RESUMEN
This paper introduces an analytical framework for assessing the cost-effectiveness of intervention strategies to reduce total joint replacement (TJR) readmissions. In such a framework, a machine learning-based readmission risk prediction model is developed to predict an individual TJR patient's risk of hospital readmission within 90 days post-discharge. Specifically, through data sampling and boosting techniques, we overcome the class imbalance problem by iteratively building an ensemble of models. Then, utilizing the results of the predictive model, and by taking into account the imbalanced misclassification costs between readmitted and nonreadmitted patients, a cost analysis framework is introduced to support decision making in selecting cost-effective intervention policies. Finally, using this framework, a case study at a community hospital is presented to demonstrate the applicability of the analysis.
Asunto(s)
Artroplastia de Reemplazo , Modelos Estadísticos , Readmisión del Paciente , Algoritmos , Artroplastia de Reemplazo/efectos adversos , Artroplastia de Reemplazo/economía , Artroplastia de Reemplazo/estadística & datos numéricos , Análisis Costo-Beneficio , Femenino , Humanos , Aprendizaje Automático , Masculino , Readmisión del Paciente/economía , Readmisión del Paciente/estadística & datos numéricos , Medición de Riesgo , Factores de RiesgoRESUMEN
In 1997, 15% of patients who received a cardiac transplant in the United States needed a mechanical circulatory support device before transplantation. One device that patients received was the left ventricular assist device (LVAD). During the LVAD support period, approximately 30% to 80% of LVAD recipients have positive test results for panel reactive antibodies (PRAs). Many of these antibodies form against human leukocyte antigens (HLA). These antigens are present on most cells and stimulate antibody production when a person receives unrelated donor cells. Several pre-LVAD and post-LVAD factors contribute to anti-HLA antibody formation. These antibody levels must be lowered before transplantation because the presence of anti-HLA antibodies makes it more difficult to find a suitable donor and increases the risk of rejection. The objectives of this article are to describe anti-HLA antibody formation in LVAD recipients, review its major consequences and treatments, and discuss nursing actions associated with anti-HLA antibody formation.
