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

Daniel Morales (Lead / Corresponding author), Robert Flynn, Jianguo Zhang, Emanuele Trucco, Jennifer K. Quint

Research output: Contribution to journalArticlepeer-review

23 Citations (Scopus)
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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 discrimination 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.
Original languageEnglish
Pages (from-to)150-155
Number of pages6
JournalRespiratory Medicine
Early online date12 Apr 2018
Publication statusPublished - 1 May 2018


  • COPD
  • Epidemiology
  • Mortality
  • Dyspnea/etiology
  • Severity of Illness Index
  • Prognosis
  • Comorbidity
  • Humans
  • Middle Aged
  • Male
  • Mortality/trends
  • Machine Learning
  • United Kingdom/epidemiology
  • Smoking/epidemiology
  • Pulmonary Disease, Chronic Obstructive/complications
  • Aged, 80 and over
  • Adult
  • Female
  • Aged
  • Risk Assessment/methods
  • Primary Health Care/methods
  • Electronic Health Records

ASJC Scopus subject areas

  • Pulmonary and Respiratory Medicine


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