Re-Defining High Risk COPD with Parameter Response Mapping Based on Machine Learning Models

ABSTRACT:

Purpose

To explore optimal threshold of FEV1% predicted value (FEV1%pre) for high-risk chronic obstructive pulmonary disease (COPD) using the parameter response mapping (PRM) based on machine learning classification model.

Patients and Methods

A total of 561 consecutive non-COPD subjects who were screened for chest diseases in our hospital between August and October 2018 and who had complete questionnaire surveys, pulmonary function tests (PFT), and paired respiratory chest CT scans were enrolled retrospectively. The CT quantitative parameter for small airway remodeling was PRM, and 72 parameters were obtained at the levels of whole lung, left and right lung, and five lobes. To identify a more reasonable thresholds of FEV1% predicted value for distinguishing high-risk COP

SUBMITTER: Pu Y 

PROVIDER: S-EPMC9547550 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature