External validation of ADO, DOSE, COTE and CODEX at predicting death in primary care patients with COPD using standard and machine learning approaches. (May 2018)

• Record Type: Journal Article
• Authors: Morales, Daniel R.; Flynn, Rob; Zhang, Jianguo; Trucco, Emmanuel; Quint, Jennifer K.; Zutis, Kris
• Is Part Of: Respiratory medicine. Volume 138(2018)
• Page Start: 150
• Page End: 155
• Publication Date: 2018-05
• Subjects: COPD -- Mortality -- Epidemiology; Chest -- Diseases -- Periodicals; Chest -- Diseases -- Great Britain -- Periodicals; Respiratory organs -- Diseases -- Periodicals; Respiratory Tract Diseases -- Periodicals; Appareil respiratoire -- Maladies -- Périodiques; Thorax -- Maladies -- Périodiques; Appareil respiratoire -- Maladies -- Traitement -- Périodiques; Electronic journals; 616.2
• DOI: 10.1016/j.rmed.2018.04.003
• Languages: English
• ISSNs: 0954-6111

Abstract

Abstract: Background: Several models for predicting the risk of death in people with chronic obstructive pulmonary disease (COPD) exist but have not undergone large scale validation in primary care. The objective of this study was to externally validate these models using statistical and machine learning approaches. Methods: We used a primary care COPD cohort identified using data from the UK Clinical Practice Research Datalink. Age-standardised mortality rates were calculated for the population by gender and discrimination of ADO (age, dyspnoea, airflow obstruction), COTE (COPD-specific comorbidity test), DOSE (dyspnoea, airflow obstruction, smoking, exacerbations) and CODEX (comorbidity, dyspnoea, airflow obstruction, exacerbations) at predicting death over 1–3 years measured using logistic regression and a support vector machine learning (SVM) method of analysis. Results: The age-standardised mortality rate was 32.8 (95%CI 32.5–33.1) and 25.2 (95%CI 25.4–25.7) per 1000 person years for men and women respectively. Complete data were available for 54879 patients to predict 1-year mortality. ADO performed the best (c-statistic of 0.730) compared with DOSE (c-statistic 0.645), COTE (c-statistic 0.655) and CODEX (c-statistic 0.649) at predicting 1-year mortality. Discrimination of ADO and DOSE improved at predicting 1-year mortality when combined with COTE comorbidities (c-statistic 0.780 ADO + COTE; c-statistic 0.727 DOSE + COTE). Discrimination did not change significantly over 1–3 years. Comparable results were observed using SVM. Conclusion: In primary care, ADO appears superior at predicting death in COPD. Performance of ADO and DOSE improved when combined with COTE comorbidities suggesting better models may be generated with additional data facilitated using novel approaches. Highlights: Mortality rates in COPD are higher for men than women. Models predicting COPD mortality exist but require validation in primary care. ADO may predict mortality in COPD better than DOSE, COTE and CODEX. Better models could be created using more data and novel approaches.