Pretransplant survival of patients with end-stage heart failure under competing risks.

Heart transplantation is the gold standard of care for end-stage heart failure in the United States. Donor hearts are a scarce resource, however the current allocation policy-proposed in 2016 and implemented in 2018-has not addressed certain disparities. Between 2005 and 2016, the number of active candidates increased 127%, whereas transplant rates decreased 27.8%. Pretransplant mortality rates declined steadily for that period from 14.6 to 9.7, especially for candidates with mechanical circulatory assistive devices (MCSDs). This study reports survival analyses of candidates for heart transplantation list under competing events of transplantation and MCSD implantation. We queried the transplant data for a cohort of adult patients (age ≥ 16) without MCSDs prior to listing for transplantation between 2005 and 2014 (n = 23,373). We used cause-specific and subdistribution hazards models as multivariate regressions for all competing events. Patients listed as low priority for transplantation are less likely to require implantation but less likely to survive after 1,000 days of listing than patients listed at higher priorities. The current policy does not address this disparity as it focuses on stratifying patients with different types of MCSD. Clinical characteristics must be considered in prioritization.

Introduction

Heart transplantation is the gold standard of care for end-stage heart failure in the United States (US). The number of active candidates waitlisted for heart transplantation increased by 127% (from 1,262 to 2,862) between 2005 and 2016, while the transplant rates (i.e., transplants per 100 waitlist years) decreased by 27.8% (from 129.0 to 93.1) over the same time period [1]. The rates of mortality (i.e., deaths per 100 waitlist years) over the same years, however, decreased as a result of increased usage of mechanical circulatory support devices (MCSDs), specifically ventricular assistive devices (VADs) [1]. In 2016, the Organ Procurement and Transplantation Network (OPTN) developed the new heart-allocation policy [2] to stratify MCSD-supported adult patients [3, 4], and it was eventually implemented in October of 2018. Tiers 1–4 of the new policy are now reserved for and based on the stratification of MCSDs. Part of the rationale for stratification is the increased use of MCSDs as a bridge-to-transplant (BTT) therapy [5], which affects the dynamics of heart allocation and transplantation. Thus, proper pretransplant survival analyses must account for competing events [6].

When considering pretransplant mortality, the event of death competes with other events of interest, such as transplantation and MCSD implantation, which alters the etiology and prognosis [5] and, ultimately, the survival of patients. Competing risks models were developed to ascertain proper mortality rates in studies where a traditional Kaplan-Meier survival estimate exhibits a strong bias [7] or where doctors cannot evaluate the potential of an event-specific intervention [8]. We are motivated to properly measure and estimate the survival probability of patients on the United Network for Organ Sharing (UNOS) waitlist under competing risks to potentially highlight systematic biases and possible areas of improvement in organ allocation and patient status classification.

This paper reports on competing-risk analyses of pretransplant survival of patients with end-stage heart failure under three events of interest: implantation, transplantation, and death. We use the transplant registry maintained by the UNOS to generate two models: the cause-specific Cox Proportional Hazards and the Fine-Gray subdistribution hazards models. We found the lowest-priority patients without MCSDs are disadvantaged under the current policy (as well as under the new policy), which prioritize patients with MCSD.

Materials and methods

Ethics

The Internal Review Board of the University of North Carolina at Charlotte exempted our study from review and permitted us to obtain data from UNOS and perform analyses on the September 2016 version of the UNOS Standard Transplant Analysis Research (STAR) files, which contain the heart transplantation waitlist in the US from which October 2018 policy change was based [9]. (The results from this study are robust to the October 2018 policy change, for which an accurate one-year UNOS survival analysis study [i.e., using stable data] only can be conducted from October 2021).

Patient selection

We used 10 years of stable waitlist data between January 1, 2005 and December 31, 2014, with the end point chosen to align with the latest available report (with respect STAR files version date) from OPTN and the Scientific Registry of Transplant Recipients (SRTR) [1]. From the initial 2005–2014 cohort of 34,833 patients waitlisted for heart transplantation, we excluded 4,818 patients aged less than 16 years, 5,745 implanted with an MCSD prior to listing, and 897 having previously received a heart transplant, resulting in a final study sample size of 23,373 (Fig 1).

Fig 1

Flow diagram for the exclusion of patients in the UNOS STAR files 2005 to 2014.

Priority for heart transplantation considers the recipients’ distances from the donor hospital, waiting times, and medical needs [10]. The medical urgency of a recipient is measured using UNOS Statuses in the heart allocation system, which, in 2005–2014, were: Status 1A (most urgent), Status 1B, Status 2 (least urgent), and Status 7 (inactive).

Disease classification

The OPTN uses six main categories of heart disease from 30 primary diagnoses, which are: cardiomyopathy, coronary heart disease, congenital heart disease, valvular heart disease, retransplant graft failure, and other. We use five of these categories—excluding retransplant graft failure, which does not occur in our final cohort of patients. The category defined as other includes patients whose diagnosis at listing was not indicated (n = 5) or indicated as unknown (n = 568).

Selection of survival prediction variables

We considered all the at-listing variables from the UNOS STAR files for heart transplantation as potential covariates for a pretransplant analysis. We used R 4.1.0 [11] to perform a mixed selection of variables available from the STAR files using the step function on the cause-specific hazard of death (i.e., the event of interest). From the resulting selection, we further excluded variables with p-values over 0.05, but we included categorical variables for which the p-values of the baseline categories are at or under 0.05.

Statistical analyses

We report cumulative incidences of death overall, as well as by sex and disease classification using non-parametric cumulative incidence functions. To identify potential systemic biases in UNOS prioritization, we also report survival analyses stratified by UNOS Status under the competing events of death, transplant, and implant using non-parametric cumulative incidence functions, cause-specific Cox Proportional Hazards model (Cox model), and Fine-Gray subdistribution hazards model (Fine-Gray model) [12]. Holistic interpretations of the last two models are available [12, 13]. We used the significant variables regressed on cause-specific hazard of death for both the Cox and Fine-Gray models.

Results

Patient characteristics

The study sample consisted of mainly Caucasians and male patients (Table 1). Patients in this study were diagnosed with five main categories of heart disease, over half of which were diagnosed with Cardiomyopathy at listing. The UNOS Status statistics reported were designated at listing. The baseline patient characteristics are stratified by event (Table 1) and by UNOS Status (Table 2).

Table 1

Baseline patient characteristics by event.

Demographic Feature		Event Free (n = 4,495)	Death (n = 1,441)	Transplant (n = 13,020)	Implant (n = 4,417)	Sample (n = 23,373)
Sex	Male	73.6%	78.1%	72.5%	76.4%	73.8%
	Female	26.4%	21.9%	27.5%	23.6%	26.2%
UNOS Status	1A	7.9%	14.4%	14.9%	16.8%	13.9%
	1B	26.1%	34.0%	41.6%	47.9%	39.4%
	2	63.0%	46.8%	41.9%	30.9%	44.2%
	7	2.9%	4.8%	1.6%	4.5%	2.6%
Ethnicity	White	68.1%	68.0%	68.6%	65.0%	67.8%
	Black	21.3%	19.6%	19.3%	25.2%	20.8%
	Hispanic	7.3%	9.1%	8.0%	6.4%	7.6%
	Other	3.3%	3.3%	4.1%	3.4%	3.8%
Age at Listing	Minimum	16 years	16 years	16 years	16 years	16 years
	Median	54 years	56 years	55 years	55 years	55 years
	Maximum	76 years	79 years	78 years	78 years	79 years
Primary Diagnosis	Cardiomyopathy	50.7%	47.6%	53.7%	58.9%	53.7%
	Coronary Heart Disease	38.1%	41.2%	36.8%	37.5%	37.5%
	Congenital Heart Disease	5.8%	5.6%	4.3%	1.3%	4.1%
	Valvular Heart Disease	1.9%	2.5%	2.4%	0.9%	2.0%
	Other	3.5%	3.1%	2.7%	1.7%	2.7%

Table 2

Baseline patient characteristics by UNOS status.

Demographic Feature		UNOS Status 1A (n = 3,244)	UNOS Status 1B (n = 9,200)	UNOS Status 2 (n = 10,326)	UNOS Status 7 (n = 603)
Sex	Male	72.7%	73.9%	74.0%	73.5%
	Female	27.3%	26.1%	26.0%	26.5%
Ethnicity	White	62.3%	62.7%	74.1%	66.3%
	Black	23.6%	25.5%	15.6%	22.2%
	Hispanic	8.9%	8.2%	6.6%	8.1%
	Other	5.1%	3.5%	3.6%	3.3%
Age at Listing	Minimum	16 years	16 years	16 years	16 years
	Median	53 years	55 years	56 years	53 years
	Maximum	79 years	78 years	78 years	73 years
Primary Diagnosis	Cardiomyopathy	58.3%	58.0%	48.4%	52.7%
	Coronary Heart Disease	33.3%	34.8%	41.0%	41.0%
	Congenital Heart Disease	3.5%	2.8%	5.6%	2.5%
	Valvular Heart Disease	2.0%	1.9%	2.2%	1.5%
	Other	2.9%	2.5%	2.8%	2.7%

Cumulative incidence of patient survival

Cumulative incidence of death for the entire study is 5.09% (95% Confidence Interval [CI]: 4.81%-5.39%) at Year 1, 7.34% (95% CI: 6.96%-7.73%) at Year 5, and 8.71% (95% CI: 7.63%-9.94%) at Year 10 (Fig 2a). Female (Year 1: 4.29% [95% CI: 3.79%-4.84%], Year 5: 6.17% [95% CI: 5.51%-6.90%], and Year 10: 7.44% [95% CI: 5.81%-9.53%]) and male (Year 1: 5.38% [95% CI: 5.04%-5.74%], Year 5: 7.74% [95% CI: 7.30%-8.21%], and Year 10: 9.22% [95% CI: 7.81%-10.89%]) patients’ cumulative incidences are shown in Fig 2b. When the cumulative incidence of death is separated by primary diagnosis, patients with Cardiomyopathy have one- and five-year cumulative incidences of 4.67% (95% CI: 4.31%-5.07%) and 6.45% (95% CI: 5.98%-6.96%), respectively, whereas those with Congenital Heart Disease have 6.47% (95% CI: 5.03%-8.30%) and 10.59% (95% CI: 8.49%-13.21%), respectively (Fig 2c).

Fig 2

Non-parametric cumulative incidence of death in 2005–2014: (a) Overall, as well as by (b) sex and (c) primary diagnosis.

Predictors of survival

Table 3 lists all of the at-listing variables available from the UNOS STAR files for heart transplantation; the covariates obtained by mixed selection and tested for significance are marked (*). Table 3 also lists the hazard ratios with respect to the events of interest and the regression models (i.e., cause-specific and subdistribution hazards models). We note that the cause-specific hazard ratio (csHR) represents the rate of the event of interest in those patients that are event-free; thus, csHR provides the estimated etiological effects of the variables [14]. In contrast, the subdistribution hazard ratio (sdHR) provides the prognostic effects of the variables [14].

Table 3

All UNOS STAR files at-listing variables and covariates obtained by mixed selection of variables.

Variable		Cause-specific Hazards Model			Subdistribution Hazards Model
		Death	Transplant	Implant	Death	Transplant	Implant
UNOS Status	1B*	0.65 (0.07)	0.63 (0.02)	0.67 (0.04)	0.87 (0.09)	0.72 (0.03)	0.95 (0.05)
	2*	0.46 (0.09)	0.31 (0.03)	0.25 (0.06)	1.05 (0.10)	0.44 (0.04)	0.65 (0.06)
	7* (baseline: 1A)	1.25 (0.11)	0.37 (0.06)	0.70 (0.08)	1.63 (0.15)	0.40 (0.08)	1.14 (0.09)
Sex: Female* (baseline: Male)		0.80 (0.07)	0.90 (0.02)	0.93 (0.05)	0.76 (0.08)	0.93 (0.03)	1.05 (0.05)
Ethnicity	White	1.17 (0.14)	0.93 (0.04)	0.88 (0.09)	0.86 (0.15)	1.01 (0.05)	1.09 (0.09)
	Black	0.97 (0.14)	0.88 (0.04)	0.87 (0.09)	0.89 (0.07)	0.96 (0.03)	1.05 (0.04)
	Hispanic (baseline = Other)	1.29 (0.15)	0.95 (0.05)	0.85 (0.10)	1.16 (0.10)	1.01 (0.04)	0.87 (0.06)
Age at Listing*		1.01 (0.00)	1.01 (0.00)	1.01 (0.00)	1.00 (0.00)	1.01 (0.00)	1.00 (0.00)
Primary Diagnosis	Coronary Artery Disease	0.91 (0.06)	0.92 (0.02)	0.86 (0.04)	1.03 (0.07)	0.97 (0.02)	0.93 (0.04)
	Congenital Heart Disease	0.98 (0.13)	0.80 (0.05)	0.37 (0.14)	1.20 (0.14)	0.95 (0.05)	0.42 (0.14)
	Valvular Heart Disease	1.30 (0.15)	0.95 (0.06)	0.49 (0.16)	1.34 (0.17)	1.12 (0.06)	0.46 (0.16)
	Other Primary Diagnosis	1.10 (0.14)	0.97 (0.05)	0.74 (0.12)	1.12 (0.16)	1.09 (0.05)	0.68 (0.12)
(baseline: Cardiomyopathy)
Height (cm)*		0.97 (0.01)	1.01 (0.00)	1.01 (0.01)	0.97 (0.01)	1.01 (0.00)	1.02 (0.01)
Weight (kg)*		1.02 (0.01)	0.98 (0.00)	0.99 (0.01)	1.03 (0.01)	0.97 (0.00)	0.99 (0.01)
Body Mass Index*		0.94 (0.02)	1.03 (0.01)	1.02 (0.02)	0.92 (0.03)	1.04 (0.01)	1.05 (0.02)
Blood Group	AB	1.21 (0.14)	1.70 (0.04)	1.27 (0.09)	0.83 (0.06)	1.56 (0.02)	0.85 (0.03)
	B	0.99 (0.08)	1.00 (0.02)	1.06 (0.05)	0.77 (0.16)	2.56 (0.05)	0.60 (0.09)
	O (baseline: A)	0.96 (0.05)	0.61 (0.02)	0.82 (0.03)	0.78 (0.09)	1.55 (0.03)	0.88 (0.05)
Total Serum Albumin (g/dL)*		0.67 (0.02)	1.45 (0.01)	0.99 (0.01)	0.64 (0.02)	1.48 (0.01)	0.79 (0.01)
Serum Creatinine (mg/dL)*		1.16 (0.01)	1.01 (0.01)	1.01 (0.02)	1.15 (0.01)	1.00 (0.01)	0.96 (0.02)
No Diabetes		0.86 (0.15)	1.14 (0.06)	1.21 (0.11)	1.27 (0.18)	0.98 (0.07)	0.90 (0.11)
Type I Diabetes		1.00 (0.19)	1.28 (0.08)	0.81 (0.16)	1.44 (0.13)	1.19 (0.06)	0.60 (0.11)
Type II Diabetes		0.89 (0.15)	1.12 (0.07)	1.34 (0.11)	1.04 (0.07)	0.95 (0.02)	1.09 (0.04)
Other Type Diabetes (baseline: Unknown)		0.82 (0.52)	0.97 (0.19)	1.57 (0.31)	0.98 (0.59)	0.99 (0.18)	1.52 (0.27)
Uses Cigarettes		1.08 (0.06)	0.90 (0.02)	0.91 (0.04)	1.22 (0.07)	0.92 (0.02)	0.97 (0.04)
Has Abstained from Cigarette Use ≥ 60 Months		1.01 (0.07)	0.99 (0.02)	1.07 (0.04)	0.99 (0.08)	0.96 (0.03)	1.07 (0.04)
Cardiac Output (CO, L/min)*		0.97 (0.02)	0.98 (0.01)	0.99 (0.01)	0.97 (0.02)	0.99 (0.01)	1.01 (0.01)
CO Obtained while on Vasodilaters or Inotropes (V/I)		0.70 (0.19)	0.96 (0.08)	0.73 (0.15)	0.81 (0.22)	1.10 (0.09)	0.84 (0.16)
Unknown if CO Obtained while on V/I		0.96 (0.16)	0.89 (0.06)	0.80 (0.12)	1.11 (0.17)	0.98 (0.07)	0.85 (0.11)
Pulmonary Artery Systolic Pressure (PASP, mmHg)*		1.01 (0.00)	1.00 (0.00)	1.00 (0.00)	1.00 (0.00)	1.00 (0.00)	1.00 (0.00)
PASP Obtained while on V/I		1.51 (0.88)	1.93 (0.29)	3.34 (0.54)	1.32 (0.79)	1.22 (0.33)	2.44 (0.58)
Unknown if PASP Obtained while on V/I		2.25 (0.66)	1.07 (0.22)	2.76 (0.41)	1.34 (0.51)	0.77 (0.21)	2.98 (0.45)
Pulmonary Artery Diastolic Pressure (PADP, mmHg)		1.00 (0.01)	1.00 (0.00)	1.00 (0.00)	1.01 (0.01)	1.00 (0.00)	1.00 (0.00)
PADP Obtained while on V/I		1.51 (0.89)	0.62 (0.30)	0.48 (0.53)	1.45 (0.81)	0.82 (0.33)	0.47 (0.60)
Unknown if PADP Obtained while on V/I		0.89 (0.66)	0.88 (0.22)	0.49 (0.42)	1.65 (0.52)	1.04 (0.21)	0.43 (0.46)
Pulmonary Artery Mean Pressure (PAMP, mmHg)		1.00 (0.01)	0.99 (0.00)	0.99 (0.00)	1.00 (0.01)	0.99 (0.00)	1.00 (0.00)
PAMP Obtained while on V/I		0.63 (0.27)	1.02 (0.10)	1.15 (0.20)	0.52 (0.27)	0.99 (0.11)	1.38 (0.19)
Unknown if PAMP Obtained while on V/I		0.69 (0.26)	1.01 (0.09)	0.82 (0.19)	0.71 (0.28)	1.00 (0.10)	1.02 (0.18)
Pulmonary Capillary Wedge Pressure (PCWP, mmHg)*		1.01 (0.00)	1.01 (0.00)	1.01 (0.00)	0.99 (0.01)	1.01 (0.00)	1.00 (0.00)
PCWP Obtained while on V/I*		1.31 (0.18)	0.93 (0.07)	0.76 (0.13)	1.56 (0.21)	1.01 (0.09)	0.72 (0.13)
Unknown if PCWP Obtained while on V/I*		1.50 (0.18)	1.14 (0.07)	1.12 (0.13)	1.22 (0.19)	1.14 (0.08)	0.95 (0.12)
On Inotropes*		1.39 (0.09)	1.12 (0.03)	0.38 (0.04)	1.77 (0.11)	3.42 (0.05)	0.27 (0.05)
On Life Support		0.89 (0.09)	0.88 (0.04)	3.01 (0.05)	0.66 (0.12)	0.28 (0.06)	4.43 (0.05)
On Other Mechanism of Life		1.19 (0.15)	0.81 (0.06)	0.66 (0.09)	1.43 (0.18)	1.22 (0.08)	0.69 (0.09)
Number of Previous Non-heart Transplants*		1.90 (0.13)	1.03 (0.07)	0.93 (0.17)	1.91 (0.12)	0.94 (0.08)	0.81 (0.18)
Had Prior Cardiac Surgeries*		1.23 (0.05)	0.94 (0.02)	1.05 (0.04)	1.22 (0.06)	0.93 (0.02)	1.08 (0.03)
Unknown History of Prior Cardiac Surgeries*		1.37 (0.19)	0.88 (0.06)	1.51 (0.12)	1.33 (0.21)	0.75 (0.08)	1.39 (0.14)
Symptomatic of Cerebrovascular Disease*		1.31 (0.10)	1.15 (0.04)	1.34 (0.07)	1.14 (0.11)	1.04 (0.04)	1.14 (0.07)
Unknown to be Symptomatic of Cerebrovascular Disease*		1.28 (0.20)	1.13 (0.09)	0.83 (0.20)	1.57 (0.23)	1.16 (0.10)	0.62 (0.21)
Has Previous Malignancy		0.92 (0.10)	0.98 (0.03)	1.04 (0.06)	0.88 (0.11)	1.00 (0.04)	1.01 (0.06)
Unknown to Have Previous Malignancy		1.32 (0.18)	0.78 (0.08)	0.47 (0.19)	1.41 (0.22)	0.99 (0.08)	0.56 (0.18)

Numeric and parenthesized numeric entries are the hazard ratios (standard errors) for each variable and respective event and model; Covariates derived via mixed-selection of variables are indicated with asterisks (*).

The interpretation of the sdHR requires some care, however [15]. We can assume that if a variable increases the subdistribution hazard, it will also increase the incidence of the event of interest, but we cannot conclude that these two are in the same magnitude. Thus, using the value of a covariate’s sdHR only approximately describes the effect of that variable on the incidence of the event of interest.

When presenting the estimated HRs, we note qualitatively similar and different effects across competing events as well as the hazards models [16]. For example, UNOS Status 2 has a protective effect on the cause-specific hazards of death (csHR 0.46 [95% CI: 0.39-0.55]), transplant (csHR 0.31 [95% CI: 0.29-0.33]), and implant (csHR 0.25 [95% CI: 0.22-0.28]), whereas its effect on the subdistribution hazard of death is insignificant (sdHR 1.05 [95% CI: 0.86-1.27]). One may interpret this insignificant effect on the incidence of death as non-indicative of pretransplant death, but, as recommended elsewhere [17], we present csHRs and sdHRs to provide decision-makers the complete estimated etiological and prognostic effects [14], respectively, of the variables in our multivariate analyses. The final set of covariates is obtained through the mixed selection of variables for the cause-specific hazard of death and is used in the statistical analyses previously described.

Predicted survival by UNOS status

For death, transplant, and implant events, the average one-year non-parametric cumulative incidences are: 5.09% (95% CI: 4.81%-5.39%), 52.26% (95% CI: 51.60%-52.93%), and 12.64% (95% CI: 12.20%-13.09%), respectively. At five years, those incidences for the same three events are: 7.34% (95% CI: 6.96%-7.73%), 61.70% (95% CI: 61.00%-62.40%), and 24.96% (95% CI: 24.30%-25.64%), respectively (Fig 3). We observe a crossover on the cumulative incidence for the death event between patients listed at UNOS Statuses 1A and 2 at 1,002 days, 6.90% (95% CI: 6.04%-7.89%) and 6.91% (95% CI: 6.38%-7.47%), respectively (Fig 3a). We observe a similar crossover in the Cox model for the death event between patients listed at UNOS Statuses 1A and 2 at 300 days, 19.09% (95% CI: 10.40%-26.94%) and 19.09% (95% CI: 8.38%-28.55%), respectively (Fig 3b). The Fine-Gray model, by design, does not exhibit this crossover (Fig 3c): Status 2 patients have a higher cumulative incidence rate for the death event than the UNOS Status 1A and 1B patient groups.

Fig 3

Cumulative incidences of death (a–c), transplantation (d–f), and implantation (g–i) by UNOS status in 2005–2014 using three survival analysis models: Non-parametric (a, d, g), cox model (b, e, h), and Fine-Gray model (c, f, i).

Transplant rates for the non-parametric cumulative incidence plot indicate that only Status 2 patients are receiving transplantation after 500 days and before 2000 days (Fig 3d). The Cox model shows a similar pattern by predicting only Status 2 patients as receiving transplantation after 600 days through around 2,000 days (Fig 3e). However, the Fine-Gray model does not illustrate this sloping difference (Fig 3f).

Implant rates, for all three models, are highest for Status 7 patients and lowest among Status 2 patients. Status 2 patients, however, do continue to receive implants through 10 years from listing on the UNOS waitlist (Fig 3g). The Cox model predicts this same pattern: as all other statuses stop receiving implants, Status 2 patients are continuing to increase their cumulative incidences (Fig 3h). The Fine-Gray model, instead, predicts a much higher gap in implant rates between Status 2 patients and all others (Fig 3i).

Discussion

Patients listed at the lowest priority (UNOS Status 2) appear disadvantaged as they eventually experience higher cumulative incidences of death compared to patients at higher priorities, yet they are least likely to require implants.

We chose to stratify the data by UNOS Status to highlight the strengths and shortcomings of the current heart allocation policy. In doing so, we discovered that patients at the lowest priority level, UNOS Status 2 patients, are at a disadvantage with respect to allocation. Fig 3a depicts that the rate of death of Status 2 patients eventually surpasses those of patients at higher priorities. Fig 3d highlights the current UNOS Status prioritization for transplantation accurately. Implant events are shown to be least viable for Status 2 patients as they make up the group with the lowest probability of receiving one. Fig 3g shows that the rate of implantation up to Day 500 for Status 2 patients is much lower than those of other Statuses. The implantation and death incidences highlight how the prioritization of Status 2 patients proves to be one of the largest challenges facing this current policy during a patient’s first few years on the waitlist.

Patients on the UNOS waitlist will have changes in condition and require reallocation to new Statuses. Our study uses patient UNOS Status only at the time of listing. However, the UNOS STAR files do not maintain historical changes in the variables we used (Table 3), thus prohibiting time-varying analyses. Our study also used the covariates found only by using the cause-specific hazards model; more-appropriate ways for identifying covariates using the subdistribution hazards model are under development [18-20]. Regardless, the event rates calculated by our models pose extreme inconsistencies within current prioritization practices. One would expect the graphs of different events to follow a highest- to lowest-priority order. For example, Fig 3d displays this trend. Those with the highest probability of receiving a transplant are also the highest priority patients, and those with the lowest probability are the lowest priority. This graph remains as the only depiction of those policies while the graphs for the other two events, death (Fig 3a) and implant (Fig 3g), show that given initial waitlist priorities, there are unexpected patient outcomes with respect to priority.

The competing risks model posits a strength of predicting survival probabilities for patients with an upward bias as indicated by the Kaplan-Meier estimate. In our study, implantation acts as a competing event once patients are listed on the waitlist. MCSD clinical recommendations [5] indicate that MCSDs are a BTT therapy for patients, which affects patient health trajectories and UNOS prioritization. Thus, our study excludes patients with MCSDs prior to listing for transplantation (i.e., patients with higher priority). However, MCSDs are affording long-term survival from heart failure [5], thus our study underestimates the disadvantages of Status 2 patients.

The US heart allocation policy has undergone some recent changes [4]. In contrast to other organ allocation policies, however, a prioritization model incorporating clinical characteristics has yet to be developed for the heart allocation policy. While different models have been proposed [21-23], a consensus on a prevailing model has yet to be reached [23]. When one model prevails, the information it requires must be collected by UNOS to properly assign waitlist priority. Recent policy changes address the excessive amount of Status 1A patients, the exorbitant exception requests, incorrect allocation of patients with MCSD based on prognosis, and geographical disparities as a consequence of the current policy [4]. Stratification by MCSD type does not address the disadvantages currently faced by Status 2 patients, who appear to have more profound reasons for not being candidates for MCSD implantation yet eventually die at a higher rate than higher-priority patients. The clinical characteristics of patients with end-stage heart failure should be accommodated as factors in the prioritization of the UNOS heart transplant waitlist.

7 Mar 2022

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Reviewer #1: Partly

Reviewer #2: Partly

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: I Don't Know

Reviewer #2: I Don't Know

**********

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The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: No

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Reviewer #2: Yes

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Reviewer #1: The authors reported the survival analyses of candidates for heart transplantation list under competing events of transplantation and MCSD implantation. The authors aimed to measure properly and estimate the survival probability of patients on the UNOS waitlist under competing risks to potentially highlight systematic biases and possible areas of improvement in organ allocation and patient status classification. However, this reviewer considers that this paper did not well meet the results for the objectives. This reviewer has several comments as described below.

Major comments.

1. The study found high mortality and low attainment of transplantation in Status 2 patients. The authors should indicate the predictors of mortality in Status 2 patients. One of them might be small left ventricular diameters, such as restrictive cardiomyopathy, who cannot fit to VAD.

2. This reviewer feels that it was to overstate to conclude that all status2 patients have high mortality. The authors should explain what clinical features of status 2 had high mortality.

3. It is important to point out that the mortality rate of status 2 is higher than that of status 1 when the observation period is prolonged. It may indicate that the current organ distribution system is acceptable up to 300 days or 1002 days. The authors should add that point.

4. As the authors indicated, the results did not reflect the changes in status during the study period, which may lead to problems with the accuracy of the results. This was a big limitation.

5. Dissociation of the results between the cause-specific Hazards Model and the Subdistribution Hazards Model was difficult to understand, which should be explained more carefully.

6. In the Discussion section, the author described that Status2 patients had a higher rate of death than patients at higher priorities in Fig 2a. Was this Fig 3a?

Reviewer #2: Describe heart-allocation policy and details about status /priority IA, 1B, 2 and 7.

Figure 2 should present cumulative Incidence of Patient Survival event for each category of UNOS.

Is the waiting list kept updated frequently as patient health conditions evolve? Did the priorities of patients analyzed in the study change accordingly?

As shown in Fig 3a, during the first 500 days, the death probability in status 2 group is the lowest. Afterwards, the death probability rises up and exceeds d status 1B and 7 starting from day 1500.

Label for each group should be distinguished. As shown in Fig.3 d, status 1b and status 2 has continuously received implantations through the entire 10 years, not as the authors stated “only Status 2 patients are receiving transplantation after 500 days and before 2000 days “.

Provide waiting time information for transplantation and implantation in each group/status. The plot between waiting time for transplantation/ implantation and death should be provided, which are more valuable and informative.

Page 8 lines 155-156 the authors wrote “ Fig 2a depicts that Status 2 patients have a higher rate of death 155 than patients at higher priorities.” However, Fig.2a did not provide any information about priorities.

On page 9 Lin 185-186 Sentence “Long proposed is the idea that a score for patients with end-stage heart failure should be developed and utilized to judge priority..” What is a score? When is it collected?

There are many grammar and typo errors. And English proof is needed. For example, on Page3 line 45 “ aged less than 16 years” ; On page 9 Lin 185-186 Sentence “Long proposed is the idea that a score for patients with end-stage heart failure should be developed and utilized to judge priority.. “

Full name for Abbreviation should be shown when it appears the first time. For example, UNOS.

**********

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Reviewer #2: No

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25 May 2022

We are grateful to the publisher, academic editor, and two anonymous reviewers for their comments to improve our manuscript. Summarized in the following sections are the itemized comments accompanied by our corresponding responses and/or updates to the manuscript.

Note: References to manuscript Line numbers are those of the change-tracked (marked-up) version.

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Academic Editor’s Comments and Authors’ Responses

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1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and

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Response: We use the PLOS ONE LaTeX template (https://journals.plos.org/plosone/s/latex), and:

• We revised our manuscript to conform to the formatting guidelines, including correcting the author notes including affiliations, addresses, and corresponding authorship, as well as manually adjusting the default LaTeX template line-spacing for the title page.

• We corrected the citations at the ends of sentences to appear before the punctuations.

• We renamed the figure files as Fig1.tiff, Fig2.tiff, and Fig3.tiff.

• We corrected the caption and legend of Table 3.

• We adjusted for the LaTeX template automation to force Table 3 to appear within the manuscript rather than after the References. However, the LaTeX template still places Tables 1–3 on the pages following their first citations because they do not fit within the page under the paragraph in which they were first cited.

2. Thank you for stating the following in the Acknowledgments Section of your manuscript:

“This work was supported by funds provided by The University of North Carolina at

Charlotte, University Professional Internship Program, Levine Scholars Program, and

Honors College. Funding sources had no involvement in this study. The corresponding

author affirms that he has listed everyone who contributed significantly to the work.”

We note that you have provided funding information that is not currently declared in your Funding Statement. However, funding information should not appear in the Acknowledgments section or other areas of your manuscript. We will only publish funding information present in the Funding Statement section of the online submission form.

Please remove any funding-related text from the manuscript and let us know how you would like to update your Funding Statement. Currently, your Funding Statement reads as follows:

“This work was supported by funds provided by The University of North Carolina at Charlotte (GLZ, Faculty Research Grant, https://research.charlotte.edu/departments/center-research-excellence-cre/locating-funding/internal-funding-programs), University Professional Internship Program (KBS, https://career.charlotte.edu/upip), Levine Scholars Program (KBS, https://levinescholars.charlotte.edu), and Honors College (KBS, https://honorscollege.charlotte.edu). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.”

Please include your amended statements within your cover letter; we will change the online submission form on your behalf.

Response: We removed the funding information from the Acknowledgements section and updated its contents as follows:

The authors are grateful to the academic editor and two anonymous reviewers for their comments to earlier versions of this manuscript. The data reported here have been supplied by the United Network for Organ Sharing as the contractor for the Organ Procurement and Transplantation Network. 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 OPTN or the U.S. Government.

We accept the funding statement as suggested, which is repeated here:

This work was supported by funds provided by The University of North Carolina at Charlotte (GLZ, Faculty Research Grant, https://research.charlotte.edu/departments/center-research-excellence-cre/locating-funding/internal-funding-programs), University Professional Internship Program (KBS, https://career.charlotte.edu/upip), Levine Scholars Program (KBS, https://levinescholars.charlotte.edu), and Honors College (KBS, https://honorscollege.charlotte.edu). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

3. In your Data Availability statement, you have not specified where the minimal data set underlying the results described in your manuscript can be found. PLOS defines a study's minimal data set as the underlying data used to reach the conclusions drawn in the manuscript and any additional data required to replicate the reported study findings in their entirety. All PLOS journals require that the minimal data set be made fully available. For more information about our data policy, please see http://journals.plos.org/plosone/s/data-availability.

Upon re-submitting your revised manuscript, please upload your study’s minimal underlying data set as either Supporting Information files or to a stable, public repository and include the relevant URLs, DOIs, or accession numbers within your revised cover letter. For a list of acceptable repositories, please see http://journals.plos.org/plosone/s/data-availability#loc-recommended-repositories. Any potentially identifying patient information must be fully anonymized.

Important: If there are ethical or legal restrictions to sharing your data publicly, please explain these restrictions in detail. Please see our guidelines for more information on what we consider unacceptable restrictions to publicly sharing data: http://journals.plos.org/plosone/s/data-availability#loc-unacceptable-data-access-restrictions. Note that it is not acceptable for the authors to be the sole named individuals responsible for ensuring data access.

We will update your Data Availability statement to reflect the information you provide in your cover letter.

Response: The UNOS Data Use Agreement signed by the UNC Charlotte liaison prohibits us from releasing the dataset without approval, which is quoted here: “You will neither release nor permit other to release the Data to any person (including media and subcontractors) except with the written approval of UNOS.”

As a result, we would like to update our Data Availability Statement (as similarly stated in another PLOS ONE article using the same transplantation dataset, https://dx.doi.org/10.1371%2Fjournal.pone.0247789):

Data cannot be shared publicly as it is owned by the United Network for Organ Sharing. We do not have permission to distribute the data, however, the data may be requested from the Organ Procurement and Transplantation Network. To obtain the data, a Data Use Agreement must be signed and approved by OPTN. Please refer the following URL: https://optn.transplant.hrsa.gov/data/request-data/.

4. We note that you have indicated that data from this study are available upon request. PLOS only allows data to be available upon request if there are legal or ethical restrictions on sharing data publicly. For more information on unacceptable data access restrictions, please see http://journals.plos.org/plosone/s/data-availability#loc-unacceptable-data-access-restrictions.

In your revised cover letter, please address the following prompts:

a) If there are ethical or legal restrictions on sharing a de-identified data set, please explain them in detail (e.g., data contain potentially sensitive information, data are owned by a third-party organization, etc.) and who has imposed them (e.g., an ethics committee). Please also provide contact information for a data access committee, ethics committee, or other institutional body to which data requests may be sent.

b) If there are no restrictions, please upload the minimal anonymized data set necessary to replicate your study findings as either Supporting Information files or to a stable, public repository and provide us with the relevant URLs, DOIs, or accession numbers. For a list of acceptable repositories, please see http://journals.plos.org/plosone/s/data-availability#loc-recommended-repositories.

We will update your Data Availability statement on your behalf to reflect the information you provide.

Response: Kindly see our response to the preceding comment, which also applies to this comment.

5. Please include your full ethics statement in the ‘Methods’ section of your manuscript file. In your statement, please include the full name of the IRB or ethics committee who approved or waived your study, as well as whether or not you obtained informed written or verbal consent. If consent was waived for your study, please include this information in your statement as well.

Response: We moved our full ethics statement from the Patient Selection subsection to a new Ethics subsection (Lines 35–42) at the beginning of the Methods section.

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Reviewer #1’s Comments and Authors’ Responses

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The authors reported the survival analyses of candidates for heart transplantation list under competing events of transplantation and MCSD implantation. The authors aimed to measure properly and estimate the survival probability of patients on the UNOS waitlist under competing risks to potentially highlight systematic biases and possible areas of improvement in organ allocation and patient status classification. However, this reviewer considers that this paper did not well meet the results for the objectives. This reviewer has several comments as described below.

Major comments.

1. The study found high mortality and low attainment of transplantation in Status 2 patients. The authors should indicate the predictors of mortality in Status 2 patients. One of them might be small left ventricular diameters, such as restrictive cardiomyopathy, who cannot fit to VAD.

Response: We include all at-listing variables considered in our models in Table 3. We state in Lines 64–70 that we considered all at-listing variables available from the data set and perform a mixed-selection method to derive the covariates for our models. Clinical characteristics, such as “small left ventricular diameters,” were not available in the dataset. Nevertheless, patients that may have been diagnosed with restrictive myopathy before or at listing would be contained within our primary diagnosis categorical variable.

2. This reviewer feels that it was to overstate to conclude that all status2 patients have high mortality. The authors should explain what clinical features of status 2 had high mortality.

Response: We corrected our previous overstatement on Lines 162–163:

Fig 3a depicts that the rate of death of Status 2 patients eventually surpasses those of patients at higher priorities.

The death hazard ratios for the variables we analyzed are tabulated in Table 3.

3. It is important to point out that the mortality rate of status 2 is higher than that of status 1 when the observation period is prolonged. It may indicate that the current organ distribution system is acceptable up to 300 days or 1002 days. The authors should add that point.

Response: We respectfully disagree: An equitable prioritization and allocation policy must attempt to maintain mortality curves ordered in accordance with priority, especially under competing risks: Status 2 patients have less alternatives. Throughout the 10-year study period, Status 2 patients have lower incidence of transplants (as imposed by their low priority), and the analyses revealed they also have lower incidence of implants.

4. As the authors indicated, the results did not reflect the changes in status during the study period, which may lead to problems with the accuracy of the results. This was a big limitation.

Response: We agree; however, the available data does not allow for time-varying analyses, which we clarified in the new sentence added on Lines 174–176:

However, the UNOS STAR files do not maintain historical changes in the variables we used (Table 3), thus prohibiting time-varying analyses.

Nonetheless, we believe we modeled the problem as appropriately as contemporary methodology allows.

5. Dissociation of the results between the cause-specific Hazards Model and the Subdistribution Hazards Model was difficult to understand, which should be explained more carefully.

Response: We revised the Predictors of Survival subsection of the Results section to clarify this dissociation in Lines 104–109:

We note that the cause-specific hazard ratio (csHR) represents the rate of the event of interest in those patients that are event-free; thus, csHR provides the estimated etiological effects of the variables. In contrast, the subdistribution hazard ratio (sdHR) provides the prognostic effects of the variables.

We also clarified the respective effects (in the context of an example) in the paragraph containing Lines 123–126:

… we present csHRs and sdHRs to provide decision-makers the complete estimated etiological and prognostic effects, respectively, of the variables in our multivariate analyses.

Additionally, we separated the discussion of exercising care in the interpretation of the subdistribution Hazards Model into its own paragraph:

clarify this dissociation on Lines 104–109:

The interpretation of the sdHR requires some care, however. We can assume that if a variable increases the subdistribution hazard, it will also increase the incidence of the event of interest, but we cannot conclude that these two are in the same magnitude. Thus, using the value of a covariate's sdHR only approximately describes the effect of that variable on the incidence of the event of interest.

6. In the Discussion section, the author described that Status2 patients had a higher rate of death than patients at higher priorities in Fig 2a. Was this Fig 3a?

Response: We corrected the figure reference to Fig 3a.

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Reviewer #2’s Comments and Authors’ Responses

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1. Describe heart-allocation policy and details about status /priority IA, 1B, 2 and 7.

Response: We describe study-relevant information on heart allocation, including the citation to the full policy, as well as UNOS Statuses in Lines 51–54.

2. Figure 2 should present cumulative Incidence of Patient Survival event for each category of UNOS.

Response: Cumulative incidences of patient survival for all UNOS Statuses are presented in Fig 3a.

3. Is the waiting list kept updated frequently as patient health conditions evolve? Did the priorities of patients analyzed in the study change accordingly? As shown in Fig 3a, during the first 500 days, the death probability in status 2 group is the lowest. Afterwards, the death probability rises up and exceeds d status 1B and 7 starting from day 1500.

Response: Kindly refer to our responses to Reviewer 1’s Comments 4 and 5 above, which apply here.

4. Label for each group should be distinguished. As shown in Fig.3 d, status 1b and status 2 has continuously received implantations through the entire 10 years, not as the authors stated “only Status 2 patients are receiving transplantation after 500 days and before 2000 days “.

Response: We distinguish UNOS Status for all panels of Fig 3 using the legend found in the upper left corner. Fig 3d displays the cumulative incidence of the transplantation event, not the implantation event. Fig 3d shows that the Status 1A and Status 1B cumulative incidence of transplantation are constant (i.e., flat curves) after 500 and 2000 days, respectively; this indicates that no transplants have since been accumulated for those statuses.

5. Provide waiting time information for transplantation and implantation in each group/status. The plot between waiting time for transplantation/ implantation and death should be provided, which are more valuable and informative.

Response: Cumulative incidence curves incorporate waiting time by design; the step increases in Figs 3a, 3d, and 3g depict on the y-axis the incremental proportion of patients who experienced the event of interest (i.e., after the last step increase), and their waiting time is equal to the Days to Event on the x-axis when the step increase occurred.

6. Page 8 lines 155-156 the authors wrote “ Fig 2a depicts that Status 2 patients have a higher rate of death 155 than patients at higher priorities.” However, Fig.2a did not provide any information about priorities.

Response: We corrected the figure reference to Fig 3a.

7. On page 9 Lin 185-186 Sentence “Long proposed is the idea that a score for patients with end-stage heart failure should be developed and utilized to judge priority..” What is a score? When is it collected?

Response: We clarified the sentence with the following replacement on Lines 199–202:

In contrast to other organ allocation policies, however, a prioritization model incorporating clinical characteristics has yet to be developed for the heart allocation policy. While different models have been proposed, a consensus on a prevailing model has yet to be reached.

8. There are many grammar and typo errors. And English proof is needed. For example, on Page3 line 45 “ aged less than 16 years” ; On page 9 Lin 185-186 Sentence “Long proposed is the idea that a score for patients with end-stage heart failure should be developed and utilized to judge priority.. “

Response: We respectfully disagree: The two examples given are grammatically correct. We proofread the manuscript again as suggested.

9. Full name for Abbreviation should be shown when it appears the first time. For example, UNOS.

Response: We corrected this on Lines 23–24 and Lines 29–30.

3 Aug 2022

Pretransplant survival of patients with end-stage heart failure under competing risks

PONE-D-21-27074R1

Dear Dr. Zenarosa,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Kind regards,

Yoshihiro Fukumoto

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

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2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Partly

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: I Don't Know

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

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5. Is the manuscript presented in an intelligible fashion and written in standard English?

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Reviewer #1: Yes

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6. Review Comments to the Author

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Reviewer #1: This reviewer has no further comment. The authors indicated to reconsider the classification of priorities in heart transplantation. It is a future challenge to determine what clinical characteristics of Status 2 patients are predictors of higher priority for heart transplantation. This reviewer hopes that this manuscript will serve as a starting point.

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Reviewer #1: No

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5 Aug 2022

PONE-D-21-27074R1

Pretransplant survival of patients with end-stage heart failure under competing risks

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