Bloom, CI; Ricciardi, F; Smeeth, L; Stone, P; Quint, JK; (2019) Predicting COPD 1-year mortality using prognostic predictors routinely measured in primary care. BMC Medicine , 17 (1) , Article 73. 10.1186/s12916-019-1310-0.

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Abstract

BACKGROUND: Chronic obstructive pulmonary disease (COPD) is a major cause of mortality. Patients with advanced disease often have a poor quality of life, such that guidelines recommend providing palliative care in their last year of life. Uptake and use of palliative care in advanced COPD is low; difficulty in predicting 1-year mortality is thought to be a major contributing factor. METHODS: We identified two primary care COPD cohorts using UK electronic healthcare records (Clinical Practice Research Datalink). The first cohort was randomised equally into training and test sets. An external dataset was drawn from a second cohort. A risk model to predict mortality within 12 months was derived from the training set using backwards elimination Cox regression. The model was given the acronym BARC based on putative prognostic factors including body mass index and blood results (B), age (A), respiratory variables (airflow obstruction, exacerbations, smoking) (R) and comorbidities (C). The BARC index predictive performance was validated in the test set and external dataset by assessing calibration and discrimination. The observed and expected probabilities of death were assessed for increasing quartiles of mortality risk (very low risk, low risk, moderate risk, high risk). The BARC index was compared to the established index scores body mass index, obstructive, dyspnoea and exacerbations (BODEx), dyspnoea, obstruction, smoking and exacerbations (DOSE) and age, dyspnoea and obstruction (ADO). RESULTS: Fifty-four thousand nine hundred ninety patients were eligible from the first cohort and 4931 from the second cohort. Eighteen variables were included in the BARC, including age, airflow obstruction, body mass index, smoking, exacerbations and comorbidities. The risk model had acceptable predictive performance (test set: C-index = 0.79, 95% CI 0.78-0.81, D-statistic = 1.87, 95% CI 1.77-1.96, calibration slope = 0.95, 95% CI 0.9-0.99; external dataset: C-index = 0.67, 95% CI 0.65-0.7, D-statistic = 0.98, 95% CI 0.8-1.2, calibration slope = 0.54, 95% CI 0.45-0.64) and acceptable accuracy predicting the probability of death (probability of death in 1 year, n high-risk group, test set: expected = 0.31, observed = 0.30; external dataset: expected = 0.22, observed = 0.27). The BARC compared favourably to existing index scores that can also be applied without specialist respiratory variables (area under the curve: BARC = 0.78, 95% CI 0.76-0.79; BODEx = 0.48, 95% CI 0.45-0.51; DOSE = 0.60, 95% CI 0.57-0.61; ADO = 0.68, 95% CI 0.66-0.69, external dataset: BARC = 0.70, 95% CI 0.67-0.72; BODEx = 0.41, 95% CI 0.38-0.45; DOSE = 0.52, 95% CI 0.49-0.55; ADO = 0.57, 95% CI 0.54-0.60). CONCLUSION: The BARC index performed better than existing tools in predicting 1-year mortality. Critically, the risk score only requires routinely collected non-specialist information which, therefore, could help identify patients seen in primary care that may benefit from palliative care.

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