Predicting Dynamic Inspiratory Muscle Strength in COPD and Asthma Patients

Dynamic inspiratory muscle strength provides important information about the functional capacity of the inspiratory muscles. In patients with chronic respiratory diseases such as COPD and asthma, respiratory muscle function may be affected by airflow limitation, altered ventilatory mechanics, dyspnea, and reduced exercise tolerance. Although maximal inspiratory pressure is commonly used to assess respiratory muscle strength, it mainly reflects static pressure-generating capacity. The S-Index is a dynamic measure obtained during a rapid and forceful inspiratory maneuver and may provide additional information about inspiratory muscle performance under flow-dependent conditions. This study was designed as an observational, cross-sectional predictive modeling study. Adult male patients with a clinical diagnosis of COPD or asthma were prospectively recruited from clinical units of Çorum Erol Olçok Training and Research Hospital, Hitit University, Türkiye. All participants provided informed consent before participation. The study was approved by the Hitit University Non-Interventional Research Ethics Committee. Participants attended a familiarization session before the measurement day. During this session, the study procedures were explained, the measurement techniques were demonstrated, and participants were informed about behaviors to avoid before testing. On the measurement day, demographic information was recorded, and participants underwent dynamic inspiratory muscle strength assessment, body composition assessment, pulmonary function testing, and a fixed-pressure inspiratory muscle performance test. The S-Index was measured using the POWERbreathe K5 device in single-breath test mode. Participants were instructed to exhale fully to residual volume and then perform the fastest and strongest possible inspiration through the device until the lungs were completely filled. Multiple trials were performed, and the highest S-Index value was used for analysis. Pulmonary function testing was performed using standardized spirometry procedures. Forced vital capacity, forced expiratory volume in one second, the FEV1/FVC ratio, peak expiratory flow, peak inspiratory flow rate, and inspiratory volume were recorded. Body composition variables, including body weight, body mass index, fat-free mass, fat mass, and body fat percentage, were assessed using multi-frequency bioelectrical impedance analysis. Inspiratory muscle performance was evaluated using a fixed-pressure inspiratory muscle loading task with the POWERbreathe Classic Light Resistance device set at 30 cmH₂O. Participants performed continuous inspiratory efforts through the device until task failure. The total number of valid inspiratory repetitions and the time to task failure were recorded. Task failure was defined as inability to maintain rhythmic breathing, marked reduction in inspiratory effort, technical errors, or voluntary termination due to inability to continue. Safety was monitored throughout the testing procedures by healthcare professionals and researchers experienced in respiratory physiology and exercise testing. Testing was stopped if clinically relevant symptoms occurred, including severe dyspnea, dizziness, chest pain, wheezing, chest tightness, coughing attacks, or inability to maintain proper technique. No adverse clinical events requiring medical intervention were reported during the study. The primary purpose of the analysis was to develop statistical and machine learning-based prediction models for estimating S-Index values. Predictor variables included demographic characteristics, body composition variables, spirometry parameters, peak inspiratory flow, inspiratory volume, number of inspiratory repetitions, and time to task failure. Regression-based models were developed and evaluated using standard prediction performance metrics, including mean squared error, mean absolute error, root mean squared error, and the coefficient of determination.