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Employing the Forced Oscillation Technique for the Assessment of Respiratory Mechanics in Adults
Published on: February 9, 2022
Prediction of spirometry outcome in Croatian patients with chronic obstructive pulmonary disease
Erim Bešić1, Davorka Muršić2, Tajana Jalušić Glunčić2
1Faculty of Pharmacy and Biochemistry, University of Zagreb.
Abstract:
The current study offers an extensive examination of the influence of 29 diverse parameters on spirometry measurement variables in a cohort of 534 patients with chronic obstructive pulmonary disease (COPD) from five different centers in Croatia. The study elucidates both the magnitude and direction of the effect exerted by the 29 predictors on forced vital capacity (FVC), forced expiratory volume in one second (FEV1), the ratio FEV1/FVC, and predicted forced expiratory flow at 50% of FVC. Additionally, the development of prediction models for these parameters has been undertaken using several statistical methods. The study identifies fat-free mass index, 6-minute walk distance, predicted diffusing capacity of the lung for carbon monoxide, arterial partial pressure of oxygen, and both arterial and tissue hemoglobin oxygen saturation percentage as robust positive predictors for all four spirometry parameters. Body mass index is recognized as a weak positive predictor for FEV1 and FEV1/FVC, commonly observed in COPD patients. As expected, smoking years is identified as a strong negative predictor for all four spirometry parameters, while age and illness duration exhibit strong predictive negative associations. Furthermore, the modified Medical Research Council, arterial partial pressure carbon dioxide, St. George's Respiratory Questionnaire, COPD assessment test, Depression Anxiety Stress Scales, and Nutritional Risk Screening are identified as weak negative predictors. The Charlson Comorbidity Index, phase angle, and number of comorbidities do not exhibit a significant impact on spirometry variables. Ultimately, the performed factorial analysis categorized the 29 parameters into five groups, which were identified as relating to lung function, health status, nutritional status, age, and smoking. Multiple regression analysis, including four newly derived parameters based on the results of factorial analysis, identified nutritional status as a positive predictor for spirometry readings, while smoking, poor health status, and age were identified as negative predictors in successive order.
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