Predicting total lung capacity from spirometry: a machine learning approach

Abstract

Background and objective: Spirometry patterns can suggest that a patient has a restrictive ventilatory impairment; however, lung volume measurements such as total lung capacity (TLC) are required to confirm the diagnosis. The aim of the study was to train a supervised machine learning model that can accurately estimate TLC values from spirometry and subsequently identify which patients would most benefit from undergoing a complete pulmonary function test.

Methods: We trained three tree-based machine learning models on 51,761 spirometry data points with corresponding TLC measurements. We then compared model performance using an independent test set consisting of 1,402 patients. The best-performing model was used to retrospectively identify restrictive ventilatory impairment in the same test set. The algorithm was compared against different spirometry patterns commonly used to predict restriction.

Results: The prevalence of restrictive ventilatory impairment in the test set is 16.7% (234/1402). CatBoost was the best-performing machine learning model. It predicted TLC with a mean squared error (MSE) of 560.1 mL. The sensitivity, specificity, and F1-score of the optimal algorithm for predicting restrictive ventilatory impairment was 83, 92, and 75%, respectively.

Conclusion: A machine learning model trained on spirometry data can estimate TLC to a high degree of accuracy. This approach could be used to develop future smart home-based spirometry solutions, which could aid decision making and self-monitoring in patients with restrictive lung diseases.

Keywords: interstitial lung disease; machine learning; restriction; spirometry; total lung capacity.

Figure 1

Illustration of the machine learning-based algorithm for predicting total lung capacity. MSE, mean squared error.

Figure 2

The total lung capacity (TLC) predictions of the CatBoost model (TLC_CatBoost) against the reference TLC measurements in the independent test set, grouped by true restriction defined as TLC < lower limit of normal (LLN). The black dashed line represents the line of ideal agreement.

Figure 3

The prediction error for each diagnosis is calculated as the difference between the average total lung capacity (TLC) value and the average TLC_CatBoost prediction for that group. Bars above and below the horizontal dotted line indicate model underestimation and overestimation, respectively. COPD, chronic obstructive pulmonary disease; ILD, interstitial lung disease; OBD, other obstructive disease; NMD, neuromuscular disease; PVD, pulmonary vascular disease; TD, thoracic deformity.