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. 2014 Dec 16;9(12):e114438.
doi: 10.1371/journal.pone.0114438. eCollection 2014.

A simplified score to quantify comorbidity in COPD

Nirupama Putcha  1 , Milo A Puhan  2 , M Bradley Drummond  1 , MeiLan K Han  3 , Elizabeth A Regan  4 , Nicola A Hanania  5 , Carlos H Martinez  3 , Marilyn Foreman  6 , Surya P Bhatt  7 , Barry Make  8 , Joe Ramsdell  9 , Dawn L DeMeo  10 , R Graham Barr  11 , Stephen I Rennard  12 , Fernando Martinez  13 , Edwin K Silverman  10 , James Crapo  8 , Robert A Wise  1 , Nadia N Hansel  1
Affiliations

A simplified score to quantify comorbidity in COPD

Nirupama Putcha et al. PLoS One. .

Abstract

Importance: Comorbidities are common in COPD, but quantifying their burden is difficult. Currently there is a COPD-specific comorbidity index to predict mortality and another to predict general quality of life. We sought to develop and validate a COPD-specific comorbidity score that reflects comorbidity burden on patient-centered outcomes.

Materials and methods: Using the COPDGene study (GOLD II-IV COPD), we developed comorbidity scores to describe patient-centered outcomes employing three techniques: 1) simple count, 2) weighted score, and 3) weighted score based upon statistical selection procedure. We tested associations, area under the Curve (AUC) and calibration statistics to validate scores internally with outcomes of respiratory disease-specific quality of life (St. George's Respiratory Questionnaire, SGRQ), six minute walk distance (6MWD), modified Medical Research Council (mMRC) dyspnea score and exacerbation risk, ultimately choosing one score for external validation in SPIROMICS.

Results: Associations between comorbidities and all outcomes were comparable across the three scores. All scores added predictive ability to models including age, gender, race, current smoking status, pack-years smoked and FEV1 (p<0.001 for all comparisons). Area under the curve (AUC) was similar between all three scores across outcomes: SGRQ (range 0·7624-0·7676), MMRC (0·7590-0·7644), 6MWD (0·7531-0·7560) and exacerbation risk (0·6831-0·6919). Because of similar performance, the comorbidity count was used for external validation. In the SPIROMICS cohort, the comorbidity count performed well to predict SGRQ (AUC 0·7891), MMRC (AUC 0·7611), 6MWD (AUC 0·7086), and exacerbation risk (AUC 0·7341).

Conclusions: Quantifying comorbidity provides a more thorough understanding of the risk for patient-centered outcomes in COPD. A comorbidity count performs well to quantify comorbidity in a diverse population with COPD.

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Conflict of interest statement

Competing Interests: RGB reports receiving funding from the National Institutes of Health and United States-Environmental Protection Agency, with an in-kind donation from Cenestra Health and royalties from UpToDate. In the past 3 years, MKH has participated in advisory boards for Boehringer Ingelheim, Pfizer, GlaxoSmithKline, Genentech, Novartis, Forest, Regeneron and Medimmune; participated on speaker's bureaus for Boehringer Ingelheim, Pfizer, GlaxoSmithKline, Novartis, Grifols therapeutics, and the National Association for Continuing Education, and WebMD; has consulted for Novartis, Evidera and United Biosource Corporation; and has received royalties from UpToDate and ePocrates. She has also participated in research sponsored by GlaxoSmithKline with funds paid to her institution. In the past three years, EKS received honoraria and consulting fees from Merck and grant support from GlaxoSmithKline. In the past three years, MBD had participated in advisory boards for Lupin Pharmaceuticals and Boehringer Ingelheim. In the last three years, BJM has participated in medical advisory boards for Aerocrine, AstraZeneca, Boehringer Ingelheim, Breathe, Coviden, GlaxoSmithKline, Forest, Ikaria, Merck, Novartis, Pfizer, Respironics, Theravance; has received grant funds provided to and controlled by National Jewish Health from AstraZeneca, Boehringer Ingelheim, Forest, NABI, National Heart, Lung, and Blood Institute, Sunovian; consulted for Forest; and participated in CME activities with Consensus Medical, Cleveland Clinic, Integrity, Carden Jennings, Mt Sinai, Cedars Sinai, WebMD, Foundation for Improving Patient Outcomes; and royalties from Up-To-Date. The remaining authors have no relevant financial disclosures. The above stated interests do not alter the authors' adherence to PLOS ONE policies on sharing data and materials.

Figures

Figure 1
Figure 1. Areas Under the Curve (AUCs) for discrimination of comorbidity scores with regards to primary outcome SGRQ.
All three scores compared to “empty” model (including age, gender, race, FEV1, pack-years smoked and current smoking status). ROC for empty model is 0.7393.
See this image and copyright information in PMC

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