Clinical Judgment Versus Lung Allocation Score in Predicting Lung Transplant Waitlist Mortality

Received: 20 January 2020 | Revised: 9 March 2020 | Accepted: 30 March 2020 DOI: 10.1111/ctr.13870

ORIGINAL ARTICLE

Clinical judgment versus lung allocation score in predicting lung transplant waitlist mortality

1University of Alberta, Edmonton, AB, Canada Abstract 2Toronto Lung Transplant Program, Canadian lung transplant centers currently use a subjective and dichotomous “Status” University of Toronto, Toronto, ON, Canada ranking to prioritize waitlisted patients for lung transplantation. The lung allocation 3University of British Columbia, Vancouver, BC, Canada score (LAS) is an objective composite score derived from clinical parameters associated with both waitlist and post-transplant survival. We performed a retrospective Correspondence Alim Hirji, 3-114F CSB, 11350 83 Ave, cohort study to determine whether clinical judgment (Status) or LAS better predicted Edmonton, AB T6G 2G3, Canada. waitlist mortality. All adult patients listed for lung transplantation between 2007 Email: [email protected] and 2012 at three Canadian lung transplant programs were included. Status and LAS were compared in their ability to predict waitlist mortality using Cox proportional hazards models and C-statistics. Status and LAS were available for 1122 patients. Status 2 patients had a higher LAS compared to Status 1 patients (mean 40.8 (4.4) vs 34.6 (12.5), P = .0001). Higher LAS was associated with higher risk of waitlist mortality (HR 1.06 per unit LAS, 95% CI 1.05, 1.07, P < .001). LAS predicted waitlist mortality better than Status (C-statistic 0.689 vs 0.674). Patients classified as Status 2 and LAS ≥ 37 had the worst survival awaiting transplant, HR of 8.94 (95% CI 5.97, 13.37). LAS predicted waitlist mortality better than Status; however, the best predictor of waitlist mortality may be a combination of both LAS and clinical judgment.

KEYWORDS allocation, Canada, lung allocation score, lung transplant, waitlist mortality

1 | INTRODUCTION regional transplant program level. All four Canadian lung transplant centers currently utilize a subjective “Status” system to indicate illness Lung transplantation has been shown to prolong overall survival and severity of patients requiring lung transplantation, with most centers improve health-related quality of life for individuals with end-stage dichotomously classifying patients as either Status 1 (ie, stable), or pulmonary disease.1,2 Between 2000 and 2017, 3238 lung trans- Status 2 (ie, deteriorating). Status is determined by a multidisciplinary plants were performed in Canada, but despite increasing numbers transplant team at the time of assessment at the respective transplant of transplants performed, over 200 patients each year remain on center, based on clinical judgment and available prognostic informa- waitlists across the country, with waitlist mortality as high as 27% tion from tests performed at the time of transplant assessment. Status in recent years.3 is then updated on subsequent clinical visits. There are no national In the absence of a national organ-sharing policy for lung trans- consensus criteria defining Status 1 and Status 2 patients, and each plantation in Canada, allocation decisions are currently made at a transplant center will list a candidate as Status 1 or 2 based on general

clinicaltransplantation.com | 1 of 10 Clinical Transplantation. 2020;00:e13870. https://doi.org/10.1111/ctr.13870 2 of 10 | HIRJI et al. consensus from the multidisciplinary transplant team relative to the performance to Status. Status ranking and baseline characteristics severity of illness of those already on the waitlist. A significant con- were obtained at time of listing and were available from program sideration is placed on trajectory of disease when Status ranking is as- databases. Given center-specific variability to the allocation of or- signed to listed patients, especially changes in lung function, increases gans for pediatric cases, these cases were excluded from the analy- in oxygen requirements over time and declines in six-minute walk dis- sis. Research ethics review board approval was obtained from each tances over tests done prior to the consideration for transplantation. participating center to undertake this study (UHN 13-7041.5, UBC Within each Status rank, transplant is offered based on best match H12-01960, UA Pro00066849). (blood type, predicted total lung capacity, serology, etc), severity of disease, and duration on waitlist, with the recipient determined by a transplant physician or surgeon at the center in the jurisdiction of the 2.2 | Primary outcome offered donor lungs. Declined donor organs are offered to the next nearest transplant center until all four centers have declined. Waitlist mortality was the primary outcome of interest. Patients re- In contrast, the lung allocation score (LAS) is a composite of 17 moved from the waitlist prior to transplant or death were excluded clinical and physiological measures that estimate a patient's urgency from the analysis, as this included both patients who improved and for transplant, as well as an estimated post-transplant survival ben- were assumed to be listed too early, and those who developed co- efit in the first post-transplant year.4,5 Introduced in May 2005 in morbidities that made them ineligible for lung transplantation. the United States, the United Network for Organ Sharing (UNOS) implemented the use of the LAS to allocate donor lungs for patients 12 years of age and older. Using well-outlined statistical models,6 a 2.3 | LAS calculations score between 0-100 is calculated to define both a patient's need and estimated survival benefit of receiving a lung transplant. The LAS scores were retrospectively calculated according to the pub- LAS weighs waitlist mortality and transplant benefit, respectively, in lished literature based on available clinical parameters at time of list- a 2:1 ratio, thus enabling the score to be used as a potential predictor ing. Patients missing non-essential clinical data required to calculate of waitlist mortality in jurisdictions not currently utilizing the LAS for the LAS assumed normal values used by the Organ Procurement and lung allocation. Transplantation Network LAS Calculator.7 In this study, we compare the Canadian Status designation to LAS scores in terms of the ability to predict waitlist survival at the time of listing. We hypothesized that LAS will better predict waitlist mor- 2.4 | Statistical analysis tality, given Status is subjective and likely variable across transplant centers. We also investigate whether there is a benefit in combining Continuous data were expressed as mean and standard devia- clinical judgment with LAS to predict death on the waitlist and look tion (SD) or median and interquartile range (IQR) as appropriate. for opportunities for further improvement of the LAS calculation. Categorical data were reported as numbers and percentages, as appropriate. Fisher's exact test and Wilcoxon's rank-sum test were used to compare baseline characteristics by Status. Analysis of vari- 2 | METHODS ance (ANOVA) was performed to assess differences between the three institutions. Survival on the waiting list was compared using 2.1 | Study population Kaplan-Meier with log-rank test. Patients were censored from analysis at the time of transplant or at the completion date of data collec- All patients listed for lung transplantation between January 1, tion, September 2015, if not transplanted. A 2-tailed P-value of <.05 2007, and December 31, 2012, at three Canadian lung transplant was considered statistically significant. programs (Toronto Lung Transplant Program, Toronto General The primary outcome was mortality rate on the waitlist using each Hospital, Toronto, Ontario, Canada; University of Alberta Lung organ allocation method. An LAS threshold identifying high from low Transplant Program, University of Alberta Hospital, Edmonton, scores was determined by constructing a receiver operating charac- Alberta, Canada; BC Lung Transplant Program, Vancouver General teristic (ROC) curve to determine the LAS with the best sensitivity and Hospital, Vancouver, British Columbia, Canada) were included in the specificity for waitlist mortality with maximal area under the curve study. During this time period, LAS for waitlisted patients was not (AUC). Cohen's Kappa coefficient was calculated to determine the calculated at Canadian centers and thus was not utilized in allocation level of agreement between those patients with a high LAS, and those decisions, allowing for an unbiased comparison of allocation mod- listed as Status 2.8 Using Cox proportional hazards regression models. Since late-2015, most Canadian centers have begun to calculate eling, a hazard ratio (HR) for mortality on the waitlist for both Status LAS and include this score on waitlists to aid transplant clinicians rank and a high LAS, both individually and combined, were calculated. in making decisions on priority for transplantation. Data were col- Models were performed unadjusted, as potential confounders were lected until September 2015 to ensure that LAS had no impact on also clinical components of the LAS, and used to determine Status. allocation decisions in order to objectively compare its predictive Harrell's concordance index (C-statistic) was used to compare the HIRJI et al. | 3 of 10 predictions of Cox models, and the Akaike Information Criterion (AIC) outcomes are outlined in Table 1. Mean LAS for all listed patients was used for goodness of fit of both Status and LAS models in pre- was 37.9 (SD 10.2). Patients with COPD were more likely to be la- dicting waitlist mortality. All analyses were performed using STATA beled Status 1 and have a lower mean LAS (33.0 ± 3.3) while patients version 14 (Stata Statistical Software: Release 14: StataCorp LP). with pulmonary fibrosis (mean LAS 41.7 ± 11.5) or pulmonary hypertension (mean LAS 34.5 ± 6.0) were more likely to be prioritized as Status 2. Those listed as Status 2 had a significantly increased mean 3 | RESULTS LAS compared with Status 1 patients (40.8 ± 4.4 vs 34.6 ± 12.5, P = .0001). The distribution of LAS for all waitlisted patients is plot- A total of 1198 patients were listed during the study period at the ted in Figure 2. Status at listing was not available for 14 patients, three centers. Of these patients initially listed, 11 were removed be- with a mean LAS of 34.62. An additional 12 patients were lacking cause of an improvement in their medical condition after an acute data in order to calculate the LAS accurately. Of these 12 patients, 4 presentation, while 29 patients were removed because of the devel- were listed as Status 2 and 8 were listed as Status 1. opment of comorbidities (eg malignancy, n = 14) that led them to be Status ranking between institutions starkly contrasted with one medically unsuitable for transplant. An additional 10 patients were another [F(1, 1120) = 9.13, P = .003] with varying proportions of pa- taken off the list because of non-compliance, ambivalence to trans- tients being listed each Status despite similar overall LAS for patients plant, moved out of the center's jurisdiction or other reasons. A total at each institution [F(2, 1119) = 4.36, P = .053]. Furthermore, only of 26 pediatric patients listed for lung transplant at the three centers the Toronto program utilizes an additional Status 3 for rapidly deteri- over the study period were excluded. Overall, 852 patients (76%) orating patients. In order to assess the severity of disease of patients were transplanted, 232 patients (20.7%) died awaiting transplant, in each Status rank at each institution, mean LAS were calculated for and at the end of data collection, September 25, 2015, 38 patients each Status at each center (Table 2). Given the above findings, for (3.4%) were still awaiting transplant (Figure 1). the purposes of our analysis, the Toronto Program Status 3 patients (n = 85) were reclassified as Status 2. After reclassification, the mean LAS of Toronto patients as Status 2 was 40.4 (SD 12.5). 3.1 | Status and LAS scores

A total of 1122 patients with available Status and LAS clinical pa- 3.2 | Waitlist mortality rameters were actively listed in the 5 year time period. A total of 514 patients (45.8%) were listed Status 1, and 608 patients (54.2%) In both Vancouver and Edmonton transplant programs, of those were listed Status 2. Baseline characteristics at time of listing and with an outcome by the end of the study period, 70.7% of the

FIGURE 1 Study flowchart 4 of 10 | HIRJI et al.

TABLE 1 Baseline characteristics Overall Status 1 Status 2

Patient characteristics n = 1122 n = 514 (45.8%) n = 608 (54.2%) P-value

Age at listing (y) 52.4 ± 13.4 53.7 ± 12.8 51.3 ± 13.8 .005 Sex (female) 431 (42.6) 193 (46.7) 238 (39.8) .05 Primary diagnosis Cystic fibrosis 203 (18.1) 95 (18.5) 108 (17.8) <.001 Chronic obstructive 284 (25.3) 208 (40.5) 76 (12.5) pulmonary disease Pulmonary fibrosis 464 (41.4) 157 (30.5) 307 (50.5) Pulmonary hypertension 71 (6.3) 19 (3.7) 52 (8.6) Other 100 (8.9) 35 (6.8) 65 (10.7) Height (cm) 168.3 ± 9.3 168.1 ± 9.5 168.5 ± 9.3 .227 Body mass index (kg/m2) 24.4 ± 4.8 24.2 ± 4.6 24.7 ± 5.0 .172 Creatinine (mmol/L) 70.7 ± 29.0 71.4 ± 33.9 69.9 ± 21.9 .405 Forced vital capacity (% 53.3 ± 18.2 55.2 ± 17.8 51.8 ± 18.4 <.001 predicted) Presence of diabetes 189 (16.9) 72 (14.1) 117 (19.3) <.001

Requiring supplemental O2 824 (74.6) 343 (66.9) 481 (81.3) <.001 at rest Requiring assisted 76 (9.4) 20 (4.7) 56 (14.6) <.001 ventilationa

Arterial pCO2 (mm Hg) 42.2 ± 12.3 41.1 ± 9.5 43.1 ± 14.1 .189 6-minute walk distance 972.6 ± 458 1102.1 ± 391 868.5 ± 481 <.001 (feet) Functional status (NYHA 4 76 (9.5) 20 (4.6) 56 (15.2) <.001 or Total Dependence with ADLs) Waitlist death 223 (20.7) 59 (11.9) 164 (27.0) <.001 Single/double lung 119 (14.9) 71 (18.3) 48 (11.7) .003 transplant (single) Median length of stay in 5 (12) 4 (9) 7 (15) <.001 ICU post-transplant (d, IQR) Median length of stay in 23 (26) 21 (18) 27 (35.5) <.001 hospital post-transplant (d, IQR) Mean Lung Allocation 37.9 (10.2) 34.6 (12.5) 40.8 (4.4) <.001 Score (SD)

Note: Statistically significant P-values (P < .05) are indicated in italics. Continuous variables are expressed as mean ± standard deviation and categorical variables as number (%) unless otherwise specified. Abbreviations: ADL, Activities of Daily Living; ICU, Intensive care Unit, NYHA, New York Heart Association. aIncludes Bipap as a modality of ventilation. waitlisted cohort was transplanted, while in the Toronto program, patients listed Status 2 compared with those listed Status 1 (27% vs which has the largest catchment area of 15.8 million Canadians,9 11.5%, P < .001). 83.7% of the listed cohort was transplanted. Median time to trans- Compared with the remainder of the cohort, those who died plant was 197 days (IQR 256) for patients listed as Status 1, and awaiting transplant were more likely to be older (mean age 56.3 ± 11.3 86 days (IQR 166) for those listed as Status 2. Median time to wait- vs 51.4 ± 13.7, P < .001), female (23% vs 17% of males, P < .03), list death across all three institutions was 201 days (IQR 421) for shorter in stature (mean 165 ± 8.7 vs 168.9 ± 9.4 cm, P < .001), and those listed Status 1, and 57 days (IQR 200) for those listed Status have a diagnosis of pulmonary fibrosis (64.3%). Of those who died on 2. Proportion of waitlist deaths over the study period was higher for the waitlist, 73.2% were listed as Status 2 compared to 49.4% in the HIRJI et al. | 5 of 10 rest of the cohort, P < .001. Mean LAS at listing of those who died on waitlist mortality (C-statistic 0.689 vs 0.674). Using the AIC, where the waitlist was 43.1 ± 14.2 compared with a mean LAS of 36.7 ± 8.4 a lower number indicates better goodness of fit of the model, LAS for those who survived, P < .001). again demonstrated to be a better fit model in predicting waitlist Higher LAS scores were associated with a higher risk of death death (LAS AIC 1951.3 vs Status AIC 1959.5). on the waitlist (HR 1.06 per unit LAS, 95% confidence interval [CI] Agreement between Status 2 patients and LAS ≥ 37 was only 1.05, 1.07, P < .001). Patients with a LAS 40-45 had a HR of death on fair (Kappa statistic = 0.30). The HR for death on the waiting list for the waitlist of 3.6 (95% CI 2.39, 5.43) compared to those listed with patients with LAS ≥ 37 was 3.64 (95% CI 2.78, 4.78) compared with LAS 30-35, while those with a LAS greater or equal to 80 had a HR those LAS < 37, while Status 2 patients had a HR for waitlist death of of death on the waitlist of 24 (95% CI 13, 44.2) compared to those 3.98 (95% CI 2.93, 5.41) compared with those listed Status 1. listed with LAS 30-35 (Table 3). Kaplan-Meier curves for survival on the waitlist, by both Status rank and LAS score are displayed in Figure 4. Combining Status and LAS data demonstrated that patients listed as Status 2 and with a 3.3 | Comparison of status- and LAS-based calculated LAS ≥ 37 had the highest mortality in the first 6 months prediction models after listing, with a HR of 8.94 (95% CI 5.97, 13.37) compared with patients listed Status 1 and with an LAS < 37. The AIC of a com- The ROC curve describing the relationship between LAS and wait- bined model of Status and LAS demonstrated better goodness of list mortality had an AUC of 0.673 (Figure 3). The sensitivities and fit in comparison with either model alone (Status-LAS AIC 1940.8). specificities of various LAS cutoffs are listed in Table 4. The LAS There was a significant difference in survival between patients with with the closest AUC to the maximum achieved was 37 (AUC 0.644), an LAS < 37 listed either Status 1 or Status 2, log-rank test < 0.001 (see with a sensitivity of 58% and specificity of 71%. This was selected Figure 4C). We thus assessed for demographic and clinical differences as the LAS cutoff to differentiate high versus low LAS values. Using between these two groups to better understand what led to differ- Harrell's C-statistic to compare the models of LAS and Status, the ences in choice of Status ranking that may explain this disparity in LAS model was found to have a better goodness of fit in predicting survival that LAS was unable to differentiate between. Mean LAS between these two groups with LAS < 37 was quite similar (Status 1 mean LAS 33.0 ± 1.7 vs Status 2 33.3 ± 1.9), although this was statistically significantly different, P = .03. While many characteristics between the two groups were similar, such as sex, height, oxygen requirement, and presence of diabetes, Status 2 patients were more likely to be younger and have a diagnosis of pulmonary fibrosis or pulmonary hypertension. Six-minute walk distances and functional status were also worse in the Status 2 group compared with those listed Status 1 (see Table 5). In the cohort with LAS ≥ 37, the most significant differences between those listed Status 1 and Status 2 were again worse 6-minute walk distance and functional status in the Status 2 group.

4 | DISCUSSION

FIGURE 2 Histogram of LAS at time of listing for all patients Pre-transplant mortality rates for individuals awaiting lung trans- listed for lung transplant plant in Canada remain high, as the per capita rates of organ

TABLE 2 Mean LAS by Status at each No. of patients (% by Overall mean institution Institution Status Institution) Mean LAS (SD) LAS (SD)

Toronto 1 334 (48.2) 34.5 (4.1) 37.5 (9.9) 2 274 (39.5) 38.3 (8.2) 3 85 (12.3) 47.1 (19.6) Edmonton 1 79 (24.8) 33.9 (4.5) 39.1 (11.3) 2 239 (75.2) 40.8 (12.2) Vancouver 1 101 (91.0) 35.6 (5.4) 37.1 (8.3) 2 10 (9.0) 52.2 (15.4)

Abbreviations: LAS, Lung allocation score; SD, Standard Deviation. 6 of 10 | HIRJI et al.

TABLE 3 Hazard ratio for mortality on waitlist based on LAS at median LAS between institutions (Table 1), Status ranking varied sig- listing nificantly, from 9% of the Status 2 waitlisted cohort in Vancouver,

Lung to 75% in Edmonton. This variability also likely impacts the correla- Allocation Hazard ratio (95% tion of Status to LAS demonstrated on kappa testing. This finding Score No. of patients CI) P-value reveals that each institution holds a different clinical threshold to 25-29.99 25 1.44 (0.52, 3.94) .481 what they define as a “deteriorating” (Status 2) patient. Organ alloca- 30-34.99 577 1.00 — tion in Canada is currently performed on a regional basis; hence, this 35-39.99 271 1.86 (1.30, 2.65) .001 variation in Status definition has little impact on waitlisted patients. 40-44.99 113 3.60 (2.39, 5.43) <.001 Selecting an appropriate LAS cutoff to dichotomize patients as high versus low risk of death is difficult in such an analysis, as 45-49.99 40 3.95 (2.17, 7.18) <.001 there is already an existing tendency to transplant those likely to 50-59.99 48 10.00 (6.20, 16.15) <.001 die earlier, and increasing LAS increases the competing risk of both 60-79.99 28 7.70 (3.81, 15.59) <.001 death and transplant. Donor-recipient matching is also dependent 80+ 20 23.98 (13.00, <.001 on other factors such as blood type and height, which can confound 44.22) the correlation between LAS and death. In our ROC curve analysis, a cutoff of LAS ≥ 37 maximized both sensitivity and specificity with the greatest AUC; however, this cutoff was only associated with a sensitivity of 58% and specificity of 71%. This indicates that patients awaiting lung transplantation are at risk of death regardless of their LAS, although there was a positive correlation with risk of mortality as LAS increased. As hypothesized, the LAS model demonstrated significantly better goodness of fit in predicting waitlist mortality compared to Status. The LAS as a continuous variable was able to identify patients in higher LAS strata who had a very high mortality risk, whereas Status lacked the granularity for this based on its bi- nary allocation. A combination of clinical judgment and LAS could predict those at greatest risk of death, as this model demonstrated the best goodness of fit for predicting waitlist mortality. Patients with both Status 2 and a LAS ≥ 37 had the worst survival awaiting transplant. Furthermore, in the listed cohort with LAS < 37, there were significant differences in mortality between those listed Status 1 and 2. Although mean LAS was very similar for these two groups, Status 2 patients were more likely to be younger, have a diagnosis of pulmonary fibrosis or pulmonary hypertension, and have a worse six minute walk distance and functional status. This indicates that emphasis FIGURE 3 Receiver operating characteristic curve of LAS on on age, diagnosis, and functional status, although considered in the waitlist mortality calculation of the LAS, may not be weighted as heavily as a clinician may consider based on clinical judgment. Weighting these more donation across the country (20.9 deceased donors per mil- heavily in the LAS calculation may give a higher score to these pa- lion)10 remain lower than the national need for lung transplants. tients than they are currently awarded, highlighting their increased Prediction of death on the lung transplant waitlist is thus impor- risk of mortality, and allowing them to be transplanted sooner. tant in order to allocate donor lungs to the patient at highest risk Although these clinical differences partially explain the ben- of mortality. While several studies have indicated the ability of efits that a subjective scoring system such as Status has to offer, LAS to predict post-transplant survival,11-14 because of its 2:1 the strength in its capability to predict mortality likely comes from weighting for pre-transplant mortality over post-transplant ben- information not even captured in the LAS. Trajectory of disease efit, increasing LAS has been designed to confer increasing risk of significantly impacts waitlist mortality19-22 and is heavily weighted waitlist mortality15,16 and can be applied as a useful tool to predict on the Status rank assigned to a patient, based on changes in lung death on the waitlist by countries that currently do not utilize LAS function parameters, oxygen requirements and six-minute walk for allocation.17,18 test changes over time prior to the referral of transplant, critical This study reveals that although all Canadian lung transplant cen- pieces of information which the LAS does not incorporate into ters utilize a Status rank to prioritize patients awaiting transplant, its calculated result but is recognized only after the LAS has be- Status is subjective and institution specific. Despite similar overall come high enough to warrant transplant. The LAS thus removes a HIRJI et al. | 7 of 10

TABLE 4 Sensitivity, specificity, ROC AUC, positive and negative likelihood ratios, and predictive value of an incremental increase in LAS cutoff for predicting waitlist death, using a prevalence of 20% for mortality

LAS Cutoff Sensitivity (%) Specificity (%) ROC AUC LR (+) LR (−) PPV (%) NPV (%)

34 73.5 50.1 0.618 1.47 0.529 26.8 88.4 35 67.7 59.0 0.633 1.65 0.548 29.0 88.0 36 61.4 66.0 0.637 1.80 0.585 30.9 87.3 37 57.8 70.9 0.644 1.98 0.595 33.0 87.1 38 49.8 75.9 0.628 2.06 0.662 33.8 85.9 39 46.2 79.2 0.627 2.22 0.679 35.5 85.6 40 42.2 82.9 0.625 2.44 0.699 37.8 85.2

Abbreviations: AUC, area under the curve; LR, likelihood ratio; NPV, negative predictive value; PPV, positive predictive value; ROC, receiver operating characteristic. significant degree of information available to the transplant physi- prioritized based on severity of illness, implementation of the LAS cian by giving an objective score based on current clinical status. was found to significantly reduce waitlist mortality by 23%.33 As a transplant clinician knows quite well, a patient with idiopathic We have demonstrated that Status would be inadequate for the pulmonary fibrosis (IPF) who has been on 4 L of oxygen for the development of national policies on inter-provincial organ sharing past 3 years has a very different risk profile than a patient with and allocation. Potential advantages to using the LAS in Canada IPF with an otherwise similar profile who has had their oxygen re- would be to standardize practices among transplant centers and to quirements increased on multiple occasions from 1 to 4 L over the eliminate clinician-to-clinician subjectivity in prioritizing patients past 3 months. These two individuals would have the same LAS, for transplant. This would then allow for consideration of the de- however likely a different Status ranking. Incorporating changes velopment of national organ sharing policies. The use of the LAS in oxygen needs or lung function over a defined period of time would also allow transplant programs to better identify which of into the LAS may more accurately identify patients at risk of early their patients are at highest risk of both waitlist mortality and poor death. post-transplant survival, while the current ranking system is based Beyond disease trajectory, other less objective measures con- on subjective assessment of waitlist mortality risk alone. However, sidered to be relevant to predicting pre-transplant mortality not in- potential disadvantages to LAS include its inflexibility and low pre- cluded in the LAS, include degree of frailty,23,24 the patient's extent dictive value of long-term survival post-transplant.13,34-36 of extra-pulmonary comorbidity,25-27 frequency of hospitalizations, As both allocation models have inherent advantages, it seems and presence of difficult to manage microorganisms,28-30 all factors Canadians may benefit from an allocation system that melds Status which can influence a clinician's decision when prioritizing a patient and LAS together. A hypothetical CANLAS of 44-2 could indicate a with end-stage lung disease. These factors are considered at the Status 2 patient with an LAS of 44, which based on our study cohort time of allocation of Status rank and demonstrate inherent benefits had an average time to death of 36 days, and could be prioritized of clinical gestalt when objective measurements may not capture all over a patient with a CANLAS of 36-2, which in our cohort had a available predictive variables. mean time to death of 255 days. The benefits of the current allocation system in Canada are This analysis has limitations. First, it is based on Status and LAS several. Status ranking in Canada is able to take extended clini- calculated at the time of listing and does not account for changes cal factors into consideration and then be used by the transplant that may have occurred as patients deteriorated further. LAS was clinician as part of the decision of whom to transplant, allowing only retrospectively calculated at time of listing since it is not re- the flexibility to consider those who are highly sensitized, have an quired for lung allocation in Canada. Similarly, updates to Status are atypical chest cavity size, or may benefit earlier from an ABO com- not mandated to be performed in a protocolized manner and were patible donor rather than wait for an ABO identical one. Thus, the difficult to capture for the purposes of this study. Nevertheless, we role of implementation of the LAS in Canada is a source of debate. felt the comparison of allocation models based on data available at Within a year of implementation of the LAS in the United States, listing was important to analyze, as predictions of waitlist mortality waitlist times were found to have decreased by more than 40%,31 are most helpful at the time of listing when time to allocation of lungs and national data demonstrated a reduction in annual deaths on the is maximized for a patient with progressive end-stage lung disease. waitlist from approximately 500 to 300.32 This improvement, how- The LAS is derived from both estimated waitlist survival prob- ever, was primarily due to moving away from a system that trans- ability over the next year, as well as the estimated post-transplant planted patients based on time on waitlist, and the removal of survival probability over the first-year post-transplant. For this rea- inappropriate candidates who were listed simply to accrue time. son, LAS does not linearly correlate with waitlist mortality as com- Even among countries that have recently implemented the LAS posite scores may be lower for those individuals with higher risk of such as Germany, where the lung transplant waitlist was already mortality both pre- and post-transplant. Nevertheless, since the LAS 8 of 10 | HIRJI et al.

(A) Kaplan−Meier Survival Estimates by Status FIGURE 4 Kaplan-Meier survival curves by (A) Status and (B) LAS, using

1.00 a cutoff of 37 and (C) combined cohorts based on Status and LAS, using a cutoff t of 37 0.75 0.50

0.25 log−rank p<0.001 Proportion Alive on Waitlis 0.00 0 2 4 6 8 years

Status 1Status 2

(B) Kaplan−Meier Survival Estimates by LAS 1.00 t 0.75 0.50 0.2 5 Proportion Alive on Waitlis Log rank p<0.001 0.0 0 0 2 4 6 8 years

LAS <37 LAS ?37

(C) Kaplan−Meier Survival Estimates by Status and LAS Combined 1.00 t 0.7 5 0.5 0 0.2 5 Proportion Alive on Waitlis Log rank p<0.001 0.0 0 0 2 4 6 8 years

Status 1 and LAS <37 Status 1 and LAS ?37 Status 2 and LAS <37 Status 2 and LAS ?37 HIRJI et al. | 9 of 10

TABLE 5 Clinical characteristics of Status 1 Status 2 patients listed Status 1 and Status 2 with LAS < 37 LAS < 37 an LAS < 37 Patient characteristics n = 422 n = 309 P-value

Age at listing (y) 53.3 ± 12.9 48.0 ± 14.6 <.001 Sex (female) 157 (54.5) 131 (45.5) .448 Primary diagnosis Cystic fibrosis 84 (19.9) 68 (22) <.001 Chronic obstructive pulmonary 204 (48.3) 64 (20.7) disease Pulmonary fibrosis 93 (22) 100 (32.4) Pulmonary hypertension 13 (3.1) 42 (13.6) Other 28 (6.6) 35 (11.3) Height (cm) 168.2 ± 9.4 167.9 ± 9.4 .652 Body mass index (kg/m2) 24.0 ± 4.6 23.9 ± 4.8 .812 Creatinine (mmol/L) 71.5 ± 36.5 72.0 ± 25.5 .869 Forced vital capacity (% predicted) 57.2 ± 17.6 57.2 ± 19.4 .996 Presence of diabetes 52 (12.4) 50 (16.2) .147

Requiring supplemental O2 at rest 264 (62.5) 203 (68.1) .124

Arterial pCO2 (mm Hg) 40.8 ± 8.8 41.6 ± 13.2 .331 6-minute walk distance (feet) 1,116.4 ± 390 960.0 ± 495 <.001 Functional status (NYHA 4 or Total 15 (4.3) 25 (13.4) <.001 Dependence with ADLs) Lung Allocation Score 33.0 ± 1.7 33.3 ± 1.9 .027

Note: Statistically significant P-values (P < .05) are indicated in italics. Continuous variables are expressed as mean ± standard deviation and categorical variables as number (%) unless otherwise specified. weighs waitlist mortality and transplant benefit respectively in a 2:1 RDL, and LGS all participated in writing and review of the manu- ratio, our study confirms that the score can be used as a potential script. AH takes full responsibility for the validity of the data and for predictor of waitlist mortality in jurisdictions not currently utilizing the results presented here within. the LAS for lung allocation. This study demonstrates that LAS is superior to clinical judgment DATA AVAILABILITY STATEMENT in predicting waitlist mortality. The continuous nature of the LAS fa- The data that support the findings of this study are available from cilitates risk stratification with more precision than Status. We have the corresponding author upon reasonable request. shown that integrating a system in Canada that includes the ability to prioritize patients through a standardized score as well as allow ORCID for clinician gestalt can best aid in identifying those individuals at Alim Hirji https://orcid.org/0000-0002-6879-2345 highest risk of waitlist mortality. This study also demonstrates that Kieran Halloran https://orcid.org/0000-0002-5615-6974 further work can be done with the LAS to better identify those at Lianne G. Singer https://orcid.org/0000-0002-2693-8676 risk of early death, and by weighting disease and functional status more heavily, and incorporating more parameters to integrate trajec- REFERENCES tory of disease into the LAS calculation, waitlist mortality in coun- 1. Vock DM, Durheim MT, Tsuang WM, et al. Survival benefit of lung tries utilizing the LAS could potentially be reduced further. transplantation in the modern era of lung allocation. Ann Am Thorac Soc. 2017;14(2):172-181. 2. Singer LG, Chowdhury NA, Faughnan ME, et al. Effects of recipient CONFLICTS OF INTEREST age and diagnosis on health-related quality-of-life benefit of lung The authors of this manuscript declare no conflicts of interest. transplantation. Am J Respir Crit Care Med. 2015;192(8):965-973. 3. Canadian Institute for Health Information. Annual Statistics on Organ Replacement in Canada: Dialysis, Transplantation and AUTHOR CONTRIBUTION Donation, 2008 to 2017. Canada. 2018. AH, HZ, JY, DCL, RDL, and LGS participated in research design. AH, 4. Egan TM, Murray S, Bustami RT, et al. Development of the new HZ, and MH participated in data collection. AH, MBO, JSL, and KH lung allocation system in the United States. Am J Transplant. participated in data analysis. AH, HZ, MBO, JSL, KH, MH, JY, DCL, 2006;6(5p2):1212-1227. 10 of 10 | HIRJI et al.

5. Eberlein M, Garrity ER, Orens JB. Lung allocation in the United 23. McAdams-DeMarco MA, Ying H, Thomas AG, et al. Frailty, inflam- States. Clin Chest Med. 2011;32(2):213-222. matory markers, and waitlist mortality among patients with end- 6. United Network for Organ Sharing. A guide to calculating the lung stage renal disease in a Prospective Cohort Study. Transplantation. allocation score. 2005. 2018;102(10):1740-1746. 7. Organ Procurement and Transplantation Network. Lung Allocation 24. Singer JP, Diamond JM, Gries CJ, et al. Frailty phenotypes, disabil- Score Calculator 2015. https://optn.transplant.hrsa.gov/resources/ ity, and outcomes in adult candidates for lung transplantation. Am J allocation-calculators/las-calculator/. Accessed September 25, Respir Crit Care Med. 2015;192(11):1325-1334. 2015. 25. De Boer K, Lee JS. Under-recognised co-morbidities in idiopathic 8. Landis JR, Koch GG. The measurement of observer agreement for pulmonary fibrosis: a review. Respirology. 2016;21(6):995-1004. categorical data. Biometrics. 1977;33(1):159-174. 26. Suzuki A, Kondoh Y. The clinical impact of major comorbidities on 9. Statistics Canada. Population estimates, quarterly. 2018. Contract idiopathic pulmonary fibrosis. Respir Investig. 2017;55(2):94-103. No.: Table 17-10-0009-01. 27. Negewo NA, Gibson PG, McDonald VM. COPD and its comor- 10. International Registry on Organ Donation and Transplantation. bidities: impact, measurement and mechanisms. Respirology. 2016 Newsletter. 2017. 2015;20(8):1160-1171. 11. Weiss ES, Allen JG, Merlo CA, Conte JV, Shah AS. Lung allocation 28. Slenter RH, Sprooten RT, Kotz D, Wesseling G, Wouters EF, score predicts survival in lung transplantation patients with pulmo- Rohde GG. Predictors of 1-year mortality at hospital admission nary fibrosis. Ann Thorac Surg. 2009;88(6):1757-1764. for acute exacerbations of chronic obstructive pulmonary disease. 12. Liu V, Zamora MR, Dhillon GS, Weill D. Increasing lung allocation Respiration. 2013;85(1):15-26. scores predict worsened survival among lung transplant recipients. 29. Song JW, Hong SB, Lim CM, Koh Y, Kim DS. Acute exacerbation of Am J Transplant. 2010;10(4):915-920. idiopathic pulmonary fibrosis: incidence, risk factors and outcome. 13. Merlo CA, Weiss ES, Orens JB, et al. Impact of U.S. Lung Allocation Eur Respir J. 2011;37(2):356-363. Score on survival after lung transplantation. J Heart Lung Transplant. 30. Lindenauer PK, Dharmarajan K, Qin L, Lin Z, Gershon AS, Krumholz 2009;28(8):769-775. HM. Risk trajectories of readmission and death in the first year 14. Braun AT, Dasenbrook EC, Shah AS, Orens JB, Merlo CA. Impact after hospitalization for chronic obstructive pulmonary disease. Am of lung allocation score on survival in cystic fibrosis lung transplant J Respir Crit Care Med. 2018;197(8):1009-1017. recipients. J Heart Lung Transplant. 2015;34(11):1436-1441. 31. Egan TM, Edwards LB. Effect of the lung allocation score on lung 15. Li SS, Miller R, Tumin D, Stewart WCL, Tobias JD, Hayes D. Lung transplantation in the United States. J Heart Lung Transplant. allocation score thresholds prioritize survival after lung transplan- 2016;35(4):433-439. tation. Chest. 2019;156(1):64-70. 32. Yusen RD, Shearon TH, Qian Y, et al. Lung transplantation in 16. Russo MJ, Worku B, Iribarne A, et al. Does lung allocation score the United States, 1999–2008. Am J Transplant. 2010;10(4 Pt maximize survival benefit from lung transplantation? J Thorac 2):1047-1068. Cardiovasc Surg. 2011;141(5):1270-1277. 33. Gottlieb J, Greer M, Sommerwerck U, et al. Introduction of the lung 17. Russo MJ, Iribarne A, Hong KN, et al. High lung allocation score is allocation score in Germany. Am J Transplant. 2014;14(6):1318-1327. associated with increased morbidity and mortality following trans- 34. Gries CJ, Rue TC, Heagerty PJ, Edelman JD, Mulligan MS, Goss CH. plantation. Chest. 2010;137(3):651-657. Development of a predictive model for long-term survival after 18. Smits JM, Nossent G, Evrard P, et al. Lung allocation score: the lung transplantation and implications for the lung allocation score. J Eurotransplant model versus the revised US model – a cross-sec- Heart Lung Transplant. 2010;29(7):731-738. tional study. Transpl Int. 2018;31(8):930-937. 35. Maxwell BG, Levitt JE, Goldstein BA, et al. Impact of the lung 19. Lynch DA, Godwin JD, Safrin S, et al. High-resolution computed to- allocation score on survival beyond 1 year. Am J Transplant. mography in idiopathic pulmonary fibrosis: diagnosis and prognosis. 2014;14(10):2288-2294. Am J Respir Crit Care Med. 2005;172(4):488-493. 36. Halpern SD. Beyond “The LAS is broken”. Ways to improve lung al- 20. Ash SY, Harmouche R, Vallejo DL, et al. Densitometric and local his- location. Am J Respir Crit Care Med. 2015;191(3):245-246. togram based analysis of computed tomography images in patients with idiopathic pulmonary fibrosis. Respir Res. 2017;18(1):45. 21. Erbes R, Schaberg T, Loddenkemper R. Lung function tests in pa- How to cite this article: Hirji A, Zhao H, Ospina MB, et al. tients with idiopathic pulmonary fibrosis. Are they helpful for pre- Clinical judgment versus lung allocation score in predicting dicting outcome? Chest. 1997;111(1):51-57. 22. Gomberg-Maitland M, Glassner-Kolmin C, Watson S, et al. Survival lung transplant waitlist mortality. Clin Transplant. in pulmonary arterial hypertension patients awaiting lung trans- 2020;00:e13870. https://doi.org/10.1111/ctr.13870 plantation. J Heart Lung Transplant. 2013;32(12):1179-1186.