Xavier Joana, Seringa Joana, Pinto Fausto José, Magalhães Teresa
NOVA National School of Public Health, Nova University Lisbon, Lisbon, Portugal.
Serviço de Cardiologia do Centro Hospitalar de Lisboa Norte, Faculdade de Medicina, Universidade de Lisboa, Lisbon, Portugal.
Front Med (Lausanne). 2023 Feb 8;10:907310. doi: 10.3389/fmed.2023.907310. eCollection 2023.
Cardiovascular diseases are still a significant cause of death and hospitalization. In 2019, circulatory diseases were responsible for 29.9% of deaths in Portugal. These diseases have a significant impact on the hospital length of stay. Length of stay predictive models is an efficient way to aid decision-making in health. This study aimed to validate a predictive model on the extended length of stay in patients with acute myocardial infarction at the time of admission.
An analysis was conducted to test and recalibrate a previously developed model in the prediction of prolonged length of stay, for a new set of population. The study was conducted based on administrative and laboratory data of patients admitted for acute myocardial infarction events from a public hospital in Portugal from 2013 to 2015.
Comparable performance measures were observed upon the validation and recalibration of the predictive model of extended length of stay. Comorbidities such as shock, diabetes with complications, dysrhythmia, pulmonary edema, and respiratory infections were the common variables found between the previous model and the validated and recalibrated model for acute myocardial infarction.
Predictive models for the extended length of stay can be applied in clinical practice since they are recalibrated and modeled to the relevant population characteristics.
心血管疾病仍然是导致死亡和住院的重要原因。2019年，循环系统疾病在葡萄牙的死亡原因中占29.9%。这些疾病对住院时间有重大影响。住院时间预测模型是辅助医疗决策的有效方法。本研究旨在验证一种针对急性心肌梗死患者入院时延长住院时间的预测模型。
进行了一项分析，以测试和重新校准先前开发的用于预测延长住院时间的模型，针对一组新的人群。该研究基于2013年至2015年葡萄牙一家公立医院收治的急性心肌梗死事件患者的行政和实验室数据进行。
在对延长住院时间预测模型进行验证和重新校准后，观察到了可比的性能指标。休克、伴有并发症的糖尿病、心律失常、肺水肿和呼吸道感染等合并症是先前模型与急性心肌梗死验证和重新校准模型之间共同发现的变量。
延长住院时间的预测模型可应用于临床实践，因为它们已根据相关人群特征进行了重新校准和建模。
