Surya P Bhatt1,2, Pallavi P Balte3, Joseph E Schwartz3,4, Byron C Jaeger5, Patricia A Cassano6, Paulo H Chaves7, David Couper8, David R Jacobs9, Ravi Kalhan10, Robert Kaplan11, Donald Lloyd-Jones12, Anne B Newman13, George O'Connor14, Jason L Sanders15, Benjamin M Smith16, Yifei Sun17, Jason G Umans18, Wendy B White19, Sachin Yende20,21, Elizabeth C Oelsner3,22. 1. Division of Pulmonary, Allergy, and Critical Care Medicine. 2. Lung Health Center, and. 3. Division of General Medicine, Columbia University Medical Center, New York, New York. 4. Department of Psychiatry and Behavioral Health, Renaissance School of Medicine, Stony Brook University, Stony Brook, New York. 5. Department of Biostatistics, University of Alabama at Birmingham, Birmingham, Alabama. 6. Division of Nutritional Sciences, Weill Cornell Medical College, Ithaca, New York. 7. Benjamin Leon Center for Geriatric Research and Education, Herbert Wertheim College of Medicine, Florida International University, Miami, Florida. 8. Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, North Carolina. 9. Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, Minnesota. 10. Division of Pulmonary and Critical Care Medicine and. 11. Albert Einstein College of Medicine, New York, New York. 12. Department of Preventive Medicine, Northwestern University, Chicago, Illinois. 13. Department of Epidemiology and. 14. Division of Pulmonary, Allergy, Sleep, and Critical Care, Boston University, Boston, Massachusetts. 15. Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, Massachusetts. 16. Department of Medicine, McGill University, Montreal, Quebec, Canada. 17. Department of Biostatistics and. 18. Georgetown Howard Universities Center for Clinical and Translational Science, Washington, DC. 19. Undergraduate Training and Education Center, Tougaloo College, Tougaloo, Mississippi; and. 20. Department of Critical Care Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania. 21. Veterans Affairs Pittsburgh Healthcare System, Pittsburgh, Pennsylvania. 22. Department of Epidemiology, Mailman School of Public Health, Columbia University Medical Center, New York, New York.
Abstract
Rationale: Early detection of chronic obstructive pulmonary disease (COPD) is a public health priority. Airflow obstruction is the single most important risk factor for adverse COPD outcomes, but spirometry is not routinely recommended for screening. Objectives: To describe the burden of subclinical airflow obstruction (SAO) and to develop a probability score for SAO to inform potential detection and prevention programs. Methods: Lung function and clinical data were harmonized and pooled across nine U.S. general population cohorts. Adults with respiratory symptoms, inhaler use, or prior diagnosis of COPD or asthma were excluded. A probability score for prevalent SAO (forced expiratory volume in 1 second/forced vital capacity < 0.70) was developed via hierarchical group-lasso regularization from clinical variables in strata of sex and smoking status, and its discriminative accuracy for SAO was assessed in the pooled cohort as well as in an external validation cohort (NHANES [National Health and Nutrition Examination Survey] 2011-2012). Incident hospitalizations and deaths due to COPD (respiratory events) were defined by adjudication or administrative criteria in four of nine cohorts. Results: Of 33,546 participants (mean age 52 yr, 54% female, 44% non-Hispanic White), 4,424 (13.2%) had prevalent SAO. The incidence of respiratory events (Nat-risk = 14,024) was threefold higher in participants with SAO versus those without (152 vs. 39 events/10,000 person-years). The probability score, which was based on six commonly available variables (age, sex, race and/or ethnicity, body mass index, smoking status, and smoking pack-years) was well calibrated and showed excellent discrimination in both the testing sample (C-statistic, 0.81; 95% confidence interval [CI], 0.80-0.82) and in NHANES (C-statistic, 0.83; 95% CI, 0.80-0.86). Among participants with predicted probabilities ⩾ 15%, 3.2 would need to undergo spirometry to detect one case of SAO. Conclusions: Adults with SAO demonstrate excess respiratory hospitalization and mortality. A probability score for SAO using commonly available clinical risk factors may be suitable for targeting screening and primary prevention strategies.
Rationale: Early detection of chronic obstructive pulmonary disease (COPD) is a public health priority. Airflow obstruction is the single most important risk factor for adverse COPD outcomes, but spirometry is not routinely recommended for screening. Objectives: To describe the burden of subclinical airflow obstruction (SAO) and to develop a probability score for SAO to inform potential detection and prevention programs. Methods: Lung function and clinical data were harmonized and pooled across nine U.S. general population cohorts. Adults with respiratory symptoms, inhaler use, or prior diagnosis of COPD or asthma were excluded. A probability score for prevalent SAO (forced expiratory volume in 1 second/forced vital capacity < 0.70) was developed via hierarchical group-lasso regularization from clinical variables in strata of sex and smoking status, and its discriminative accuracy for SAO was assessed in the pooled cohort as well as in an external validation cohort (NHANES [National Health and Nutrition Examination Survey] 2011-2012). Incident hospitalizations and deaths due to COPD (respiratory events) were defined by adjudication or administrative criteria in four of nine cohorts. Results: Of 33,546 participants (mean age 52 yr, 54% female, 44% non-Hispanic White), 4,424 (13.2%) had prevalent SAO. The incidence of respiratory events (Nat-risk = 14,024) was threefold higher in participants with SAO versus those without (152 vs. 39 events/10,000 person-years). The probability score, which was based on six commonly available variables (age, sex, race and/or ethnicity, body mass index, smoking status, and smoking pack-years) was well calibrated and showed excellent discrimination in both the testing sample (C-statistic, 0.81; 95% confidence interval [CI], 0.80-0.82) and in NHANES (C-statistic, 0.83; 95% CI, 0.80-0.86). Among participants with predicted probabilities ⩾ 15%, 3.2 would need to undergo spirometry to detect one case of SAO. Conclusions: Adults with SAO demonstrate excess respiratory hospitalization and mortality. A probability score for SAO using commonly available clinical risk factors may be suitable for targeting screening and primary prevention strategies.
Entities:
Keywords:
airflow obstruction; chronic obstructive pulmonary disease; probability score; subclinical
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