Machine Learning Model Validated to Predict Outcomes of Liver Transplantation Recipients with Hepatitis C: The Romanian National Transplant Agency Cohort Experience.

BACKGROUND AND OBJECTIVES: In the early period after liver transplantation, patients are exposed to a high rate of complications and several scores are currently available to predict adverse postoperative outcomes. However, an ideal, universally accepted and validated score to predict adverse events in liver transplant recipients with hepatitis C is lacking. Therefore, we aimed to establish and validate a machine learning (ML) model to predict short-term outcomes of hepatitis C patients who underwent liver transplantation. MATERIALS AND METHODS: We conducted a retrospective observational two-center cohort study involving hepatitis C patients who underwent liver transplantation. Based on clinical and laboratory parameters, the dataset was used to train a deep-learning model for predicting short-term postoperative complications (within one month following liver transplantation). Adverse events prediction in the postoperative setting was the primary study outcome. RESULTS: A total of 90 liver transplant recipients with hepatitis C were enrolled in the present study, 80 patients in the training cohort and ten in the validation cohort, respectively. The age range of the participants was 12-68 years, 51 (56,7%) were male, and 39 (43.3%) were female. Throughout the 85 training epochs, the model achieved a very good performance, with the accuracy ranging between 99.76% and 100%. After testing the model on the validation set, the deep-learning classifier confirmed the performance in predicting postoperative complications, achieving an accuracy of 100% on unseen data. CONCLUSIONS: We successfully developed a ML model to predict postoperative complications following liver transplantation in hepatitis C patients. The model demonstrated an excellent performance for accurate adverse event prediction. Consequently, the present study constitutes the foundation for careful and non-invasive identification of high-risk patients who might benefit from a more intensive postoperative monitoring strategy.

Role of pulmonary rehabilitation in chronic obstructive pulmonary disease - a historical perspective.

Introduction: Pulmonary rehabilitation is known as an effective therapy for patients with chronic obstructive pulmonary disease (COPD). This article is a brief introduction into the history of medical and pulmonary rehabilitation, presenting the evolution of applied therapies and methods from ancient to present times. It also highlights the role of physical effort in the prevention and treatment of lung diseases, with special consideration to COPD. Methods: For this literature review, the international databases Medline and Scopus were used to identify relevant articles, between January 1981 to December 2021; eighty articles were considered: thirty-six reviews, eight original research and six general articles which met the criteria for inclusion. A total of thirty references were excluded because they were not relevant. Results: Available published data suggest a rich history of rehabilitation reaching for thousands of years even though it was developed as a medical branch only in the 20th century. Pulmonary rehabilitation is currently an important component of the management of COPD patients, with a positive impact on symptoms, frequency of exacerbations, severity and mortality rates. Conclusions: Even though this type of intervention is known to be beneficial for this type of patients more studies need to be conducted in this field.