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

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. YoungJessica 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

Research output: Contribution to journal › Article › peer-review

10 Scopus citations

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 chronic Obstructive Lung Disease (GOLD) criteria.

Original language	English (US)
Pages (from-to)	346-361
Number of pages	16
Journal	Chronic Obstructive Pulmonary Diseases
Volume	7
Issue number	4
DOIs	https://doi.org/10.15326/JCOPDF.7.4.2020.0146
State	Published - 2020
Externally published	Yes

Bibliographical note

Funding Information:
Abbreviations: COPD Genetic Epidemiology, COPDGene®; SubPopulations and InteRmediate Outcome Measures In COPD Study, SPIROMICS; chronic obstructive pulmonary disease, COPD; Global initiative for chronic Obstructive Lung Disease, GOLD; body mass index, BMI; Body mass index-Obstruction-Dyspnea-Exercise capacity index, BODE; computed tomography, CT; forced expiratory volume in 1 second, FEV1; forced vital capacity, FVC; preserved ratio-impaired spirometry, PRISm; 6-minute walk distance, 6MWD; Framingham Risk Score, FRS; longitudinal follow-up program, LFU; modified Medical Resource Council dyspnea scale, mMRC; cardiovascular disease, CVD; segmental airway wall thickening, sAWT; Social Security Death Index, SSDI; area-under-the-curve, AUC; confidence interval, CI; standard deviation, SD Funding Support: The COPDGene® study is funded by National Heart, Lung, and Blood Institute grants U01 HL089897 and U01 HL089856. The COPDGene® study (NCT00608764) is also supported by the COPD Foundation through contributions made to an Industry Advisory Committee comprised of AstraZeneca, Boehringer-Ingelheim, Genentech, GlaxoSmithKline, Novartis, Pfizer, Siemens, and Sunovion. Date of Acceptance: May 15, 2020 Citation: Strand M, Austin E, Moll M, et al. A risk prediction model for mortality among smokers in the COPCGene® study. Chronic Obstr Pulm Dis. 2020;7(4):346-361. doi: https://doi.org/10.15326/jcopdf.7.4.2020.0146

Funding Information:
The COPDGene® study is funded by National Heart, Lung, and Blood Institute grants U01 HL089897 and U01 HL089856. The COPDGene® study (NCT00608764) is also supported by the COPD Foundation through contributions made to an Industry Advisory Committee comprised of AstraZeneca, Boehringer-Ingelheim, Genentech, GlaxoSmithKline, Novartis, Pfizer, Siemens, and Sunovion.

Publisher Copyright:
© 2020 JCOPDF.

Keywords

COPD
COPD genetic epidemiology study
COPDGene
PRISm
Preserved ratio-impaired spirometry
Risk score
Spirometry

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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Strand, M., Austin, E., Moll, M., Pratte, K. A., Regan, E. A., Hayden, L. P., Bhatt, S. P., Boriek, A. M., Casaburi, R., Silverman, E. K., Fortis, S., Ruczinski, I., Koegler, H., Rossiter, H. B., Occhipinti, M., Hanania, N. A., Gebrekristos, H. T., Lynch, D. A., Kunisaki, K. M., ... Crapo, J. D. (2020). A risk prediction model for mortality among smokers in the COPDGene® study. Chronic Obstructive Pulmonary Diseases, 7(4), 346-361. https://doi.org/10.15326/JCOPDF.7.4.2020.0146

Strand, M, Austin, E, Moll, M, Pratte, KA, Regan, EA, Hayden, LP, Bhatt, SP, Boriek, AM, Casaburi, R, Silverman, EK, Fortis, S, Ruczinski, I, Koegler, H, Rossiter, HB, Occhipinti, M, Hanania, NA, Gebrekristos, HT, Lynch, DA, Kunisaki, KM, Young, KA, Sieren, JC, Ragland, M, Hokanson, JE, Lutz, SM, Make, BJ, Kinney, GL, Cho, MH, Pistolesi, M, DeMeo, DL, Sciurba, FC, Comellas, AP, Diaz, AA, Barjaktarevic, I, Bowler, RP, Kanner, RE, Peters, SP, Ortega, VE, Dransfield, MT & Crapo, JD 2020, 'A risk prediction model for mortality among smokers in the COPDGene® study', Chronic Obstructive Pulmonary Diseases, vol. 7, no. 4, pp. 346-361. https://doi.org/10.15326/JCOPDF.7.4.2020.0146

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title = "A risk prediction model for mortality among smokers in the COPDGene{\textregistered} study",

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{\textregistered}) 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 chronic Obstructive Lung Disease (GOLD) criteria.",

keywords = "COPD, COPD genetic epidemiology study, COPDGene, PRISm, Preserved ratio-impaired spirometry, Risk score, Spirometry",

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

note = "Funding Information: Abbreviations: COPD Genetic Epidemiology, COPDGene{\textregistered}; SubPopulations and InteRmediate Outcome Measures In COPD Study, SPIROMICS; chronic obstructive pulmonary disease, COPD; Global initiative for chronic Obstructive Lung Disease, GOLD; body mass index, BMI; Body mass index-Obstruction-Dyspnea-Exercise capacity index, BODE; computed tomography, CT; forced expiratory volume in 1 second, FEV1; forced vital capacity, FVC; preserved ratio-impaired spirometry, PRISm; 6-minute walk distance, 6MWD; Framingham Risk Score, FRS; longitudinal follow-up program, LFU; modified Medical Resource Council dyspnea scale, mMRC; cardiovascular disease, CVD; segmental airway wall thickening, sAWT; Social Security Death Index, SSDI; area-under-the-curve, AUC; confidence interval, CI; standard deviation, SD Funding Support: The COPDGene{\textregistered} study is funded by National Heart, Lung, and Blood Institute grants U01 HL089897 and U01 HL089856. The COPDGene{\textregistered} study (NCT00608764) is also supported by the COPD Foundation through contributions made to an Industry Advisory Committee comprised of AstraZeneca, Boehringer-Ingelheim, Genentech, GlaxoSmithKline, Novartis, Pfizer, Siemens, and Sunovion. Date of Acceptance: May 15, 2020 Citation: Strand M, Austin E, Moll M, et al. A risk prediction model for mortality among smokers in the COPCGene{\textregistered} study. Chronic Obstr Pulm Dis. 2020;7(4):346-361. doi: https://doi.org/10.15326/jcopdf.7.4.2020.0146 Funding Information: The COPDGene{\textregistered} study is funded by National Heart, Lung, and Blood Institute grants U01 HL089897 and U01 HL089856. The COPDGene{\textregistered} study (NCT00608764) is also supported by the COPD Foundation through contributions made to an Industry Advisory Committee comprised of AstraZeneca, Boehringer-Ingelheim, Genentech, GlaxoSmithKline, Novartis, Pfizer, Siemens, and Sunovion. Publisher Copyright: {\textcopyright} 2020 JCOPDF.",

year = "2020",

doi = "10.15326/JCOPDF.7.4.2020.0146",

language = "English (US)",

volume = "7",

pages = "346--361",

journal = "Chronic Obstructive Pulmonary Diseases",

issn = "2372-952X",

publisher = "COPD Foundation",

number = "4",

}

TY - JOUR

T1 - A risk prediction model for mortality among smokers in the COPDGene® study

AU - Strand, Matthew

AU - Austin, Erin

AU - Moll, Matthew

AU - Pratte, Katherine A.

AU - Regan, Elizabeth A.

AU - Hayden, Lystra P.

AU - Bhatt, Surya P.

AU - Boriek, Aladin M.

AU - Casaburi, Richard

AU - Silverman, Edwin K.

AU - Fortis, Spyridon

AU - Ruczinski, Ingo

AU - Koegler, Harald

AU - Rossiter, Harry B.

AU - Occhipinti, Mariaelena

AU - Hanania, Nicola A.

AU - Gebrekristos, Hirut T.

AU - Lynch, David A.

AU - Kunisaki, Ken M.

AU - Young, Kendra A.

AU - Sieren, Jessica C.

AU - Ragland, Margaret

AU - Hokanson, John E.

AU - Lutz, Sharon M.

AU - Make, Barry J.

AU - Kinney, Gregory L.

AU - Cho, Michael H.

AU - Pistolesi, Massimo

AU - DeMeo, Dawn L.

AU - Sciurba, Frank C.

AU - Comellas, Alejandro P.

AU - Diaz, Alejandro A.

AU - Barjaktarevic, Igor

AU - Bowler, Russell P.

AU - Kanner, Richard E.

AU - Peters, Stephen P.

AU - Ortega, Victor E.

AU - Dransfield, Mark T.

AU - Crapo, James D.

N1 - Funding Information: Abbreviations: COPD Genetic Epidemiology, COPDGene®; SubPopulations and InteRmediate Outcome Measures In COPD Study, SPIROMICS; chronic obstructive pulmonary disease, COPD; Global initiative for chronic Obstructive Lung Disease, GOLD; body mass index, BMI; Body mass index-Obstruction-Dyspnea-Exercise capacity index, BODE; computed tomography, CT; forced expiratory volume in 1 second, FEV1; forced vital capacity, FVC; preserved ratio-impaired spirometry, PRISm; 6-minute walk distance, 6MWD; Framingham Risk Score, FRS; longitudinal follow-up program, LFU; modified Medical Resource Council dyspnea scale, mMRC; cardiovascular disease, CVD; segmental airway wall thickening, sAWT; Social Security Death Index, SSDI; area-under-the-curve, AUC; confidence interval, CI; standard deviation, SD Funding Support: The COPDGene® study is funded by National Heart, Lung, and Blood Institute grants U01 HL089897 and U01 HL089856. The COPDGene® study (NCT00608764) is also supported by the COPD Foundation through contributions made to an Industry Advisory Committee comprised of AstraZeneca, Boehringer-Ingelheim, Genentech, GlaxoSmithKline, Novartis, Pfizer, Siemens, and Sunovion. Date of Acceptance: May 15, 2020 Citation: Strand M, Austin E, Moll M, et al. A risk prediction model for mortality among smokers in the COPCGene® study. Chronic Obstr Pulm Dis. 2020;7(4):346-361. doi: https://doi.org/10.15326/jcopdf.7.4.2020.0146 Funding Information: The COPDGene® study is funded by National Heart, Lung, and Blood Institute grants U01 HL089897 and U01 HL089856. The COPDGene® study (NCT00608764) is also supported by the COPD Foundation through contributions made to an Industry Advisory Committee comprised of AstraZeneca, Boehringer-Ingelheim, Genentech, GlaxoSmithKline, Novartis, Pfizer, Siemens, and Sunovion. Publisher Copyright: © 2020 JCOPDF.

PY - 2020

Y1 - 2020

N2 - 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 chronic Obstructive Lung Disease (GOLD) criteria.

AB - 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 chronic Obstructive Lung Disease (GOLD) criteria.

KW - COPD

KW - COPD genetic epidemiology study

KW - COPDGene

KW - PRISm

KW - Preserved ratio-impaired spirometry

KW - Risk score

KW - Spirometry

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M3 - Article

AN - SCOPUS:85096068640

SN - 2372-952X

VL - 7

SP - 346

EP - 361

JO - Chronic Obstructive Pulmonary Diseases

JF - Chronic Obstructive Pulmonary Diseases

IS - 4

ER -

A risk prediction model for mortality among smokers in the COPDGene® study

Abstract

Bibliographical note

Keywords

UN SDGs

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