Biomechanical Phenotyping of Forced Expiration for Precision Pulmonary Rehabilitation: A Machine Learning Approach to Identify Structural and Kinetic Drivers.

Abstract

Background: Standard spirometry fundamentally overlooks the mechanical dynamics of forced expiration. This study derived novel biomechanical parameters to establish functional phenotypes and predict clinical respiratory impairments.

Methods: Utilizing 16,596 acceptable spirometry records from NHANES (2007 to 2012), parameters reflecting kinetic power, mass constraint, and airway instability were mathematically derived. Principal component analysis, K-means clustering, and a Multilayer Perceptron neural network were sequentially applied.

Results: Three distinct biomechanical phenotypes emerged: Load-Constrained (45.4%), Mechanically Efficient (23.5%), and Dynamic Collapse (31.0%). Aging significantly degraded kinetic power, demonstrating a steeper functional decline in males (p < 0.001). The neural network achieved 93.2% testing accuracy in classifying spirometric abnormalities. Crucially, Dynamic Airway Collapse Ratio (100% normalized importance), BMI (89.4%), and kinetic power (86.2%) fundamentally outperformed traditional demographic predictors such as chronological age (20.4%) and biological sex (7.1%).

Conclusions: Structural and dynamic kinetic factors drive pulmonary dysfunction far more accurately than conventional demographics. Classifying these mechanical phenotypes facilitates highly targeted precision cardiopulmonary rehabilitation.