Reducing the wait: TCV can expand the donor pool for heart transplant candidates

Impact of size matching on survival post-heart transplant in infants: Estimated total cardiac-volume ratio outperforms donor-recipient weight ratio

BACKGROUND

Cardiac volume-based estimation offers an alternative to donor-recipient weight ratio (DRWR) in pediatric heart transplantation (HT) but has not been correlated to posttransplant outcomes. We sought to determine whether estimated total cardiac volume (eTCV) ratio is associated with HT survival in infants.

METHODS

The United Network for Organ Sharing database was used to identify infants (aged <1 year) who received HT in 1987-2020. Donor and recipient eTCV were calculated from weight using previously published data. Patient cohort was divided acc ording to the significant range of eTCV ratio; characteristics and survival were compared.

RESULTS

A total of 2845 infants were identified. Hazard ratio with cubic spline showed prognostic relationship of eTCV ratio and DRWR with the overall survival. The cut point method determined an optimal eTCV ratio range predictive of infant survival was 1.05 to 1.85, whereas no range for DRWR was predictive. Overall, 75.6% of patients had an optimal total cardiac volume ratio, while 18.1% were in the lower (LR) and 6.3% in the higher (HR) group. Kaplan-Meier analysis showed better survival for patients within the optimal vs LR (p = 0.0017) and a similar significantly better survival when compared to HR (p = 0.0053). The optimal eTCV ratio group (n = 2,151) had DRWR, ranging from 1.09 to 5; 34.3% had DRWR of 2% to 3%, and 5.0% had DRWR of >3.

CONCLUSIONS

Currently, an upper DRWR limit has not been established in infants. Therefore, determining the optimal eTCV range is important to identify an upper limit that significantly predicts survival benefit. This finding suggests a potential increase in donor pool for infant recipients since over 40% of donors in the optimal eTCV range include DRWR values >2 that are traditionally not considered for candidate listing.

Deep Learning for Automated Measurement of Total Cardiac Volume for Heart Transplantation Size Matching

Total Cardiac Volume (TCV)-based size matching using Computed Tomography (CT) is a novel technique to compare donor and recipient heart size in pediatric heart transplant that may increase overall utilization of available grafts. TCV requires manual segmentation, which limits its widespread use due to time and specialized software and training needed for segmentation. This study aims to determine the accuracy of a Deep Learning (DL) approach using 3-dimensional Convolutional Neural Networks (3D-CNN) to calculate TCV, with the clinical aim of enabling fast and accurate TCV use at all transplant centers. Ground truth TCV was segmented on CT scans of subjects aged 0-30 years, identified retrospectively. Ground truth segmentation masks were used to train and test a custom 3D-CNN model consisting of a DenseNet architecture in combination with residual blocks of ResNet architecture. The model was trained on a cohort of 270 subjects and a validation cohort of 44 subjects (36 normal, 8 heart disease retained for model testing). The average Dice similarity coefficient of the validation cohort was 0.94 ± 0.03 (range 0.84-0.97). The mean absolute percent error of TCV estimation was 5.5%. There is no significant association between model accuracy and subject age, weight, or height. DL-TCV was on average more accurate for normal hearts than those listed for transplant (mean absolute percent error 4.5 ± 3.9 vs. 10.5 ± 8.5, p = 0.08). A deep learning-based 3D-CNN model can provide accurate automatic measurement of TCV from CT images. This initial study is limited as a single-center study, though future multicenter studies may enable generalizable and more accurate TCV measurement by inclusion of more diverse cardiac pathology and increasing the training data.

Deep Learning for Automated Measurement of Total Cardiac Volume for Heart Transplantation Size Matching

Background

Total Cardiac Volume (TCV) based size matching using Computed Tomography (CT) is a novel technique to compare donor and recipient heart size in pediatric heart transplant that may increase overall utilization of available grafts. TCV requires manual segmentation, which limits its widespread use due to time and specialized software and training needed for segmentation.

Objective

This study aims to determine the accuracy of a Deep Learning (DL) approach using 3-dimensional Convolutional Neural Networks (3D-CNN) to calculate TCV, with the clinical aim of enabling fast and accurate TCV use at all transplant centers.

Materials and Methods

Ground truth TCV was segmented on CT scans of subjects aged 0-30 years, identified retrospectively. Ground truth segmentation masks were used to train and test a custom 3D-CNN model consisting of a Dense-Net architecture in combination with residual blocks of ResNet architecture.

Results

The model was trained on a cohort of 270 subjects and a validation cohort of 44 subjects (36 normal, 8 heart disease retained for model testing). The average Dice similarity coefficient of the validation cohort was 0.94 ± 0.03 (range 0.84-0.97). The mean absolute percent error of TCV estimation was 5.5%. There is no significant association between model accuracy and subject age, weight, or height. DL-TCV was on average more accurate for normal hearts than those listed for transplant (mean absolute percent error 4.5 ± 3.9 vs. 10.5 ± 8.5, p = 0.08).

Conclusion

A deep learning based 3D-CNN model can provide accurate automatic measurement of TCV from CT images.

Female donor hearts can improve survival for male pediatric heart transplant recipients

BACKGROUND

Both gender- and weight-matching between donor and recipient are thought to impact survival in pediatric heart transplantation, with clinical dogma holding that male donor hearts and "ideal" weight-matching yield superior survival. The composite impacts of gender and weight on post-transplant survival (PTS) are understudied.

METHODS

All pediatric (age <18) heart recipients between 1989 and 2021 with the complete recipient and donor gender and weight data were identified in the United Network for Organ Sharing database. Patients were grouped by recipient-donor gender (M & F) and donor-to-recipient weight ratio (DRWR; undersized [<0.8], ideal-sized [0.8-1.5], oversized [>1.5]).

RESULTS

A total of 10 697 patients were identified. Among male recipients, PTS was greatest with oversized DRWR from either male or female donors (median 22.4 and 20.6 years; p < .001 vs. others) and lowest for undersized DRWR from either male or female donors (median 13.4 and 13.2 years; p < .001 vs. others). The majority (64%) of male recipients received ideal-sized DRWR, among which female donor hearts yielded superior survival to males (median 18.9 vs. 17.4 years, p = .014). No differences in PTS existed for female recipients on the basis of gender-match, DRWR, and gender/DRWR together (all p > .1).

CONCLUSIONS

When considered together, gender and DRWR pairings impact PTS in male-but not female-pediatric heart transplant recipients. For males receiving ideal-sized DRWR organs (most common pairing, >60%), male recipients achieve superior survival when female donor hearts are transplanted. These findings suggest that if weight is being used for size-matching, donor gender should also be considered, particularly for male recipients.

Evidence supporting total cardiac volumes instead of weight for transplant size-matching

Total cardiac volume (TCV)-based size matching for heart transplantation offers individualization in size matching that increases the number of suitable donors. Here we describe our clinical protocol for using TCV to determine an acceptable donor weight range for heart transplant candidates. We compare candidate imaging-derived TCV to a nomogram of subjects with normal TCV to determine a precise maximum donor weight at the time of listing. For nearly half of our transplant patients, we have increased weight range by an average of 70% with no oversizing related adverse events, such as delayed chest closure to avoid tamponade or bronchial compression. Widespread adoption of TCV-based size matching can lead to a more efficient heart allocation system by the data-driven bypass of poor size matches.