• James A Diao, Yixuan He, Rohan Khazanchi, Nguemeni TiakoMax JordanMJFrom the Department of Biomedical Informatics, Harvard Medical School (J.A.D., P.R., L.M.-K., C.J.P., M.F., A.K.M.), the Computational Health Informatics Program, Boston Children's Hospital (J.A.D., A.K.M.), the Analytic and Tran, Jonathan I Witonsky, Emma Pierson, Pranav Rajpurkar, Jennifer R Elhawary, Luke Melas-Kyriazi, Albert Yen, Alicia R Martin, Sean Levy, Chirag J Patel, Maha Farhat, Luisa N Borrell, Michael H Cho, Edwin K Silverman, Esteban G Burchard, and Arjun K Manrai.
• From the Department of Biomedical Informatics, Harvard Medical School (J.A.D., P.R., L.M.-K., C.J.P., M.F., A.K.M.), the Computational Health Informatics Program, Boston Children's Hospital (J.A.D., A.K.M.), the Analytic and Translational Genetics Unit (Y.H., A.R.M.) and the Division of Pulmonary and Critical Care Medicine, Department of Medicine (M.F.), Massachusetts General Hospital, Harvard Internal Medicine-Pediatrics Combined Residency Program, Brigham and Women's Hospital, Boston Children's Hospital, and Boston Medical Center (R.K.), the François-Xavier Bagnoud Center for Health and Human Rights, Harvard University (R.K.), the Department of Medicine (M.J.N.T.) and the Channing Division of Network Medicine and the Division of Pulmonary and Critical Care Medicine, Department of Medicine (M.H.C., E.K.S.), Brigham and Women's Hospital, and the Division of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, Beth Israel Deaconess Medical Center (S.L.), Boston, and the Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge (Y.H., A.R.M.) - all in Massachusetts; the Departments of Pediatrics (J.I.W.), Medicine (J.R.E., E.G.B.), and Bioengineering and Therapeutic Sciences (J.R.E., E.G.B.), University of California, San Francisco, San Francisco; the Department of Computer Science, Cornell University, Ithaca (E.P.), and the Department of Population Health Sciences, Weill Cornell Medical College (E.P.), and the Department of Epidemiology and Biostatistics, Graduate School of Public Health and Health Policy, City University of New York (L.N.B.), New York - all in New York; the Department of Engineering Science, University of Oxford, Oxford, United Kingdom (L.M.-K.); and the Medical Scientist Training Program, University of Illinois at Chicago, Chicago (A.Y.).
• N. Engl. J. Med. 2024 Jun 13; 390 (22): 208320972083-2097.

BackgroundAdjustment for race is discouraged in lung-function testing, but the implications of adopting race-neutral equations have not been comprehensively quantified.MethodsWe obtained longitudinal data from 369,077 participants in the National Health and Nutrition Examination Survey, U.K. Biobank, the Multi-Ethnic Study of Atherosclerosis, and the Organ Procurement and Transplantation Network. Using these data, we compared the race-based 2012 Global Lung Function Initiative (GLI-2012) equations with race-neutral equations introduced in 2022 (GLI-Global). Evaluated outcomes included national projections of clinical, occupational, and financial reclassifications; individual lung-allocation scores for transplantation priority; and concordance statistics (C statistics) for clinical prediction tasks.ResultsAmong the 249 million persons in the United States between 6 and 79 years of age who are able to produce high-quality spirometric results, the use of GLI-Global equations may reclassify ventilatory impairment for 12.5 million persons, medical impairment ratings for 8.16 million, occupational eligibility for 2.28 million, grading of chronic obstructive pulmonary disease for 2.05 million, and military disability compensation for 413,000. These potential changes differed according to race; for example, classifications of nonobstructive ventilatory impairment may change dramatically, increasing 141% (95% confidence interval [CI], 113 to 169) among Black persons and decreasing 69% (95% CI, 63 to 74) among White persons. Annual disability payments may increase by more than $1 billion among Black veterans and decrease by $0.5 billion among White veterans. GLI-2012 and GLI-Global equations had similar discriminative accuracy with regard to respiratory symptoms, health care utilization, new-onset disease, death from any cause, death related to respiratory disease, and death among persons on a transplant waiting list, with differences in C statistics ranging from -0.008 to 0.011.ConclusionsThe use of race-based and race-neutral equations generated similarly accurate predictions of respiratory outcomes but assigned different disease classifications, occupational eligibility, and disability compensation for millions of persons, with effects diverging according to race. (Funded by the National Heart Lung and Blood Institute and the National Institute of Environmental Health Sciences.).Copyright © 2024 Massachusetts Medical Society.

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