Race-Specific versus Race-Neutral Pulmonary Function Predicted Values in Patients with Operable Lung Cancer
<b>Rationale:</b> Percent predicted forced expiratory volume in 1 second (FEV<sub>1</sub>pp) is used for surgical risk assessment in patients with lung cancer. FEV<sub>1</sub>pp is adjusted for race, despite concerns regarding inaccurate estimations of lung health. <b>Objectives:</b> To compare prediction of race-specific versus race-neutral FEV<sub>1</sub>pp for pulmonary complications after lung cancer resection. <b>Methods:</b> Patients who underwent lung resection in the Society of Thoracic Surgeons General Thoracic Surgery Database were identified (2002-2008). We used Global Lung Initiative equations to derive race-specific and race-neutral FEV<sub>1</sub>pp and compared their performance to predict pulmonary complications. Postoperative FEV<sub>1</sub>pp was calculated with patients categorized into low- (>60%), intermediate- (30-60%), and high- (<30%) risk groups. <b>Results:</b> Of 24,276 patients, most were White (<i>n</i> = 21,130; 87.0%) or Black (<i>n</i> = 1,912; 7.9%). Race-specific equations reduced the mean FEV<sub>1</sub>pp by 5.3% for White patients and increased it 6.2% for Black patients compared with race-neutral equations. Multivariate models using race-neutral FEV<sub>1</sub>pp performed similarly to race-specific models in predicting pulmonary complications (sublobar resection: C-statistic 0.72; lobectomy: C-statistic 0.65; and bilobectomy/pneumonectomy: C-statistic 0.67; for both models) with similar adjusted odds ratios of FEV<sub>1</sub>pp for both equation types. In 5,422 patients with calculable postoperative FEV<sub>1</sub>pp, 617 (11.4%) were recategorized into a higher (<i>n</i> = 65) or lower (<i>n</i> = 552) risk group when using race-neutral equations. Of those moving into a lower-risk group, 98.0% were White. All patients reclassified into higher-risk groups were Black. <b>Conclusions:</b> Race-neutral FEV<sub>1</sub>pp performed equally well as race-specific equations in predicting pulmonary complications and disentangled the effect of respiratory function from race on outcomes.
Authors
- David E. Ost (ORCID: https://orcid.org/0000-0001-6351-5552)
- Mark I. Block (ORCID: https://orcid.org/0000-0002-6885-0584)
- Aaron Baugh (ORCID: https://orcid.org/0000-0002-9527-691X)
- Ara Asadur Vaporciyan (ORCID: https://orcid.org/0000-0002-2060-3584)
- Ajay Sheshadri (ORCID: https://orcid.org/0000-0002-8091-0180)
- Levi N. Bonnell (ORCID: https://orcid.org/0000-0002-4519-086X)
- Ravi Rajaram (ORCID: https://orcid.org/0000-0003-2782-7756)
- Liang Li (ORCID: https://orcid.org/0000-0001-5453-3839)
- Robert Habib
- Elizabeth A. David
Institutions
- The University of Texas MD Anderson Cancer Center (US)
- University of California, San Francisco (US)
- Society of Thoracic Surgeons (US)
- University of California System (US)
- Memorial Healthcare System (US)
- Department of Physics, Mathematics and Informatics (BY)
- Pulmonary Associates (US)
- University of Colorado Denver (US)
Publication Details
- Journal
- Annals of the American Thoracic Society
- Published
- 2025-03-28
- DOI
- https://doi.org/10.1513/annalsats.202408-846oc
- Citations
- 1
- Primary Topic
- Lung Cancer Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 1.52