Machine learning based detection of true ventilatory restriction - 22/10/25
, Muhammad F.A. Chaudhary a, c , AKM Shahariar Azad Rabby a, b , Sandeep Bodduluri a, c , Arie Nakhmani a, d , Chengcui Zhang b , Surya P. Bhatt a, c, ⁎ Abstract |
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 FEV1/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 (FEV1, FVC, FEV1/FVC, FEV1 % 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.
Le texte complet de cet article est disponible en PDF.Highlights |
• | Spirometry is not sensitive for the detection of true ventilatory restriction. |
• | Demographics combined with spirometry improve detection of true restriction. |
• | Machine learning model trained on these features accurately identifies restriction. |
• | Implementation reduces dependency on comprehensive lung volume testing. |
• | It may be particularly valuable in resource-limited settings. |
Keywords : Ventilatory restriction, Spirometric restriction, Machine learning, Lung volume
Plan
Vol 248
Article 108314- novembre 2025 Retour au numéro
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