Performance of existing risk scores around heart transplantation: validation study in a 4-year cohort

International Society for Heart and Lung Transplantation Guidelines for the Evaluation and Care of Cardiac Transplant Candidates-2024

The "International Society for Heart and Lung Transplantation Guidelines for the Evaluation and Care of Cardiac Transplant Candidates-2024" updates and replaces the "Listing Criteria for Heart Transplantation: International Society for Heart and Lung Transplantation Guidelines for the Care of Cardiac Transplant Candidates-2006" and the "2016 International Society for Heart Lung Transplantation Listing Criteria for Heart Transplantation: A 10-year Update." The document aims to provide tools to help integrate the numerous variables involved in evaluating patients for transplantation, emphasizing updating the collaborative treatment while waiting for a transplant. There have been significant practice-changing developments in the care of heart transplant recipients since the publication of the International Society for Heart and Lung Transplantation (ISHLT) guidelines in 2006 and the 10-year update in 2016. The changes pertain to 3 aspects of heart transplantation: (1) patient selection criteria, (2) care of selected patient populations, and (3) durable mechanical support. To address these issues, 3 task forces were assembled. Each task force was cochaired by a pediatric heart transplant physician with the specific mandate to highlight issues unique to the pediatric heart transplant population and ensure their adequate representation. This guideline was harmonized with other ISHLT guidelines published through November 2023. The 2024 ISHLT guidelines for the evaluation and care of cardiac transplant candidates provide recommendations based on contemporary scientific evidence and patient management flow diagrams. The American College of Cardiology and American Heart Association modular knowledge chunk format has been implemented, allowing guideline information to be grouped into discrete packages (or modules) of information on a disease-specific topic or management issue. Aiming to improve the quality of care for heart transplant candidates, the recommendations present an evidence-based approach.

Survival analysis for pediatric heart transplant patients using a novel machine learning algorithm: A UNOS analysis

BACKGROUND

Impact of pretransplantation risk factors on mortality in the first year after heart transplantation remains largely unknown. Using machine learning algorithms, we selected clinically relevant identifiers that could predict 1-year mortality after pediatric heart transplantation.

METHODS

Data were obtained from the United Network for Organ Sharing Database for years 2010-2020 for patients 0-17 years receiving their first heart transplant (N = 4150). Features were selected using subject experts and literature review. Scikit-Learn, Scikit-Survival, and Tensorflow were used. A train:test split of 70:30 was used. N-repeated k-fold validation was performed (N = 5, k = 5). Seven models were tested, Hyperparameter tuning performed using Bayesian optimization and the concordance index (C-index) was used for model assessment.

RESULTS

A C-index above 0.6 for test data was considered acceptable for survival analysis models. C-indices obtained were 0.60 (Cox proportional hazards), 0.61 (Cox with elastic net), 0.64 (gradient boosting), 0.64 (support vector machine), 0.68 (random forest), 0.66 (component gradient boosting), and 0.54 (survival trees). Machine learning models show an improvement over the traditional Cox proportional hazards model, with random forest performing the best on the test set. Analysis of the feature importance for the gradient boosted model found that the top 5 features were the most recent serum total bilirubin, the travel distance from the transplant center, the patient body mass index, the deceased donor terminal Serum glutamic pyruvic transaminase/Alanine transaminase (SGPT/ALT), and the donor PCO2.

CONCLUSIONS

Combination of machine learning and expert-based methodology of selecting predictors of survival for pediatric heart transplantation provides a reasonable prediction of 1- and 3-year survival outcomes. SHapley Additive exPlanations can be an effective tool for modeling and visualizing nonlinear interactions.

Cardiopulmonary Exercise Testing in the Age of New Heart Failure Therapies: Still a Powerful Tool?

BACKGROUND

New therapies with prognostic benefits have been recently introduced in heart failure with reduced ejection fraction (HFrEF) management. The aim of this study was to evaluate the prognostic power of current listing criteria for heart transplantation (HT) in an HFrEF cohort submitted to cardiopulmonary exercise testing (CPET) between 2009 and 2014 (group A) and between 2015 and 2018 (group B).

METHODS

Consecutive patients with HFrEF who underwent CPET were followed-up for cardiac death and urgent HT.

RESULTS

CPET was performed in 487 patients. The composite endpoint occurred in 19.4% of group A vs. 7.4% of group B in a 36-month follow-up. Peak VO2 (pVO2) and VE/VCO2 slope were the strongest independent predictors of mortality. International Society for Heart and Lung Transplantation (ISHLT) thresholds of pVO2 ≤ 12 mL/kg/min (≤14 if intolerant to β-blockers) and VE/VCO2 slope > 35 presented a similar and lower Youden index, respectively, in group B compared to group A, and a lower positive predictive value. pVO2 ≤ 10 mL/kg/min and VE/VCO2 slope > 40 outperformed the traditional cut-offs. An ischemic etiology subanalysis showed similar results.

CONCLUSION

ISHLT thresholds showed a lower overall prognostic effectiveness in a contemporary HFrEF population. Novel parameters may be needed to improve risk stratification.

Postoperative high-sensitivity troponin T predicts 1-year mortality and days alive and out of hospital after orthotopic heart transplantation

BACKGROUND

Orthotopic heart transplantation (HTX) is the gold standard to treat end-stage heart failure. Numerous risk stratification tools have been developed in the past years. However, their clinical utility is limited by their poor discriminative ability. High sensitivity troponin T (hsTnT) is the most specific biomarker to detect myocardial cell injury. However, its prognostic relevance after HTX is not fully elucidated. Thus, this study evaluated the predictive value of postoperative hsTnT for 1-year survival and days alive and out of hospital (DAOH) after HTX.

METHODS

This retrospective cohort study included patients who underwent HTX at the University Hospital Duesseldorf, Germany between 2011 and 2021. The main exposure was hsTnT concentration at 48 h after HTX. The primary endpoints were mortality and DAOH within 1 year after surgery. Receiver operating characteristic (ROC) curve analysis, logistic regression model and linear regression with adjustment for risk index for mortality prediction after cardiac transplantation (IMPACT) were performed.

RESULTS

Out of 231 patients screened, 212 were included into analysis (mean age 55 ± 11 years, 73% male). One-year mortality was 19.7% (40 patients) and median DAOH was 298 days (229-322). ROC analysis revealed strongest discrimination for mortality by hsTnT at 48 h after HTX [AUC = 0.79 95% CI 0.71-0.87]. According to Youden Index, the cutoff for hsTnT at 48 h and mortality was 1640 ng/l. After adjustment for IMPACT score multivariate logistic and linear regression showed independent associations between hsTnT and mortality/DAOH with odds ratio of 8.10 [95%CI 2.99-21.89] and unstandardized regression coefficient of -1.54 [95%CI -2.02 to -1.06], respectively.

CONCLUSION

Postoperative hsTnT might be suitable as an early prognostic marker after HTX and is independently associated with 1-year mortality and poor DAOH.

Validation of existing risk scores for mortality prediction after a heart transplant in a Chinese population

OBJECTIVES

The objectives of this study were to validate 3 existing heart transplant risk scores with a single-centre cohort in China and evaluate the efficacy of the 3 systems in predicting mortality.

METHODS

We retrospectively studied 428 patients from a single centre who underwent heart transplants from January 2015 to December 2019. All patients were scored using the Index for Mortality Prediction After Cardiac Transplantation (IMPACT) and the United Network for Organ Sharing (UNOS) and risk stratification scores (RSSs). We assessed the efficacy of the risk scores by comparing the observed and the predicted 1-year mortality. Binary logistic regression was used to evaluate the predictive accuracy of the 3 risk scores. Model discrimination was assessed by measuring the area under the receiver operating curves. Kaplan-Meier survival analyses were performed after the patients were divided into different risk groups.

RESULTS

Based on our cohort, the observed mortality was 6.54%, whereas the predicted mortality of the IMPACT and UNOS scores and the RSSs was 10.59%, 10.74% and 12.89%, respectively. Logistic regression analysis showed that the IMPACT [odds ratio (OR), 1.25; 95% confidence interval (CI), 1.15-1.36; P < 0.001], UNOS (OR, 1.68; 95% CI, 1.37-2.07; P < 0.001) and risk stratification (OR, 1.61; 95% CI, 1.30-2.00; P < 0.001) scores were predictive of 1-year mortality. The discriminative power was numerically higher for the IMPACT score [area under the curve (AUC) of 0.691)] than for the UNOS score (AUC 0.685) and the RSS (AUC 0.648).

CONCLUSIONS

We validated the IMPACT and UNOS scores and the RSSs as predictors of 1-year mortality after a heart transplant, but all 3 risk scores had unsatisfactory discriminative powers that overestimated the observed mortality for the Chinese cohort.

Clinical Utility and Validation of the Krakow DCM Risk Score—A Prognostic Model Dedicated to Dilated Cardiomyopathy

Background: One of the most common causes of heart failure is dilated cardiomyopathy (DCM). In DCM, the mortality risk is high and reaches approximately 20% in 5 years. A patient’s prognosis should be established for appropriate HF management. However, so far, no validated tools have been available for the DCM population. Methods: The study population consisted of 735 DCM patients: 406 from the derivation cohort (previously described) and 329 from the validation cohort (from 2009 to 2020, with outcome data after a mean of 42 months). For each DCM patient, the individual mortality risk was calculated based on the Krakow DCM Risk Score. Results: During follow-up, 49 (15%) patients of the validation cohort died. They had shown significantly higher calculated 1-to-5-year mortality risks. The Krakow DCM Risk Score yielded good discrimination in terms of overall mortality risk, with an AUC of 0.704–0.765. Based on a 2-year mortality risk, patients were divided into non-high (≤6%) and high (>6%) mortality risk groups. The observed mortality rates were 8.3% (n = 44) vs. 42.6% (n = 75), respectively (HR 3.37; 95%CI 1.88–6.05; p < 0.0001). Conclusions: The Krakow DCM Risk Score was found to have good predictive accuracy. The 2-year mortality risk > 6% has good discrimination for the identification of high-risk patients and can be applied in everyday practice.

Development and validation of specific post-transplant risk scores according to the circulatory support status at transplant: A UNOS cohort analysis

BACKGROUND

The clinical use of post-transplant risk scores is limited by their poor statistical performance. We hypothesized that developing specific prognostic models for each type of circulatory support at transplant may improve risk stratification.

METHODS

We analyzed the UNOS database including contemporary, first, non-combined heart transplantations (2013-2018). The endpoint was death or retransplantation during the first year post-transplant. Three different circulatory support statuses at transplant were considered: no support, durable mechanical support and temporary support (inotropes, temporary mechanical support). We generated 1,000 bootstrap samples that we randomly split into derivation and test sets. In each sample, we derived an overall model and 3 specific models (1 for each type of circulatory support) using Cox regressions, and compared, in the test set, their statistical performance for each type of circulatory support.

RESULTS

A total of 13,729 patients were included; 1,220 patients (8.9%) met the composite endpoint. Circulatory support status at transplant was associated with important differences in baseline characteristics and distinct prognosis (p = 0.01), interacted significantly with important predictive variables included in the overall model, and had a major impact on post-transplant predictive models (type of variables included and their corresponding hazard ratios). However, specific models suffered from poor discriminative performance and significantly improved risk stratification (discrimination, reclassification indices, calibration) compared to overall models in a very limited proportion of bootstrap samples (<15%). These results were consistent across several sensitivity analyzes.

CONCLUSION

Circulatory support status at transplant reflected different disease states that influenced predictive models. However, developing specific models for each circulatory support status did not significantly improve risk stratification.

Twenty-year survival following orthotopic heart transplantation in the United States

BACKGROUND

This study evaluated 20-year survival after adult orthotopic heart transplantation (OHT).

METHODS

The United Network of Organ Sharing Registry database was queried to study adult OHT recipients between 1987 and 1998 with over 20-year posttransplant follow-up. The primary and secondary outcomes were 20-year survival and cause of death after OHT, respectively. Multivariable logistic regression was used to identify significant independent predictors of long-term survival, and long-term survival was compared among cohorts stratified by number of predictors using Kaplan Meier survival analysis.

RESULTS

20,658 patients undergoing OHT were included, with a median follow-up of 9.0 (IQR, 3.2-15.4) years. Kaplan-Meier estimates of 10-, 15-, and 20-year survival were 50.2%, 30.1%, and 17.2%, respectively. Median survival was 10.1 (IQR, 3.9-16.9) years. Increasing recipient age (>65 years), increasing donor age (>40 years), increasing recipient body mass index (>30), black race, ischemic cardiomyopathy, and longer cold ischemic time (>4 h) were adversely associated with a 20-year survival. Of these 6 negative predictors, presence of 0 risk factors had the greatest 10-year (59.7%) and 20-year survival (26.2%), with decreasing survival with additional negative predictors. The most common cause of death in 20-year survivors was renal, liver, and/or multisystem organ failure whereas graft failure more greatly impacted earlier mortality.

CONCLUSIONS

This study identifies six negative preoperative predictors of 20-year survival with 20-year survival rates exceeding 25% in the absence of these factors. These data highlight the potential for very long-term survival after OHT in patients with end-stage heart failure and may be useful for patient selection and prognostication.

Risk prediction models for survival after heart transplantation: A systematic review

Risk prediction scores have been developed to predict survival following heart transplantation (HT). Our objective was to systematically review the model characteristics and performance for all available scores that predict survival after HT. Ovid Medline and Epub Ahead of Print and In-Process & Other Non-Indexed Citations, Ovid Embase, Cochrane Database of Systematic Reviews, and Cochrane Central Register of Controlled Clinical Trials were searched to December 2018. Eligible articles reported a score to predict mortality following HT. Of the 5392 studies screened, 21 studies were included that derived and/or validated 16 scores. Seven (44%) scores were validated in external cohorts and 8 (50%) assessed model performance. Overall model discrimination ranged from poor to moderate (C-statistic/area under the receiver operating characteristics 0.54-0.77). The IMPACT score was the most widely validated, was well calibrated in two large registries, and was best at discriminating 3-month survival (C-statistic 0.76). Most scores did not perform particularly well in any cohort in which they were assessed. This review shows that there are insufficient data to recommend the use of one model over the others for prediction of post-HT outcomes.

Risk Indices in Deceased-donor Organ Allocation for Transplantation: Review From an Australian Perspective

Over the last decade, organ donation and transplantation rates have increased in Australia and worldwide. Donor and recipient characteristics for most organ types have generally broadened, resulting in the need to consider more complex data in transplant decision-making. As a result of some of these pressures, the Australian software used for donor and recipient data management is currently being updated. Because of the in-built capacity for improved data management, organ allocation processes will have the opportunity to be significantly reviewed, in particular the possible use of risk indices (RIs) to guide organ allocation and transplantation decisions. We aimed to review RIs used in organ allocation policies worldwide and to compare their use to current Australian protocols. Significant donor, recipient, and transplant variables in the indices were summarized. We conclude that Australia has the opportunity to incorporate greater use of RIs in its allocation policies and in transplant decision-making processes. However, while RIs can assist with organ allocation and help guide prognosis, they often have significant limitations which need to be properly appreciated when deciding how to best use them to guide clinical decisions.

Improving risk prediction in heart failure using machine learning

BACKGROUND

Predicting mortality is important in patients with heart failure (HF). However, current strategies for predicting risk are only modestly successful, likely because they are derived from statistical analysis methods that fail to capture prognostic information in large data sets containing multi-dimensional interactions.

METHODS AND RESULTS

We used a machine learning algorithm to capture correlations between patient characteristics and mortality. A model was built by training a boosted decision tree algorithm to relate a subset of the patient data with a very high or very low mortality risk in a cohort of 5822 hospitalized and ambulatory patients with HF. From this model we derived a risk score that accurately discriminated between low and high-risk of death by identifying eight variables (diastolic blood pressure, creatinine, blood urea nitrogen, haemoglobin, white blood cell count, platelets, albumin, and red blood cell distribution width). This risk score had an area under the curve (AUC) of 0.88 and was predictive across the full spectrum of risk. External validation in two separate HF populations gave AUCs of 0.84 and 0.81, which were superior to those obtained with two available risk scores in these same populations.

CONCLUSIONS

Using machine learning and readily available variables, we generated and validated a mortality risk score in patients with HF that was more accurate than other risk scores to which it was compared. These results support the use of this machine learning approach for the evaluation of patients with HF and in other settings where predicting risk has been challenging.

Prediction of mortality following pediatric heart transplant using machine learning algorithms

BACKGROUND

Optimizing transplant candidates' priority for donor organs depends on the accurate assessment of post-transplant outcomes. Due to the complexity of transplantation and the wide range of possible serious complications, recipient outcomes are difficult to predict accurately using conventional multivariable regression. Therefore, we evaluated the utility of 3 ML algorithms for predicting mortality after pediatric HTx.

METHODS

We identified patients <18 years of age receiving HTx in 2006-2015 in the UNOS Registry database. Mortality within 1, 3, or 5 years was predicted using classification and regression trees, RFs, and ANN. Each model was trained using cross-validation, then validated in a separate testing set. Model performance was primarily evaluated by the area under the receiver operating characteristic (AUC) curve.

RESULTS

The training set included 2802 patients, whereas 700 were included in the testing set. RF achieved the best fit to the training data with AUCs of 0.74, 0.68, and 0.64 for 1-, 3-, and 5-year mortality, respectively, and performed best in the testing data, with AUCs of 0.72, 0.61, and 0.60, respectively. Nevertheless, sensitivity was poor across models (training: 0.22-0.58; testing: 0.07-0.49).

DISCUSSION

ML algorithms demonstrated fair predictive utility in both training and testing data, but the sensitivity of these algorithms was generally poor. With the registry missing data on many determinants of long-term survival, the ability of ML methods to predict mortality after pediatric HTx may be fundamentally limited.