Increased Oxygen Consumption Ability With Pulmonary Rehabilitation Improves Submaximal Exercise Capacity in Advanced COPD.

Background and Purpose: Chronic obstructive pulmonary disease (COPD) is the third global cause of deaths. Pulmonary rehabilitation (PR) is the standard treatment for this disease. However, the dropout rate remains high (33-50%) because of traffic issues and patients' low mobility. Therefore, telerehabilitation seems to be a better way to deliver PR. Using videos to deliver PR is not only easy to manipulate on the cellphone or ipad, but also improve patients' attention and compliance. Thus, the aim of this study is to assess whether tailor-made PR video rehabilitation program improves patients' quality of life (QoL), muscle strengths, cardiopulmonary functions, and compliance. Methods: This is a randomized controlled trial with 6-week follow-up. Participants were medically stable COPD patients and able to use LINE and YOUTUBE in cellphone. In the process of recruitment, they were all assessed by time-up-and-go test. The performance of time-up-and-go test should be lower than 12 seconds to rule out the participants with high risks of falling. They were randomly assigned to control group or intervention group to conduct 6-week home-based PR program. Patients in control group received the education booklet with words and pictures. Patients in intervention group watched YOUTUBE videos to rehab and recorded the intensity after the exercise by rating of perceived exertion (RPE) scores. During the intervention, a physical therapist would have weekly telephone calls or LINE calls for 6 times to monitor and modify the intensity of exercise. The videos included 5-minute breathing reeducation, 20-minute interval strengthening exercise, and 5-minute education animation. All patients were assessed the performance of spirometry, cardiopulmonary exercise test (CPET), questionnaires about symptoms, QoL, muscle strengths, and distances of 6-minute walk test (6MWT) before intervention, after intervention and, after 6-week follow-up. Results: After the 6-week intervention, the values of maximal inspiratory pressure (MIP) and maximal expiratory pressure (MEP), muscle strengths, and distances of 6MWT were increased. The total work (watts) and maximal oxygen uptake (VO2max) (mL/kg/min) in CPET were increased. The minute ventilation to carbon dioxide production slope (VE/VCO2) was decreased. The time point of anaerobic threshold (AT) after intervention was postponed because the PR program improved the utilization of aerobic metabolism. Conclusion: The video-delivered PR program is more convenience for COPD patient to carry out home-based PR. Also, the intervention will improve cardiopulmonary functions, QoL, and physical activity. Clinical Relevance: Our study provides an effective way to deliver home-based PR program that will improve multiple health-related functions and participation in COPD patients, decrease drop-out rate and potentially increase the utilization of PR.

Cardiopulmonary exercise test with stress echocardiography in COVID-19 survivors at 6 months follow-up

COMPLICATIONS OF CARDIOPULMONARY EXERCISE TESTING

Full text Figures and data Side by side Abstract Editor's evaluation Introduction Materials and methods Results Discussion Data availability References Decision letter Author response Article and author information Metrics Abstract Background: Oxygen uptake (VO2) is one of the most important measures of fitness and critical vital sign. Cardiopulmonary exercise testing (CPET) is a valuable method of assessing fitness in sport and clinical settings. There is a lack of large studies on athletic populations to predict VO2max using somatic or submaximal CPET variables. Thus, this study aimed to: (1) derive prediction models for maximal VO2 (VO2max) based on submaximal exercise variables at anaerobic threshold (AT) or respiratory compensation point (RCP) or only somatic and (2) internally validate provided equations. Methods: Four thousand four hundred twenty-four male endurance athletes (EA) underwent maximal symptom-limited CPET on a treadmill (n=3330) or cycle ergometer (n=1094). The cohort was randomly divided between: variables selection (nrunners = 1998; ncyclist = 656), model building (nrunners = 666; ncyclist = 219), and validation (nrunners = 666; ncyclist = 219). Random forest was used to select the most significant variables. Models were derived and internally validated with multiple linear regression. Results: Runners were 36.24±8.45 years; BMI = 23.94 ± 2.43 kg·m−2; VO2max=53.81±6.67 mL·min−1·kg−1. Cyclists were 37.33±9.13 years; BMI = 24.34 ± 2.63 kg·m−2; VO2max=51.74±7.99 mL·min−1·kg−1. VO2 at AT and RCP were the most contributing variables to exercise equations. Body mass and body fat had the highest impact on the somatic equation. Model performance for VO2max based on variables at AT was R2=0.81, at RCP was R2=0.91, at AT and RCP was R2=0.91 and for somatic-only was R2=0.43. Conclusions: Derived prediction models were highly accurate and fairly replicable. Formulae allow for precise estimation of VO2max based on submaximal exercise performance or somatic variables. Presented models are applicable for sport and clinical settling. They are a valuable supplementary method for fitness practitioners to adjust individualised training recommendations. Funding: No external funding was received for this work. Editor's evaluation The authors have established new formulas to predict maximum oxygen uptake for cyclists and runners based on submaximal exercise testing and anthropometric characteristics. This is an important study with a large and comprehensive dataset, which may be helpful for many exercise labs. The work is convincing, using appropriate and validated methodology in line with the current state-of-the-art, as shown by references to common exercise books. https://doi.org/10.7554/eLife.86291.sa0 Decision letter Reviews on Sciety eLife's review process Introduction The oxygen uptake (VO2) is considered an important metric in assessing cardiorespiratory fitness, health status, or endurance performance potential (Guazzi et al., 2012). With the application of standardised procedures and interpretation protocols, during graded exercise tests (GXT), the (maximal oxygen uptake) VO2max can be established (Bentley et al., 2007). GXT is the most widely used assessment to examine the dynamic relationship between exercise and integrated physiological systems (Albouaini et al., 2007; Bentley et al., 2007). The information from GXT during cardiopulmonary exercise testing (CPET) can be applied across the spectrum of sport performance, occupational safety screening, research, and clinical diagnostics (Guazzi et al., 2017). VO2 max is often used as a boundary between severe and extreme intensity domains and by definition requires maximal effort from the tested subject (Gaesser and Poole, 1996). However, it is not always recommended or possible to undertake a test to exhaustion (Guazzi et al., 2012). For the athletes, the proximity of competition or injury history can allow submaximal testing, but not testing to exhaustion (Sassi et al., 2006). Testing that requires maximal effort may be disruptive to the training process or interfere with race performance (Coutts et al., 2007; Lamberts et al., 2011). Due to practical constraints, tests to exhaustion or peak-power-output tests are often performed only two or three times a year (Coutts et al., 2007). However, VO2 values are widely used in sport science and the decision-making process (Mann et al., 2013). VO2 is widely considered one of the major endurance performance determinants (Joyner and Coyle, 2008). Using VO2max to guide the selection process, prescribing training intensity, assessing training adaptations, or predicting race times is a common practice in high-performance sports (Bassett and Howley, 2000; Bentley et al., 2007; Hawley and Noakes, 1992; Noakes et al., 1990). VO2max is also one of the critical vital signs coordinating the function of the cardiovascular, respiratory, and muscular systems, it is an indicator of overall body health status (Kaminsky et al., 2017). Quantifying VO2max provides additional input regarding clinical decision-making, risk stratification, evaluation of therapy, and physical activity guidelines (Guazzi et al., 2012). For patients undertaking a test to exhaustion is rarely needed or possible due to health restraints or cardiac risk (Guazzi et al., 2016). For many years researchers have studied indirect methods of estimating VO2max(Sartor et al., 2013). Protocols such as the Astrand-Ryhming Test, Six-Minute Walk Test, or YMCA Step Test have been established and validated (Astrand and Ryhming, 1954; Beutner et al., 2015; Carey, 2022; Jalili et al., 2018). Moreover, estimation of the VO2 and heart rate (HR) values below the ventilatory threshold can be based on cardiorespiratory kinetics assessment using randomised changes in the work rate known as a pseudo-random binary sequences testing (Hoffmann et al., 2022). However, with the development of technology, the accessibility of laboratory testing and mobile testing improved (Montoye et al., 2020; Pritchard et al., 2021). Therefore, new opportunities to develop more precise yet simple and accessible methods and models to assess VO2max occur (Jurov et al., 2023). This appears to be especially important considering the low prediction accuracy of most of the VO2max formulae that were validated in our previous study (Wiecha et al., 2023). Recently, we have been observing the development of prediction methods with the usage of machine learning (ML) and artificial intelligence (AI) (Ashfaq et al., 2022). Both ML and AI are used in sport science as forecasting and decision-making support tools (Abut and Akay, 2015; Bobowik and Wiszomirska, 2022; Chmait and Westerbeek, 2021; Hammes et al., 2022; Rossi et al., 2021). There is growing evidence that VO2max prediction based on ML models, especially support vector ML and artificial neural network models, exhibits more robust and accurate results compared to MLR only (Abut and Akay, 2015; Ashfaq et al., 2022). Therefore, in this research, with the support of ML, we look for algorithms and prediction patterns that allow us to use values obtained during submaximal CPET and somatic measurements to estimate maximal VO2max values in male runners and cyclists. We stipulate that prediction models allow for accurate calculation of VO2max based on somatic or submaximal CPET variables. Materials and methods We have applied the development and validation of the prediction TRIPOD guidelines to conduct the study (see Supplementary Material 1TRIPOD Checklist for Prediction Model Development and Validation) (Collins et al., 2015). The study is based on retrospective data analysis from the CPET registry collected from 2013 to 2021 at the medical clinic (Sportslab, Warsaw, Poland). All CPET have been performed at the individual request of participants, as a part of regular training monitoring or performance assessment. Ethical approval Request a detailed protocol The Institutional Review Board of the Bioethical Committee at the Medical University of Warsaw (AKBE/32/2021) has approved the study protocol. The regulations of the Declaration of Helsinki were met during all parts of the study. Each study participant delivered written consent to undergo CPET and participate in the study. Derivation cohort Request a detailed protocol We selected the cohort with the use of rigorous exclusion/inclusion criteria. Due to the insufficient number of women in our database and the number of potential variables in the regression models for adequate power, we had to limit ourselves to conduct analysis in the male population only (Martens and Logan, 2021). Out of 6439 healthy, adult male cyclists and long-distance runners that undergone CPET, 4423 met the criteria as further: (1) age ≥18 years, (2) declared regular cycling or running training for ≥3 months, (3) had no extreme outliers ≤ or ≥±3 standard deviations (SD) from mean for all of the testing variables (beyond ≥±3 SD in VO2max), (4) lack of any injury, medical condition, or addiction in medical history that may affect exercise capacity, (5) not taking any medications with a modifying effect on exercise capacity, (6) maximum exertion achieved during CPET. We defined the maximum exertion in CPET as the fulfilment of the minimum six of the following criteria: (1) respiratory exchange ratio (RER) ≥1.10, (2) present VO2 plateau (growth <100 mL·min–1 in VO2 despite increased running speed or cycling power), (3) respiratory frequency (fR) ≥45 breaths·min–1, (4) declared subjective exertion intensity during CPET ≥18 in the Borg scale (Borg, 1970), (5) blood lactate concentration [La-]b ≥8 mmol·L–1, (6) growth in speed/power ≥10% of respiratory compensation point (RCP) values after exceeding the RCP, (7) peak heart rate (HRpeak) ≥15 beats·min–1 below predicted maximal heart rate (HRmax) (Lach et al., 2021). Participants’ selection procedure has been shown in Figure 1. Figure 1 Download asset Open asset Flowchart of the preliminary inclusion and exclusion process. Abbreviations: EA, endurance athlete; CPET, cardiopulmonary exercise testing; SD, standard deviation; TE, treadmill; RER, respiratory exchange ratio; VO2, oxygen uptake (mL·min−1·kg−1); [La−]b, lactate concentration (mmol·L−1); fR, breathing frequency (breaths·min−1); RCP, respiratory compensation point; HRpeak, peak heart rate (beats·min−1); HRmax, maximal heart rate (bpm). At both stages of the selection, some participants met several (>1) exclusion criteria. Somatic measurements and CPET protocols Request a detailed protocol Body mass was measured with a body composition (BC) analyser (Tanita, MC 718, Japan) with the multifrequency of 5 kHz/50 kHz/250 kHz via the bioimpedance analysis and normal testing mode. The participants’ skin was cleaned with alcohol before placing the electrodes on the skin. Prior to the test, the participants received instructions to refrain from exercising for 2 hr, consume a light meal rich in carbohydrates 2–3 hr beforehand, and maintain hydration by drinking isotonic beverages. Additionally, they were advised to abstain from medications, caffeine, and cigarettes on the day of the test. Running CPET (TE) was performed on a mechanical treadmill (h/p/Cosmos Quasar, Germany). Cycling CPET (CE) was performed on Cyclus-2 (RBM elektronik-automation GmbH, Leipzig, Germany). Hans Rudolph V2 mask (Hans Rudolph, Inc, Shawnee, KS, USA), breath-by-breath method with Cosmed Quark CPET gas exchange analysing device (Cosmed Srl, Rome, Italy), and Quark PFT Suite to Omnia 1.6 software were utilised. The gas analyser device was regularly calibrated with the reference gas (16% O2; 5% CO2) in accordance with the manufacturer’s instructions (Airgas USA, LLC, Plumsteadville, PA, USA). From 2013 to 2021, three Cosmed Quark CPET units were used. HR was measured with the Cosmed torso belt (Cosmed srl, Rome, Italy). [La-]b was measured via enzymatic-amperometric electrochemical technique with Super GL2 analyser (Müller Gerätebau GmbH, Freital, Germany). The [La-]b analyser was regularly calibrated before each measurement series. The 40 m2 indoor, air-conditioned laboratory with 20–22°C temperature and 40–60% humidity, and 100 m ASL provided the same conditions for all BC and CPET. Each CPET began with a 5 min personalised warm-up (walk or easy jog with ‘conversational’ intensity for running, easy pedalling with ‘conversational’ intensity for cycling). Then after the preparation (about 5 min), the continuous progressive step test was conducted. Due to the population diversity (training status), the running test speed started from 7 to 12 km·hr–1 with a 1% treadmill incline. The choice of initial starting speed was determined by the interview and sports results achieved. For example, those running less than 60 min at a distance of 10 km started the test at 7 km/hr, while those running 10 km for less than 35 min started the test at an initial speed of 12 km/hr. The pace increased by 1 km·hr–1 every 2 min with no change in incline. The cycling test began at 60–150 W, depending on the athletes training status. The power increased by 20–30 W every 2 min. It was recommended to maintain a constant cadence of 80–90 (repetition·min–1) during the test. The tests were terminated due to exhaustion: volitional inability to continue the activity or/and VO2 and HR plateau with increasing load or/and observed disturbance of coordination in running or/and inability to maintain the set cadence. Due to the graded protocol used, the cycling power and running speed values have been calculated as a function of time to better reflect the actual level for the test moment being determined (Kuipers et al., 1985). Before the test, after every step, and 3 min after the termination of the effort technician took a 20 µL blood sample from a fingertip. Samples were collected during the test without interrupting the effort. The samples were taken from the initial puncture. The first blood drop was collected into the swab and the second blood drop was drawn for further analysis into the capillary. VO2max was recorded as the highest value (15 s intervals) before the termination of the test. HRmax was recorded as the highest value obtained at the end of the test, without averaging. The anaerobic threshold (AT) was established with the following criteria: (1) common start of VE/VO2 and VE/VCO2 curves, (2) end-tidal partial pressure of oxygen raised constantly with the end-tidal partial pressure of carbon dioxide (Beaver et al., 1986). The was established with the following criteria: (1) PetCO2 must decrease after reaching maximal amount, (2) the presence of fast nonlinear growth in VE (second deflection), (3) the VE/VCO2 ratio achieved minimum and started to rise, and (4) a nonlinear increase in VCO2 versus VO2 (lack of linearity) (Beaver et al., 1986). The [La-]b was estimated for AT and RCP in relation to power or speed (Wiecha et al., 2022). Data analysis Request a detailed protocol Our comprehensive ML approach enables the evaluation of each formula by preliminary variables precision (at the stage of selection), then accuracy (during the model’s building) and recall (in internal validation). Individual CPET results were saved into the Excel file (Microsoft Corporation, Redmond, WA, USA) and a custom-made script was used to generate the database in Excel (Python programming). Further, mean, SD, and 95% confidence intervals (CI) were calculated. The normality of the distribution of the data was examined using the Shapiro-Wilk test and intergroup differences were calculated using the Student’s t-test for independent variables. Three-step variable selection procedures based on random forests were applied using the R package VSURF in RStudio software (R Core Team, Vienna, Austria; version 3.6.4) (Genuer et al., 2016). For each level of measurement (AT, RCP) and their combination (AT+RCP), significant variables were identified separately. The first step was dedicated to eliminate irrelevant variables from the dataset. Second step aimed to select all variables related to the response for interpretation purposes. The third step refined the selection by eliminating redundancy in the set of variables selected by the second step, for prediction purposes (Genuer et al., 2017). Each time for variables selection, the anthropometric variables as in Tables 1–2 and the CPET parameters given in Tables 3–4 from a specific level of measurement (AT; RCP) and their combinations were visible. Table 1 Basic anthropometric characteristics for runners. Variable (unit)Derivation group n=1998Testing group n=666Validation group n=666MeanCISDMeanCISDMeanCISDAge (years)36.235.6–36.98.4535.935.5–36.38.0535.534.9–36.28.14Height (cm)180.0179.6–180.56.04179.4179.1–179.76.13179.7179.2–180.26.61BM (kg)77.777.0–78.49.3577.777.3–78.19.2977.977.1–78.610.1BMI (kg·m–2)23.923.8–24.12.4324.124.0–24.22.4124.123.9–24.32.56BF (%)15.415.1–15.74.5515.515.3–15.74.5215.415.1–15.84.55FM (kg)12.211.9–12.64.6812.312.1–12.54.6512.311.9–12.74.92FFM (kg)65.565.0–66.06.4365.465.1–65.76.3165.665.1–66.16.86 BM, body mass; BMI, body mass index; BF, body fat; FM, fat mass; FFM, fat-free mass; CI, 95% confidence interval; SD, standard deviation. Table 2 Basic anthropometric characteristics for cyclists. Variable (unit)Derivation group n=656Testing group n=219Validation group n=219MeanCISDMeanCISDMeanCISDAge (years)37.336.6–38.09.1337.135.9–38.49.5037.636.5–38.88.46Height (cm)179.9179.4–180.46.27180.1179.2–181.06.96180.2179.4–181.06.13BM (kg)78.878.1–79.69.8079.177.7–80.510.479.878.4–81.310.9BMI (kg·m–2)24.324.1–24.62.6324.424.0–24.72.8024.624.2–25.02.96BF (%)16.415.7–17.14.9916.115.7–16.54.8116.215.5–16.84.87FM (kg)13.312.6–14.15.6613.012.6–13.45.2713.312.5–14.05.85FFM (kg)65.864.9–66.66.2565.865.4–66.36.0666.665.7–67.46.58 BM, body mass; BMI, body mass index; BF, body fat; FM, fat mass; FFM, fat-free mass; CI, 95% confidence interval; SD, standard deviation. Table 3 Cardiopulmonary exercise testing (CPET) characteristics for runners. Variable (unit)Derivation group n=1998Testing group n=666Validation group n=666MeanCISDMeanCISDMeanCISDrVO2AT (mL·min–1·kg–1)38.438.1–38.85.0138.538.3–38.74.8838.137.7–38.55.16RERAT0.870.86–0.870.040.870.86–0.870.040.870.86–0.870.04HRAT (beats·min–1)151.5150.8–152.310.3151.0150.6–151.510.8152.0151.2–152.810.8VEAT (L·min–1)79.178.1–80.012.278.377.8–78.912.077.276.3–78.212.0SPEEDAT (km·h–1)11.010.9–11.11.4511.011.0–11.11.3610.910.8–11.01.42LAAT (mmol·L–1)2.082.02–2.140.631.801.76–1.830.622.352.27–2.420.72rVO2RCP (mL·min–1·kg–1)47.547.0–48.05.8847.747.4–48.06.1547.346.8–47.86.16RERRCP1.001.00–1.000.041.001.00–1.000.041.001.00–1.000.03HRRCP (beats·min–1)173.4172.7–174.19.21173.2172.8–173.69.30174.3173.5–175.09.50VERCP (L·min–1)114.7113.5–116.015.9113.9113.1–114.616.7112.7111.4–114.016.2SPEEDRCP (km·h–1)14.013.9–14.11.7714.114.0–14.11.7013.913.8–14.11.75LARCP (mmol·L–1)4.724.63–4.821.044.404.34–4.451.044.814.69–4.931.14rVO2max (mL·min–1·kg–1)53.853.3–54.36.6754.354.0–54.66.9553.853.3–54.37.09 CI, 95% confidence interval; SD, standard deviation; rVO2AT, oxygen uptake at anaerobic threshold relative to body mass; RERAT, respiratory exchange ratio at anaerobic threshold; HRAT, heart rate at anaerobic threshold; VEAT, pulmonary ventilation at anaerobic threshold; SPEEDAT, velocity at anaerobic threshold; LAAT, blood lactate concentration at anaerobic threshold; rVO2RCP, oxygen uptake at respiratory compensation point relative to body mass; RERRCP, respiratory exchange ratio at respiratory compensation point; HRRCP, heart rate at respiratory compensation point; VERCP, pulmonary ventilation at respiratory compensation point; SPEEDRCP, velocity at respiratory compensation point; LARCP, blood lactate concentration at respiratory compensation point; rVO2max, maximal oxygen uptake relative to body mass. Table 4 Cardiopulmonary exercise testing (CPET) characteristics for cyclists. Variable (unit)Derivation group n=656Testing group n=219Validation group n=219MeanCISDMeanCISDMeanCISDrVO2AT (mL·min–1·kg–1)33.032.5–33.45.8433.232.4–33.95.6833.732.9–34.55.89RERAT0.870.87–0.870.040.870.87–0.880.040.870.87–0.880.04HRAT CI, 95% confidence interval; SD, standard deviation; rVO2AT, oxygen uptake at anaerobic threshold relative to body mass; RERAT, respiratory exchange ratio at anaerobic threshold; HRAT, heart rate at anaerobic threshold; VEAT, pulmonary ventilation at anaerobic threshold; power at anaerobic threshold relative to body mass; LAAT, blood lactate concentration at anaerobic threshold; rVO2RCP, oxygen uptake at respiratory compensation point relative to body mass; RERRCP, respiratory exchange ratio at respiratory compensation point; HRRCP, heart rate at respiratory compensation point; VERCP, pulmonary ventilation at respiratory compensation point; LARCP, blood lactate concentration at respiratory compensation point; power at respiratory compensation point relative to body mass; rVO2max, maximal oxygen uptake relative to body mass. selection variables were in the further only selected parameters were into multiple linear regression The data for MLR model building were randomly into that is testing, validation and of the a only significant were in the Derived are by the of mean and mean analysis was used to the model’s precision and accuracy during validation and tests to the fulfilment of MLR test the of in MLR test assessment between and test of Each model was examined the and any have not been 2 package in RStudio (R Core Team, Vienna, Austria; version version for and software version were used in was considered as the Results Somatic measurements and CPET results data of the runners models for testing, and validation are in Table while cyclists are in Table The runners of and for testing, and validation the cyclists and differences between of runners and cyclists were in BMI and between testing in all between validation only in CPET results for runners models are in Table 3 and for cyclists in Table Runners in the cohort achieved relative to body mass VO2max of in testing group and in validation group cyclists mean was and for testing, and validation to body mass oxygen uptake at anaerobic threshold in runners for ± ± and ± of in testing, and validation it was ± ± and ± of rVO2max, relative to body mass oxygen uptake at respiratory compensation point in runners for ± ± and ± of for testing, and validation while in cyclists for ± ± and ± of rVO2max, There were no significant differences in values between testing, and validation the runners and cyclists between runners and cyclists results were all significant Prediction models based on AT and RCP Full of MLR prediction models for cyclists are in Table for runners in Table The models prediction performance is as with and for cyclists from for somatic parameters to for RCP equations. For runners from for to for AT and equations. for cyclists models was the for RCP and the highest for For from for AT and to for equation. observed for cyclists was the for RCP in the validation group and the highest for while in runners the for AT and and the highest for The performance of prediction is in Figure Figure 2 Download asset Open asset of prediction for Abbreviations: maximal oxygen anaerobic threshold; RCP, respiratory compensation point; All values are in performance for running while the performance for cycling equations. performance of the prediction model for for for AT and for somatic-only equation. Table 5 VO2max prediction for cyclists. linear regression group group = = = = based on anaerobic threshold; RCP, based on respiratory compensation point; based on somatic variables mean mean maximal oxygen uptake relative to body mass rVO2AT, oxygen uptake at anaerobic threshold relative to body mass power at anaerobic threshold relative to body mass rVO2RCP, oxygen uptake at respiratory compensation point relative to body mass VERCP, pulmonary ventilation at respiratory compensation point BF, body fat BM, body mass Table VO2max prediction for runners. linear regression group group = = = = based on anaerobic threshold; RCP, based on respiratory compensation point; based on somatic variables mean mean maximal oxygen uptake relative to body mass rVO2AT, oxygen uptake at anaerobic threshold relative to body mass SPEEDAT, velocity at anaerobic threshold FFM, fat mass VEAT, pulmonary ventilation at anaerobic threshold HRAT, heart rate at anaerobic threshold BF, body fat rVO2RCP, oxygen uptake at respiratory compensation point relative to body mass SPEEDRCP, velocity at respiratory compensation point Models validation of each model for cyclists is in Table while for runners in Table the performance of our prediction was to that observed in the

<i>Circulation: Heart Failure</i> Editors’ Picks

Power of Outcome Measurements to Detect Clinically Significant Changes in Pulmonary Rehabilitation of Patients With COPD

The Tidal Volume Response to Incremental Exercise in COPD

Background— Children and young adults with single-ventricle physiology have abnormal exercise capacity after the Fontan operation. A medication capable of decreasing pulmonary vascular resistance should allow improved cardiac filling and improved exercise capacity. Methods and Results— This study was a double-blind, placebo-controlled, crossover trial conducted in children and young adults after Fontan. Subjects were randomized to receive placebo or sildenafil (20 mg three times daily) for 6 weeks. After a 6-week washout, subjects crossed over for an additional 6 weeks. Each subject underwent an exercise stress test at the start and finish of each phase. After taking sildenafil, subjects had a significantly decreased respiratory rate and decreased minute ventilation at peak exercise. At the anaerobic threshold, subjects had significantly decreased ventilatory equivalents of carbon dioxide. There was no change in oxygen consumption during peak exercise, although there was a suggestion of improved oxygen consumption at the anaerobic threshold. Improvement at the anaerobic threshold was limited to the subgroup with single left or mixed ventricular morphology and to the subgroup with baseline serum brain natriuretic peptide levels ≥100 pg/mL. Conclusions— In this cohort, sildenafil significantly improved ventilatory efficiency during peak and submaximal exercise. There was also a suggestion of improved oxygen consumption at the anaerobic threshold in 2 subgroups. These findings suggest that sildenafil may be an important agent for improving exercise performance in children and young adults with single-ventricle physiology after the Fontan operation. Clinical Trial Registration— URL: http://clinicaltrials.gov . Unique identifier: NCT00507819.

BackgroundChronic obstructive pulmonary disease (COPD) often involves the cardiopulmonary dysfunction that deteriorates health-related quality of life (HRQL) and exercise capacity. Work efficiency (WE) indicates the efficiency of overall oxygen consumption (VO2) during exercise. This study investigated whether different WEs have different effects on pulmonary rehabilitation (PR).MethodsForty-five patients with stable COPD were scheduled for PR. The PR programs consisted of twice-weekly sessions for three months. These patients were comprehensively evaluated by cardiopulmonary exercise testing and COPD assessment test (CAT) before and after PR. We compared these parameters between patients with a normal versus poor WE.ResultsTwenty-one patients had a normal WE and twenty-four patients had a poor WE (<8.6 mL/min/watt). Patients with a poor WE had earlier anaerobic metabolism, a poorer oxygen pulse, lower exercise capacity, more exertional dyspnea, and a poorer HRQL than those with a normal WE. PR improved exercise capacity, HRQL, anaerobic threshold, exertional dyspnea and leg fatigue in patients with either normal or poor WE. However, significant improvement of WE, oxygen pulse, respiratory frequency (Rf) during exercise, chest tightness, activity and sleepiness by CAT were noted only in patients with a poor WE. Among the patients with a poor WE, 29% patients had WE returned to normal after PR.ConclusionPatients with different WE had different responses to PR. PR improved exercise capacity and HRQL regardless of a normal or poor WE. However, WE, oxygen pulse, Rf during exercise, chest tightness, activity and sleepiness were only improved in patients with a poor WE.

BackgroundLung cancer significantly impairs exercise capacity and health-related quality of life (HRQL). Pulmonary rehabilitation (PR) has demonstrated positive effects on exercise capacity and HRQL in lung cancer patients. However, its impact on cardiopulmonary function needs further exploration. The aim of this study was to explore the effects of PR on cardiopulmonary function, exercise capacity and HRQL in patients with lung cancer.MethodsPatients with lung cancer were enrolled in a 12-week PR program. Each participant underwent a thorough evaluation, which included spirometry, cardiopulmonary exercise testing, respiratory muscle strength test, and evaluation of HRQL using the Chronic Obstructive Pulmonary Disease Assessment Test (CAT).ResultsFifty-six patients completed the PR program. Following PR, exercise capacity significantly improved, as evidenced by increased peak oxygen uptake and work rate (both p < 0.05). Exertional symptoms were notably reduced, including leg soreness and dyspnea at peak exercise, accompanied by a decrease in the CAT score (all p < 0.05). Furthermore, improvements in cardiopulmonary function were observed, encompassing respiratory muscle strength, ventilatory equivalent, tidal volume, stroke volume index, and cardiac index at peak exercise (all p < 0.05).ConclusionsPR demonstrated notable enhancements in cardiopulmonary function, exertional symptoms, exercise capacity, and HRQL in patients with lung cancer.

The oxygen uptake to work rate (VO2/WR) relationship observed throughout peak exercise testing is already being applied for rate adaptive pacemaker programming. However, the detailed curve design of VO2/WR with respect to the anaerobic threshold (AT) has not yet been investigated. It was the purpose of this study to determine the VO2/WR slope below and above the AT in a healthy control group. Seventy-eight healthy control subjects (45.9 +/- 17.4 years; 34 women: 49.9 +/- 18.6 years 44 men: 43.6 +/- 16.6 years) were exercised on a treadmill with "breath-by-breath" gas exchange monitoring using the symptom limited "ramping incremental treadmill exercise" (RITE) protocol. The slope of the VO2/WR relationship from rest to peak exercise (r-p), rest to AT (slope A), and AT to peak exercise (slope B) in mL oxygen uptake per watt of external treadmill work was determined by linear regression analysis. [table: see text] The oxygen uptake to work rate relationship throughout peak exercise in the entire study group generated a significant slope change at the AT (31%, P < 0.0001) with a decreasing slope during higher work load intensities. Female subjects demonstrated a greater percentage of slope change at AT (43%), as compared to men (22%, P < 0.01). When using the oxygen uptake to work rate relationship for the programming of the pacemaker's rate response to exercise, the significant slope change at the AT should be considered to more appropriately pace during higher work intensities supported by anaerobic metabolism. Female pacemaker patients should be programmed to generate a steeper VO2/WR slope below AT with a greater slope change at AT, as compared to men. Abnormally high oxygen uptake to work rate ratios above the AT may be possibly used as an indicator of overpacing.

Steep ramp test protocol for preoperative risk assessment and short-term high-intensity interval training to evaluate, improve, and monitor cardiorespiratory fitness in surgical oncology.

To investigate the safety and effects of early submaximal cardiopulmonary exercise test (CPET) and cardiac rehabilitation for patients with acute myocardial infarction (AMI) after percutaneous coronary intervention (PCI). 94 patients with AMI after PCI were randomly divided into 2 groups: exercise group undergoing anaerobic rehabilitation training based on anaerobic threshold (AT) exercise prescription for 3 months, and control group, conducting exercise according to the needs of the patients themselves. Three months later, the exercise cardiopulmonary function was evaluated. In the first CPET 89 patients attained their anaerobic threshold (AT) and their heart rates were lower than their target heart rates following the exercise test. The oxygen consumption at the anaerobic threshold (VO2AT) 3 months later of the exercise group was [(12.6 +/- 2.9) ml x min(-1) x kg(1)], significantly greater and that before the exercise [(10.5 x 2.9) ml x min x kg(-1), P = 0.000]. The peak oxygen uptake (VO2 pea) 3 months of the exercise group was (20 +/- 4) ml x min(-1) x kg(-1), signficantly greater then that before exercise [(14 +/- 4) ml x min(-1) x kg(-1), P = 0.000]. The LAT 3 months of the exercise group was (42 +/- 16) J x s(-1), significantly higher than that before exercise p [(33 +/- 20) J x s(-1), P = 0.000]. The workload at peak level (Lpeak) 3 months of the exercise group was (89 +/- 14) J x s(-1) significantly greater than thatbefore exercise [(66 +/- 21) J x s(-1), P = 0.000]. And the VO2pea and Lpeak of 3 months later of the control group were [(19 +/- 4) ml x min(-l) x kg(-1)) and (80 +/- 14) J x s(-1)] respectively, both significantly higher than those before exercise [(14 +/- 4) ml x min(-1) x kg(-1) and (64 +/- 21) J x s(-1), both P = 0.000]. The early submaximal CPET and cardiac rehabilitation for patients with AMI after PCI are not only safe but also can improve their exercise capacity.

Objective Patients with normal ejection fraction (EF) by conventional echocardiography may present with symptoms and fi ndings of decreased cardiac functional capacity. We aimed to investigate the association between cardiac functional capacity determined by cardiopulmonary exercise test (CPET) and parameters of tissue Doppler (TD) imaging in patients with normal EF.Methods In all, 52 patients with normal EF were included. Conventional and TD imaging were performed. Peak systolic (S), early (E’) and late (A’) diastolic velocities were obtained from septal and lateral mitral annulus and tricuspid annulus by pulsed-wave TD. CPET was performed. Exercise time, peak oxygen consumption (peak VO2), anaerobic threshold (AT), metabolic equivalents (MET) values were determined and were compared with TD imaging parameters.Results We did not fi nd any association between conventional echocardiographic measurements and cardiac functional capacity. However, peak S, E’ and A velocity from the septal and tricuspid annulus and E’ velocity from the lateral annulus correlated with exercise time, peak VO2, AT and MET (all P <0.05). E/E’ from the left ventricle correlated inversely with exercise time, peak VO2, AT and MET (all P <0.05). S, E’, A’ velocities from septal and tricuspid annulus, E’ velocity from lateral annulus were lower in patients with MET ≤ 7 than in patients with MET > 7 (all P <0.05). Conclusion Systolic and diastolic velocities measured by TD imaging correlated with cardiac functional capacity as determined by CPET in patients with normal EF by conventional echocardiography. TD imaging could be more susceptible to determine cardiac functional capacity in these patients.

<bold>Objective</bold> To observe the effect of cardiac exercise rehabilitation program based on cardiopulmonary exercise testing (CPET) on patients with stable coronary heart disease complicated with hypertension. <bold>Methods</bold> A total of 80 patients with stable coronary artery disease complicated with hypertension in the Xuzhou Central Hospital from February to September 2023 were randomly divided into control group and observation group, with 40 cases in each group. The control group received routine medication for 12 weeks. The observation group received cardiac exercise rehabilitation program based on CPET in addition to the treatment of the control group, including aerobic exercise and resistance training, once every other day, 3 times a week for 12 weeks. Before and after treatment, CPET was used to evaluate cardiac autonomic function [such as rest heart rate (HR_rest), rest systolic blood pressure (SBP_rest), rest diastolic blood pressure (DBP_rest), peak heart rate (HR_peak), peak systolic blood pressure (SBP_peak), peak diastolic blood pressure (DBP_peak), heart rate recovery from the 1st to 6th min (HRR_t)]. CPET was used to evaluate cardiopulmonary function and exercise ability [peak metabolic equivalent (METs_peak), peak work (Work_peak), peak oxygen pulse (VO₂/HR_peak), peak oxygen uptake (VO_{2 peak}), anaerobic threshold (AT) and carbon dioxide ventilation equivalents slope (VE/VCO_{2 slope})]. The 36-item short-form health survey (SF-36) was used to evaluate quality of life. Pittsburgh sleep quality index (PSQI) was used to evaluate sleep quality. <bold>Results</bold> Compared with that before treatment, SBP_rest, DBP_peak, HR_rest, HR_peak and VE/VCO_{2 slope} in the observation group decreased significantly after treatment, HRR₁-HRR₆, VO_{2 peak}, AT, VO₂/HR_peak, METs_peak, Work_peak, SF-36 scores (physical functioning, role physical, bodily pain, general health, social functioning and mental health) increased significantly, PSQI score decreased significantly (<italic>P</italic>&lt;0.05). Compared with the control group, SBP_rest, DBP_peak, HR_rest and VE/VCO_{2 slope} in the observation group were significantly lower after treatment, HRR₁-HRR₄, VO_{2 peak}, AT, VO₂/HR_peak, METs_peak, Work_peak, SF-36 scores (physical functioning, role physical, bodily pain, general health, social functioning and mental health) were significantly higher after treatment (<italic>P</italic>&lt;0.05), PSQI score decreased significantly (<italic>P</italic>&lt;0.05). <bold>Conclusion</bold> The cardiac exercise rehabilitation program based on CPET can improve cardiac autonomic function, cardiopulmonary function, exercise ability, sleep and quality of life of patients with stable coronary heart disease complicated with hypertension, which is recommended for clinical application.