EDITORIALS 11. Klompas M, Kleinman K, Murphy MV. Descriptive epidemiology and attributable morbidity of ventilator-associated events. Infect Control Hosp Epidemiol 2014;35:502–510. 12. Kipnis E, Nseir S. Ventilator-associated events (VAEs): VAE victis? (Woe to the conquered?). Crit Care Med 2014;42:1949–1950. 13. Boyer AF, Schoenberg N, Babcock H, McMullen KM, Micek ST, Kollef MH. A prospective evaluation of ventilator-associated conditions and infection-related ventilator-associated conditions. Chest 2014; (In press). 14. Muscedere J, Sinuff T, Heyland DK, Dodek PM, Keenan SP, Wood G, Jiang X, Day AG, Laporta D, Klompas M; Canadian Critical Care
Trials Group. The clinical impact and preventability of ventilatorassociated conditions in critically ill patients who are mechanically ventilated. Chest 2013;144:1453–1460. 15. Mekontso Dessap A, Katsahian S, Roche-Campo F, Varet H, Kouatchet A, Tomicic V, Beduneau G, Sonneville R, Jaber S, Darmon M, et al. Ventilator-associated pneumonia during weaning from mechanical ventilation: role of fluid management. Chest 2014;146:58–65.
Copyright © 2015 by the American Thoracic Society
Beyond “The LAS Is Broken” Ways to Improve Lung Allocation It is difficult to improve on, let alone solve, a healthcare problem. But when successes come, they often follow a common sequence: problems are described, inferences are made about causal mechanisms, those putative mechanisms are targeted by one or more experimental interventions, interventions that appear successful get implemented in the real world, and implementation is refined so the interventions actually help people. Viewed from this framework, the article by Maxwell and colleagues (pp. 302–308) in this issue of the Journal nicely exemplifies research that accomplishes the first and second of these steps to healthcare improvement (1). The authors describe a new (or at least worsening) problem in lung transplantation: the high costs of post-transplantation care. And they point to a plausible culprit: the introduction of the Lung Allocation Score (LAS) in May 2005, which led to the transplantation of older, sicker patients at risk for more posttransplant complications (2, 3). The study provides novel evidence that after LAS implementation, lung transplant recipients had longer stays during their index hospitalization and higher hospitalization charges per day, consistent with more resource-intensive care. In-hospital mortality was unchanged, and after discharge, patients more commonly received healthcare resources, including home nursing services and stays in rehabilitation and long-term acute care facilities. Among the study’s several strengths are its use of a broad national sample of transplant admissions, confirmation of the generalizability of this National Inpatient Sample to all US lung transplant recipients through comparison with the Scientific Registry for Transplant Recipients’ Standard Analysis File, and use of two analytic methods that bolster confidence in the paper’s suggestion that the rising in-hospital charges observed after LAS implementation were, at least in part, attributable to LAS implementation. The first of these methods, jointpoint regression, uses a hypothesis-free framework to empirically identify points in time when changes in outcomes manifest. This approach identified 2005 as a point of interest,
S.D.H. was supported by a Greenwall Foundation Faculty Scholar Award in Bioethics.
Editorials
confirming that changes occurred near the time of LAS implementation. The authors then used a difference-indifferences approach (4) to show that for at least some outcomes, the changes occurring at this time among lung transplant recipients were greater than those occurring among recipients of other transplantable organs who would not have been affected by the LAS. The study is limited by focusing on charges rather than costs, and on a narrow set of charges at that: only those that accrue during the index hospitalization in which the transplant was performed. However, this initial foray into the broader questions of how costs and cost-effectiveness can or should be used to evaluate organ allocation policies is interesting and novel. Further, the concept that the LAS or other organ allocation strategies (e.g., the Model of End-stage Liver Disease) that prioritize measures of urgency may not perform well on resource use metrics will clearly resonate with practicing transplant professionals. Despite successfully completing the first two steps of the foregoing recipe for healthcare improvement, it seems doubtful that Maxwell and colleagues’ evidence of increased resource use will lead to interventions that ultimately benefit patients. Many observers have already concluded that the LAS is broken because it prioritizes transplantation of patients with reduced survival (3), excludes key prognostic variables (5, 6), and has failed to improve on preexisting problems such as sex-based (7) and geographic (8, 9) disparities in access and the transplantation of many patients at centers that achieve poor outcomes (10). In addition, despite recent reductions in wait-list mortality and time on the wait-list among those transplanted (2), there is no evidence that LAS implementation has improved outcomes for the overall population of patients who might benefit from lung transplantation. If recognition of these numerous clinical and ethical problems has not prompted substantive revisions to the LAS, it is unlikely that new evidence of economic consequences will. Instead, what we need for real progress in this field is to move beyond identifying new ways in which the LAS fails and to start establishing consensus and evidence in at least four key areas. First, to make real progress requires that we agree on how best to evaluate the success of any lung allocation strategy.
245
EDITORIALS Several strategies to improve the supply or allocation of transplantable lungs, including more widespread use of single lung transplantation for patients with chronic obstructive pulmonary disease (11) or of organs from donors with certain blemishes such as modest smoking histories, might increase the number of patients transplanted at the cost of reduced average survival among those transplanted. We need consensus on which side of this tradeoff ought to be prioritized, and how many extra lives saved would be needed to justify a given decrease (e.g., of a couple months) in average posttransplant survival. Second, the transplant community needs to first quantify, and then limit, the among-center variability in the use of nonstandard organs, such as organs from older donors, donors who smoked, or donors after circulatory determination of death. Identifying what causes such center-level variability, whether it is expertise in using ex vivo lung perfusion, being close to getting flagged as an outlier on program-specific reports from the Scientific Registry for Transplant Recipients, or general risk aversion, could lead to clinical or policy interventions that expand the pool of suitable organs without curtailing outcomes. Third, we need better models for determining which patients derive a net benefit from transplantation over the long term. In the post-LAS era, we have limited evidence regarding which patients derive a net transplant benefit (12, 13). This knowledge gap impedes our abilities to optimize the equity of wait-listing practices and allocation algorithms and to measure the cost-effectiveness of lung transplantation. Finally, once we have determined who actually benefits from lung transplantation, we need to start evaluating allocation strategies by studying their effects on the entire population of such patients. Traditional approaches to evaluating organ allocation policies look only at their effects on wait-listed patients. However, this approach ignores the fact that most organ rationing occurs by limiting who gets on the wait-list in the first place (e.g., through restrictions based on age, psychosocial support, duration of smoking abstinence, and other factors). Evidence in end-stage liver disease shows an inverse relationship between the proportion of patients who might benefit from transplant who are actually wait-listed and the proportion of wait-listed patients who get transplanted (14). This suggests that programs can make their wait-list mortality and transplant rate statistics look better by restricting who they put on the wait-list, without in any way helping the full population of patients who might benefit from transplantation. In summary, by showing that the LAS has led to increased resource use without improved outcomes after lung transplantation, Maxwell and colleagues add to what ought to already have been considerable motivation for improving the supply and allocation of transplantable lungs in the United States (1). Now it is time to start agreeing on what outcomes we most care about, implementing strategies that promote more rational and consistent organ use, identifying the full population of patients who derive a net
246
benefit from lung transplantation, and developing allocation strategies that optimally funnel organs toward such potential beneficiaries. n Author disclosures are available with the text of this article at www.atsjournals.org. Scott D. Halpern, M.D., Ph.D. Department of Medicine Department of Biostatistics and Epidemiology and Department of Medical Ethics and Health Policy Perelman School of Medicine at the University of Pennsylvania Philadelphia, Pennsylvania
References 1. Maxwell BG, Mooney JJ, Lee PHU, Levitt JE, Chhatwani L, Nicolls MR, Zamora MR, Valentine V, Weill D, Dhillon GS. Increased resource use in lung transplant admissions in the Lung Allocation Score era. Am J Respir Crit Care Med 2015;191:302–308. 2. Valapour M, Skeans MA, Heubner BM, Smith JM, Schnitzler MA, Hertz MI, Edwards LB, Snyder JJ, Israni AK, Kasiske BL. OPTN/SRTR 2012 Annual Data Report: lung. Am J Transplant 2014;14(Suppl 1): 139–165. 3. Kotloff RM. Risk stratification of lung transplant candidates: implications for organ allocation. Ann Intern Med 2013;158:699–700. 4. Dimick JB, Ryan AM. Methods for evaluating changes in health care policy: the difference-in-differences approach. JAMA 2014;312: 2401–2402. 5. Chen H, Shiboski SC, Golden JA, Gould MK, Hays SR, Hoopes CW, De Marco T. Impact of the lung allocation score on lung transplantation for pulmonary arterial hypertension. Am J Respir Crit Care Med 2009; 180:468–474. 6. Tsuang WM, Vock DM, Finlen Copeland CA, Lederer DJ, Palmer SM. An acute change in lung allocation score and survival after lung transplantation: a cohort study. Ann Intern Med 2013;158:650–657. 7. Wille KM, Harrington KF, deAndrade JA, Vishin S, Oster RA, Kaslow RA. Disparities in lung transplantation before and after introduction of the lung allocation score. J Heart Lung Transplant 2013;32:684–692. 8. Russo MJ, Meltzer D, Merlo A, Johnson E, Shariati NM, Sonett JR, Gibbons R. Local allocation of lung donors results in transplanting lungs in lower priority transplant recipients. Ann Thorac Surg 2013;95: 1231–1234, discussion 1234–1235. 9. Thabut G, Munson J, Haynes K, Harhay MO, Christie JD, Halpern SD. Geographic disparities in access to lung transplantation before and after implementation of the lung allocation score. Am J Transplant 2012;12:3085–3093. 10. Thabut G, Christie JD, Kremers WK, Fournier M, Halpern SD. Survival differences following lung transplantation among US transplant centers. JAMA 2010;304:53–60. 11. Munson JC, Christie JD, Halpern SD. The societal impact of single versus bilateral lung transplantation for chronic obstructive pulmonary disease. Am J Respir Crit Care Med 2011;184:1282–1288. 12. Russo MJ, Worku B, Iribarne A, Hong KN, Yang JA, Vigneswaran W, Sonett JR. Does lung allocation score maximize survival benefit from lung transplantation? J Thorac Cardiovasc Surg 2011;141:1270–1277. 13. Thabut G, Christie JD, Mal H, Fournier M, Brugiere ` O, Leseche G, Castier Y, Rizopoulos D. Survival benefit of lung transplant for cystic fibrosis since lung allocation score implementation. Am J Respir Crit Care Med 2013;187:1335–1340. 14. Goldberg DS, French B, Lewis JD, Halpern SD. Geographic differences in access to transplant care among Medicaid enrollees with end-stage liver disease. Hepatology 2014;60:258A.
Copyright © 2015 by the American Thoracic Society
American Journal of Respiratory and Critical Care Medicine Volume 191 Number 3 | February 1 2015
Trials Group. The clinical impact and preventability of ventilatorassociated conditions in critically ill patients who are mechanically ventilated. Chest 2013;144:1453–1460. 15. Mekontso Dessap A, Katsahian S, Roche-Campo F, Varet H, Kouatchet A, Tomicic V, Beduneau G, Sonneville R, Jaber S, Darmon M, et al. Ventilator-associated pneumonia during weaning from mechanical ventilation: role of fluid management. Chest 2014;146:58–65.
Copyright © 2015 by the American Thoracic Society
Beyond “The LAS Is Broken” Ways to Improve Lung Allocation It is difficult to improve on, let alone solve, a healthcare problem. But when successes come, they often follow a common sequence: problems are described, inferences are made about causal mechanisms, those putative mechanisms are targeted by one or more experimental interventions, interventions that appear successful get implemented in the real world, and implementation is refined so the interventions actually help people. Viewed from this framework, the article by Maxwell and colleagues (pp. 302–308) in this issue of the Journal nicely exemplifies research that accomplishes the first and second of these steps to healthcare improvement (1). The authors describe a new (or at least worsening) problem in lung transplantation: the high costs of post-transplantation care. And they point to a plausible culprit: the introduction of the Lung Allocation Score (LAS) in May 2005, which led to the transplantation of older, sicker patients at risk for more posttransplant complications (2, 3). The study provides novel evidence that after LAS implementation, lung transplant recipients had longer stays during their index hospitalization and higher hospitalization charges per day, consistent with more resource-intensive care. In-hospital mortality was unchanged, and after discharge, patients more commonly received healthcare resources, including home nursing services and stays in rehabilitation and long-term acute care facilities. Among the study’s several strengths are its use of a broad national sample of transplant admissions, confirmation of the generalizability of this National Inpatient Sample to all US lung transplant recipients through comparison with the Scientific Registry for Transplant Recipients’ Standard Analysis File, and use of two analytic methods that bolster confidence in the paper’s suggestion that the rising in-hospital charges observed after LAS implementation were, at least in part, attributable to LAS implementation. The first of these methods, jointpoint regression, uses a hypothesis-free framework to empirically identify points in time when changes in outcomes manifest. This approach identified 2005 as a point of interest,
S.D.H. was supported by a Greenwall Foundation Faculty Scholar Award in Bioethics.
Editorials
confirming that changes occurred near the time of LAS implementation. The authors then used a difference-indifferences approach (4) to show that for at least some outcomes, the changes occurring at this time among lung transplant recipients were greater than those occurring among recipients of other transplantable organs who would not have been affected by the LAS. The study is limited by focusing on charges rather than costs, and on a narrow set of charges at that: only those that accrue during the index hospitalization in which the transplant was performed. However, this initial foray into the broader questions of how costs and cost-effectiveness can or should be used to evaluate organ allocation policies is interesting and novel. Further, the concept that the LAS or other organ allocation strategies (e.g., the Model of End-stage Liver Disease) that prioritize measures of urgency may not perform well on resource use metrics will clearly resonate with practicing transplant professionals. Despite successfully completing the first two steps of the foregoing recipe for healthcare improvement, it seems doubtful that Maxwell and colleagues’ evidence of increased resource use will lead to interventions that ultimately benefit patients. Many observers have already concluded that the LAS is broken because it prioritizes transplantation of patients with reduced survival (3), excludes key prognostic variables (5, 6), and has failed to improve on preexisting problems such as sex-based (7) and geographic (8, 9) disparities in access and the transplantation of many patients at centers that achieve poor outcomes (10). In addition, despite recent reductions in wait-list mortality and time on the wait-list among those transplanted (2), there is no evidence that LAS implementation has improved outcomes for the overall population of patients who might benefit from lung transplantation. If recognition of these numerous clinical and ethical problems has not prompted substantive revisions to the LAS, it is unlikely that new evidence of economic consequences will. Instead, what we need for real progress in this field is to move beyond identifying new ways in which the LAS fails and to start establishing consensus and evidence in at least four key areas. First, to make real progress requires that we agree on how best to evaluate the success of any lung allocation strategy.
245
EDITORIALS Several strategies to improve the supply or allocation of transplantable lungs, including more widespread use of single lung transplantation for patients with chronic obstructive pulmonary disease (11) or of organs from donors with certain blemishes such as modest smoking histories, might increase the number of patients transplanted at the cost of reduced average survival among those transplanted. We need consensus on which side of this tradeoff ought to be prioritized, and how many extra lives saved would be needed to justify a given decrease (e.g., of a couple months) in average posttransplant survival. Second, the transplant community needs to first quantify, and then limit, the among-center variability in the use of nonstandard organs, such as organs from older donors, donors who smoked, or donors after circulatory determination of death. Identifying what causes such center-level variability, whether it is expertise in using ex vivo lung perfusion, being close to getting flagged as an outlier on program-specific reports from the Scientific Registry for Transplant Recipients, or general risk aversion, could lead to clinical or policy interventions that expand the pool of suitable organs without curtailing outcomes. Third, we need better models for determining which patients derive a net benefit from transplantation over the long term. In the post-LAS era, we have limited evidence regarding which patients derive a net transplant benefit (12, 13). This knowledge gap impedes our abilities to optimize the equity of wait-listing practices and allocation algorithms and to measure the cost-effectiveness of lung transplantation. Finally, once we have determined who actually benefits from lung transplantation, we need to start evaluating allocation strategies by studying their effects on the entire population of such patients. Traditional approaches to evaluating organ allocation policies look only at their effects on wait-listed patients. However, this approach ignores the fact that most organ rationing occurs by limiting who gets on the wait-list in the first place (e.g., through restrictions based on age, psychosocial support, duration of smoking abstinence, and other factors). Evidence in end-stage liver disease shows an inverse relationship between the proportion of patients who might benefit from transplant who are actually wait-listed and the proportion of wait-listed patients who get transplanted (14). This suggests that programs can make their wait-list mortality and transplant rate statistics look better by restricting who they put on the wait-list, without in any way helping the full population of patients who might benefit from transplantation. In summary, by showing that the LAS has led to increased resource use without improved outcomes after lung transplantation, Maxwell and colleagues add to what ought to already have been considerable motivation for improving the supply and allocation of transplantable lungs in the United States (1). Now it is time to start agreeing on what outcomes we most care about, implementing strategies that promote more rational and consistent organ use, identifying the full population of patients who derive a net
246
benefit from lung transplantation, and developing allocation strategies that optimally funnel organs toward such potential beneficiaries. n Author disclosures are available with the text of this article at www.atsjournals.org. Scott D. Halpern, M.D., Ph.D. Department of Medicine Department of Biostatistics and Epidemiology and Department of Medical Ethics and Health Policy Perelman School of Medicine at the University of Pennsylvania Philadelphia, Pennsylvania
References 1. Maxwell BG, Mooney JJ, Lee PHU, Levitt JE, Chhatwani L, Nicolls MR, Zamora MR, Valentine V, Weill D, Dhillon GS. Increased resource use in lung transplant admissions in the Lung Allocation Score era. Am J Respir Crit Care Med 2015;191:302–308. 2. Valapour M, Skeans MA, Heubner BM, Smith JM, Schnitzler MA, Hertz MI, Edwards LB, Snyder JJ, Israni AK, Kasiske BL. OPTN/SRTR 2012 Annual Data Report: lung. Am J Transplant 2014;14(Suppl 1): 139–165. 3. Kotloff RM. Risk stratification of lung transplant candidates: implications for organ allocation. Ann Intern Med 2013;158:699–700. 4. Dimick JB, Ryan AM. Methods for evaluating changes in health care policy: the difference-in-differences approach. JAMA 2014;312: 2401–2402. 5. Chen H, Shiboski SC, Golden JA, Gould MK, Hays SR, Hoopes CW, De Marco T. Impact of the lung allocation score on lung transplantation for pulmonary arterial hypertension. Am J Respir Crit Care Med 2009; 180:468–474. 6. Tsuang WM, Vock DM, Finlen Copeland CA, Lederer DJ, Palmer SM. An acute change in lung allocation score and survival after lung transplantation: a cohort study. Ann Intern Med 2013;158:650–657. 7. Wille KM, Harrington KF, deAndrade JA, Vishin S, Oster RA, Kaslow RA. Disparities in lung transplantation before and after introduction of the lung allocation score. J Heart Lung Transplant 2013;32:684–692. 8. Russo MJ, Meltzer D, Merlo A, Johnson E, Shariati NM, Sonett JR, Gibbons R. Local allocation of lung donors results in transplanting lungs in lower priority transplant recipients. Ann Thorac Surg 2013;95: 1231–1234, discussion 1234–1235. 9. Thabut G, Munson J, Haynes K, Harhay MO, Christie JD, Halpern SD. Geographic disparities in access to lung transplantation before and after implementation of the lung allocation score. Am J Transplant 2012;12:3085–3093. 10. Thabut G, Christie JD, Kremers WK, Fournier M, Halpern SD. Survival differences following lung transplantation among US transplant centers. JAMA 2010;304:53–60. 11. Munson JC, Christie JD, Halpern SD. The societal impact of single versus bilateral lung transplantation for chronic obstructive pulmonary disease. Am J Respir Crit Care Med 2011;184:1282–1288. 12. Russo MJ, Worku B, Iribarne A, Hong KN, Yang JA, Vigneswaran W, Sonett JR. Does lung allocation score maximize survival benefit from lung transplantation? J Thorac Cardiovasc Surg 2011;141:1270–1277. 13. Thabut G, Christie JD, Mal H, Fournier M, Brugiere ` O, Leseche G, Castier Y, Rizopoulos D. Survival benefit of lung transplant for cystic fibrosis since lung allocation score implementation. Am J Respir Crit Care Med 2013;187:1335–1340. 14. Goldberg DS, French B, Lewis JD, Halpern SD. Geographic differences in access to transplant care among Medicaid enrollees with end-stage liver disease. Hepatology 2014;60:258A.
Copyright © 2015 by the American Thoracic Society
American Journal of Respiratory and Critical Care Medicine Volume 191 Number 3 | February 1 2015
