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Original Clinical Science—General

Pretransplant Opioid Use and Survival After Lung Transplantation

Vahidy, Sana MD1; Li, David BSc1; Hirji, Alim MD, MSc1; Kapasi, Ali MD1; Weinkauf, Justin MD1; Laing, Bryce MD2; Lien, Dale MD1; Halloran, Kieran MD, MSc1

Author Information
doi: 10.1097/TP.0000000000003050
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The risk of pretransplant opioid use in lung transplant candidates is not well defined and opioid use does not feature in the International Society for Heart and Lung Transplantation selection guidelines.1 Studies on pretransplant opioid use in kidney and liver transplant candidates have yielded variable results but some suggest increased risk of graft failure or death.2-6 There has also been harm associated with opioid use in patients with advanced chronic obstructive pulmonary disease.7,8 Furthermore, there is lack of consensus on the approach to opioid use at time of listing for lung transplant based on the results of a recent national US survey.9 Guidance is needed on this subject in light of the changing landscape of opioid prescription based on recent recommendations.10,11

The lung transplant candidate population may be unique in their opioid use and risk. Opioids are a recommended therapy for end-stage dyspnea, both in lung transplant candidates and otherwise, so the prevalence of use may be greater.12-15 Opioids also pose unique respiratory risks that may affect the immediate posttransplant course in the event of either opioid-related respiratory depression and/or withdrawal.16 Finally, as with other organ transplants, the development of opioid use disorder could increase risk of immunosuppressive nonadherence given patients’ “failure to fulfill major… obligations” as described in the Diagnostic and Statistical Manual of Mental Disorders definition.17 These issues could potentially impact short- and long-term survival, as a recent small study of 21 lung transplant recipients suggested where opioid use before lung transplantation increased the risk of forced expiratory volume in 1 second decline and mortality posttransplantation.18

Given these unknowns and potential implications, we sought to analyze the relationship between pretransplant opioid use in lung transplant candidates and time to death or retransplantation after transplant, as well as secondary outcomes including duration of ventilation, intensive care unit (ICU) and hospital length of stay (LOS), time to chronic lung allograft dysfunction (CLAD) onset and continued opioid use at 1 year in those using at time of listing. We hypothesized that opioid use at time of listing would be associated with an increased risk of death or retransplantation as well as a more complex perioperative course and an increased risk of progression to CLAD.



We reviewed adult lung transplant recipients consecutively transplanted between November 2004 and August 2015 in the University of Alberta Lung Transplant program in Edmonton, Canada. We routinely record patients’ complete medication list at time of listing in our prospectively collected database. All other data were obtained from either our program database or patients’ charts. We excluded patients transplanted in 2016 and afterwards as we implemented a limited opioid policy, which would result in inhomogeneity of the risk and/or control groups. Patient consent was waived given the retrospective design and inclusion of deceased cases. Institutional review board approval was provided at study outset (Pro00070337).

Opioid Use

We designated the risk group as any opioid use at time of listing based on review of medications recorded at that timepoint, including type and dose in oral morphine equivalents (OMEs). This included patients listed while on invasive respiratory support in an ICU setting. Opioid use status was not reassessed at time of transplant. Time between opioid exposure assessment and transplant is reflected by waitlist duration. For as needed use (pro re nata, PRN), OMEs were calculated based on the assumption of maximal dosing in 24 hours. Our program did not have a specific opioid policy over the study timeframe and accepted patients were not required to change therapy. Our program also had policies requiring reduction or complete abstinence of alcohol intake and complete abstinence of marijuana in accepted patients, in addition to the absence of a substance use disorder as per guidelines.


Our primary outcome was retransplant-free survival after transplant (time to death or retransplantation). Secondary outcomes included duration of ventilation, ICU and hospital LOS; 3-month and 1-year survival; continuing opioid use at 1 year in those on therapy at assessment; and time to CLAD onset, defined as time from date of transplant to criteria for CLAD being met, as per the International Society for Heart and Lung Transplantation consensus statement.19


We compared continuous variables using t-tests or Wilcoxon tests depending on normality and binary or categorical variables using Fisher exact test or Pearson chi-square test. We analyzed the association between any versus no pretransplant opioid use as a dichotomous risk factor and overall retransplant-free survival from date of transplant by proportional hazards regression model, adjusted for variables known to be or potentially associated with survival: recipient age, gender, underlying diagnosis, transplant type, and recipient bridging status (none, bilevel positive airway pressure, or invasive ventilation/extracorporeal membrane oxygenation). We also adjusted for variables with notable differences in baseline characteristics to mitigate confounding. We tested the proportional hazards assumption for all variables. We also ran a secondary survival models using total dose of OMEs as the risk factor of interest (both assuming maximum as well as one-half maximum PRN usage in 24 h) as well as subtypes of opioid use (regular [non-PRN], opioids stronger than codeine, or regular/noncodeine opioid use). We ran a death-censored time-to-CLAD-onset model. Missing data for modeled variables were handled via listwise deletion. All analyses were performed using JMP 12 (SAS Institute, Inc., Cary, NC).


Four hundred and twenty-five patients underwent transplant during the study timeframe, mainly double lung transplant recipients (91%) reflecting center preference. Patient follow-up was complete to December 2015. No subjects were excluded on the basis of missing data for the primary variable of interest, opioid use, or absence of primary outcome assessment. The prevalence of opioid use at time of listing was 14% (n = 61) with a median daily OME dose of 31 mg (interquartile range 18–54 mg). Recipients using opioids at time of listing differed from those not using opioids with respect to ethnicity and bridging status (use of mechanical ventilation or extracorporeal membrane oxygenation) (Table 1). Codeine was the most common opioid, followed by morphine, hydromorphone, and oxycodone (Table 2).

Baseline characteristics
Opioid use details

Retransplant-free Survival

One hundred thirty-seven patients (32%) died and 1 patient underwent retransplant during the study timeframe. Opioid use at time of listing did not increase risk of death or retransplantation in unadjusted (hazard ratio [HR] 1.15 [95% confidence interval [CI], 0.70-1.81], P = 0.5622) or adjusted models (HR 1.12 [95% CI, 0.65-1.83], P = 0.6570), controlling for recipient age, gender, ethnicity, underlying diagnosis, transplant type, and bridging status. Kaplan-Meier estimation with log rank test is depicted in Figure 1 (P = 0.5551). Analyzing opioid use as a continuous variable as total dose of OMEs in the multivariable model showed no association, both assuming maximum possible PRN dose in 24 hours (P = 0.7391) or one-half of maximum (P = 0.8409). Altering the risk group to 1 of noncodeine opioids, regular opioids (excluding PRN use) or noncodeine/regular opioids did not alter the results in unadjusted and adjusted models. Inclusion of either waitlist duration or transplant era (2010 and prior versus post-2010) as additional covariates did not alter the results. Post hoc power calculation showed that based on our cohort size and group proportions, we had 85% power to detect a 15% increase in mortality in the opioid use group at an alpha level of 0.05, assuming a baseline mortality of 30% over the study period.

Kaplan Meier curve for patients on opioid therapy (dotted line) vs not on opioid therapy (solid line) at time of listing.

Perioperative Course

All postoperative outcomes were similar between groups except hospital LOS (opioid users 35 d versus nonopioid users 27 d, P = 0.014, Table 3). Continued opioid use at 1-year posttransplant was common in survivors who were on therapy at time of listing (27/56 or 48%, Table 2).

Postoperative outcomes

Time to CLAD Onset

Ninety-six patients (24%) progressed to CLAD over the study timeframe. Opioid use at time of listing did not increase the unadjusted risk of CLAD onset (HR 0.87 [95% CI 0.44-1.56], P = 0.6628) in a death-censored model.


Our findings suggest that pretransplant opioid use does not impact retransplant-free survival in the largest cohort available on the subject. This is timely, as more direction is needed for clinicians evaluating candidates receiving opioid therapy.

There are several potential reasons for the lack of observed association. First and foremost, opioid use in lung transplant candidates is likely in at least part due to this cohort receiving opioids for respiratory indications and palliation of dyspnea, and in the hands of skilled surgeons, anesthesiologists, critical care physicians and respirologists, the short- and long-term risks of pretransplant opioid use are likely mitigatable by active management. We were not able to capture indication of opioid use due the nature of our data, but moving forward, it will be important to analyze outcomes stratified by said indication. Finally, it is important to note that the daily dose as measured by OME in the overall population was relatively low, in keeping with a respiratory cohort but not necessarily generalizable to candidates on very high opioid doses, with whom programs may be more cautious.

The association between opioid use and survival outcomes has been evaluated in several other studies. Fleming et al6 found preliver transplant opioid use was independently associated with time to graft loss and mortality in subjects with a Model of End-Stage Liver Disease score ≤ 15. There was, however, no effect in the overall cohort, which is in line with our findings. Randall et al noted an increase in mortality in a large registry study in pretransplant opioid users, but the effect was limited to those with level 3 (10–70 daily OMEs) or level 4 (>70 daily OMEs) use. This level of use was present in <35% of the overall cohort and no effect was reported for the overall cohort. Barrantes et al found a 2-fold increased risk of death in pretransplant opioid users compared with nonusers in kidney transplant recipients; however, the primary outcome, time to death or graft failure, was not different between groups either before or after adjustment. Finally, Lentine et al3 noted an increased risk of death and all-cause graft failure at 1 year posttransplant in renal transplant recipients; however, as with Randall et al, this effect was limited to the level 3 and level 4 users.2,3 Our study in contrast lacked a sufficient number of high dose OME users which meant a survival analysis by OME quartile was not feasible. However, lower overall OMEs may also be reflective of a lung transplant cohort compared with other organ groups. It is also possible that the risks of pretransplant opioid use are organ specific. In addition to differences in metabolism in renal and liver transplant candidates, there are almost certainly differences between the pulmonary and nonpulmonary populations in terms of indication for opioid therapy with respect to treatment of dyspnea versus pain and prior addiction, with resultant differences in agent, dose, and usage history. Finally, Drees et al18 report the only existing lung transplant data with an adjusted HR of 7.1 for pretransplant opioid use. We feel this is a problematic analysis though, as the authors’ conclusions are based on a total sample size of 21 with only 6 deaths. Given the model was also adjusted for age and gender, it is almost certainly overfit. Considering all evidence though, the effects of pretransplant opioid use in all organ groups appear to be complex, but given the aforementioned factors, we do not feel our study contradicts prior work. Rather, it is an additional piece of an expanding puzzle.

Posttransplant opioid use was not evaluated in the presented work but is an important area for future study. As many as 71% of renal transplant candidates on opioids before transplantation demonstrated continued use at 1 year posttransplant, while our study found 48% of lung transplant candidates on therapy before transplant continued at 1 year.3 Noncompliance has been suggested to be increased in chronic pain populations, and whether continued opioid use posttransplant plays a role in the pathways and mechanisms of graft loss requires further study.20-22 Our data are at least somewhat reassuring that pretransplant opioid use was not associated with the unadjusted risk of posttransplant CLAD.

Other healthcare outcomes aside from survival are relevant, particularly in lung transplantation where cost-effectiveness has been questioned.23 We noted patients using opioids at time of listing had longer hospital stays (35 versus 27 d), not apparently due to greater PGD risk, more prolonged ventilation or longer ICU stay. It is important to note this is a secondary finding, but taken at face value, we can speculate based on our experience that this could relate to difficulty with pain management strategies and resultant slower progress with physiotherapy or, alternatively, overt opioid toxicity such as constipation. However, our data lack the granularity to explain the mechanisms behind this difference, which will require more detailed inpatient data with symptoms scores and physical performance metrics.

Our study has important limitations. First, our exposure variable—any opioid use—was selected to be inclusive and relevant to a selection committee prospectively evaluating candidates. However, as noted above, stratification by indication, dose, duration, and opioid type will be important to analyze for their respective risks in a larger study with adequately sized subgroups. Second, it is possible that posttransplant outcomes are mediated as much or more so by posttransplant opioid use as they are by pretransplant use and our study was not designed to address this question. Further work on the role of posttransplant opioids in a lung transplant population is much needed. Third, our study lacks information on the mechanisms of opioid-related risks, such as respiratory depression, confusion, or withdrawal syndromes; these parameters await a dedicated study. Fourth, the use of opioids at time of listing does not necessarily mean the patient will be receiving opioids at time of transplant. Though the recorded medications in our database are intended to reflect stable prescriptions, it is possible some patients discontinued use in the interim between listing and transplant, or that nonusers started them. However, given the majority of patients would be receiving this therapy for chronic conditions, that is, pain or dyspnea, we feel they are likely a valid reflection of prescribing status at transplant. More importantly though, our goal was to assess the implications of a risk factor evaluable at time of the listing decision, which our data captures.

Pretransplant opioid use was not associated with retransplant-free survival after lung transplantation. We emphasize that decisions about opioid use in candidates are and should remain program-specific but feel that the presented data does not support exclusion of potential candidates on the basis of opioid use in the dose range captured in the study. Further work is required to assess higher dose- and indication-related effects.

The authors of this article have no relevant financial disclosures. This study was unfunded. We thank Alberta Health Services for the use of resources, the clinical team for their patient care, and the patients for use of their data.


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