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The Acute COPD Exacerbation Prediction Tool (ACCEPT): a modelling study

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Summary

Background

Accurate prediction of exacerbation risk enables personalised care for patients with chronic obstructive pulmonary disease (COPD). We developed and validated a generalisable model to predict individualised rate and severity of COPD exacerbations.

Methods

In this risk modelling study, we pooled data from three COPD trials on patients with a history of exacerbations. We developed a mixed-effect model to predict exacerbations over 1 year. Severe exacerbations were those requiring inpatient care. Predictors were history of exacerbations, age, sex, body-mass index, smoking status, domiciliary oxygen therapy, lung function, symptom burden, and current medication use. Evaluation of COPD Longitudinally to Identify Predictive Surrogate End-points (ECLIPSE), a multicentre cohort study, was used for external validation.

Results

The development dataset included 2380 patients, 1373 (58%) of whom were men. Mean age was 64·7 years (SD 8·8). Mean exacerbation rate was 1·42 events per year and 0·29 events per year were severe. When validated against all patients with COPD in ECLIPSE (mean exacerbation rate was 1·20 events per year, 0·27 events per year were severe), the area-under-curve (AUC) was 0·81 (95% CI 0·79–0·83) for at least two exacerbations and 0·77 (95% CI 0·74–0·80) for at least one severe exacerbation. Predicted exacerbation and observed exacerbation rates were similar (1·31 events per year for all exacerbations and 0·25 events per year for severe exacerbations vs 1·20 events per year and 0·27 events per year). In ECLIPSE, in patients with previous exacerbation history (mean exacerbation rate was 1·82 events per year, 0·40 events per year were severe), AUC was 0·73 (95% CI 0·70–0·76) for two or more exacerbations and 0·74 (95% CI 0·70–0·78) for at least one severe exacerbation. Calibration was accurate for severe exacerbations (predicted 0·37 events per year vs observed 0·40 events per year) and all exacerbations (predicted 1·80 events per year vs observed 1·82 events per year).

Interpretation

This model can be used as a decision tool to personalise COPD treatment and prevent exacerbations.

Funding

Canadian Institutes of Health Research.

Introduction

Chronic obstructive pulmonary disease (COPD) is characterised by symptoms of breathlessness and cough, which worsen acutely during exacerbations.1 COPD is known to be a heterogeneous disorder with large variations in risk of exacerbation across patients.2 In clinical practice, a history of two or more exacerbations and one severe exacerbation per year is used to guide therapeutic choices for exacerbation prevention.3 However, this approach is clinically restricted owing to substantial heterogeneity in risk even within those who frequently exacerbate.4

Prognostic clinical prediction tools enable personalised approaches to disease management. Despite potential benefits, no such tool is routinely used in clinical management of COPD. Whereas, for COPD-related mortality, clinical scoring schemes, such as the BODE index, are available and frequently used.5 A 2017 systematic review by Guerra and colleagues6 identified 27 prediction tools for COPD exacerbations. Among these tools, only two reported on model validation and none were deemed ready for personalised COPD management in clinic.6

In this study, we describe a new model, the Acute COPD Exacerbation Prediction Tool (ACCEPT), to predict, at an individual level, rate and severity of COPD exacerbation, report on its performance in an independent external cohort, and explain, using case studies, its potential clinical application. As a decision tool, ACCEPT provides a personalised risk profile that allows clinicians to tailor treatment regimens to individual needs of patients.

Section snippets

Participants and study design

In reporting our prediction model, we followed recommendations set by the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) statement.7 We developed the model using data from patients with COPD, without previous or existing history of asthma, and who had at least one exacerbation over the past 12 months. We then externally validated the model in patients with COPD regardless of their exacerbation history and in a subset of patients with

Results

We excluded 96 patients who were lost to follow-up (n=33) or had missing values (n=63; figure 1). The final development dataset included 2380 patients (1107 from MACRO, 847 from STATCOPE, and 426 from OPTIMAL). Total mean age was 64·7 years (SD 8·8) and 1373 (58%) were men. Patients had a total of 3056 exacerbations, 628 of which were severe. In the external validation dataset, ECLIPSE, 109 patients had missing values. Thus, the final sample included 1819 patients with COPD (mean age was 63·3

Discussion

The most important finding of the study was the development and validation of ACCEPT that uses simple and widely available clinical and demographic variables to predict risk and severity of exacerbations over a 12-month period, enabling personalisation of care for patients with COPD. ACCEPT was superior to using an individual's history of exacerbation to predict future risk of exacerbations and, in particular, for severe exacerbations (we observed an increase in AUC of 0·11 in all patients with

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