Continuously Updated Estimation of Heart Transplant Waitlist Mortality

Evolution of serious and life-threatening COVID-19 pneumonia as the SARS-CoV-2 pandemic progressed: an observational study of mortality to 60 days after admission to a 15-hospital US health system

OBJECTIVE

In order to predict at hospital admission the prognosis of patients with serious and life-threatening COVID-19 pneumonia, we sought to understand the clinical characteristics of hospitalised patients at admission as the SARS-CoV-2 pandemic progressed, document their changing response to the virus and its variants over time, and identify factors most importantly associated with mortality after hospital admission.

DESIGN

Observational study using a prospective hospital systemwide COVID-19 database.

SETTING

15-hospital US health system.

PARTICIPANTS

26 872 patients admitted with COVID-19 to our Northeast Ohio and Florida hospitals from 1 March 2020 to 1 June 2022.

MAIN OUTCOME MEASURES

60-day mortality (highest risk period) after hospital admission analysed by random survival forests machine learning using demographics, medical history, and COVID-19 vaccination status, and viral variant, symptoms, and routine laboratory test results obtained at hospital admission.

RESULTS

Hospital mortality fell from 11% in March 2020 to 3.7% in March 2022, a 66% decrease (p<0.0001); 60-day mortality fell from 17% in May 2020 to 4.7% in May 2022, a 72% decrease (p<0.0001). Advanced age was the strongest predictor of 60-day mortality, followed by admission laboratory test results. Risk-adjusted 60-day mortality had all patients been admitted in March 2020 was 15% (CI 3.0% to 28%), and had they all been admitted in May 2022, 12% (CI 2.2% to 23%), a 20% decrease (p<0.0001). Dissociation between observed and predicted decrease in mortality was related to temporal change in admission patient profile, particularly in laboratory test results, but not vaccination status or viral variant.

CONCLUSIONS

Hospital mortality from COVID-19 decreased substantially as the pandemic evolved but persisted after hospital discharge, eclipsing hospital mortality by 50% or more. However, after accounting for the many, even subtle, changes across the pandemic in patients' demographics, medical history and particularly admission laboratory results, a patient admitted early in the pandemic and predicted to be at high risk would remain at high risk of mortality if admitted tomorrow.

Failing with Cyanosis-Heart Failure in End-Stage Unrepaired or Partially Palliated Congenital Heart Disease

Heart failure in cyanotic congenital heart disease (CHD) is diagnosed clinically rather than relying solely on ventricular function assessments. Patients with cyanosis often present with clinical features indicative of heart failure. Although myocardial injury and dysfunction likely contribute to cyanotic CHD, the primary concern is the reduced delivery of oxygen to tissues. Symptoms such as fatigue, lassitude, dyspnea, headaches, myalgias, and a cold sensation underscore inadequate tissue oxygen delivery, forming the basis for defining heart failure in cyanotic CHD. Thus, it is pertinent to delve into the components of oxygen delivery in this context.

Ex vivo lung perfusion and the Organ Care System: a review

With the increasing prevalence of heart failure and end-stage lung disease, there is a sustained interest in expanding the donor pool to alleviate the thoracic organ shortage crisis. Efforts to extend the standard donor criteria and to include donation after circulatory death have been made to increase the availability of suitable organs. Studies have demonstrated that outcomes with extended-criteria donors are comparable to those with standard-criteria donors. Another promising approach to augment the donor pool is the improvement of organ preservation techniques. Both ex vivo lung perfusion (EVLP) for the lungs and the Organ Care System (OCS, TransMedics) for the heart have shown encouraging results in preserving organs and extending ischemia time through the application of normothermic regional perfusion. EVLP has been effective in improving marginal or borderline lungs by preserving and reconditioning them. The use of OCS is associated with excellent short-term outcomes for cardiac allografts and has improved utilization rates of hearts from extended-criteria donors. While both EVLP and OCS have successfully transitioned from research to clinical practice, the costs associated with commercially available systems and consumables must be considered. The ex vivo perfusion platform, which includes both EVLP and OCS, holds the potential for diverse and innovative therapies, thereby transforming the landscape of thoracic organ transplantation.

Increasing Utilization of Extended Criteria Donor Hearts for Transplantation: The OCS Heart EXPAND Trial

BACKGROUND

Extended criteria donor (ECD) hearts available with donation after brain death (DBD) are underutilized for transplantation due to limitations of cold storage.

OBJECTIVES

This study evaluated use of an extracorporeal perfusion system on donor heart utilization and post-transplant outcomes in ECD DBD hearts.

METHODS

In this prospective, single-arm, multicenter study, adult heart transplant recipients received ECD hearts using an extracorporeal perfusion system if hearts met study criteria. The primary outcome was a composite of 30-day survival and absence of severe primary graft dysfunction (PGD). Secondary outcomes were donor heart utilization rate, 30-day survival, and incidence of severe PGD. The safety outcome was the mean number of heart graft-related serious adverse events within 30 days. Additional outcomes included survival through 2 years benchmarked to concurrent nonrandomized control subjects.

RESULTS

A total of 173 ECD DBD hearts were perfused; 150 (87%) were successfully transplanted; 23 (13%) did not meet study transplantation criteria. At 30 days, 92% of patients had survived and had no severe PGD. The 30-day survival was 97%, and the incidence of severe PGD was 6.7%. The mean number of heart graft-related serious adverse events within 30 days was 0.17 (95% CI: 0.11-0.23). Patient survival was 93%, 89%, and 86% at 6, 12, and 24 months, respectively, and was comparable with concurrent nonrandomized control subjects.

CONCLUSIONS

Use of an extracorporeal perfusion system resulted in successfully transplanting 87% of donor hearts with excellent patient survival to 2 years post-transplant and low rates of severe PGD. The ability to safely use ECD DBD hearts could substantially increase the number of heart transplants and expand access to patients in need. (International EXPAND Heart Pivotal Trial [EXPANDHeart]; NCT02323321; Heart EXPAND Continued Access Protocol; NCT03835754).

Use of Donor Hearts ≥50 Years Old for Septuagenarians in Heart Transplantation

BACKGROUND

The impact of using donors ≥50 years old on heart transplantation outcomes of septuagenarians is unknown, which may have a potential to expand the donor pool.

METHODS

From January 2011 to December 2021, 817 septuagenarians received donor hearts <50 years old (DON<50) and 172 septuagenarians received donor hearts ≥50 years old (DON≥50) in the United Network for Organ Sharing database. Propensity score matching was performed using recipient characteristics (167 pairs). The Kaplan-Meier method and Cox proportional hazards model were used to analyze death and graft failure.

RESULTS

The number of heart transplants in septuagenarians has been increasing (54 per year in 2011 to 137 per year in 2021). In a matched cohort, the donor age was 30 years in DON<50 and 54 years in DON≥50. In DON≥50, cerebrovascular disease was the main cause of death (43%), whereas head trauma (38%) and anoxia (37%) were the causes in DON<50 (P < .001). The median heart ischemia time was comparable (DON<50, 3.3 hours; DON≥50, 3.2 hours; P = .54). In matched patients, 1- and 5-year survival rates were 88.0% (DON<50) vs 87.2% (DON≥50) and 79.2% (DON<50) vs 72.3% (DON≥50), respectively (log-rank, P = .41). In the multivariable Cox proportional hazards models, donors ≥50 years old were not associated with death in matched (hazard ratio, 1.05; 95% CI, 0.67-1.65; P = .83) and nonmatched groups (hazard ratio, 1.11; 95% CI, 0.82-1.50; P = .49).

CONCLUSIONS

The use of donor hearts older than 50 years can be an effective option for septuagenarians, thereby potentially increasing organ availability without compromising outcomes.

Association of high-priority exceptions with waitlist mortality among heart transplant candidates

BACKGROUND

The US heart allocation system ranks candidates using six categorical status levels. Transplant programs can request exceptions to increase a candidate's status level if they believe their candidate has the same medical urgency as candidates who meet the standard criteria for that level. We aimed to determine if exception candidates have the same medical urgency as standard candidates.

METHODS

Using the Scientific Registry of Transplant Recipients, we constructed a longitudinal waitlist history dataset of adult heart-only transplant candidates listed between October 18, 2018 and December 1, 2021. We estimated the association between exceptions and waitlist mortality with a mixed-effects Cox proportional hazards model that treated status and exceptions as time-dependent covariates.

RESULTS

Out of 12,458 candidates listed during the study period, 2273 (18.2%) received an exception at listing and 1957 (15.7%) received an exception after listing. After controlling for status, exception candidates had approximately half the risk of waitlist mortality as standard candidates (hazard ratio [HR] 0.55, 95% confidence interval [CI] [0.41, 0.73], p < .001). Exceptions were associated with a 51% lower risk of waitlist mortality among Status 1 candidates (HR 0.49, 95% CI [0.27, 0.91], p = .023) and a 61% lower risk among Status 2 candidates (HR 0.39, 95% CI [0.24, 0.62], p < .001).

CONCLUSIONS

Under the new heart allocation policy, exception candidates had significantly lower waitlist mortality than standard candidates, including exceptions for the highest priority statuses. These results suggest that candidates with exceptions, on average, have a lower level of medical urgency than candidates who meet standard criteria.

A dynamic Norwood mortality estimation: Characterizing individual, updated, predicted mortality trajectories after the Norwood operation

Objective

Post-Norwood mortality remains high and unpredictable. Current models for mortality do not incorporate interstage events. We sought to determine the association of time-related interstage events, along with (pre)operative characteristics, with death post-Norwood and subsequently predict individual mortality.

Methods

From the Congenital Heart Surgeons' Society Critical Left Heart Obstruction cohort, 360 neonates underwent Norwood operations from 2005 to 2016. Risk of death post-Norwood was modeled using a novel application of parametric hazard analysis, in which baseline and operative characteristics and time-related adverse events, procedures, and repeated weight and arterial oxygen saturation measurements were considered. Individual predicted mortality trajectories that dynamically update (increase or decrease) over time were derived and plotted.

Results

After the Norwood, 282 patients (78%) progressed to stage 2 palliation, 60 patients (17%) died, 5 patients (1%) underwent heart transplantation, and 13 patients (4%) were alive without transitioning to another end point. In total, 3052 postoperative events occurred and 963 measures of weight and oxygen saturation were obtained. Risk factors for death included resuscitated cardiac arrest, moderate or greater atrioventricular valve regurgitation, intracranial hemorrhage/stroke, sepsis, lower longitudinal oxygen saturation, readmission, smaller baseline aortic diameter, smaller baseline mitral valve z-score, and lower longitudinal weight. Each patient's predicted mortality trajectory varied as risk factors occurred over time. Groups with qualitatively similar mortality trajectories were noted.

Conclusions

Risk of death post-Norwood is dynamic and most frequently associated with time-related postoperative events and measures, rather than baseline characteristics. Dynamic predicted mortality trajectories for individuals and their visualization represent a paradigm shift from population-derived insights to precision medicine at the patient level.

Prognostic implications of inactive status in highest urgency categories among heart transplantation recipients in the new donor heart allocation system

BACKGROUND

Patients on the waiting list for heart transplantation (HT) can become inactive or made status seven because of medical reasons, such adverse events, complications, or psychosocial circumstances. If the condition that caused the inactivation is resolved, patients are re- activated. Information about the prognostic implications of Status 7 in the new donor heart allocation system has not been described. To bridge this knowledge gap, we performed an analysis of the United Network of Organ Sharing (UNOS) registry.

METHODS

Data on adult patients who underwent HT between October 18th, 2018 and October 2021, were queried from the UNOS registry. The main outcomes were post- transplant all-cause mortality, 1-year all-cause mortality and treated acute rejection. Since re-transplantation is a competing event for all-cause mortality, we performed competing risk survival analysis and reported sub distribution hazard ratios (SHR) from the Fine and Gray model to examine the relationship between inactive status and all-cause mortality.

RESULTS

A total of 5267 adult patients underwent HT and were previously listed as Status 1 or Status 2 in the new allocation system. We identified 946 HT recipients temporarily inactivated while on HT list (18%). The number of temporarily inactive patients remained stable since the implementation of the new donor allocation system (p = .37). Approximately, two-thirds of temporarily inactive patients (65.9%) were inactivated for being too sick, whereas other frequent justifications for inactivity included left ventricular assist device implantation (7.8%) and insurance related issues (4.8%). Temporarily inactive HT recipients were more likely to be African Americans, males, have a higher body mass index (BMI) and significantly longer waiting time (391.6 ± 600 vs. 72.3 ± 223 days, p < .001) compared with never inactivated patients. In the unadjusted analyses 30-day mortality did not differ between groups, but both 1-year and overall all-cause mortality was significantly higher in temporarily inactive patients (1-year: SHR: 1.3; 95% confidence intervals [CI]: 1.03, 1.64; p = .028, overall mortality SHR: 1.31; 95% CI: 1.06, 1.64; p = .014). After adjustment for donor and recipient characteristics, a trend towards higher 1-year and overall mortality remained (1-year: SHR 1.32; 95% CI .99, 1.76, p = .006, overall mortality SHR: 1.29; 95% CI: .98-1.68, p = .065). No differences in treated acute allograft rejection at 1 year were found between groups.

CONCLUSIONS

Temporary inactive status while waiting for HT occurs in approximately one in five HT recipients listed in higher urgency categories after the implementation of the new allocation system. A signal of adverse long-term outcomes was found, and this could be explained by differences in recipient characteristics. Further research is required to elucidate pathways involved and possible implications for clinical practice.

Prolonged waitlisting is associated with mortality in extracorporeal membrane oxygenation-supported heart transplantation candidates

OBJECTIVE

Heart transplantation (HTx) candidates supported with venoarterial extracorporeal membrane oxygenation (ECMO) may be listed at highest status 1 but are at inherent risk for ECMO-related complications. The effect of waitlist time on postlisting survival remains unclear in candidates with ECMO support who are listed using the new allocation system.

METHODS

Adult candidates listed with ECMO for a first-time, single-organ HTx from October 18, 2018, to March 21, 2021, in the Scientific Registry of Transplant Recipients database were included and stratified according to waitlist time (≤7 vs ≥8 days). Postlisting outcomes were compared between cohorts.

RESULTS

Among 175 candidates waitlisted for ≤7 days, 162 (92.6%) underwent HTx whereas 13 (7.4%) died/deteriorated compared with 41 (57.8%) and 21 (29.6%) of the 71 candidates waitlisted for ≥8 days, respectively (P < .01). Blood type O candidates (odds ratio [OR], 2.94; 95% CI, 1.54-5.61) were more likely to wait ≥8 days whereas candidates with concurrent intra-aortic balloon pump were less likely (OR, 0.30; 95% CI, 0.10-0.89). Obesity was additionally associated among those listed at status 1 (OR, 2.04; 95% CI, 1.00-4.17). Waitlisting for ≥8 days was independently associated with 90-day postlisting mortality conditional on survival to day 8 postlisting (hazard ratio, 5.59; 95% CI, 2.59-12.1). Candidates listed at status 1 showed similar trends (hazard ratio, 5.49; 95% CI, 2.39-12.6). There was no significant difference in 90-day post-HTx survival depending on whether a candidate waited for ≥8 days versus ≤7 days (92.7 vs 92.0%; log rank P = .87).

CONCLUSIONS

Among ECMO-supported candidates, obtaining HTx within 1 week of listing might improve overall survival.

Actuators for Implantable Devices: A Broad View

The choice of actuators dictates how an implantable biomedical device moves. Specifically, the concept of implantable robots consists of the three pillars: actuators, sensors, and powering. Robotic devices that require active motion are driven by a biocompatible actuator. Depending on the actuating mechanism, different types of actuators vary remarkably in strain/stress output, frequency, power consumption, and durability. Most reviews to date focus on specific type of actuating mechanism (electric, photonic, electrothermal, etc.) for biomedical applications. With a rapidly expanding library of novel actuators, however, the granular boundaries between subcategories turns the selection of actuators a laborious task, which can be particularly time-consuming to those unfamiliar with actuation. To offer a broad view, this study (1) showcases the recent advances in various types of actuating technologies that can be potentially implemented in vivo, (2) outlines technical advantages and the limitations of each type, and (3) provides use-specific suggestions on actuator choice for applications such as drug delivery, cardiovascular, and endoscopy implants.

Mortality rate of Boer, Central Highland goat and their crosses in Ethiopia: Nonparametric survival analysis and piecewise exponential model

Background

Methods

Results

Conclusion

Data-driven versus a domain-led approach to k-means clustering on an open heart failure dataset

Domain-driven data mining of health care data poses unique challenges. The aim of this paper is to explore the advantages and the challenges of a ‘domain-led approach’ versus a data-driven approach to a k-means clustering experiment. For the purpose of this experiment, clinical experts in heart failure selected variables to be used during the k-means clustering, whilst during the ‘data-driven approach’ feature selection was performed by applying principal component analysis to the multidimensional dataset. Six out of seven features selected by physicians were amongst 26 features that contributed most to the significant principal components within the k-means algorithm. The data-driven approach showed advantage over the domain-led approach for feature selection by removing the risk of bias that can be introduced by domain experts. Whilst the ‘domain-led approach’ may potentially prohibit knowledge discovery that can be hidden behind variables not routinely taken into consideration as clinically important features, the domain knowledge played an important role at the interpretation stage of the clustering experiment providing insight into the context and preventing far fetched conclusions. The “data-driven approach” was accurate in identifying clusters with distinct features at the physiological level. To promote the domain-led data mining approach, as a result of this experiment we developed a practical checklist guiding how to enable the integration of the domain knowledge into the data mining project.

Patient selection for heart transplant: balancing risk

PURPOSE OF REVIEW

Heart failure incidence continues to rise despite a relatively static number of available donor hearts. Selecting an appropriate heart transplant candidate requires evaluation of numerous factors to balance patient benefit while maximizing the utility of scarce donor hearts. Recent research has provided new insights into refining recipient risk assessment, providing additional tools to further define and balance risk when considering heart transplantation.

RECENT FINDINGS

Recent publications have developed models to assist in risk stratifying potential heart transplant recipients based on cardiac and noncardiac factors. These studies provide additional tools to assist clinicians in balancing individual risk and benefit of heart transplantation in the context of a limited donor organ supply.

SUMMARY

The primary goal of heart transplantation is to improve survival and maximize quality of life. To meet this goal, a careful assessment of patient-specific risks is essential. The optimal approach to patient selection relies on integrating recent prognostication models with a multifactorial assessment of established clinical characteristics, comorbidities and psychosocial factors.

What can machines learn about heart failure? A systematic literature review

This paper presents a systematic literature review with respect to application of data science and machine learning (ML) to heart failure (HF) datasets with the intention of generating both a synthesis of relevant findings and a critical evaluation of approaches, applicability and accuracy in order to inform future work within this field. This paper has a particular intention to consider ways in which the low uptake of ML techniques within clinical practice could be resolved. Literature searches were performed on Scopus (2014-2021), ProQuest and Ovid MEDLINE databases (2014-2021). Search terms included ‘heart failure’ or ‘cardiomyopathy’ and ‘machine learning’, ‘data analytics’, ‘data mining’ or ‘data science’. 81 out of 1688 articles were included in the review. The majority of studies were retrospective cohort studies. The median size of the patient cohort across all studies was 1944 (min 46, max 93260). The largest patient samples were used in readmission prediction models with the median sample size of 5676 (min. 380, max. 93260). Machine learning methods focused on common HF problems: detection of HF from available dataset, prediction of hospital readmission following index hospitalization, mortality prediction, classification and clustering of HF cohorts into subgroups with distinctive features and response to HF treatment. The most common ML methods used were logistic regression, decision trees, random forest and support vector machines. Information on validation of models was scarce. Based on the authors’ affiliations, there was a median 3:1 ratio between IT specialists and clinicians. Over half of studies were co-authored by a collaboration of medical and IT specialists. Approximately 25% of papers were authored solely by IT specialists who did not seek clinical input in data interpretation. The application of ML to datasets, in particular clustering methods, enabled the development of classification models assisting in testing the outcomes of patients with HF. There is, however, a tendency to over-claim the potential usefulness of ML models for clinical practice. The next body of work that is required for this research discipline is the design of randomised controlled trials (RCTs) with the use of ML in an intervention arm in order to prospectively validate these algorithms for real-world clinical utility.

Emerging roles of left ventricular assist device therapy as bridge to transplant in an Asian city with scarce heart transplant donor

Background

Left ventricular assist device (LVAD) has been increasingly used in patients with advanced heart failure. This study aimed to assess the impact of implementation of LVAD therapy on heart transplantation (HTx) service in Hong Kong (HK).

Methods

LVAD program was started in 2010 in HK and patients who had been put on HTx waiting list since the start of HTx program in HK from 1992 to 2020 were included for analysis. Survival on HTx waiting list between pre-LVAD era 1992–2009 and post-LVAD era 2010–2020 were analyzed by Kaplan-Meier method and compared by log-rank test. Multivariate analysis by time-dependent Cox-proportional hazard model was used to identify independent predictors of HTx waiting list mortality.

Results

A total of 478 heart transplant listing episodes involving 457 patients were included for analysis. There were 232 heart transplantations (HTxs), including one re-transplantation, during the study period. There were 110 patients who received LVAD as bridge to transplantation (BTT) and 30 of them had undergone subsequent HTx. The 1-, 2- and 3-year survival on waiting list were 82.3%, 61.7% and 43.0% respectively in the pre-LVAD era (n=178), while the 1-, 2- and 3-year survival were significantly improved at 85.7%, 81.8% and 78% respectively in the post-LVAD era (n=300), (P=0.003). Time-dependent multivariate analysis revealed that LVAD support was independently associated with significant reduction of waiting list mortality [odds ratio (OR): 0.21; 95% confidence interval (CI): 0.10–0.44, P<0.001]. There was no significant difference when comparing survival after LVAD as BTT and survival after HTx up to 8 years (76.1% vs. 72% at 8 years respectively, P=0.732).

Conclusions

Waiting list survival improved in the post-LVAD era driven by the implementation of LVAD service. Long-term survival for LVAD recipients as BTT were comparable to heart transplant recipients in HK.

Differential Impact of Class I and Class II Panel Reactive Antibodies on Post-Heart Transplant Outcomes

BACKGROUND

Sensitized patients awaiting heart transplantation spend a longer time on the waitlist and have higher mortality. We are now able to further characterize sensitization by discriminating antibodies against class I and II, but the differential impact of these has not been assessed systematically.

METHODS AND RESULTS

Using United Network for Organ Sharing data (2004-2015), we analyzed 17,361 adult heart transplant patients whose class I and II panel reactive antibodies were reported. Patients were divided into 4 groups: class I and II ≤25% (group 1); class I ≤25% and class II ˃25% (group 2); class II ≤25% and class I >25% (group 3); and both class I and II >25% (group 4). Outcomes assessed were treated rejection at 1-year mortality, all-cause mortality, and rejection-related mortality. Compared with group 1, only group 4 was associated with a higher risk of treated rejection at 1 year (odds ratio 1.31, 95% confidence interval [CI] 1.05-1.64), all-cause mortality (hazard ratio 1.24, 95% CI 1.06-1.46), and mortality owing to rejection (subhazard ratio 1.84, 95% CI 1.18-2.85), whereas groups 2 and 3 were not (P > .05).

CONCLUSIONS

Combined elevation in class I and II panel reactive antibodies seem to increase the risk of treated rejection and all-cause mortality, whereas risk with isolated elevation is unclear.

Heart Transplantation: An In-Depth Survival Analysis

OBJECTIVES

This study aims to understand the complex factors affecting heart transplant survival and to determine the importance of possible sex-specific risk factors.

BACKGROUND

Heart transplant allocation is primarily focused on preventing waitlist mortality. To prevent organ wastage, future allocation must balance risk of waitlist mortality with post-transplantation mortality. However, more information regarding risk factors after heart transplantation is needed.

METHODS

We included all adults (30,606) in the Scientific Registry of Transplant Recipients database who underwent isolated heart transplantation from January 1, 2004, to July 1, 2018. Mortality (8,278 deaths) was verified with the complete Social Security Death Index with a median follow-up of 3.9 years. Temporal decomposition was used to identify phases of survival and phase-specific risk factors. The random survival forests method was used to determine importance of mortality risk factors and their interactions.

RESULTS

We identified 3 phases of mortality risk: early post-transplantation, constant, and late. Sex was not a significant risk factor. There were several interactions predicting early mortality such as pretransplantation mechanical ventilation with presence of end-organ function (bilirubin, renal function) and interactions predicting later mortality such as diabetes and older age (donor and recipient). More complex interactions predicting early-, mid-, and late-mortality existed and were identified with machine learning (i.e., elevated bilirubin, mechanical ventilation, and dialysis).

CONCLUSIONS

Post-heart transplant mortality risk is complex and dynamic, changing with time and events. Sex is not an important mortality risk factor. To prevent organ wastage, end-organ dysfunction should be resolved before transplantation as much as possible.

Variables of importance in the Scientific Registry of Transplant Recipients database predictive of heart transplant waitlist mortality

The prelisting variables essential for creating an accurate heart transplant allocation score based on survival are unknown. To identify these we studied mortality of adults on the active heart transplant waiting list in the Scientific Registry of Transplant Recipients database from January 1, 2004 to August 31, 2015. There were 33 069 candidates awaiting heart transplantation: 7681 UNOS Status 1A, 13 027 Status 1B, and 12 361 Status 2. During a median waitlist follow-up of 4.3 months, 5514 candidates died. Variables of importance for waitlist mortality were identified by machine learning using Random Survival Forests. Strong correlates predicting survival were estimated glomerular filtration rate (eGFR), serum albumin, extracorporeal membrane oxygenation, ventricular assist device, mechanical ventilation, peak oxygen capacity, hemodynamics, inotrope support, and type of heart disease with less predictive variables including antiarrhythmic agents, history of stroke, vascular disease, prior malignancy, and prior tobacco use. Complex interactions were identified such as an additive risk in mortality based on renal function and serum albumin, and sex-differences in mortality when eGFR >40 mL/min/1.73 m. Most predictive variables for waitlist mortality are in the current tiered allocation system except for eGFR and serum albumin which have an additive risk and complex interactions.

Noteworthy Literature Published in 2018 for Thoracic Organ Transplantation

Publications of note from 2018 are reviewed for the cardiothoracic transplant anesthesiologist. Strategies to expand the availability of donor organs were highlighted, including improved donor management, accumulating experience with increased-risk donors, ex vivo perfusion techniques, and donation after cardiac death. A number of reports examined posttransplant outcomes, including outcomes other than mortality, with new data-driven risk models. Use of extracorporeal support in cardiothoracic transplantation was a prominent theme. Major changes in adult heart allocation criteria were implemented, aiming to improve objectivity and transparency in the listing process. Frailty and prehabilitation emerged as targets of comprehensive perioperative risk mitigation programs.