Abstract
Impacts of the prescription opioid epidemic have not yet been examined in the context of heart transplantation. We examined a novel database in which national U.S. transplant registry records were linked to a large pharmaceutical claims warehouse (2007–2016) to characterize prescription opioid use before and after heart transplant, and associations (adjusted hazard ratio, 95% LCLaHR95% UCL) with death and graft loss. Among 13 958 eligible patients, 40% filled opioids in the year before transplant. Use was more common among recipients who were female, white, or unemployed, or who underwent transplant in more recent years. Of those with the highest level of pretransplant opioid use, 71% continued opioid use posttransplant. Pretransplant use had graded associations with 1-year posttransplant outcomes; compared with no use, the highest-level use (>1000 mg morphine equivalents) predicted 33% increased risk of death (aHR 1.101.331.61) in the year after transplant. Risk relationships with opioid use in the first year posttransplant were stronger, with highest level use predicting 70% higher mortality (aHR 1.461.701.98) over the subsequent 4 years (from >1 to 5 years posttransplant). While associations may, in part, reflect underlying conditions or behaviors, opioid use history is relevant in assessing and providing care to transplant candidates and recipients.
Keywords: anesthesia/pain management, clinical research/practice, epidemiology, health services and outcomes research, heart transplantation, heart transplantation/cardiology, opioids, registries, risk assessment/risk stratification, risk factors, Scientific Registry for Transplant Recipients (SRTR)
1 |. INTRODUCTION
Heart transplantation is the definitive therapy for end-stage heart failure,1 and the number of heart transplants performed in the United States has steadily increased over the past 5 years.2 To determine candidacy to receive the scarce resource of a donor heart, a thorough and exhaustive evaluation is performed prior to candidate listing.3 Critical evaluation components include confirming that end stage heart disease is not remediable by more conservative measures and establishing the absence of contraindications, including irreversible pulmonary hypertension, active infection, malignancy, severe peripheral arterial disease, cerebrovascular disease, and non-adherence to medical therapy.4 Other risk factors for adverse outcomes may be considered in program-specific protocols or on a case-by-case basis.3 The evaluation also includes a psychosocial component addressing the patient’s ability to give informed consent and comply with posttransplant recommendations, and the presence of adequate support. Active illicit drug use is often considered a contraindication to transplant.
In the United States and internationally, the epidemic of prescription opioid use and abuse is a critical health concern.5,6 Pain is one of the most prevalent health problems in the United States; in 2011, the National Academy of Medicine found that more than one hundred million Americans experience pain that interferes with their activities.7 Use of opioids to treat pain increased in the 1990s as physicians were encouraged to assess and address pain as a fifth vital sign.8 As opioid use began to rise, the complications of overdose and adverse events mirrored the increase. In 2017, 47 600 drug overdoses involving opioid use occurred in the US, an increase of 45% from 2016.9 Pain is a common finding in end-stage heart failure, reported by 84% of patients.10 In the Pain Assessment, Incidence, and Nature in Heart Failure (PAIN-HF) study, patients reported that the only medication that provided pain relief was opioids.11 In one cohort, nearly 25% of patients hospitalized with decompensated heart failure had an opioid prescription on admission.12
Presurgical use of opioid analgesics is increasingly recognized as a predictor of postoperative complications and increased resource utilization in diverse populations, including patients undergoing general and orthopedic surgery, living donor nephrectomy procedures, and some forms of solid organ transplant.13–16 Risks of opioid-related toxicity may be especially pronounced in patients with end-stage organ failure due to altered drug protein binding, metabolism, and excretion, leading to accumulation of parent agents and potentially toxic metabolites.17,18 Patients who use high levels of prescription opioids may also be at increased risk of nonadherence to prescribed care. Previously, we identified associations of high levels of opioid prescriptions before and after kidney transplant with increased risks of death and graft failure,19,20 and with clinical complications such as cardiovascular, respiratory, and neurological events, and recreational substance abuse.21 Similar associations of prescription opioid use with graded increases in death and graft failure were observed among liver transplant recipients.15
Notably, to date, the relationship between prescription opioid use and outcomes among heart transplant patients is not well described. To advance understanding of the impact of the prescription opioid epidemic on heart transplant recipients, we examined a novel database that integrates national transplant registry data with pharmacy fill records as a non-obtrusive measure of medication use that does not rely on self-reporting. Our goals were to quantify pretransplant exposure to prescription opioids, identify correlates of high levels of use, and determine whether the level of opioid exposure before transplant predicts posttransplant outcomes.
2 |. METHODS
2.1 |. Data sources
We conducted a retrospective cohort study using linked healthcare databases in the United States to ascertain patient characteristics, pharmacy fill records, and outcome events for heart transplant recipients. This study used data from the Scientific Registry of Transplant Recipients (SRTR). The SRTR system includes data on all donors, waitlist candidates, and transplant recipients in the US, submitted by the members of the Organ Procurement and Transplantation Network (OPTN). The Health Resources and Services Administration (HRSA), US Department of Health and Human Services, provides oversight to the activities of the OPTN and SRTR contractors.
Pharmacy fill data were assembled by linking SRTR records for heart transplant recipients with billing claims from a large nationwide US pharmaceutical claims data warehouse that collects prescription drug fill records including self-paid fills and those reimbursed by private and public payers. The warehouse comprises National Council for Prescription Drug Program format prescription claims aggregated from multiple sources including claims warehouses, retail pharmacies, and prescription benefit managers for approximately 60% of US retail pharmacy transactions. Individual claim records include the date of a given pharmacy fill with the national drug code identifying agent and dosage. After Institutional Review Board and HRSA approvals, fill records were linked with SRTR records for heart transplant recipients. We applied a deterministic de-identification strategy wherein patient identifiers (last name, first name, date of birth, sex, and ZIP code of residence) were transformed before delivery to the Saint Louis University researchers with Health Information Portability and Accountability Act and HITECH-certified encryption technology from the pharmaceutical data warehouse. The patient de-identification software employs multiple encryption algorithms in succession to guarantee that the resulting “token” containing encrypted patient identifiers can never be decrypted. However, the algorithm yields the same results for a given set of data elements, such that linkages by unique anonymous tokens are possible.
All direct identifiers were removed before the final dataset was available for analysis. Because of the large sample size, the anonymity of the patients studied, and the non-intrusive nature of the research, a waiver of informed consent was granted per the Department of Health and Human Services Code of Federal Regulations (title 45, part 46, paragraph 46.116).
2.2 |. Population and measures
We included heart transplant recipients who underwent transplant between 2007 and 2016 and who had 1 year of pretransplant pharmacy fill records in the pharmaceutical data warehouse. We examined a subgroup of the cohort with available data in the records to ascertain prescription opioid use in the 1 year before and after transplant. Transplant recipient clinical and demographic characteristics, and characteristics of the donated organ and other transplant factors, were defined by the OPTN Transplant Candidate Registration and Transplant Recipient Registration forms (Table 1).
TABLE 1.
Distribution of clinical traits of heart transplant recipients by level of pretransplant opioid use
| No use | Level 1 | Level 2 | Level 3 | Level 4 | |
|---|---|---|---|---|---|
| (N = 8375) | (N = 2130) | (N = 1027) | (N = 606) | (N = 1820) | |
| Recipient factors at transplant | |||||
| Age, y | ‡ | ‡ | ‡ | ‡ | |
| <18 | 91.6 | 4.8 | 1.2 | 0.7 | 1.7 |
| 18–30 | 57.6 | 17.7 | 7.4 | 4.5 | 12.9 |
| 31–40 | 48.5 | 17.7 | 8.7 | 5.6 | 19.5 |
| 41–50 | 52.6 | 16.4 | 8.0 | 5.6 | 17.3 |
| 51–60 | 56.1 | 16.0 | 8.0 | 5.0 | 14.9 |
| 61–70 | 60.4 | 16.1 | 8.1 | 4.0 | 11.4 |
| ≥71 | 65.9 | 15.9 | 7.9 | 3.2 | 7.1 |
| Sex | |||||
| Male | 59.6 | 15.1 | 7.4 | 4.5 | 13.4* |
| Female | 61.0 | 15.6 | 7.2 | 4.0 | 12.2‡ |
| Race | * | ‡ | * | ‡ | |
| White | 59.8 | 14.8 | 7.6 | 4.3 | 13.5 |
| African American | 56.4 | 16.6 | 8.0 | 4.8 | 14.2 |
| Hispanic | 68.3 | 14.1 | 4.8 | 3.6 | 9.3 |
| Other | 64.7 | 18.1 | 6.2 | 3.2 | 7.8 |
| Education level | ‡ | ‡ | * | ‡ | |
| College or higher | 58.6 | 16.9 | 7.7 | 4.4 | 12.4 |
| Grade/high schools | 59.5 | 14.2 | 7.6 | 4.4 | 14.3 |
| Unknown | 70.8 | 11.4 | 4.4 | 3.5 | 9.9 |
| Employment status | ‡ | ‡ | ‡ | ‡ | |
| Working | 58.7 | 17.8 | 6.9 | 4.8 | 11.9 |
| Not working | 56.3 | 16.4 | 8.1 | 4.8 | 14.4 |
| Unknown | 83.5 | 6.7 | 3.1 | 1.6 | 5.1 |
| Body mass index, kg/m2 | * | † | ‡ | ‡ | |
| <35 | 60.7 | 15.2 | 7.2 | 4.2 | 12.6 |
| ≥35 | 48.4 | 16.4 | 9.6 | 6.6 | 19.0 |
| Unknown | 75.0 | 9.1 | 4.6 | 0.0 | 11.4 |
| Patient last status | * | ‡ | ‡ | ||
| Status 1A | 58.8 | 15.0 | 7.7 | 4.8 | 13.8 |
| Status 1B | 60.6 | 15.9 | 7.0 | 3.8 | 12.7 |
| Status 2 | 71.8 | 14.8 | 6.0 | 1.5 | 5.9 |
| Comorbid conditions | |||||
| Diabetes mellitus | 54.4 | 16.3‡ | 8.3‡ | 5.2‡ | 15.7‡ |
| COPD | 50.2 | 16.6* | 9.3* | 5.4 | 18.5‡ |
| Hypertension | 57.6 | 15.5 | 7.7* | 4.7* | 14.5‡ |
| PVD | 55.2 | 13.5 | 9.4 | 2.8 | 19.1* |
| Total bilirubin, mg/dL | |||||
| <3 | 59.9 | 15.3 | 7.4 | 4.3 | 13.1 |
| ≥3 | 62.1 | 15.2 | 6.9 | 5.2 | 10.6 |
| eGFR, mL/min per 1.73 m2 | ‡ | ‡ | ‡ | ‡ | |
| ≥90 | 68.8 | 12.0 | 5.8 | 3.2 | 10.2 |
| 60 to <90 | 56.4 | 16.5 | 7.8 | 4.9 | 14.3 |
| 40 to <60 | 57.8 | 16.0 | 8.0 | 4.5 | 13.7 |
| <40 | 60.8 | 15.9 | 5.9 | 4.6 | 12.8 |
| Unknown | 60.0 | 40.0 | 0.0 | 0.0 | 0.0 |
| Transplant/donor factors | |||||
| Ventilator | 62.2 | 14.8 | 7.9 | 4.0 | 11.2* |
| VAD life support | 48.5 | 14.7‡ | 10.3‡ | 6.9‡ | 19.7‡ |
| LVAD time frame | * | ‡ | ‡ | ‡ | |
| At transplant | 49.9 | 11.4 | 12.5 | 7.7 | 18.5 |
| Within 3 min of transplant | 55.8 | 11.3 | 11.0 | 6.6 | 15.4 |
| >3 min prior to transplant | 45.2 | 16.0 | 11.7 | 6.5 | 20.6 |
| None or unknown | 61.6 | 15.4 | 6.8 | 4.0 | 12.2 |
| HLA mismatch | |||||
| <5 | 59.9 | 15.0 | 7.5 | 4.3 | 13.3 |
| ≥5 | 59.9 | 15.8 | 7.2 | 4.4 | 12.7 |
| Unknown | 60.8 | 13.3 | 7.8 | 4.3 | 13.8 |
| Donor age, y | ‡ | ‡ | ‡ | ‡ | |
| <18 | 77.9 | 10.5 | 4.0 | 2.4 | 5.2 |
| 18–30 | 56.5 | 16.6 | 8.2 | 4.5 | 14.2 |
| 31–40 | 56.3 | 16.2 | 7.5 | 5.0 | 15.1 |
| 41–50 | 58.5 | 14.8 | 7.9 | 4.9 | 13.9 |
| ≥51 | 60.9 | 14.7 | 7.5 | 3.8 | 13.1 |
| Gender mismatch (FD/MR) | 65.5 | 13.5† | 6.2* | 3.4* | 11.5* |
| Weight mismatch (D/R < 0.7) | 50.6 | 16.3 | 9.3* | 5.1 | 18.7‡ |
| Era of transplant | ‡ | * | ‡ | ||
| 2008–2011 | 63.2 | 13.7 | 7.8 | 3.9 | 11.4 |
| 2012–2014 | 58.4 | 15.8 | 7.1 | 4.8 | 14.1 |
| 2015–2017 | 58.8 | 16.3 | 7.2 | 4.3 | 13.5 |
Note: Data presented as row percentages (%).P-values for comparison of trait distribution in each level of opioid use to no use:
P < .05–.002;
P < .001–.0002;
P < .0001.
Level 1, >0 to 300 ME mg; level 2, >300 to 600 ME mg; level 3, >600 to 1000 ME mg; level 4, >1000 ME mg per year. ME, morphine equivalent.
Abbreviations: COPD, chronic obstructive pulmonary disease; D/R, donor to recipient; eGFR, estimated glomerular filtration rate; FD/MR, female donor to male recipient; HLA, human leukocyte antigen; LVAD, left ventricular assist device; PVD, peripheral vascular diseases; VAD, ventricular assist device.
Outpatient pharmacy fills for opioid analgesics in the 1 year before transplant were ascertained from the fill records. Fills were also aggregated in the first year posttransplant, excluding the first 90 days to ensure removal of initial surgery-related use.15 Opioid use was normalized to morphine equivalents (MEs), according to conversion ratios, as previously described (Table S1).15,19–21 Pre- and posttransplant MEs were aggregated, separately within each period, for each recipient and expressed as dose (mg) of ME exposure over the year. We ranked annual ME exposure among recipients who filled opioid prescriptions by levels as: level 1, ≤300 mg; level 2, 301–600 mg; level 3, 601–1000 mg; and level 4, >1000 mg ME per year, similar to previous methods.15,16,19,21 The primary outcome was all-cause mortality, as reported by transplant programs to OPTN and supplemented with the Social Security Death Master File; we also examined graft loss as identified in the OPTN database.
2.3 |. Statistical analyses
Datasets were merged and analyzed with SAS (Statistical Analysis Software) version 9.4 (SAS Institute Inc., Cary, NC). Distributions of clinical and demographic traits among recipients with each level of pretransplant opioid exposure, compared with no opioid use, were compared by chi-squared test. Propensity score models for the likelihood of any opioid use in the pretransplant period and the first year posttransplant were constructed by multivariate logistic regression. Adjusted associations of pre- and posttransplant opioid use with posttransplant death and graft loss (adjusted hazard ratio with 95% upper and lower confidence limits, LCLaHRUCL) were quantified by multivariate Cox regression including adjustment for recipient, donor, and transplant clinical factors, and stratified by propensity score for opioid use as per previous methods.15,19,20 In all outcome analyses, we interpreted two-tailed P-values <.05 as statistically significant. In the analysis of pretransplant opioid use, outcomes were assessed at the 1-year posttransplant date. In the analysis of posttransplant opioid use, outcomes were assessed between the >1 and 5-year posttransplant dates. Observation time was censored at the end of follow-up (December 31, 2017). Sensitivity analyses explored limitation of the exclusion of postoperative opioid fills to the first 30 days, and also examined posttransplant survival between >1 and 2 years after transplant.
3 |. RESULTS
3.1 |. Baseline characteristics of heart transplant recipients
A STROBE checklist22 for the study is provided in Table S2. In the study period, 26 786 US adult heart transplant recipients were recorded in the SRTR database. Of these, 13 958 (52.1%) also had available pretransplant medication data (Table 1) and 13 065 (48.8%) had available posttransplant medication data (Table S3). The sample with linked pharmacy data was generally similar to all heart transplant recipients in the SRTR, except that fewer patients aged <18 years were included in the study sample (Table S5). In the study sample with available pretransplant medication data, mean age at the time of transplant was 49.2 years (standard deviation, 17.8 years); 28.6% were female, 66.6% were white, and 21.0% were African American. In the study cohort, 5583 recipients (40.0%) filled ≥1 opioid prescription in the year before transplant; 13.0% of doses were at level 4. Compared with recipients with no pretransplant prescription opioid use, those with any use were more often aged >18 (especially 31–40) years, male, of white race, unemployed, and less likely to be college educated (Table 1, Table S4). Recipients with any pretransplant opioid use were also more likely to be obese (body mass index [BMI] >35 kg/m2), to have comorbid conditions (diabetes mellitus, coronary artery disease, cerebral vascular disease, peripheral vascular disease, chronic obstructive pulmonary disease), to be allocation Status 1A, and to be on a ventricular assist device (VAD) life support, but less likely to use a ventilator. Pretransplant opioid users also more commonly underwent transplant in more recent years (Table S4). Associations with these demographic and clinical traits were even more significant among recipients with the highest level of use (ie, level 4). Among recipients with level 4 opioid use, 19.7% had VAD support prior to heart transplant compared with 6.9%, 10.3%, and 14.7% with VAD support among those with level 1 to 3 opioid use, respectively.
3.2 |. Mortality according to pretransplant opioid use level
Overall, 6.7% of recipients died within the first year posttransplant; most graft failures (95%) were concomitant with death (1-year graft loss incidence was 7.0%). Reported causes of death included 28% graft failure/cardiovascular, 7% infection, and 65% missing, and did not differ significantly by opioid use level. Compared with recipients with no pretransplant opioid use, those with level 4 use had an increased risk of death (8.5% vs 6.0%, P = .0002) (Figure 1A). After adjustment for demographic and clinical factors, level 4 pretransplant opioid use was associated with a 33% increased risk of death within the first year posttransplant (aHR 1.101.331.61, P < .05) compared with no use (Table 2, Figure 2A). Because of the strong correlation of death and graft loss, associations of level 4 opioid use with graft loss were similar (aHR 1.151.271.52, P < .05). Other recipient characteristics associated with death within 1 year of transplant included age older than 70 years, black or Hispanic race/ethnicity, BMI ≥35 kg/m2, elevated total bilirubin and estimated glomerular filtration rate (eGFR) <30 mL/min/1.73 m2 (Table 2). Donor characteristics associated with adverse outcomes included age older than 50 years and sex mismatch (eg, female donor in to a male recipient) (Table 2).
FIGURE 1.
Incidence of death posttransplant according to prescription opioid use before and after heart transplant. Level 1, >0 to 300 mg ME; level 2, >300 to 600 mg ME; level 3, >600 to 1000 mg ME; level 4, >1000 mg ME. ME, morphine equivalent
TABLE 2.
Adjusted association of pre- and posttransplant opioid use and clinical factors with death after heart transplant
| Death over 1st year posttransplant, by opioid use in year before HTx | Death >1 to 5 years posttransplant, by opioid use in 1st year after HTx | |||
|---|---|---|---|---|
| Opioid use | ||||
| No use | Reference | Reference | ||
| Level 1 | 1.11 (0.92–1.34) | 1.26 (1.06–1.49)* | ||
| Level 2 | 1.32 (1.05–1.66)* | 1.50 (1.20–1.87)† | ||
| Level 3 | 1.27 (0.94–1.71) | 1.62 (1.25–2.10)† | ||
| Level 4 | 1.33 (1.10–1.61)* | 1.70 (1.46–1.98)‡ | ||
| Recipient factors | ||||
| Age, y | ||||
| <18 | 0.69 (0.33–1.45) | 0.97 (0.61–1.55) | ||
| 18–30 | 1.21 (0.79–1.85) | 1.60 (1.22–2.11)† | ||
| 31–40 | Reference | Reference | ||
| 41–50 | 1.07 (0.78–1.48) | 0.79 (0.63–0.99)* | ||
| 51–60 | 1.15 (0.82–1.60) | 0.66 (0.53–0.82)† | ||
| 61–70 | 1.65 (1.11–2.44)* | 0.76 (0.60–0.98)* | ||
| ≥71 | 1.59 (0.87–2.93) | 1.07 (0.70–1.64) | ||
| Sex | ||||
| Male | Reference | Reference | ||
| Female | 0.97 (0.82–1.14) | 0.99 (0.87–1.13) | ||
| Race | ||||
| White | Reference | Reference | ||
| African American | 1.20 (1.02–1.41)* | 1.43 (1.26–1.63)‡ | ||
| Hispanic | 1.23 (0.93–1.62) | 0.97 (0.75–1.24) | ||
| Other | 0.83 (0.56–1.23) | 0.89 (0.64–1.23) | ||
| Education level | ||||
| College or higher | Reference | Reference | ||
| Grade/high schools | 1.11 (0.93–1.32) | 0.99 (0.85–1.15) | ||
| Unknown | 0.79 (0.59–1.06) | 1.21 (1.01–1.44)* | ||
| Employment status | ||||
| Working | 0.96 (0.72–1.27) | 0.81 (0.59–1.10) | ||
| Not working | Reference | Reference | ||
| Unknown | 1.28 (0.92–1.78) | 1.24 (0.91–1.68) | ||
| Body mass index, kg/m2 | ||||
| <35 | Reference | Reference | ||
| ≥35 | 1.34 (1.02–1.77)* | 0.96 (0.75–1.23) | ||
| Unknown | 1.02 (0.35–2.97) | 0.79 (0.35–1.82) | ||
| Patient last status | ||||
| Status 1A | Reference | Reference | ||
| Status 1B | 1.07 (0.89–1.27) | 0.92 (0.82–1.04) | ||
| Status 2 | 1.26 (0.86–1.85) | 0.91 (0.71–1.16) | ||
| Comorbid conditions | ||||
| Diabetes | 0.98 (0.83–1.17) | 1.33 (1.13–1.56)† | ||
| COPD | 0.98 (0.68–1.41) | 1.18 (0.89–1.56) | ||
| Hypertension | 1.20 (1.04–1.38)* | 1.23 (1.10–1.38)† | ||
| PVD | 0.98 (0.64–1.49) | 1.48 (1.11–1.99)* | ||
| Total bilirubin, mg/dL | ||||
| <3 | Reference | Reference | ||
| ≥3 | 1.82 (1.38–2.41)‡ | 0.97 (0.73–1.28) | ||
| eGFR, mL/min per 1.73 m2 | ||||
| ≥90 | Reference | Reference | ||
| 60 to <90 | 0.97 (0.78–1.20) | 1.02 (0.86–1.20) | ||
| 40 to <60 | 1.29 (1.04–1.60)* | 1.12 (0.93–1.34) | ||
| <40 | 1.78 (1.23–2.58)* | 1.69 (1.21–2.36)* | ||
| Unknown | 3.50 (0.76–16.19) | 1.65 (0.39–6.99) | ||
| Transplant/donor factors | ||||
| Ventilator | 0.76 (0.54–1.06) | 0.98 (0.78–1.23) | ||
| VAD life support | 1.17 (1.00–1.37) | 0.94 (0.81–1.08) | ||
| LVAD time frame | ||||
| At transplant | 1.17 (0.67–2.03) | 0.97 (0.61–1.54) | ||
| Within 3 min of transplant | 0.61 (0.36–1.05) | 0.85 (0.57–1.27) | ||
| >3 min prior to transplant | Reference | Reference | ||
| None or unknown | 0.95 (0.74–1.22) | 0.92 (0.72–1.17) | ||
| HLA mismatch | ||||
| <5 | Reference | Reference | ||
| ≥5 | 1.07 (0.93–1.23) | 1.09 (0.97–1.22) | ||
| Unknown | 1.05 (0.83–1.32) | 0.93 (0.76–1.13) | ||
| Donor age, y | ||||
| <18 | 1.09 (0.82–1.43) | 0.75 (0.60–0.95)* | ||
| 18–30 | Reference | Reference | ||
| 31–40 | 1.12 (0.94–1.33) | 1.17 (1.02–1.36)* | ||
| 41–50 | 1.19 (0.99–1.43) | 1.20 (1.03–1.40)* | ||
| ≥51 | 1.51 (1.19–1.90)† | 1.49 (1.21–1.83)† | ||
| Gender mismatch (FD/MR) | 1.27 (1.06–1.53)* | 1.02 (0.87–1.20) | ||
| Weight mismatch (D/R < 0.7) | 1.28 (0.94–1.73) | 1.17 (0.88–1.55) | ||
| Era of transplant | ||||
| 2008–2011 | Reference | |||
| 2012–2014 | 0.96 (0.79–1.17) | |||
| 2015–2017 | 0.90 (0.73–1.10) | |||
| 2007–2010 | Reference | |||
| 2011–2013 | 0.79 (0.68–0.92)* | |||
| 2014–2016 | 0.72 (0.60–0.87)† | |||
Abbreviations: aHR, adjusted hazard ratio; CL, confidence limit; COPD, chronic obstructive pulmonary disease; D/R, donor to recipient; eGFR, estimated glomerular filtration rate; FD/MR, female donor to male recipient; HLA, human leukocyte antigen; LVAD, left ventricular assist device; PVD, peripheral vascular diseases; VAD, ventricular assist device.
P < .05–.002;
P = .001–.0002;
P < .0001.
FIGURE 2.
Adjusted associations of prescription opioid use before and after transplant with posttransplant mortality (referent = no use). Level 1, >0 to 300 mg ME; level 2, >300 to 600 mg ME; level 3, >600 to 1000 mg ME; level 4, >1000 mg ME per year. ME, morphine equivalent. aHR, adjusted hazard ratio
3.3 |. Patterns of opioid use before and after transplant
Among recipients who used level 4 opioids pretransplant, 70.8% filled any opioid prescription in the first year posttransplant, including 53.4% with level 3 or 4 use. Clinical correlates of posttransplant opioid use included factors associated with pretransplant use, such as age, white race, less than college education, unemployment, obesity, and VAD life support, although eGFR <40 mL/min/1.73 m2 was a unique correlate of posttransplant use. Table S3 demonstrates clinical traits of heart transplant recipients by level of posttransplant opioid use. Associations remained after multivariate adjustment, and included more recent year of transplant (Table S4). Pretransplant opioid use was strongly correlated with higher posttransplant opiate use (Figure 3). Nearly 47.6% of those with level 4 pretransplant use continued high-level use over the first year posttransplant.
FIGURE 3.
Posttransplant opioid use according to pretransplant opioid use. Level 1, >0 to 300 mg ME; level 2, >300 to 600 mg ME; level 3, >600 to 1000 mg ME; level 4, >1000 mg ME per year. ME, morphine equivalent. For posttransplant use, the first 90 days is excluded to remove immediate surgical-related fills
3.4 |. Mortality according to posttransplant opioid use level
Overall, 14.4% of recipients died during the second to the fifth years posttransplant, and 15.1% had reported graft loss. Compared with non-users, recipients with all levels of opioid use posttransplant were at increased risk of death (Figure 1B) and graft loss, especially those with level 4 opioid use versus no use (death rate 22.2% vs. 12.5%, P < .0001; graft loss rate 21.2% vs. 13.5%, P < .0001). After adjustment for demographic and clinical factors, risk of death in the posttransplant years bore graded increases with higher levels of posttransplant opioid use. Level 4 use was associated with a 70% increased mortality risk after transplant (aHR 1.461.701.98, P < .0001), compared with no use (Table 2, Figure 2). Again, associations of level 4 opioid use and graft loss (aHR 1.341.551.80, P < .0001) were similar to mortality associations. Mortality associations were similar reducing the exclusion of postoperative opioid fills from 90 days to the first 30 days posttransplant (Table S6). Mortality relationships were persistent and somewhat stronger considering risk over >1 to 2 years after transplant, such that compared with no use, level 4 opioid use in the predicted 97% increased mortality over the next year (aHR 1.501.972.58; P < .0001; Table S6).
4 |. DISCUSSION
The national epidemic of prescription opioid use is affecting patients across domains of care. Because of common use and potential for increased risks, prescription opioids are a concern among patients being considered for organ transplant, including those with end-stage heart failure, and donor organs remain a scarce resource. We examined prescription opioid fills in a contemporary US sample of >13 000 heart transplant recipients and observed several key findings: 1) 40% of the cohort filled an opioid prescription before transplant, and use was more common among recipients who were female, white, or unemployed, or who underwent transplant in more recent years. 2) Pretransplant prescription opioid use was associated with an approximately 33% increased risk of death over the first year posttransplant. 3) Nearly 50% of patients who filled an opioid prescription pretransplant also filled a prescription posttransplant, and most high-level opioid users continued to fill at high levels. 4) Posttransplant prescription opioid use in the first year had even stronger prognostic significance, with higher opioid use bearing graded associations with increased mortality. The highest posttransplant opioid use level predicted 72% increased death risk over the subsequent 4 years. These patterns are similar to associations previously reported among kidney and liver transplant recipients.
In this national cohort, we found that 40% of US heart transplant recipients were prescribed an opioid before transplant. This was similar to the percentage of kidney transplant recipients pre-scribed pretransplant opioids, but higher than the percentage of liver transplant recipients.15,16 Heart transplant recipients who filled prescription opioids were more often aged 31–40 years and female, with lower education and employment levels. These findings are consistent with data from studies evaluating pain medication prescriptions in the postoperative setting.23 Interestingly, we found consistent increases in the prevalence of comorbid conditions, including diabetes mellitus and peripheral vascular disease, in patients prescribed the highest dose of opioids, similar to observations for other patient cohorts not awaiting transplant.24 Additionally, the presence of VAD therapy prior to transplant was associated with an 83% greater likelihood of opioid prescription. The increased use of opioids in VAD patients awaiting heart transplant has not previously been demonstrated; it is unclear whether the need for opioids was directly related to the treatment of pain associated with VAD implant. Also of note, prescription opioid use was higher among more recent recipients, consistent with national increases in prescription opioid use in the study period.
A key finding of graded associations of prescription opioid use with risk of adverse outcomes after heart transplant is a key observation of this study. This finding has not previously been demonstrated in patients who undergo heart transplant, but importantly, bears striking similarity to patterns among kidney and liver transplant recipients.15,19,20 After heart transplant, 49% of patients continued to fill opioid prescriptions, including 71% of those with pretransplant level 4 use; this was the case after excluding immediate postoperative use in the first 90 days. These findings are similar to observations in other transplant populations.15,16 Given the observational design, we cannot attribute causality in the risk relationships. Poorer outcomes in transplant recipients who use prescription opioids may reflect comorbidity associated with chronic pain, or psychiatric conditions such as depression.25 In addition, opioid use may be related to patient behaviors associated with adverse outcomes, such as noncompliance with appointments or immunosuppressive medications, which may increase the risk of complications.26 At one program, opioid use in the first year after kidney transplant was associated with increased readmission rates but no difference in rejection rates.11 Further research is needed to understand the factors contributing to the increased risk of death and graft loss in heart transplant recipients, especially those with sustained use. Nonetheless, recognition of posttransplant opioid use as a graded risk across transplant populations is prognostically important.
This study has limitations. Because we cannot define causality, we cannot conclude that cessation of opioid use or change to non-opioid use prior to transplant would lead to improved outcomes. Unmeasured confounders, including drug dependence or noncompliance, may have affected the outcomes described. Causes of death were underreported in the database, and we lacked granular clinical information such as biopsy results to categorize causes of graft failure. Although we can identify electronic pharmacy claims and fill records, we cannot confirm that patients were adherent to their prescription medications. Similarly, we cannot account for illicit drug use or prescription fills at pharmacies not included in our datasets, which may underestimate total drug exposure. These patients likely would have been categorized as high-level users if they filled most of their opioid prescriptions at the same pharmacy as their immunosuppressive medications.
In summary, although associations may in part reflect underlying conditions, use of high levels of prescription opioids before and after heart transplant is a marker for increased risk of adverse outcomes. Our study informs transplant teams that many patients receiving opioids prior to transplant will continue to fill opioids after transplant beyond the immediate perioperative period. Further work should seek to identify underlying mechanisms, assess the impact of decreasing opioid use before transplant or converting to other pain management regimens, and determine management approaches to improving patient outcomes. For now, these data suggest that heart transplant patients who require high levels of opioids warrant careful evaluation of pain management strategies, perhaps by a multidisciplinary team including a pain management specialist, as well as focused monitoring of clinical status after transplant.
Supplementary Material
ACKNOWLEDGMENTS
This work was conducted under the auspices of the Hennepin Healthcare Research Institute, contractor for the Scientific Registry of Transplant Recipients (SRTR), as a deliverable under contract no. HHSH250201000018C (U.S. Department of Health and Human Services, Health Resources and Services Administration, Healthcare Systems Bureau, Division of Transplantation). As a U.S. Government sponsored work, there are no restrictions on its use. The interpretation and reporting of these data are the responsibility of the author(s) and in no way should be seen as an official policy of or interpretation by the SRTR or the U.S. Government. This work was supported by a grant from the National Institutes of Health (NIH)/National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) R01DK120518. NNL was supported by a KRESCENT New Investigator Award. The opinions, results, and conclusions reported in this article are those of the authors and are independent of the funding sources. The authors thank SRTR colleague Nan Booth, MSW, MPH, ELS, for manuscript editing.
Abbreviations:
- aHR
adjusted hazard ratio
- BMI
body mass index
- eGFR
estimated glomerular filtration rate
- HRSA
Health Resources and Services Administration
- LCL
lower confidence limit
- LVAD
left ventricular assist device
- ME
morphine equivalents
- OPTN
Organ Procurement and Transplantation Network
- SAS
Statistical Analysis Software
- SRTR
Scientific Registry of Transplant Recipients
- UCL
upper confidence limit
- VAD
ventricular assist device
Footnotes
DISCLOSURES
The authors of this manuscript have no conflicts of interest to disclose as described by the American Journal of Transplantation.
DATA AVAILABILITY STATEMENT
This study was approved by the Saint Louis University Institutional Review Board. Individual participant deidentified data will not be shared by the authors due to restrictions of Data Use Agreements. SRTR registry data can be obtained from the SRTR.
SUPPORTING INFORMATION
Additional supporting information may be found online in the Supporting Information section at the end of the article.
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