Objective: Although the importance of assessing inspiratory flow in the selection of treatments for chronic obstructive pulmonary disease (COPD) is understood, evaluation of this factor is not yet widespread or standardized. The objective of the present work was to evaluate the peak inspiratory flow (PIF) of patients with COPD and to explore the variables associated with a suboptimal PIF. Methods: An observational, cross-sectional study was carried out at specialized nursing consultations over a period of 6 months. We collected clinical data as well as data on symptoms, treatment adherence, and patient satisfaction with their inhalers via questionnaires. PIF was determined using the In-Check Dial G16^® device (Clement Clarke International, Ltd., Harlow, UK). In each case, the PIF was considered suboptimal when it was off-target for any of the prescribed inhalers. The association with suboptimal PIF was evaluated using multivariate logistic regression and the results were expressed as the odds ratio (OR) with 95% confidence interval (CI). Results: A total of 122 COPD patients were included in this study, of whom 34 (27.9%) had suboptimal PIF. A total of 229 inhalers were tested, of which 186 (81.2%) were dry powder devices. The multivariate analysis found an association between suboptimal PIF and age (OR = 1.072; 95% CI (1.019, 1.128); p = 0.007) and forced vital capacity (OR = 0.961; 95% CI (0.933, 0.989); p = 0.006). Conclusions: About a third of patients in complex specialized COPD care have suboptimal PIFs, which is related to age and forced vital capacity.

Background: Spirometry is the most widely used pulmonary function test for diagnosing respiratory diseases. However, the quality of the results mainly depends on the correct execution of the maneuver, making quality control essential. Traditionally, this process relies on subjective and laborious visual inspection. Methods: To overcome these limitations, this work proposes an intelligent clinical decision support system to assist in spirometry quality control. The proposed system generates a graphical construct that integrates the spirometry curves (flow-volume and volume-time curves) along with patient demographic information (sex, age, and BMI) extracted from the spirometry report. The resulting image is processed by a convolutional neural network based on the ResNet-18 architecture, whose output quantifies the risk of the performed test being unacceptable. This approach allows for simple integration of the system into clinical practice while accounting for individual patient characteristics during classification. Results: The results obtained in the test set are promising, with an AUC of 0.94 (95% CI: 0.80–1.00) and a sensitivity and specificity at the selected cut-off point of 75.00% (95% CI: 40–100%) and 100.00% (95% CI: 100–100%), respectively. Conclusions: Despite this, it should be noted that the system is still in a conceptual phase of development and therefore requires broader validation in real clinical environments as well as the incorporation of more diverse datasets to evaluate its robustness and generalization before its final implementation.