Abstract Introduction Inhaled therapy is essential for chronic pulmonary disease management, but is often compromised due to insufficient peak inspiratory flow (PIF). Current multi-measurement PIF assessment methods are clinically laborious. This study developed a mathematical model to predict PIF value and suboptimal PIF risk from minimal measurements, thereby simplifying inhalation capability evaluation. Methods This study included data from 281 participants, comprising In-Check DIAL measurements of PIF at resistance (R) 1-3, spirometry parameters, and demographic characteristics. The cohort was randomly divided into training (70%) and validation (30%) sets. Two models were developed: (1) multiple linear regressions to predict PIF values at R1 and R3; (2) a logistic regression to identify suboptimal PIF probability (PIF 60 L/min at R3). Models were developed using stepwise regression and validated using R² for linear regression and area under the curve (AUC) for logistic regression. Results In the general cohort, the multiple linear regression model for R1 PIF was: R1PIF = 13. 108 + 0. 888 * R2PIF + 0. 630 * PEF + 0. 088 * Height, with an R² of 0. 774 upon validation. The model for R3 PIF was: R3PIF = 3. 941 + 0. 825 * R2PIF + (-0. 079) * FEV1/FVC, with an R² of 0. 771 upon validation. Better R2 were observed in patients with chronic obstructive pulmonary disease (COPD) (R2=0. 872 for R1 and 0. 833 for R3) than other cases (R2=0. 721 for R1 and 0. 740 for R3). The subgroup of patients with COPD (n = 67 in training set and 29 in validation set) was further analyzed. The model for R1 PIF was: R1PIF = 28. 700 + 0. 873 * R2PIF + 2. 681 * FEF50, with an R² of 0. 903 upon validation. The model for R3 PIF was: R3PIF = 4. 375 + 0. 768 * R2PIF, with an R² of 0. 865 upon validation. In the general cohort, 28% participants exhibited suboptimal PIF at R3. The logistic model for suboptimal PIF risk at R3 incorporated the value of PIF at R2 and FEV1/FVC and achieved an AUC of 0. 911 on the validation set. Conclusions These models enabled accurate PIF and risk prediction from a single measurement, especially in patients with COPD, overcoming multi-step limitations to guide personalized inhaler selection and optimize disease management. This abstract is funded by: Shanghai Municipal Science and Technology Major Project (ZD2021CY001), Science and Technology Commission of Shanghai Municipality (20Z11901000, Shanghai Municipal Key Clinical Specialty (shslczdzk02201)
Lu et al. (Fri,) studied this question.