Dataset Information

Statin Short-term Inhibition of Insulin Sensitivity and Secretion During Acute Phase of ST-Elevation Myocardial Infarction.

ABSTRACT: Hyperglycemia during myocardial infarction (MI) has a strong and direct association with mortality. In stable patients and experimental models, statins favor the elevation of glycaemia. The present study investigated whether short-course treatment with statins during MI can influence glucose homeostasis and thus the clinical outcome. In this prospective study, euglycemic hyperinsulinemic clamp (EHC) was performed at second (D2) and sixth (D6) day after MI in patients randomized to simvastatin (S)10 or 80?mg/day during hospitalization (n?=?27). In addition, patients (n?=?550) were treated without (WS) or with simvastatin (S) at 20, 40 or 80?mg/day had HOMA2S on admission (D1) and fifth (D5) day after MI. According to EHC, insulin sensitivity increased by 20?±?60% in S10 and decreased by -6?±?28% in S80 (p?=?0.025). Consistently, the changes in HOMA2S between D1 and D5 were 40?±?145% (WS), 22?±?117% (S20), 16?±?61% (S40) and -2%?±?88% (S80) (p?=?0.001). In conclusion, statin during the acute phase of MI reduces insulin sensitivity in a dose-dependent manner.

SUBMITTER: Sposito AC

PROVIDER: S-EPMC6841947 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

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Publications

Statin Short-term Inhibition of Insulin Sensitivity and Secretion During Acute Phase of ST-Elevation Myocardial Infarction.

Sposito Andrei C AC Carvalho Luiz Sergio F LSF Moura Filipe A FA Campos-Staffico Alessandra M AM Cintra Riobaldo M R RMR Nadruz Wilson W Almeida Osorio R OR Quinaglia E Silva Jose C JC

Scientific reports 20191108 1

Hyperglycemia during myocardial infarction (MI) has a strong and direct association with mortality. In stable patients and experimental models, statins favor the elevation of glycaemia. The present study investigated whether short-course treatment with statins during MI can influence glucose homeostasis and thus the clinical outcome. In this prospective study, euglycemic hyperinsulinemic clamp (EHC) was performed at second (D2) and sixth (D6) day after MI in patients randomized to simvastatin (S ...[more]

PMID: 31704948

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Project description:BackgroundConventional risk score for predicting short and long-term mortality following an ST-segment elevation myocardial infarction (STEMI) is often not population specific.ObjectiveApply machine learning for the prediction and identification of factors associated with short and long-term mortality in Asian STEMI patients and compare with a conventional risk score.MethodsThe National Cardiovascular Disease Database for Malaysia registry, of a multi-ethnic, heterogeneous Asian population was used for in-hospital (6299 patients), 30-days (3130 patients), and 1-year (2939 patients) model development. 50 variables were considered. Mortality prediction was analysed using feature selection methods with machine learning algorithms and compared to Thrombolysis in Myocardial Infarction (TIMI) score. Invasive management of varying degrees was selected as important variables that improved mortality prediction.ResultsModel performance using a complete and reduced variable produced an area under the receiver operating characteristic curve (AUC) from 0.73 to 0.90. The best machine learning model for in-hospital, 30 days, and 1-year outperformed TIMI risk score (AUC = 0.88, 95% CI: 0.846-0.910; vs AUC = 0.81, 95% CI:0.772-0.845, AUC = 0.90, 95% CI: 0.870-0.935; vs AUC = 0.80, 95% CI: 0.746-0.838, AUC = 0.84, 95% CI: 0.798-0.872; vs AUC = 0.76, 95% CI: 0.715-0.802, p < 0.0001 for all). TIMI score underestimates patients' risk of mortality. 90% of non-survival patients are classified as high risk (>50%) by machine learning algorithm compared to 10-30% non-survival patients by TIMI. Common predictors identified for short- and long-term mortality were age, heart rate, Killip class, fasting blood glucose, prior primary PCI or pharmaco-invasive therapy and diuretics. The final algorithm was converted into an online tool with a database for continuous data archiving for algorithm validation.ConclusionsIn a multi-ethnic population, patients with STEMI were better classified using the machine learning method compared to TIMI scoring. Machine learning allows for the identification of distinct factors in individual Asian populations for better mortality prediction. Ongoing continuous testing and validation will allow for better risk stratification and potentially alter management and outcomes in the future.

Dataset Information

Statin Short-term Inhibition of Insulin Sensitivity and Secretion During Acute Phase of ST-Elevation Myocardial Infarction.

Publications

Statin Short-term Inhibition of Insulin Sensitivity and Secretion During Acute Phase of ST-Elevation Myocardial Infarction.

Similar Datasets

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