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. 2022 Mar 4;10(1):e142.
doi: 10.15190/d.2022.1. eCollection 2022 Jan-Mar.

New model predicts in-hospital complications in myocardial infarction

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New model predicts in-hospital complications in myocardial infarction

Geovedy Martinez-Garcia et al. Discoveries (Craiova). .

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.

Keywords: ST elevation myocardial infarction; in-hospital complications; leukoglycemic index; prediction..

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Conflict of interest statement

Conflict of interests: The authors declare no competing interests.

Figures

Figure 1
Figure 1. The receiver operating characteristic (ROC) curve of the predictive scale as a marker to predict in-hospital complications in STEMI patients

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