Quantitative CT Lung Imaging and Machine Learning Improves Prediction of Emergency Room Visits and Hospitalizations in COPD

Amir Moslemi

doi:10.1016/j.acra.2022.05.009

Quantitative CT Lung Imaging and Machine Learning Improves Prediction of Emergency Room Visits and Hospitalizations in COPD

2023-04-01

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HernandezPaul (Harvey O. Coxson)

M

MarciniukDarcy (Miranda Kirby) *

DOI：10.1016/j.acra.2022.05.009

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Rationale: Predicting increased risk of future healthcare utilization in chronic obstructive pulmonary disease (COPD) patients is an impor-tant goal for improving patient management. Objective: Our objective was to determine the importance of computed tomography (CT) lung imaging measurements relative to other demographic and clinical measurements for predicting future health services use with machine learning in COPD. Materials and Methods: In this retrospective study, lung function measurements and chest CT images were acquired from Canadian Cohort of Obstructive Lung Disease study participants from 2010 to 2017 (https://clinicaltrials.gov, NCT00920348). Up to two follow-up visits (1.5-and 3-year follow-up) were performed and participants were asked for details related to healthcare utilization. Healthcare utili-zation was defined as any COPD hospitalization or emergency room visit due to respiratory problems in the 12 months prior to the follow-up visits. CT analysis was performed (VIDA Diagnostics Inc.); a total of 108 CT quantitative emphysema, airway and vascular measure-ments were investigated. A hybrid feature selection method with support vector machine classifier was used to predict healthcare utiliza-tion. Performance was determined using accuracy, F1-measure and area under the receiver operating characteristic curve (AUC) and Matthews's correlation coefficient (MC). Results: Of the 527 COPD participants evaluated, 179 (35%) used healthcare services at follow-up. There were no significant dif-ferences between the participants with or without healthcare utilization at follow-up for age (p = 0.50), sex (p = 0.44), BMI (p = 0.05) or pack-years (p = 0.76). The accuracy for predicting subsequent healthcare utilization was 80% + 3% (F1-measure = 74%, AUC = 0.80, MC = 0.6) when all measurements were considered, 76% + 6% (F1-measure = 72%, AUC = 0.77, MC = 0.55) for CT measurements alone and 65% + 5% (F1-measure = 60%, AUC = 0.67, MC = 0.34) for demographic and lung function measurements alone. Conclusion: The combination of CT lung imaging and conventional measurements leads to greater prediction accuracy of subsequent health services use than conventional measurements alone, and may provide needed prognostic information for patients suffering from COPD.

Keywords:

Computed tomography

Machine learning

COPD

Quantitative imaging

Hospitalization

Quantitative CT Lung Imaging and Machine Learning Improves Prediction of Emergency Room Visits and Hospitalizations in COPD

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