A simplified score to quantify comorbidity in COPD

Nirupama Putcha, Milo A. Puhan, M. Bradley Drummond, Mei Lan K. Han, Elizabeth A. Regan, Nicola A. Hanania, Carlos H. Martinez, Marilyn Foreman, Surya P. Bhatt, Barry Make, Joe Ramsdell, Dawn L. DeMeo, R. Graham Barr, Stephen I. Rennard, Fernando Martinez, Edwin K. Silverman, James Crapo, Robert A. Wise, Nadia N. Hansel

Research output: Contribution to journalArticle

22 Citations (Scopus)

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 patientcentered 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 FEV 1 (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.

Original languageEnglish (US)
Article numbere114438
JournalPloS one
Volume9
Issue number12
DOIs
StatePublished - Dec 16 2014

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Chronic Obstructive Pulmonary Disease
Comorbidity
Area Under Curve
Pulmonary diseases
questionnaires
Statistics
quality of life
Calibration
comorbidity
Quality of Life
Aptitude
dyspnea
biomedical research
Dyspnea
respiratory tract diseases
Biomedical Research
calibration
statistics
Smoking
Mortality

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)
  • Agricultural and Biological Sciences(all)
  • General

Cite this

Putcha, N., Puhan, M. A., Drummond, M. B., Han, M. L. K., Regan, E. A., Hanania, N. A., ... Hansel, N. N. (2014). A simplified score to quantify comorbidity in COPD. PloS one, 9(12), [e114438]. https://doi.org/10.1371/journal.pone.0114438

A simplified score to quantify comorbidity in COPD. / Putcha, Nirupama; Puhan, Milo A.; Drummond, M. Bradley; Han, Mei Lan K.; Regan, Elizabeth A.; Hanania, Nicola A.; Martinez, Carlos H.; Foreman, Marilyn; Bhatt, Surya P.; Make, Barry; Ramsdell, Joe; DeMeo, Dawn L.; Barr, R. Graham; Rennard, Stephen I.; Martinez, Fernando; Silverman, Edwin K.; Crapo, James; Wise, Robert A.; Hansel, Nadia N.

In: PloS one, Vol. 9, No. 12, e114438, 16.12.2014.

Research output: Contribution to journalArticle

Putcha, N, Puhan, MA, Drummond, MB, Han, MLK, Regan, EA, Hanania, NA, Martinez, CH, Foreman, M, Bhatt, SP, Make, B, Ramsdell, J, DeMeo, DL, Barr, RG, Rennard, SI, Martinez, F, Silverman, EK, Crapo, J, Wise, RA & Hansel, NN 2014, 'A simplified score to quantify comorbidity in COPD', PloS one, vol. 9, no. 12, e114438. https://doi.org/10.1371/journal.pone.0114438
Putcha N, Puhan MA, Drummond MB, Han MLK, Regan EA, Hanania NA et al. A simplified score to quantify comorbidity in COPD. PloS one. 2014 Dec 16;9(12). e114438. https://doi.org/10.1371/journal.pone.0114438
Putcha, Nirupama ; Puhan, Milo A. ; Drummond, M. Bradley ; Han, Mei Lan K. ; Regan, Elizabeth A. ; Hanania, Nicola A. ; Martinez, Carlos H. ; Foreman, Marilyn ; Bhatt, Surya P. ; Make, Barry ; Ramsdell, Joe ; DeMeo, Dawn L. ; Barr, R. Graham ; Rennard, Stephen I. ; Martinez, Fernando ; Silverman, Edwin K. ; Crapo, James ; Wise, Robert A. ; Hansel, Nadia N. / A simplified score to quantify comorbidity in COPD. In: PloS one. 2014 ; Vol. 9, No. 12.
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AU - Putcha, Nirupama

AU - Puhan, Milo A.

AU - Drummond, M. Bradley

AU - Han, Mei Lan K.

AU - Regan, Elizabeth A.

AU - Hanania, Nicola A.

AU - Martinez, Carlos H.

AU - Foreman, Marilyn

AU - Bhatt, Surya P.

AU - Make, Barry

AU - Ramsdell, Joe

AU - DeMeo, Dawn L.

AU - Barr, R. Graham

AU - Rennard, Stephen I.

AU - Martinez, Fernando

AU - Silverman, Edwin K.

AU - Crapo, James

AU - Wise, Robert A.

AU - Hansel, Nadia N.

PY - 2014/12/16

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N2 - 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 patientcentered 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 FEV 1 (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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