Machine learning based detection of true ventilatory restriction.

Rationale: Spirometry is only 50 % accurate for the detection of true ventilatory restriction, necessitating additional lung volume tests.

Objective: To develop a detection tool for true lung restriction using spirometry and patient demographics.

Methods: We analyzed spirometry and lung volume data from 21,062 participants. Restrictive spirometric pattern (RSP) was defined by FEV/FVC ≥0.70 and FVC %predicted <80. Lung volumes were acquired using multi-breath nitrogen washout. True ventilatory restriction (TVR) was defined by total lung capacity <80 % predicted. We developed a LightGBM machine-learning model incorporating five spirometry (FEV, FVC, FEV/FVC, FEV % predicted and FVC % predicted), and three demographic (age, sex, and BMI) features. The model was trained on 80 % of the cohort (n = 16,849) and evaluated on 20 % (n = 4213) held-out set. The performance of the model was assessed using receiver operating characteristic (ROC) analyses.

Results: Of 21,062 participants, 12,643 (60 %) had TVR, of whom 5,255 (41.6 %) had RSP. The accuracy of RSP alone in detection of TVR was 0.61 (95% CI 0.60-0.63) with sensitivity of 0.42 (95% CI 0.40-0.43) and specificity of 0.91 (95% CI 0.90-0.92). The LightGBM model outperformed RSP alone, with an accuracy of 0.78 (95% CI 0.77-0.80), area under the ROC curve (AUC) of 0.89 (95% CI 0.88-0.90), sensitivity of 0.74 (95% CI 0.72-0.75), and specificity of 0.86 (95% CI 0.84-0.87).

Conclusions: A machine learning model using demographics and spirometry can accurately detect true ventilatory restriction and lower the need for additional lung volume testing.