AI Article Synopsis

  • Ischemic cardiopathy is the leading global cause of death, and this study investigates the leukoglycemic index's ability to predict in-hospital complications for patients with ST elevation myocardial infarction.
  • The research analyzed 900 patients using logistic regression and identified an optimal leukoglycemic index cut point of 1188, which indicated higher risks for complications. The predictive model includes age, leukoglycemic index, Killip-Kimball classification, and hypertension history.
  • Although the leukoglycemic index has limited predictive power, the proposed model shows strong potential for estimating in-hospital complications and could be beneficial for health systems in developing countries at no extra cost.

Article 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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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9482690PMC
http://dx.doi.org/10.15190/d.2022.1DOI Listing

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