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The stress test is key to the clinical evaluation and management of patients with known or potential cardiovascular disease. By measuring the heart's ability to respond to external stress, it can provide vital insights into the general physical condition of patients, highlighting abnormalities in blood flow, risk of coronary artery disease, and more. The Pocket Guide to Stress Testing gives cardiology professionals a complete breakdown of this everyday procedure that they can carry with them and consult on the go. This second edition has been fully revised to reflect the most up-to-date information available on the best approaches to conducting and interpreting various forms of stress test. With chapters spanning topics such as testing guidelines, nuclear imaging techniques, and emergency and aftercare protocols, the clear and practical contents cover all aspects of the subject.

Purpose/background Prehabilitation aims to improve physical condition in the preoperative period and, therefore, decrease the loss of cardiopulmonary capacity postoperatively, with the aim of reducing complications and promoting an early recovery. This study aims to evaluate the impact of home-based prehabilitation on the physical condition of patients treated surgically for colorectal cancer. Methods A prospective and randomized clinical study was conducted on 60 patients during two periods from October 2018 to February 2019 and from September 2019 to September 2020, in a single university hospital. Patients were randomized into two study groups (30 per group): prehabilitation vs. standard care. Changes in physical condition, measured at diagnosis, the day before surgery, and at 6–8 weeks after surgery using the cardiopulmonary exercise testing (CPET) and the 6-minute walk test (6MWT) were evaluated. Results Prehabilitation reduced postoperative complications (17.4% vs. 33.3%, p = 0.22) and hospital stay (5.74 vs. 6.67 days, p = 0.30). 6MWT showed a significant improvement in the prehabilitation group (+78.9 m). Six weeks after surgery, prehabilitation showed a significant improvement in the 6MWT (+68.9 m vs. −27.2 m, p = 0.01). Significant differences were also observed in the ergospirometry between the diagnosis and postoperative study (+0.79 METs vs. −0.84 METs, p = 0.001). A strong correlation was observed between CPET and 6MWT (0.767 ( p < 0.001)). Conclusion Home prehabilitation achieved lower overall postoperative complications than standard care and reached significant improvements in 6MWT and CET. A strong correlation was observed between CET and 6MWT, which allows validation of 6MWT as a valid and reliable measure of functional exercise capacity in colorectal patients when other, more specific and expensive tests are not available. Trial registration Registered in ClinicalTrials.gov in August 2018 with registration number https://clinicaltrials.gov/study/NCT03618329?cond=Prehabilitation%20cancer&term=arroyo&distance=50&rank=1  (NCT03618329). Initial results published in Supportive Care in Cancer: Effect of home-based prehabilitation in an enhanced recovery after surgery program for patients undergoing colorectal cancer surgery during the COVID-19 pandemic. DOI: https://doi.org/10.1007/s00520-021-06343-1 .

Exercise limitation in COVID-19 survivors is poorly explained. In this retrospective study, cardiopulmonary exercise testing (CPET) was coupled with an oxidative stress assessment in COVID-19 critically ill survivors (ICU group). Thirty-one patients were included in this group. At rest, their oxygen uptake (VO2) was elevated (8 [5.6–9.7] mL/min/kg). The maximum effort was reached at low values of workload and VO2 (66 [40.9–79.2]% and 74.5 [62.6–102.8]% of the respective predicted values). The ventilatory equivalent for carbon dioxide remained within normal ranges. Their metabolic efficiency was low: 15.2 [12.9–17.8]%. The 50% decrease in VO2 after maximum effort was delayed, at 130 [120–170] s, with a still-high respiratory exchange ratio (1.13 [1–1.2]). The blood myeloperoxidase was elevated (92 [75.5–106.5] ng/mL), and the OSS was altered. The CPET profile of the ICU group was compared with long COVID patients after mid-disease (MLC group) and obese patients (OB group). The MLC patients (n = 23) reached peak workload and predicted VO2 values, but their resting VO2, metabolic efficiency, and recovery profiles were similar to the ICU group to a lesser extent. In the OB group (n = 15), no hypermetabolism at rest was observed. In conclusion, the exercise limitation after a critical COVID-19 bout resulted from an altered metabolic profile in the context of persistent inflammation and oxidative stress. Altered exercise and metabolic profiles were also observed in the MLC group. The contribution of obesity on the physiopathology of exercise limitation after a critical bout of COVID-19 did not seem relevant.

Limited data are available on athlete’s heart for rugby athletes. This study aimed to investigate cardiac structure and its relationship with cardiorespiratory fitness in young Japanese rugby athletes. A prospective cross-sectional study using echocardiography and cardiopulmonary exercise testing (CPET) was conducted on 114 male collegiate rugby players. There was a higher prevalence of increased left ventricular (LV), atrial, and aortic dimensions in the young athletes than that in previously published reports, whereas the wall thickness was within the normal range. Anthropometry and CPET analyses indicated that the forwards and backs presented muscular and endurance phenotypes, respectively. Indexed LV and aortic dimensions were significantly larger in the backs than in the forwards, and the dimensions significantly correlated with oxygen uptake measured by CPET. On the four-tiered classification for LV hypertrophy, abnormal LV geometry was found in 16% of the athletes. Notably, the resting systolic blood pressure was significantly higher in athletes with concentric abnormal geometry than in the other geometry groups, regardless of their field positions. Japanese young athletes may exhibit unique phenotypes of cardiac remodeling in association with their fitness characteristics. The four-tiered LV geometry classification potentially offers information regarding the subclinical cardiovascular risks of young athletes.

ObjectivesWe explored relationships between biochemical markers and cardiac responses of children with and without obstructive sleep apnoea (OSA) during exercise. We hypothesised that serum markers of sympathetic nervous system activity and low-grade inflammation would correlate with cardiac responses to exercise in children with or without OSA.MethodologyThe study included 40 of 71 children with previously characterised responses to cardiopulmonary exercise testing. Measures included serum cytokine levels using a multiplex bead-based assay (interleukins IL-1β, IL-2, IL-4, IL-6, IL-8, IL-10, TNF-α and IFN-γ). Serum amyloid A (SAA) was quantified by nephelometry, and metanephrine/normetanephrine levels were measured by liquid chromatography, mass-spectroscopy. Comparisons were made between children with and without OSA, and with and without obesity. Relationships between biomarkers and various cardiac parameters were explored by linear regression.ResultsAmongst the 40 children in this study, OSA was present in 23. Compared to the 17 children without OSA, those with OSA had higher resting serum IL-6 levels compared to those without (median 3.22 pg/ml vs. 2.31, p < 0.05). Regarding correlations with cardiac function after adjusting for OSA, IL-8 negatively correlated to heart rate (HR) response following exercise (p = 0.03) and IFN-γ negatively correlated with Stroke Volume Index (SVI) (p = 0.03). Both metanephrine and normetanephrine levels positively correlated with SVI (p = 0.04, p = 0.047; respectively) and QI (p = 0.04, p = 0.04; respectively) during exercise when adjusting for OSA.ConclusionsChildren with OSA have raised morning levels of serum IL-6. Separately, higher levels of IFN-γ and IL-8 and lower levels of metanephrine and normetanephrine related to poorer cardiac function during exercise.

This 2001 book provides a practical and systematic approach to the acquisition, interpretation, and reporting of physiologic responses to exercise. Pulmonologists, cardiologists, and sports physicians, as well as respiratory therapists and other allied health professionals will find this book an indispensable resource when learning to select proper instruments, identify the most appropriate test protocols, and integrate and interpret physiologic response variables. The final chapter presents clinical cases to illuminate useful strategies for exercise testing and interpretation. Useful appendices offer laboratory forms, algorithms and calculations, as well as answers to FAQs. A glossary of terms, symbols, and definitions is also included. Exercise Testing and Interpretation: A Practical Approach offers clearly defined responses (both normal and abnormal) to over thirty performance variables including aerobic, cardiovascular, ventilatory, and gas-exchange variables. Practical, portable, and easy-to-read, this essential guidebook can be used as a complement to more detailed books on the topic, or stand on its own.

Meijer R, van Hooff M, Papen-Botterhuis NE. Int J Gen Med. 2022;15:3727—3737. The authors have advised that the Bland-Altman analysis presented in the published article requires a methodological clarification. During subsequent research involving individuals with long-COVID in an ongoing study, they observed a consistent underestimation of cardiorespiratory fitness (CRF) expressed in oxygen uptake during maximal exercise (VO2peak) by the self-reported questionnaires, as indicated by the raw data. However, the Bland-Altman analysis used in the validation study suggested overestimation of CRF—an inconsistency that warranted further examination. Upon re-evaluating their methodology, they identified the root cause of this discrepancy: the direction of subtraction used in the analysis. Specifically, they subtracted the questionnaire-derived predictions from the CPET (gold standard) values (CPET − prediction). While this is mathematically valid, the conventional approach in Bland-Altman analysis is to subtract the gold standard from the prediction (prediction − CPET), allowing for a clearer interpretation of bias direction.1,2 Adopting the conventional subtraction method provided results that aligned with the raw data, showing an underestimation of CRF, particularly in higher-performing individuals. In response to these findings, they have updated the Bland-Altman plots to reflect the conventional subtraction approach and revised the accompanying labels to explicitly indicate the direction of subtraction.1,2 These revisions enhance the clarity and transparency of their analysis while aligning with standard practices in Bland-Altman methodology. Moreover, they made small clarifications to the statistical model they used and removed a redundant sentence in the interpretation of the limits of agreement. Page 3727, Abstract, Results section, the text “Bias between predicted and measured VO2peak was -0.24 (−9.23‒8.75; 95% limits of agreement) mL·kg−1·min−1” should read “Bias between predicted and measured VO2peak was 0.24 (−8.75‒9.23; 95% limits of agreement) mL·kg−1·min−1”. Page 3730, first paragraph, line 3, the text “We performed stepwise selection with 10-fold cross-validation with 100 repeats, retaining 20% of the data at each loop for validation” should read “We performed stepwise selection using 10-fold cross-validation with 100 repeats, implemented with the repeatedcv method from the caret package in R. This means that for each repetition, the data was split in 10 equal-sized folds. Of this 10, each one was sequentially excluded and used to evaluate the model fit, obtained on the remaining 9 folds”. Page 3731, Table 1, the updated table should read as follows. Table 1 Participant Characteristics in the training and testing set, displayed separately Page 3732, Validation Prediction Model section, first paragraph, 3rd sentence, the text “Bias of the FitMáx was −0.24 mL·kg−1·min−1, which is smaller than the DASI (3.32 mL·kg−1·min−1) and VSAQ (3.44 mL·kg−1·min−1)” should read “Bias of the FitMáx was 0.24 mL·kg−1·min−1, which is smaller than the DASI (−3.32 mL·kg−1·min−1) and VSAQ (−3.44 mL·kg−1·min−1)”. Page 3732, Validation Prediction Model section, second paragraph, last sentence, the text “The density plots on the y-axis of the FitMáx indicate that most of the results from the subjects are within the 95% LoA” should be deleted. Page 3733, Figure 2, the updated figure is as follows. Figure 2 Scatterplots (A−C) show the relationship between predicted (FitMáx, DASI, and VSAQ) and measured VO2peak (in ml·min−¹·kg−¹). Each plot includes a solid line representing the best-fit linear regression (least squares); Bland-Altman plots (D−F) for DASI, VSAQ, and FitMáx illustrate the agreement between predicted and measured VO2peak (also in ml·min−¹·kg−¹), with a solid line for the mean bias, dashed lines for the limits of agreement (±1.96 SD), and a dotted line indicating zero bias. Histograms positioned above and to the right of each axes display the distribution of values per CPET indication. Colors indicate the reason for the CPET visit. Page 3733, Table 3, the updated table should read as follows. Table 3 Statistics validation of the prediction model including walking, stair climbing and cycling capacity separately. Page 3735, Abbreviations section, line 3, the text “FRIENDS” should read “FRIEND”. The authors recognize the importance of rigorous and transparent reporting in scientific research and sincerely apologize for any confusion caused by the initial presentation of these analyses. References 1. Bland JM, Altman DG. Statistical methods for assessing agreement bettheyen two methods of clinical measurement. Lancet. 1986;1(8476):307–310. 2. Kroutheyr JS. Why Bland-Altman plots should use X, not (Y+X)/2 when X is a reference method. Stat Med. 2008;27(5):778–780.

BackgroundThe most recent International Society for Heart Lung Transplantation (ISHLT) guidelines defines a maximal cardiopulmonary exercise test (CPET) as a respiratory exchange ratio (RER) of >1.05 with the achievement of anaerobic threshold (Mehra et al., 2016). Based off a maximal CPET, the ISHLT recommend a peak oxygen consumption (VO2peak) of ≤14 ml/min/kg for patient’s intolerant of beta-blockers and ≤12 ml/min/kg in the presence of a beta-blocker for guiding heart transplantation listing. Recently, Thomas and Sylvester (2020) demonstrated that a RER of 1.05 underestimated peak VO2peak and would impact patients’ risk stratification for surgery, or diagnostic outcomes. It is unknown whether this is true for patients with advanced heart failure referred for heart transplantation assessments.MethodsA retrospective analysis of patients with advanced heart failure that underwent a CPET as part of their heart transplant assessment between May 2019 and July 2021 was performed via cycle ergometry. Patients were included if they met all of the following: symptom-limited test, peak RER >1.15 and >6-minute test duration. Oxygen consumption (VO2), minute ventilation and heart rate were collected at RER’s of 1.00, 1.05, 1.10, 1.15, 1.20 and peak-exercise. A Friedman test was used to compare data across all RER points, while independent t-tests were used to compare differences in data between specific RER’s.ResultsA CPET was performed in 151 patients, of which 59 patients met the inclusion criteria. Baseline characteristics can be seen in Table 1. VO2 (ml/min/kg) significantly increased as RER increased from 1.05 to peak (p<0.001) (Figure 1). Based on ISHLT guidelines for using VO2peak to guide heart transplantation listing, inappropriate referrals for heart transplantation would have occurred in 5%, 26%, 29% and 31% of patients when taking VO2 at an RER of 1.05 compared to 1.10, 1.15, 1.20 and peak RER, respectively.Abstract 109 Figure 1Mean (±SEM) VO2 (ml/min/kg) across different RER's. * Significant difference 1.05. ‡ Significant difference versus 1.10Abstract 109 Table 1Baseline characteristics of patients referred for a heart transplant assessment and who met the inclusion criteriaSex (M:F) 45:14 Age (Years) 50 (14) BMI 27.5 (4.3) Test Time (minutes) 8.1 (1.89) Beta-Blocker Use (Y:N) 54:5 VO2 peak (ml/min/kg) 16.3 (5.2) VO2 peak (% predicted) 54.7 (17.0) Peak Heart Rate (bpm) 116 (25.5) Peak RER 1.24 (0.11) Data are reported as median (IQR). ConclusionIn our cohort of patients with advanced heart failure, VO2peak progressively increased in parallel to RER from 1.05 to peak. Using an RER of 1.05 to determine a maximal CPET in patients referred for heart transplantation will underestimate true peak VO2peak in a proportion of cases and may lead to inappropriate heart transplantation referrals.Conflict of InterestNone

Background Physical activity has been shown to reduce the risk of dementia and the pathological accumulation of amyloid in both animals and humans. One potential explanation for this outcome is that physical activity enhances glymphatic function. In this study we investigated whether a single session of physical exercise, could alter the glymphatic system, operationalized here as the visibility of perivascular spaces (PVS) on magnetic resonance imaging (MRI). Method In this prospective cohort study, we included 20 young participants (mean age 25.8±3.5 years, female 50%), who underwent repeated MRI scans at three different time points: baseline, immediately after cardiopulmonary exercise testing until exhaustion, and 24 hours later (Figure 1). We estimated PVS volumes in the centrum semiovale (CSO) and basal ganglia (BG) using a well‐validated software. For each subject, we first aligned all T2‐weighted images using FreeSurfer's mri_robust_template tool. Using SynthSeg on T1‐weighted images, we obtained white matter parcellations and aggregated them to create time‐point‐specific BG and CSO ROI masks. To ensure consistency across time points, we limited the analysis to regions that were consistent across all time points. We then segmented PVS on T2‐weighted images using the RORPO filter followed by thresholding. All segmentations were visually assessed and manually corrected. We tested for differences using the Wilcoxon signed‐rank test. Result PVS volumes measured at the three time points had high agreement with one another (Lin's concordance in BG ROI > 0.94 and in CSO ROI > 0.98). Average BG‐PVS volumes at baseline were 133.38 mm3 [95%‐CI: 109.19,157.57]. Following acute exercise, these decreased to 123.10 mm3 [95%‐CI: 99.62,146.57], showing a significant reduction of 10.28 mm3 [95%‐CI: 3.24,17.33] (Figure 2; W=181, p = 0.003). After 24 hours, BG‐PVS volumes increased to 130.34 mm3 [95%‐CI: 107.96,152.72], similar to baseline levels (Figure 2; W=107, p = 0.644). CSO‐PVS volumes, on the other hand, showed no significant changes between baseline and after exercise or 24 hours later (Figure 2). Conclusion Our work indicates that a single bout of physical exercise can exert subtle yet measurable volumetric changes on PVS in young participants. Whether this change reflects enhanced cerebrovascular or glymphatic function or not remains unclear, but will be explored in future research.