Processo orçamentário no estado do Rio Grande do Sul: uma proposta alternativa de participação popular na elaboração e fiscalização do orçamento público estadual

Este artigo se constitui, em primeiro lugar, num esforço de mostrar o funcionamento do processo de elaboração do orçamento público anual do Estado do Rio Grande do Sul. No caso do RS, tanto o Orçamento Participativo (OP-RS) como o Fórum Democrático são instrumentos de consulta popular. O primeiro é de iniciativa do Governo do Estado e o segundo, da Assembléia Legislativa. Em segundo lugar, propõe-se uma alternativa de participação popular autônoma na elaboração e fiscalização do orçamento público estadual. Em linhas gerais, tal proposta implica transformar a atual estrutura estatal de consulta popular num canal aberto para as propostas de emendas populares ao orçamento público anual a serem elaboradas por entidades representativas e grupos de, no mínimo, quinhentos eleitores.

Pre-operative Machine Learning for Heart Transplant Patients Bridged with Temporary Mechanical Circulatory Support

Background: Existing prediction models for post-transplant mortality in patients bridged to heart transplantation with temporary mechanical circulatory support (tMCS) perform poorly. A more reliable model would allow clinicians to provide better pre-operative risk assessment and develop more targeted therapies for high-risk patients. Methods: We identified adult patients in the United Network for Organ Sharing database undergoing isolated heart transplantation between 01/2009 and 12/2017 who were supported with tMCS at the time of transplant. We constructed a machine learning model using extreme gradient boosting (XGBoost) with a 70:30 train:test split to predict 1-year post-operative mortality. All pre-transplant variables available in the UNOS database were included to train the model. Shapley Additive Explanations was used to identify and interpret the most important features for XGBoost predictions. Results: A total of 1584 patients were included, with a median age of 56 (interquartile range: 46–62) and 74% male. Actual 1-year mortality was 12.1%. Out of 498 available variables, 43 were selected for the final model. The area under the receiver operator characteristics curve (AUC) for the XGBoost model was 0.71 (95% CI: 0.62–0.78). The most important variables predictive of 1-year mortality included recipient functional status, age, pulmonary capillary wedge pressure (PCWP), cardiac output, ECMO usage, and serum creatinine. Conclusions: An interpretable machine learning model trained on a large clinical database demonstrated good performance in predicting 1-year mortality for patients bridged to heart transplantation with tMCS. Machine learning may be used to enhance clinician judgement in the care of markedly high-risk transplant recipients