Cardiopulmonary Exercise Test Data Averaging Methods and Preoperative Risk Assessment

Colorectal Cancer, Colorectal Neoplasms Malignant, Surgery

colorectal surgery, Prehabilitation, CPET, CardioPulmonary Exercise Testing

Cardiopulmonary exercise testing (CPET) is used for preoperative risk assessment in patients with colorectal cancer who need to undergo surgery. For presentation and interpretation purposes, CPET data are averaged by using a time- or breath-based average. It is uncertain to what extent differences in data averaging methods influence the numerical value of preoperative CPET-derived variables used for risk assessment. Therefore, the primary aim of this study is to investigate the influence of different CPET data averaging intervals on the numerical values of CPET-derived variables used for preoperative risk assessment in patients scheduled for elective colorectal cancer surgery. The secondary aim is to elucidate the impact of data-averaging intervals on classification of patients into a low- or high-risk category for postoperative complications based on known risk assessment thresholds.

Surgery for colorectal cancer is associated with a high incidence of postoperative complications. Risk assessment by means of a cardiopulmonary exercise test (CPET) is an essential part of the preoperative diagnostic work-up of colorectal cancer patients. During CPET, a patient exercises against a progressively increasing work rate until volitional exhaustion, while breath-by-breath respiratory gasses are analyzed. The large number of data points that are collected by the breath-by-breath sampling rate can be a burden for data visualization, as they can be noisy. Therefore, data averaging is performed to optimize graphical data display and to aid CPET interpretation. To date, there are no studies quantifying the extent to which differences in data averaging influence the numerical value of preoperative CPET-derived variables for risk assessment based on aerobic fitness, such as the the oxygen uptake (VO2) at the ventilatory anaerobic threshold (VAT), VO2 at peak exercise (VO2peak), and the oxygen uptake efficiency slope (OUES), and of preoperative CPET-derived variables for risk assessment based on ventilatory efficiency, such as the ventilatory equivalent for carbon dioxide production at the VAT (VE/VCO2VAT) and the slope of the relationship between the minute ventilation and carbon dioxide production (VE/VCO2-slope). Therefore, the primary aim of this study is to investigate the influence of different CPET data averaging intervals on the numerical values of CPET-derived variables used for preoperative risk assessment in patients scheduled for elective colorectal cancer surgery. The secondary aim is to elucidate the impact of data-averaging intervals on the classification of patients into a low- or high-risk category for postoperative complications based on known risk assessment thresholds. Participants Data from patients considered for colorectal cancer surgery who are ≥18 years of age, have a score ≤7 metabolic equivalents of task on the veterans-specific activity questionnaire, and therefore performed preoperative CPET as a part of a prehabilitation study will be collected. Preoperative CPET was conducted before any intervention was initiated. Procedures Preoperative CPET data will be anonymized and patient characteristics other than anthropometric measures will be concealed. A medical physiologist and a clinical exercise physiologist will determine the CPET variables VO2VAT, VO2peak, respiratory exchange ratio at peak exercise (RERpeak), VE/VCO2VAT, VE/VCO2-slope, and the OUES in all 20 CPETs using a predefined set of guidelines Final determination of each parameter will be based on consensus between the two observers. In case of disagreement between observers, a third observer will be consulted. Determination of the aforementioned CPET variables will be repeated using each of the five different data-averaging intervals. Data averaging-intervals consists of a stationary time-based average over 10, 20, and 30 seconds, and of a rolling average over 3 and 7 breaths that were defined as follows. The stationary time-based average will be calculated by averaging the breath-by-breath data over 10, 20, or 30 seconds. A rolling average is defined as averaging a fixed number of single breath measurements (i.e., 3 and 7), then discarding the first breath and adding a new breath to obtain a new breath averaging block. CPET interpretation will be performed using Blue Cherry software version 1.3.3.3 (Geratherm Respiratory GmbH, Bad Kissingen, Germany). Apart from the CPET data, the preoperative patient characteristics age, sex, body mass index, smoking status (never, former, current), age-adjusted Charlson comorbidity index, comorbidities, American Society of Anesthesiologists classification, veterans-specific activity questionnaire score, hemoglobin levels (mmol/L), and tumor location will be recorded. To assess the influence of different CPET data averaging intervals on the numerical values of CPET-derived variables, mean differences of the numerical values of VO2VAT, VO2peak, RERpeak, VE/VCO2VAT, VE/VCO2-slope, and OUES between different data-averaging intervals will be analyzed by means of within factors repeated measures analysis of variance (ANOVA). In case of a statistically significant difference between methods (p\<0.05), post-hoc testing will be performed using the Bonferroni test to identify the exact differences. To evaluate the influence of data-averaging intervals on risk assessment, participants will be classified as being at low or high risk for postoperative complications based on their VO2VAT, VO2peak, VE/VCO2VAT, and OUES. For each CPET-derived variable, numerical values will be determined for each of the five data-averaging will be compared with known preoperative risk assessment thresholds (patients will be classified as high-risk when having a VO2VAT \<11.1 mL/kg/min, a VO2peak \<18.2 mL/kg/min, a VE/VCO2VAT \>30.9, and/or an OUES/kg \<20.6). Friedman's test will be used to determine whether differences in risk assessment exist between data-averaging methods. Differences between data-averaging methods will be assumed statistically significant when p\<0.05.

Netherlands