New model predicts in-hospital complications in myocardial infarction.

Discoveries (Craiova, Romania) Pub Date : 2022-03-04 eCollection Date: 2022-01-01 DOI:10.15190/d.2022.1
Geovedy Martinez-Garcia, Miguel Rodriguez-Ramos, Maikel Santos-Medina, Annia Maria Carrero-Vazquez, Yanitsy Chipi-Rodriguez
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引用次数: 3

Abstract

Introduction and objectives: Ischemic cardiopathy constitutes the leading cause of death worldwide. Our aim was to evaluate the prognostic capacity of the leukoglycemic index as well as to create a predictive model of in-hospital complications in patients with ST elevation myocardial infarction.

Materials and methods: This was a multicentral and cohort study, which included patients inserted in the Cuban Registry of acute myocardial infarction. The study investigated 900 patients with a validation population represented by 233 external subjects. In order to define the performance of the leukoglycemic index were evaluated the discrimination with the statistical C and the calibration by Hosmer - Lemeshow test. A model of logistic binary regression was employed in order to define the predictive factors.  RESULTS: Optimal cut point of the leukoglycemic index to predict in-hospital complications was 1188 (sensibility 60%; specificity 61.6%; area under the curve 0.623; p < 0.001). In-hospital complications were significantly higher in the group with the leukoglycemic index ≥ 1188; a higher value was significantly associated with a higher risk to develop an in-hospital complication [RR (IC 95%) = 2.4 (1.804-3.080); p<0.001]. The predictive model proposed is composed by the following factors: age ≥ 66 years, leukoglycemic index ≥ 1188, Killip-Kimball classification ≥ II and medical history of hypertension. This scale had a good discrimination in both, the training and the validation population.

Conclusion: The leukoglycemic index possesses a low performance when used to assess the risks for in hospital complications in patients with ST elevation myocardial infarction. The new predictive model has a good performance, which can be applied to estimate risk of in-hospital complications. This model would be able to contribute to the health systems of developing countries without additional cost; it enables prediction of the patients having a higher risk of complications and a negative outcome during the hospitable admission.

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新模型预测住院心肌梗死并发症。
简介和目标:缺血性心脏病是世界范围内死亡的主要原因。我们的目的是评估血糖指数的预后能力,并建立ST段抬高型心肌梗死患者住院并发症的预测模型。材料和方法:这是一项多中心队列研究,纳入了古巴急性心肌梗死登记处的患者。该研究调查了由233名外部受试者代表的900名患者的验证人群。用统计C值和Hosmer - Lemeshow检验进行校正,以确定血糖指数的性能。采用logistic二元回归模型来确定预测因素。结果:血糖指数预测院内并发症的最佳切点为1188(敏感性60%;特异性61.6%;曲线下面积0.623;P < 0.001)。院内并发症在血糖指数≥1188组明显增加;数值越高,发生院内并发症的风险越高[RR (IC 95%) = 2.4 (1.804-3.080);结论:应用血糖指数评价ST段抬高型心肌梗死患者院内并发症风险的效果较差。该预测模型具有良好的性能,可用于院内并发症风险的预测。这种模式将能够在不增加费用的情况下为发展中国家的卫生系统作出贡献;它可以预测患者在好客入院期间有较高的并发症风险和负面结果。
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