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Rationale: The American Thoracic Society recommended a single reference equation for spirometry, but the impact on patients is not known. Objectives: To estimate the effect of changing to a single reference equation among veterans with chronic obstructive pulmonary disease (COPD). Methods: A cross-sectional study was conducted including veterans aged ⩾40 to ⩽89 years with COPD and spirometry results from 21 facilities between 2010 and 2019. We collected race and ethnicity data from the electronic health record. We estimated the percentage change in the number of veterans with lung function meeting clinical thresholds used to determine eligibility for lung resection for cancer, lung volume reduction surgery (LVRS), and lung transplantation referral. We estimated the change for each level of U.S. Department of Veterans Affairs service connection and financial impact. Results: We identified 44,892 veterans (Asian, 0.5%; Black, 11.8%; White, 80.8%; and Hispanic, 1.8%). When changing to a single reference equation, Asian and Black veterans had reduced predicted lung function that could result in less surgical lung resection (4.4% and 11.1%, respectively) while increasing LVRS (1.7% and 3.8%) and lung transplantation evaluation for Black veterans (1.2%). White veterans had increased predicted lung function and could experience increased lung resection (8.1%), with less LVRS (3.3%) and lung transplantation evaluation (0.9%). Some Asian and Black veterans could experience increases in monthly disability payments (+$540.38 and +$398.38), whereas White veterans could see a decrease (-$588.79). When aggregated, Hispanic veterans experienced changes attributable to their racial identity and, because this sample was predominantly Hispanic White, had similar results to White veterans. Conclusions: Changing the reference equation could affect access to treatment and disability benefits, depending on race. If adopted, the use of discrete clinical thresholds needs to be reassessed, considering patient-centered outcomes.

Rationale: Hemoptysis is a common and important complication in persons with cystic fibrosis (PwCF). Despite this, there is limited literature on the impact of hemoptysis on contemporary cystic fibrosis (CF) outcomes. Objectives: Evaluate whether hemoptysis increases the risk of lung transplant or death without a transplant in PwCF. Methods: We reviewed a dataset of PwCF ages 12 years or older from the CFFPR (CF Foundation Patient Registry) that included 29,587 individuals. We identified hemoptysis as our predictor of interest and categorized PwCF as either no hemoptysis, any hemoptysis (submassive and/or massive), or massive hemoptysis. We subsequently evaluated whether hemoptysis, as defined above, was associated with death without transplant or receipt of lung transplant via logistic regression. We adjusted for age, sex, body mass index, forced expiratory volume in one second (FEV 1 ), number of exacerbations, supplemental oxygen use, CF-related diabetes, and Pseudomonas aeruginosa colonization status. Subgroup analyses were performed in advanced lung disease, defined as PwCF with an FEV 1  <40% predicted. Results: PwCF with any form of hemoptysis were more likely to progress to lung transplant or die without transplant than PwCF who did not have hemoptysis (odds ratio [OR], 1.3 [95% confidence interval (CI), 1.1-1.7]). The effect size of these associations was larger when hemoptysis events were classified as "massive" (massive hemoptysis OR, 2.2 [95% CI, 1.2-3.8]) or in PwCF with advanced lung disease (massive hemoptysis in advanced lung disease OR, 3.2 [95% CI 1.3-8.2]). Conclusions: Hemoptysis is associated with an increased risk of lung transplant and death without a transplant in PwCF, especially among those with massive hemoptysis or advanced lung disease. Our results suggest that hemoptysis functions as a useful predictor of serious outcomes in PwCF and may be important to incorporate into risk prediction models and/or transplant decisions in CF.

Background: Improved predictive models are needed in lung transplantation in the setting of a proposed allocation system that incorporates longer-term post-transplant survival in the United States. Allocation systems require accurate mortality predictions to justly allocate organs., Methods: Utilizing the United Network for Organ Sharing database (2005-2017), we fit models to predict 1-year mortality based on the Lung Allocation Score (LAS), the Chan, et al, 2019 model, a novel "clinician" model (a priori clinician selection of pre-transplant covariates), and two machine learning models (Least Absolute Shrinkage and Selection Operator; LASSO and Random Forests) for predicting 1-year and 3-year post-transplant mortality. We compared predictive accuracy among models. We evaluated the calibration of models by comparing average predicted probability vs observed outcome per decile. We repeated analyses fit for 3-year mortality, disease category, including donor covariates, and LAS era., Results: The area under the cure for all models was low, ranging from 0.55 to 0.62. All exhibited reasonable negative predictive values (0.87-0.90), but the positive predictive value for was poor (all <0.25). Evaluating LAS calibration found 1-year post-transplant estimates consistently overestimated risk of mortality, with greater differences in higher deciles. LASSO, Random Forests, and clinician models showed no improvement when evaluated by disease category or with the addition of donor covariates and performed worse for 3-year outcomes., Conclusions: The LAS overestimated patients' risk of post-transplant death, thus underestimating transplant benefit in the sickest candidates. Novel models based on pre-transplant recipient covariates failed to improve prediction. There should be wariness in post-transplant survival predictions from available models., Competing Interests: Disclosure statement CHG, EDL, CAM, and KJR report additional grant support from the United States CFF. CAM reports grant support from the NIH Lung Transplant Outcomes Group. CHG reports grant support from the European Commission and NIH National Heart, Lung, and Blood Institute; National Institute of Diabetes and Digestive and Kidney Diseases; and National Center for Research Resources. CHG reports personal or other fees from Gilead Sciences, Novartis, Boehringer Ingelheim, and Vertex Pharmaceuticals. KJR reports grant support from the NIH. None of these financial relationships influenced the interpretation or reporting of the current study. Grant/Research Support; Current/Ongoing - CHEST Foundation Grant in CF in partnership with Vertex Pharmaceuticals, Cystic Fibrosis Foundation (CFF), and the National Institutes of Health (NIH) (K23HL138154). A portion of the findings were presented during the 2022 ISHLT 42nd Annual Meeting and Scientific Sessions as a Mini Oral presentation by JMB., (Copyright © 2022 International Society for Heart and Lung Transplantation. Published by Elsevier Inc. All rights reserved.)

Objective: Quality-improvement efforts are hindered by a lack of consensus on meaningful and feasible measures of care. The objective of this study was to develop a core set of quality measures for mental health and substance-related care that are meaningful to stakeholders, feasible to implement, and broadly representative of diverse dimensions of the mental health system., Method: A 12-member panel of stakeholders from national organizations evaluated 116 process measures in a 2-stage modified Delphi consensus development process. Drawing on a conceptual framework and literature review, panelists rated each measure on 7 domains using a 9-point scale (1 = best). Measures were then mapped to a framework of system dimensions to identify a core set with the highest ratings for system characteristics within each dimension., Results: Twenty-eight measures were identified assessing treatment (12), access (2), assessment (2), continuity (4), coordination (2), prevention (1), and safety (5). Overall, mean ratings for meaningfulness were: clinical importance 2.29; perceived gap between actual and optimal care 2.59; association between improved performance and outcome 2.61. For feasibility, mean ratings were clarity of specifications 3.39; acceptability of data collection burden 4.77; and adequacy of case mix adjustment 4.20. The measures address a range of treatment modalities, clinical settings, diagnostic categories, vulnerable populations, and other dimensions of mental healthcare., Conclusions: A structured consensus process identified a core set of quality measures that are meaningful and feasible to multiple stakeholders, as well as broadly representative of the mental healthcare system. By yielding quantitative assessments of meaningfulness, feasibility and degree of consensus among stakeholders, these results can inform ongoing national efforts to adopt common quality measures for mental healthcare.