Development of the ProPal-COPD tool to identify patients with COPD for proactive palliative care
Authors Duenk RG, Verhagen C, Bronkhorst EM, Djamin RS, Bosman GJ, Lammers E, Dekhuijzen PNR, Vissers KCP, Engels Y, Heijdra Y
Received 20 April 2017
Accepted for publication 30 May 2017
Published 20 July 2017 Volume 2017:12 Pages 2121—2128
Checked for plagiarism Yes
Review by Single-blind
Peer reviewers approved by Dr Charles Downs
Peer reviewer comments 2
Editor who approved publication: Dr Richard Russell
RG Duenk,1 C Verhagen,1 EM Bronkhorst,2 RS Djamin,3 GJ Bosman,4 E Lammers,5 PNR Dekhuijzen,6 KCP Vissers,1 Y Engels,1,* Y Heijdra6,*
1Department of Anesthesiology, Pain and Palliative Medicine, 2Department of Health Evidence, Radboud University Medical Center, Nijmegen, 3Department of Respiratory Medicine, Amphia Hospital, Breda, 4Department of Respiratory Medicine, Slingeland Hospital, Doetinchem, 5Department of Respiratory Medicine, Gelre Hospitals, Zutphen, 6Department of Pulmonary Diseases, Radboud University Medical Center, Nijmegen, the Netherlands
*These authors contributed equally to this work
Background: Our objective was to develop a tool to identify patients with COPD for proactive palliative care. Since palliative care needs increase during the disease course of COPD, the prediction of mortality within 1 year, measured during hospitalizations for acute exacerbation COPD (AECOPD), was used as a proxy for the need of proactive palliative care.
Patients and methods: Patients were recruited from three general hospitals in the Netherlands in 2014. Data of 11 potential predictors, a priori selected based on literature, were collected during hospitalization for AECOPD. After 1 year, the medical files were explored for the date of death. An optimal prediction model was assessed by Lasso logistic regression, with 20-fold cross-validation for optimal shrinkage. Missing data were handled using complete case analysis.
Results: Of 174 patients, 155 patients were included; of those 30 (19.4%) died within 1 year. The optimal prediction model was internally validated and had good discriminating power (AUC =0.82, 95% CI 0.81–0.82). This model relied on the following seven predictors: the surprise question, Medical Research Council dyspnea questionnaire (MRC dyspnea), Clinical COPD Questionnaire (CCQ), FEV1% of predicted value, body mass index, previous hospitalizations for AECOPD and specific comorbidities. To ensure minimal miss out of patients in need of proactive palliative care, we proposed a cutoff in the model that prioritized sensitivity over specificity (0.90 over 0.73, respectively). Our model (ProPal-COPD tool) was a stronger predictor of mortality within 1 year than the CODEX (comorbidity, age, obstruction, dyspnea, and previous severe exacerbations) index.
Conclusion: The ProPal-COPD tool is a promising multivariable prediction tool to identify patients with COPD for proactive palliative care.
Keywords: COPD, exacerbation, proactive palliative care, prognosis, mortality
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