A Risk Prediction Model for Mortality Among Smokers in the COPDGene® Study

Matthew Strand; Erin Austin; Matthew Moll; Katherine A Pratte; Elizabeth A Regan; Lystra P Hayden; Surya P Bhatt; Aladin M Boriek; Richard Casaburi; Edwin K Silverman; Spyridon Fortis; Ingo Ruczinski; Harald Koegler; Harry B Rossiter; Mariaelena Occhipinti; Nicola A Hanania; Hirut T Gebrekristos; David A Lynch; Ken M Kunisaki; Kendra A Young; Jessica C Sieren; Margaret Ragland; John E Hokanson; Sharon M Lutz; Barry J Make; Gregory L Kinney; Michael H Cho; Massimo Pistolesi; Dawn L DeMeo; Frank C Sciurba; Alejandro P Comellas; Alejandro A Diaz; Igor Barjaktarevic; Russell P Bowler; Richard E Kanner; Stephen P Peters; Victor E Ortega; Mark T Dransfield; James D Crapo

doi:10.15326/jcopdf.7.4.2020.0146

A Risk Prediction Model for Mortality Among Smokers in the COPDGene® Study

Chronic Obstr Pulm Dis. 2020 Oct;7(4):346-361. doi: 10.15326/jcopdf.7.4.2020.0146.

Authors

Matthew Strand¹, Erin Austin², Matthew Moll³, Katherine A Pratte¹, Elizabeth A Regan¹, Lystra P Hayden³, Surya P Bhatt⁴, Aladin M Boriek⁵, Richard Casaburi⁶, Edwin K Silverman³, Spyridon Fortis⁷, Ingo Ruczinski⁸, Harald Koegler⁹, Harry B Rossiter^{6

10}, Mariaelena Occhipinti¹¹, Nicola A Hanania⁵, Hirut T Gebrekristos¹², David A Lynch¹, Ken M Kunisaki¹³, Kendra A Young¹⁴, Jessica C Sieren⁷, Margaret Ragland¹⁴, John E Hokanson¹⁴, Sharon M Lutz¹⁵, Barry J Make¹, Gregory L Kinney¹⁴, Michael H Cho³, Massimo Pistolesi¹¹, Dawn L DeMeo^{3

15}, Frank C Sciurba¹⁶, Alejandro P Comellas⁷, Alejandro A Diaz³, Igor Barjaktarevic¹⁷, Russell P Bowler¹, Richard E Kanner¹⁸, Stephen P Peters¹⁹, Victor E Ortega¹⁹, Mark T Dransfield⁴, James D Crapo¹

Affiliations

¹ National Jewish Health, Denver, Colorado.
² University of Colorado at Denver.
³ Brigham and Women's Hospital, Boston, Massachusetts.
⁴ University of Alabama at Birmingham.
⁵ Baylor College of Medicine, Houston, Texas.
⁶ The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, California.
⁷ University of Iowa, Iowa City.
⁸ Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland.
⁹ Boehringer-Ingelheim, Ingelheim, Germany.
¹⁰ University of Leeds, Leeds, United Kingdom.
¹¹ University of Florence, Florence, Italy.
¹² Morehouse School of Medicine, Atlanta, Georgia.
¹³ Minneapolis Veterans Administration Health Care System, Minnesota.
¹⁴ University of Colorado Anschutz Medical Campus, Aurora.
¹⁵ Harvard Medical School, Harvard University, Boston, Massachusetts.
¹⁶ University of Pittsburgh, Pittsburgh, Pennsylvania.
¹⁷ David Geffen School of Medicine, University of California-Los Angeles, Los Angeles.
¹⁸ School of Medicine, University of Utah, Salt Lake City.
¹⁹ Wake Forest School of Medicine, Wake Forest University, Winston-Salem, North Carolina.

Abstract

Background: Risk factor identification is a proven strategy in advancing treatments and preventive therapy for many chronic conditions. Quantifying the impact of those risk factors on health outcomes can consolidate and focus efforts on individuals with specific high-risk profiles. Using multiple risk factors and longitudinal outcomes in 2 independent cohorts, we developed and validated a risk score model to predict mortality in current and former cigarette smokers.

Methods: We obtained extensive data on current and former smokers from the COPD Genetic Epidemiology (COPDGene^®) study at enrollment. Based on physician input and model goodness-of-fit measures, a subset of variables was selected to fit final Weibull survival models separately for men and women. Coefficients and predictors were translated into a point system, allowing for easy computation of mortality risk scores and probabilities. We then used the SubPopulations and InteRmediate Outcome Measures In COPD Study (SPIROMICS) cohort for external validation of our model.

Results: Of 9867 COPDGene participants with standard baseline data, 17.6% died over 10 years of follow-up, and 9074 of these participants had the full set of baseline predictors (standard plus 6-minute walk distance and computed tomography variables) available for full model fits. The average age of participants in the cohort was 60 for both men and women, and the average predicted 10-year mortality risk was 18% for women and 25% for men. Model time-integrated area under the receiver operating characteristic curve statistics demonstrated good predictive model accuracy (0.797 average), validated in the external cohort (0.756 average). Risk of mortality was impacted most by 6-minute walk distance, forced expiratory volume in 1 second and age, for both men and women.

Conclusions: Current and former smokers exhibited a wide range of mortality risk over a 10- year period. Our models can identify higher risk individuals who can be targeted for interventions to reduce risk of mortality, for participants with or without chronic obstructive pulmonary disease (COPD) using current Global initiative for obstructive Lung Disease (GOLD) criteria.

Keywords: COPD Genetic Epidemiology study; COPDGene; PRISm; copd; preserved ratio-impaired spirometry; risk score; spirometry.

Abstract

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