Rationale
Predicting increased risk of future healthcare utilization in chronic obstructive
pulmonary disease (COPD) patients is an important 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
utilization 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 measurements were investigated. A hybrid feature selection method with
support vector machine classifier was used to predict healthcare utilization. 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 differences 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.
Key Words
Abbreviations:
COPD (Chronic Obstructive Pulmonary Disease), CT (Computed Tomography), CanCOLD (Canadian Cohort of Obstructive Lung Disease), ER (Emergency Room), BMI (Body Mass Index), HDHTDM (History of Heart Disease/ Systemic Hypertension/ Diabetes Mellitus), PFT (Pulmonary Function Test), FEV1 (Forced Expiratory Volume in 1 second), FVC (Forced Vital Capacity), FEF25-75 (Forced Expiratory Flow at 25–75% of FVC), ATS (American Thoracic Society), RV (Residual Volume), TLC (Total Lung Capacity), FRC (Functional Residual Capacity), DLCO (Diffusing Capacity of the lung for carbon monoxide), HU (Hounsfield Units), LAA950 (Low Attenuation Areas below -950HU), LAA910 (Low Attenuation Areas below -910HU), HU15 (HU value corresponding to the 15th percentile on the frequency distribution curve), LAC (Low Attenuation Cluster), LAA856 (Low Attenuation Areas of the lung below -856 HU), DPM (Disease Probability Measure), DPM-Emph (DPM of Emphysema), DPM-fsad (DPM of functional small airway disease), VV (Vessel Volume), TAC (Total Airway Xount), Pi-10 (estimated airway wall thickness for an idealized airway with an Internal Perimeter of 10 mm), IG (Information Gain), rref (Reduced Row Echelon Form), GA (Genetic Algorithm), SVM (Support Vector Machine), ROC (Receiver Operating Characteristic), AUC (Area Under the Curve), MC (Matthews Correlation Coefficient), TPR (True Positive Rate)To read this article in full you will need to make a payment
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Article info
Publication history
Published online: June 08, 2022
Accepted:
May 15,
2022
Received in revised form:
May 3,
2022
Received:
April 5,
2022
Footnotes
Data analyzed during the study were provided by the CanCOLD study. Requests for data should be directed to the provider indicated in the Acknowledgments.
Identification
Copyright
© 2022 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.
