Optimal Cut-Off Value for Homeostasis Model Assessment (HOMA) Index of Insulin-Resistance in a Population of Patients Admitted Electively in a Portuguese Cardiology Ward

INTRODUCTION: Insulin resistance is the pathophysiological key to explain metabolic syndrome. Although clearly useful, the Homeostasis Model Assessment index (an insulin resistance measurement) hasn't been systematically applied in clinical practice. One of the main reasons is the discrepancy in cut-off values reported in different populations. We sought to evaluate in a Portuguese population the ideal cut-off for Homeostasis Model Assessment index and assess its relationship with metabolic syndrome. MATERIAL AND METHODS: We selected a cohort of individuals admitted electively in a Cardiology ward with a BMI < 25 Kg/m2 and no abnormalities in glucose metabolism (fasting plasma glucose < 100 mg/dL and no diabetes). The 90th percentile of the Homeostasis Model Assessment index distribution was used to obtain the ideal cut-off for insulin resistance. We also selected a validation cohort of 300 individuals (no exclusion criteria applied). RESULTS: From 7 000 individuals, and after the exclusion criteria, there were left 1 784 individuals. The 90th percentile for Homeostasis Model Assessment index was 2.33. In the validation cohort, applying that cut-off, we have 49.3% of individuals with insulin resistance. However, only 69.9% of the metabolic syndrome patients had insulin resistance according to that cut-off. By ROC curve analysis, the ideal cut-off for metabolic syndrome is 2.41. Homeostasis Model Assessment index correlated with BMI (r = 0.371, p < 0.001) and is an independent predictor of the presence of metabolic syndrome (OR 19.4, 95% CI 6.6 - 57.2, p < 0.001). DISCUSSION: Our study showed that in a Portuguese population of patients admitted electively in a Cardiology ward, 2.33 is the Homeostasis Model Assessment index cut-off for insulin resistance and 2.41 for metabolic syndrome. CONCLUSION: Homeostasis Model Assessment index is directly correlated with BMI and is an independent predictor of metabolic syndrome.

Invasive Assessment of the Coronary Microcirculation Using the Index of Microcirculatory Resistance: Description and Validation of an Animal Model

INTRODUCTION: The index of microcirculatory resistance (IMR) enables/provides quantitative, invasive, and real-time assessment of coronary microcirculation status. AIMS: The primary aim of this study was to validate the assessment of IMR in a large animal model, and the secondary aim was to compare two doses of intracoronary papaverine, 5 and 10 mg, for induction of maximal hyperemia and its evolution over time. METHODS: Measurements of IMR were performed in eight pigs. Mean distal pressure (Pd) and mean transit time (Tmn) were measured at rest and at maximal hyperemia induced with intracoronary papaverine, 5 and 10 mg, and after 2, 5, 8 and 10 minutes. Disruption of the microcirculation was achieved by selective injection of 40-μm microspheres via a microcatheter in the left anterior descending artery. RESULTS: In each animal 14 IMR measurements were made. There were no differences between the two doses of papaverine regarding Pd response and IMR values - 11 ± 4.5 U with 5 mg and 10.6 ± 3 U with 10 mg (p=0.612). The evolution of IMR over time was also similar with the two doses, with significant differences from resting values disappearing after five minutes of intracoronary papaverine administration. IMR increased with disrupted microcirculation in all animals (41 ± 16 U, p=0.001). CONCLUSIONS: IMR provides invasive and real-time assessment of coronary microcirculation. Disruption of the microvascular bed is associated with a significant increase in IMR. A 5-mg dose of intracoronary papaverine is as effective as a 10-mg dose in inducing maximal hyperemia. After five minutes of papaverine administration there is no significant difference from resting hemodynamic status.

Acute Kidney Injury after Pediatric Cardiac Surgery: Risk Factors and Outcomes. Proposal for a Predictive Model

Objectives: To characterize the epidemiology and risk factors for acute kidney injury (AKI) after pediatric cardiac surgery in our center, to determine its association with poor short-term outcomes, and to develop a logistic regression model that will predict the risk of AKI for the study population. Methods: This single-center, retrospective study included consecutive pediatric patients with congenital heart disease who underwent cardiac surgery between January 2010 and December 2012. Exclusion criteria were a history of renal disease, dialysis or renal transplantation. Results: Of the 325 patients included, median age three years (1 day---18 years), AKI occurred in 40 (12.3%) on the first postoperative day. Overall mortality was 13 (4%), nine of whom were in the AKI group. AKI was significantly associated with length of intensive care unit stay, length of mechanical ventilation and in-hospital death (p<0.01). Patients’ age and postoperative serum creatinine, blood urea nitrogen and lactate levels were included in the logistic regression model as predictor variables. The model accurately predicted AKI in this population, with a maximum combined sensitivity of 82.1% and specificity of 75.4%. Conclusions: AKI is common and is associated with poor short-term outcomes in this setting. Younger age and higher postoperative serum creatinine, blood urea nitrogen and lactate levels were powerful predictors of renal injury in this population. The proposed model could be a useful tool for risk stratification of these patients.