Clinical Validation of Single-Chamber Model-Based Algorithms Used to Estimate Respiratory Compliance

TL;DR

This study validates and compares 15 single-chamber model algorithms for non-invasive respiratory compliance estimation using a large clinical dataset, revealing their performance under various clinical scenarios and proposing improvements.

Contribution

Provides a comprehensive clinical validation dataset and evaluates multiple algorithms, identifying the most effective methods and introducing a variance reduction technique.

Findings

Certain algorithms perform better under specific ventilation modes.

Variance reduction improves consistency of algorithm results.

Insights into the impact of ventilator asynchrony on algorithm accuracy.

Abstract

Non-invasive estimation of respiratory physiology using computational algorithms promises to be a valuable technique for future clinicians to detect detrimental changes in patient pathophysiology. However, few clinical algorithms used to non-invasively analyze lung physiology have undergone rigorous validation in a clinical setting, and are often validated either using mechanical devices, or with small clinical validation datasets using 2-8 patients. This work aims to improve this situation by first, establishing an open, and clinically validated dataset comprising data from both mechanical lungs and nearly 40,000 breaths from 18 intubated patients. Next, we use this data to evaluate 15 different algorithms that use the "single chamber" model of estimating respiratory compliance. We evaluate these algorithms under varying clinical scenarios patients typically experience during hospitalization. In particular, we explore algorithm performance under four different types of patient ventilator asynchrony. We also analyze algorithms under varying ventilation modes to benchmark algorithm performance and to determine if ventilation mode has any impact on the algorithm. Our approach yields several advances by 1) showing which specific algorithms work best clinically under varying mode and asynchrony scenarios, 2) developing a simple mathematical method to reduce variance in algorithmic results, and 3) presenting additional insights about single-chamber model algorithms. We hope that our paper, approach, dataset, and software framework can thus be used by future researchers to improve their work and allow future integration of "single chamber" algorithms into clinical practice.