eChindhy S, Taub PR, Lavie CJ, et al. Current challenges in cardiac rehabilitation: strategies to overcome social factors and attendance barriers. \u003cem\u003eExpert Rev Cardiovasc Ther\u003c/em\u003e 2020;18(11):777-89. doi: 10.1080/14779072.2020.1816464 [published Online First: 20200914]\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Cardiopulmonary exercise testing, Cardiac rehabilitation, Prediction equation, Peak oxygen uptake","lastPublishedDoi":"10.21203/rs.3.rs-7561891/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7561891/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003ePeak oxygen uptake (VO₂ peak) is a key indicator for evaluating cardiopulmonary function and cardiovascular prognosis. The use of the gold standard test method, the cardiopulmonary exercise test (CPET), is limited in clinical practice. Most existing nonexercise prediction equations are based on healthy or North American populations, and their applicability in Chinese patients with cardiovascular disease (CVD) is unknown. This study aimed to establish a VO₂ peak prediction equation applicable to Chinese CVD patients.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eThis retrospective multicentre study included CPET data from 21,402 CVD patients from 20 medical centres from January 2018\u0026ndash;December 2024. Patients were randomly divided into a development group (n\u0026thinsp;=\u0026thinsp;14,981) and a verification group (n\u0026thinsp;=\u0026thinsp;6421) at a ratio of 7:3. The regression analysis method was used to establish a Chinese CVD prediction equation to predict the VO₂ peak value of CVD patients, and this equation was compared with prediction equations established in other cohorts.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eAge, sex, height, weight, and CVD diagnosis were included as factors in the predictive equation. The regression equation was as follows: \u003cem\u003eVO\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e \u003cem\u003epeak (mL/min)\u0026thinsp;=\u0026thinsp;246.206 \u0026ndash; (9.858 \u0026times; age [years]) + (187.643*sex [male\u0026thinsp;=\u0026thinsp;1; female\u0026thinsp;=\u0026thinsp;0]) + (4.500 \u0026times; height [cm]) + (10.250 \u0026times; weight [kg]) + (94.969 \u0026times; SA [yes\u0026thinsp;=\u0026thinsp;1, no\u0026thinsp;=\u0026thinsp;0]) - (39.293 \u0026times; PCI [yes\u0026thinsp;=\u0026thinsp;1, no\u0026thinsp;=\u0026thinsp;0]) - (110.124\u0026times; MI [yes\u0026thinsp;=\u0026thinsp;1, no\u0026thinsp;=\u0026thinsp;0]) - (208.447 \u0026times; HF [yes\u0026thinsp;=\u0026thinsp;1, no\u0026thinsp;=\u0026thinsp;0]), adjusted R2\u0026thinsp;=\u0026thinsp;0.51, SEE\u0026thinsp;=\u0026thinsp;264 mL/min.\u003c/em\u003e The Wasserman (152%), Friend health (197%), and Friend CVD equations (117%) overestimated the VO₂ peak of Chinese CVD patients, whereas the Chinese CVD Eq.\u0026nbsp;(104%) predicted values closer to the measured values.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eIn this study, a VO₂ peak prediction equation for Chinese CVD patients was developed. Compared with existing equations, this equation has a prediction smaller error and is more suitable for the Chinese population.\u003c/p\u003e","manuscriptTitle":"Nonexercise prediction equation for peak oxygen uptake in Chinese patients with cardiovascular disease","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-07 01:27:24","doi":"10.21203/rs.3.rs-7561891/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"325648065691223544540321309485432618751","date":"2025-11-10T12:27:30+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-06T09:40:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"310662418662058521956261961219683323384","date":"2025-11-05T04:02:36+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-27T15:59:20+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-30T13:44:26+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-30T13:30:00+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-30T13:29:29+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cardiovascular Disorders","date":"2025-09-08T08:32:46+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4430f9b0-da47-4dc1-8010-8773b453b10b","owner":[],"postedDate":"November 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-11-07T01:27:24+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-07 01:27:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7561891","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7561891","identity":"rs-7561891","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}