Neurological Complications in Patients with Heart Transplantation

Patterns and Risk Factors for Rehospitalizations Within the First 90 Days Following Discharge After Heart Transplantation

BACKGROUND

Heart transplantation (HT) recipients are at risk for urgent rehospitalizations following discharge. However, data on prevalence, risk factors and clinical outcomes associated with post-HT rehospitalization are limited.

METHODS

This study aims to describe patterns of urgent rehospitalizations in HT recipients at a cardiology reference center in Brazil. Rehospitalizations and deaths occurring within the first 90 days following hospital discharge were identified. Regression models were used to identify variables associated with urgent rehospitalizations.

RESULTS

A total of 239 patients were included. Of those, 118 (49.4%) presented with a rehospitalization within 90 days following hospital discharge and 5 (2.01%) died. Most patients who were rehospitalized had one new hospital admission (86.0%). The main cause of urgent rehospitalization was infection (55.0%). In the multivariate analysis, elevated C-reactive protein at discharge and the occurrence of intracranial bleeding at index hospitalization were associated with an increased risk of readmission. Longer duration of index hospitalization and use of lower doses of azathioprine were associated with a lower risk of rehospitalization.

CONCLUSION

Around half of HT recipients were rehospitalized within the first 90 days after hospital discharge. Understanding factors associated with post-HT rehospitalization may help the implementation of strategies to avoid patient morbidity and hospital costs.

Artificial Intelligence Approaches for Predicting the Risks of Durable Mechanical Circulatory Support Therapy and Cardiac Transplantation

Background: The use of AI-driven technologies in probing big data to generate better risk prediction models has been an ongoing and expanding area of investigation. The AI-driven models may perform better as compared to linear models; however, more investigations are needed in this area to refine their predictability and applicability to the field of durable MCS and cardiac transplantation. Methods: A literature review was carried out using Google Scholar/PubMed from 2000 to 2023. Results: This review defines the knowledge gaps and describes different AI-driven approaches that may be used to further our understanding. Conclusions: The limitations of current models are due to missing data, data imbalances, and the uneven distribution of variables in the datasets from which the models are derived. There is an urgent need for predictive models that can integrate a large number of clinical variables from multicenter data to account for the variability in patient characteristics that influence patient selection, outcomes, and survival for both durable MCS and HT; this may be fulfilled by AI-driven risk prediction models.

Heart Transplantation-Postoperative Considerations

Heart transplantation (HT) remains the best treatment of patients with severe heart failure who are deemed to be transplant candidates. The authors discuss postoperative management of the HT recipient by system, emphasizing areas where care might differ from other cardiac surgery patients. Working together, critical care physicians, heart transplant surgeons and cardiologists, advanced practice providers, pharmacists, transplant coordinators, nursing staff, physical therapists, occupational therapists, rehabilitation specialists, nutritionists, health psychologists, social workers, and the patient and their loved ones partner to increase the likelihood of a successful outcome.