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

      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)
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