Impact of institutional variables on centre performance in long-term survival after heart transplant

Abstract

OBJECTIVES

The gold standard metric for centre-level performance in orthotopic heart transplantation (OHT) is 1-year post-OHT survival. However, it is unclear whether centre performance at 1 year is predictive of longer-term outcomes. This study evaluated factors impacting longer-term centre-level performance in OHT.

METHODS

Patients who underwent OHT in the USA between 2010 and 2021 were identified using the United Network of Organ Sharing data registry. The primary outcome was 5-year survival conditional on 1-year survival following OHT. Multivariable Cox proportional hazard models assessed the impact of centre-level 1-year survival rates on 5-year survival rates. Mixed-effect models were used to evaluate between-centre variability in outcomes.

RESULTS

Centre-level risk-adjusted 5-year mortality conditional on 1-year survival was not associated with centre-level 1-year survival rates [hazard ratio: 0.99 (0.97–1.01, P = 0.198)]. Predictors of 5-year mortality conditional on 1-year survival included black recipient race, pre-OHT serum creatinine, diabetes and donor age. In mixed-effect modelling, there was substantial variability between centres in 5-year mortality rates conditional on 1-year survival, a finding that persisted after controlling for recipient, donor and institutional factors (P < 0.001). In a crude analysis using Kaplan–Meier, the 5-year survival conditional on 1-year survival was: low volume: 86.5%, intermediate volume: 87.5%, high volume: 86.7% (log-rank P = 0.52). These measured variables only accounted for 21.4% of the between-centre variability in 5-year mortality conditional on 1-year survival.

CONCLUSIONS

Centre-level risk-adjusted 1-year outcomes do not correlate with outcomes in the 1- to 5-year period following OHT. Further research is needed to determine what unmeasured centre-level factors contribute to longer-term outcomes in OHT.

Orthotopic heart transplant (OHT) centre performance in the USA is a publicly available metric that plays a significant role in a centre’s continued certification, reimbursement and patient perception of a potential OHT centre [1, 2].

Graphical abstract

INTRODUCTION

Orthotopic heart transplant (OHT) centre performance in the USA is a publicly available metric that plays a significant role in a centre’s continued certification, reimbursement and patient perception of a potential OHT centre [1, 2]. Currently, the Scientific Registry of Transplant Recipients (SRTR) is mandated to maintain publicly available programme outcomes to uphold safety standards, increase transparency and identify underperformers for investigation and intervention. The significance of these outcomes to transplant centres has led to many stakeholders re-evaluating the process by which these metrics are calculated. Models by the SRTR to estimate programme performance for OHT include waitlist outcomes, graft failure rates and 1-year survival based on 2.5-year cohorts [3]. However, there has been growing concern over the accuracy of these models [4, 5]. Still today, OHT is a relatively rare procedure where the impact of an event (i.e. mortality) can be dramatically different based on the volume of a particular centre. In addition, dynamic interactions of donor, recipient and institutional factors that impact a centre’s outcomes exist, and such factors are not taken into consideration via the current models [6, 7].

In this study, we examine the validity of 5-year conditional survival as a potential metric for centre-level performance and evaluate its comparison with 1-year survival. We include a risk-adjusted analysis that incorporates donor, recipient and institutional factors, namely centre volume. In addition, these variables will be used to analyse the degree of accounted-for and unaccounted-for variability in outcomes between centres.

MATERIALS AND METHODS

Ethical statement

This study was deemed exempt from review by the Medical University of South Carolina Institutional Review Board (Pro00119979, Approved 12 April 2022). Informed consent was waived.

Study design

Patients (≥18 years old) who underwent isolated OHT between 1 January 2010 and 2021 were retrospectively identified using the United Network of Organ Sharing (UNOS) data registry. Recipient, donor and centre characteristics were extracted. Patients who underwent OHT at centres performing <10 OHT per year were excluded from this analysis. The primary outcome was risk-adjusted centre 5-year survival conditional on 1-year survival. Secondary outcomes included predictors and inter-centre variability of 5-year mortality conditional on 1-year survival.

Statistical analysis

The centres were divided into 3 quantiles based on centre-year volume. This involved arranging the centres in order of their centre-year volume and then distributing them evenly into 3 groups: the quantile with the lowest volume, the quantile with a moderate volume and the quantile with the highest volume. Kaplan–Meier survival curves were also compared after stratifying patient centre quantiles (low-, intermediate- and high-volume). To examine the impact of surviving the 1st year after heart transplantation on the likelihood of surviving for an additional 4 years, a multivariable Cox proportional hazard model was used to assess the risk-adjusted impact of centre-level 5-year mortality conditional on 1-year survival. Mixed-effect models were also used to evaluate variability in outcomes among transplant centres. By treating the centres as random effects in the models, the unique characteristics and variations between each centre were captured. This approach acknowledges that outcomes can differ between centres due to factors such as expertise, resources and protocols specific to each institution. To control for confounding and account for the influence of recipient, donor and institutional factors on outcomes, fixed effect parameters were also included in the models. These fixed effect parameters represented variables known to potentially impact transplantation outcomes, such as recipient age, comorbidities, donor age, cause of death and institutional factors like centre volume and experience.

The percentage contribution of patient and centre-level factors between centre variability in outcomes was calculated as Equation (1), where x was the standard deviation of the model, including only individual centres, and y was the standard deviation of the model, including the variables used for risk adjustment [8–10]. The statistical analyses were performed using R version 4.0.2.

$$\mathrm{\%}\mathrm{ }\mathrm{Contribution}\mathrm{ }=\left(\frac{\left(x^2-y^2\right)}{x^2}\right)\times 100$$ (1)

RESULTS

A total of 12 608 patients underwent OHT at 59 centres across the USA. Of these, 11 249 (89.2%) survived their 1st year after OHT. There was a strong correlation between unadjusted centre-level 1-year survival rates and centre-level 5-year survival conditional on 1-year survival (correlation coefficient, R = 0.82, P < 0.001, Fig. 1). Centre-level risk-adjusted 5-year mortality conditional on 1-year survival was not associated with centre-level 1-year survival rates [hazard ratio (HR): 0.99 (0.97–1.01, P = 0.198)]. The 5-year survival rate conditional on 1-year survival was: low volume: 86.5%, intermediate volume: 87.5%, high volume: 86.7% (log-rank P = 0.520) (Fig. 2). Predictors of 5-year mortality conditional on 1-year survival included Black recipient race [HR: 1.60 (1.27–2.02, P < 0.001)], increasing pretransplant serum creatinine levels [HR: 1.30 (1.09–1.54, P = 0.003)], diabetes [HR: 1.32 (1.07–1.63, P = 0.009)] and increasing donor age [HR: 1.02 (1.01–1.03, P < 0.001)] (Table 1). Additionally, acute rejection within the 1st year significantly impacted 1-year survival [HR: 1.39 (1.28–1.51), P < 0.001] and 5-year survival conditional on 1-year survival [HR: 1.30 (1.27–1.33), P < 0.001]. Similarly, drug-treated rejection within the 1st year was found to have a significant effect on 1-year survival [HR: 3.04 (2.86–3.24), P < 0.001] and 5-year survival conditional on 1-year survival [HR: 2.10 (2.06–2.15), P < 0.001]. In mixed-effect modelling, there was substantial variability between centres in 5-year mortality rates conditional on 1-year survival, a finding that persisted after risk adjustment for recipient and donor factors and institutional OHT volume (P < 0.001). These measured variables only accounted for 21.4% of the variability in 5-year mortality conditional on 1-year survival between transplant centres (Table 2).

Figure 1:

Correlation between 1-year and conditional 5-year survival. Pearson correlation (R = 0.82, P < 0.001) between 1-year post-transplant survival rates and the 5-year survival rates conditional on surviving the 1st year across various OHT centres was performed, suggesting a strong correlation between unadjusted centre 1-year and centre 5-year survival rates. Each line in the graph corresponds to a specific OHT centre, depicting its unique survival profile.

Figure 2:

Conditional 5-year survival following orthotopic heart transplant based on centre volume. Kaplan–Meier survival curves of unadjusted 5-year and 5-year conditional patient survival rates were comparable in patients who underwent OHT in low-, intermediate- and high-volume centres: log-rank P = 0.52.

Table 1:

Multivariable model for risk-adjusted 5-year mortality conditional on 1-year survival.

	Hazard ratio (95% CI, P-value)
Centre 1-year mortality (%)	0.99 (0.97–1.01, P = 0.198)
Centre volume
Low	Reference
Intermediate	0.90 (0.71–1.16, P = 0.422)
High	1.10 (0.86–1.39, P = 0.455)
Age (years)	0.99 (0.99–1.00, P = 0.083)
Recipient race
White	Reference
Black	1.60 (1.27–2.02, P < 0.001)
Hispanic	1.04 (0.71–1.53, P = 0.828)
Other	1.10 (0.68–1.76, P = 0.700)
BMI (kg/m2)	1.00 (0.98–1.02, P = 0.993)
Creatinine (mg/dl)	1.30 (1.09–1.54, P = 0.003)
Dialysis prior to OHT	1.00 (0.52–1.95, P = 0.989)
Diabetes	1.32 (1.07–1.63, P = 0.009)
ICU at time of transplant	1.04 (0.84–1.29, P = 0.718)
Panel-reactive antibodies (%)	1.00 (1.00–1.01, P = 0.064)
Heart ischaemic time, hours	1.05 (0.96–1.16, P = 0.291)
Donor age, years	1.02 (1.01–1.03, P < 0.001)
Donor race
White	Reference
Black	1.22 (0.95–1.57, P = 0.116)
Hispanic	1.13 (0.85–1.50, P = 0.407)
Other	0.97 (0.56–1.69, P = 0.918)
Donor BMI, mean (SD)	1.01 (0.99–1.02, P = 0.475)
Sex-matched	1.03 (0.82–1.30, P = 0.804)
Race-matched	1.05 (0.83–1.31, P = 0.692)
HLA-matched	0.98 (0.74–1.30, P = 0.898)

Predictive factors for 5-year mortality conditional on 1-year survival.

BMI: body mass index; ICU: intensive care unit; OHT: orthotopic heart transplantation; SD: standard deviation. HLA: Human Leukocyte Antigen.

Table 2:

Mixed-effect model evaluating between-centre variability.

	Odds ratio	Lower bound	Upper bound	P-value	Random-effect, SD (95% CI)	P-value
Model A
Individual centres					0.044 (0.037–0.054)	> 0.001
Model B: recipient factors
Individual centres					0.039 (0.031–0.048)	>0.001
Age	1.000021	0.9999142	1.0001279	0.70
Race
Black	1.0033141	1.0000252	1.0066139	0.05
Hispanic	1.0057591	1.000817	1.0107255	0.02
Other	1.0015243	0.9953042	1.0077834	0.60
BMI	1.0001553	0.999886	1.0004246	0.29
Creatinine	0.9980802	0.995244	1.0009246	0.19
Prior dialysis	0.99283	0.9834479	1.0023017	0.14
Diabetes	0.9963471	0.9933972	0.9993058	0.02
ICU at time of OHT	1.0001411	0.9972245	1.0030663	0.92
CPRA	0.9999683	0.9999116	1.000025	0.27
Ischaemic time	0.9988156	0.9974093	1.0002239	0.10
Model C: donor factors
Individual centres					0.044 (0.037–0.054)	>0.001
Age, years	0.9999377	0.9998397	1.000036	0.21
Race
Black	1.0017702	0.9988194	1.00473	0.24
Hispanic	1.0005823	0.9975325	1.003641	0.71
Other	1.0014753	0.9955822	1.007403	0.62
BMI	0.9999423	0.9997669	1.000118	0.52
Model D: centre volume
Individual centres					0.044 (0.037–0.054)	>0.001
Intermediate volume centres	1.0088756	1.005429	1.0123341	>0.99
High volume centres	0.9916786	0.9870449	0.9963341	<0.001
Model E
Individual centres					0.039 (0.031–0.048)	>0.001
Age	1.0000471	0.9999405	1.0001538	0.37
Recipient race
Black	1.0032736	1.0000267	1.006531	0.05
Hispanic	1.0057019	1.0008261	1.0106015	0.02
Other	1.0013998	0.9952674	1.0075701	0.66
Recipient BMI	1.0001647	0.9998916	1.0004378	0.24
Creatinine	0.9981271	0.9953301	1.0009319	0.19
Prior dialysis	0.9922481	0.9830016	1.0015815	0.10
Diabetes	0.9960853	0.9931775	0.9990016	0.009
ICU at time of OHT	0.9999181	0.997038	1.0028064	0.96
CPRA	0.9999735	0.9999174	1.0000295	0.35
Ischaemic time	0.9985301	0.997139	0.9999232	0.04
Donor age	0.9999765	0.9998542	1.0000987	0.71
Donor race
Black	1.0014122	0.9979125	1.0049241	0.43
Hispanic	0.998391	0.9947183	1.0020774	0.39
Other	1.0007501	0.9937862	1.0077629	0.83
Donor BMI	1.0000085	0.9997973	1.0002197	0.94
Intermediate volume centres	1.0281868	1.0231733	1.0332248	> 0.99
High volume centres	1.0086989	1.0016605	1.0157869	0.02

Mixed-effects model displaying between-centre variability with the individual centres serving as the random effect parameters and risk-adjusted recipient, donor and institutional factors serving as fixed effect parameters.

BMI: body mass index; CPRA: calculated panel-reactive antibody; ICU: intensive care unit.

DISCUSSION

This study focused on comparing the statistical reliability of 1-year and 5-year conditional on 1-year survival as mortality metrics to delineate the most effective strategy for predicting long-term survival following OHT. Our findings support that acute and drug-treated rejections within the 1st year significantly influence both 1-year and 5-year survival. However, 1-year mortality outcomes and 1- to 5-year post-transplant mortality outcomes are not correlated when controlling for other factors. This suggests that while managing rejections effectively may enhance survival rates during these periods, the varied responses of individual patients, their ongoing healthcare needs and the emergence of new health issues over time lead to a decoupling of early survival rates from longer-term outcomes. Moreover, our analysis did not identify a significant difference in survival outcomes for low-, intermediate- and high-volume centres. However, this finding may be significantly impacted by the high level of variation between centre outcomes that were primarily due to unaccounted-for variables.

In a UNOS database analysis of orthotopic lung transplant outcomes, Wakeam et al. [6] investigated the reliability of current methods of assessing centre survival outcomes. They found that 5-year survival was a significantly more reliable indicator of programme performance than 1-year survival, particularly for low-volume centres. Similarly, in the context of assessing OHT centre performance, our analysis supports the proposition that utilizing survival outcomes beyond the 1st year post-OHT should be considered and may improve upon previous prognostic models. Such an approach may mitigate the undue influence of statistical outliers and better reflect the quality of long-term patient care. Additionally, Jawitz et al. [11] performed a retrospective review of lung transplant recipients and compared factors that predicted 1-year survival and 10-year survival. They found that only donor age and performance of single-lung transplant were significant for both 1-year and 10-year survival [11]. This may become particularly relevant with the increased percentage of recipients bridged to OHT with durable and temporary mechanical support. While these patients are known to have slightly worse short-term outcomes and may initially present with poorer short-term prognoses compared to patients who do not receive bridge therapy, such individuals often display equivalent long-term survival [12, 13].

In our mixed-models analysis, our study found that the captured donor, recipient and institutional factors accounted for only 21.4% of the variability between OHT centres. Yet, the remaining 78.6% of between-centre variability was unaccounted for. This could result from the complexity of post-OHT care that includes a multitude of intangible factors, such as the socioeconomic demographics of a centre, perioperative and technical practices, and the ability to maintain adequate patient follow-up and treatment adherence [14–16]. Socioeconomic and racial disparities are well-documented predictors of worse overall survival throughout solid organ transplants [17]. As our study indicated, Black race was found to be the strongest negative predictor of long-term survival among our patient population. Here, further investigation into the mechanism behind this racial disparity might be clarified by considering the influence of socioeconomic status (SES) on the increased relative risk for early mortality reported in this subgroup. Wayda et al. [18] utilized the Agency for Healthcare Research and Quality SES Area Deprivation Index in 2014 and found strong evidence for a 2-fold increased risk of early mortality following OHT among their lowest SES quartile. Interestingly, they found that Black race was independently associated with an increased risk of early mortality despite adjustment for baseline clinical risk factors and SES. We propose that newer models, including the updated Area Deprivation Index or Index of Deep Disadvantage, could be used to provide more accurate insight into this disparity [19, 20].

Previous research corroborates that a prior diagnosis of diabetes, increased pre-OHT creatinine levels and increasing donor age are negative predictors of long-term survival following OHT [21–23]. Further, our data support that a previous diagnosis of diabetes does not independently confer an increased risk of mortality at 1-year post-OHT [HR 1.04 (0.89–1.21, P = 0.631)] but is a significant negative predictor of 5-year conditional survival 1.32 (1.07–1.63, P = 0.009). This aligns with Khush et al.’s research, showing diabetes’ different effects on short-term versus long-term post-transplant survival [22]. Here, Khush et al. also noted regional variations, such as improved 5-year conditional survival in European centres compared to American centres, suggesting a preference in Europe for long-term outcomes despite higher initial mortality [22]. These insights offer a unique perspective on evaluating outcomes and might explain some of the unaccounted-for variations among centres. While there may be an advantage to including longer-term outcomes in programme performance metrics, it should be emphasized that the ideal programme evaluation would encompass a multitude of factors. The recent development of composite scores, such as The Society of Thoracic Surgeons’ ‘Star system’, suggests that multidimensional analysis on a surgeon-level basis could be more effective in elucidating these differences [24].

Limitations

Several limitations to our study must be examined. One must 1st consider unmeasured confounding variables not included in the SRTR database, as these could account for some of the intra-centre variability provided below (Table 2). Our study focused on 1-year survival and 5-year mortality conditional on 1-year survival at a population level. We did not investigate the variability of 1-year mortality at an individual level within each of the centres included in the study. Each centre performing OHT may exhibit drastically different clinical outcomes, with outliers affecting the centre-level 1-year mortality. As such, it is important to keep in mind that our data, while compelling, may not be generalizable to individual patients, and survival varies on a patient-to-patient basis following OHT. In addition, our analysis excluded centres with fewer than 10 OHT procedures performed per year to avoid the impact of statistical outliers. This was based on previous literature addressing this topic; however, this may make the results of this study less generalizable [25, 26].

CONCLUSIONS

Centre-level 1-year outcomes do not correlate with centre-level outcomes in the 1- to 5-year period following OHT. Although our specified recipient and donor variables and institutional volume impact outcomes in the 1- to 5-year period, they only account for ∼20% of between-centre variability. Clearly, attenuating the remaining 80% of the variability between centres might require investigating novel methods, but it is critical to creating more reliable metrics for post-OHT survival. These unaccounted variables may also shed light on non-survival-based outcomes such as quality of life and functional status and address the aforementioned outcome disparities. Expanding our understanding of inter-centre variability would help identify target areas for improvement with individual centres and even play a substantial role in preoperative risk stratification. Future research is needed to identify these unmeasured centre-level factors contributing to longer-term outcomes in OHT and the ideal method for quantifying centre-level performance.

Glossary

ABBREVIATIONS

HR

Hazard ratio

OHT

Orthotopic heart transplant

SES

Socioeconomic status

SRTR

Scientific Registry of Transplant Recipients

UNOS

United Network of Organ Sharing

FUNDING

This project did not receive specific funding.

Conflict of interest: Arman Kilic is a consultant and speaker for Abiomed, Abbott, 3iVE and LivaNova. No other authors had disclosures.

DATA AVAILABILITY

Data supporting the findings in this study are available from the United Network for Organ Sharing.

Author contributions

Weston E. McDonald: Conceptualization; Formal analysis; Investigation; Methodology; Visualization; Writing—original draft; Writing—review and editing. Khaled Shorbaji: Data curation; Formal analysis; Investigation; Methodology; Software; Validation; Visualization; Writing—original draft. Maxwell Kilcoyne: Formal analysis; Investigation; Validation; Writing—original draft; Writing—review and editing. William Few: Investigation; Writing—original draft; Writing—review and editing. Brett Welch: Data curation; Formal analysis; Investigation; Project administration; Software; Validation; Writing—review and editing. Zubair Hashmi: Formal analysis; Writing—review and editing. Arman Kilic: Conceptualization; Formal analysis; Investigation; Methodology; Supervision; Validation; Writing—review and editing.

Reviewer information

Interactive CardioVascular and Thoracic Surgery thanks Kevin Ruixuan An, Marianna Buonocore and Kamran Ahmadov for their contribution to the peer review process of this article.

 

Presented at the Southern Thoracic Surgery Association 69th Annual Meeting, Fort Lauderdale, FL, USA, 19 November 2022.