Xiang L, Lu S, Mittwede PN, Clemmer JS, Husband GW, Hester RL. ␤ 2-Adrenoreceptor blockade improves early posttrauma hyperglycemia and pulmonary injury in obese rats. Am J Physiol Heart Circ Physiol 307: H621-H627, 2014. First published June 14, 2014; doi:10.1152/ajpheart.00208.2014.-Early hyperglycemia after trauma increases morbidity and mortality. Insulin is widely used to control posttrauma glucose, but this treatment increases the risk of hypoglycemia. We tested a novel method for early posttrauma hyperglycemia control by suppressing hepatic glycogenolysis via ␤ 2-adrenoreceptor blockade [ICI-118551 (ICI)]. We have shown that, after severe trauma, obese Zucker (OZ) rats, similar to obese patients, exhibit increased acute lung injury compared with lean Zucker (LZ) rats. We hypothesized that OZ rats exhibit a greater increase in early posttrauma glucose compared with LZ rats, with the increased posttrauma hyperglycemia suppressed by ICI treatment. Orthopedic trauma was applied to both hindlimbs in LZ and OZ rats. Fasting plasma glucose was then monitored for 6 h with or without ICI (0.2 mg·kg Ϫ1 ·h Ϫ1 iv.) treatment. One day after trauma, plasma IL-6 levels, lung neutrophil numbers, myeloperoxidase (MPO) activity, and wetto-dry weight ratios were measured. Trauma induced rapid hepatic glycogenolysis, as evidenced by decreased liver glycogen levels, and this was inhibited by ICI treatment. Compared with LZ rats, OZ rats exhibited higher posttrauma glucose, IL-6, lung neutrophil infiltration, and MPO activity. Lung wet-to-dry weight ratios were increased in OZ rats but not in LZ rats. ICI treatment reduced the early hyperglycemia, lung neutrophil retention, MPO activity, and wet-to-dry weight ratio in OZ rats to levels comparable with those seen in LZ rats, with no effect on blood pressure or heart rate. These results demonstrate that ␤ 2-adrenoreceptor blockade effectively reduces the early posttrauma hyperglycemia, which is associated with decreased lung injury in OZ rats. obesity; trauma; glucose; lung injury; ␤ 2-adrenoreceptor UNINTENTIONAL INJURIES are the leading cause of mortality in individuals under 50 yr of age in the United States, with staggering medical expenses attributable to traumatic injury (10a, 43). Obese patients with severe orthopedic trauma are at a higher risk than lean patients for increased inflammation, multiorgan failure, prolonged hospital stay, and increased mortality (5,8,10,12). Although the mechanisms responsible for the increased posttrauma complications in obese patients are unclear, intensive and specific treatments have been emphasized for these patients.Posttrauma hyperglycemia has been recognized as a risk factor that exacerbates complications, organ dysfunction, and mortality (29,41,44). Several clinical studies have suggested that, in critically ill obese patients, impaired glucose homeostasis appears to be a better predictor of increased complications and mortality than body mass index (38,41,45). In addition, there is evidence that the early hyperglycemia within the f...

We present a small integrative model of human cardiovascular physiology. The model is population-based; rather than using best fit parameter values, we used a variant of the Metropolis algorithm to produce distributions for the parameters most associated with model sensitivity. The population is built by sampling from these distributions to create the model coefficients. The resulting models were then subjected to a hemorrhage. The population was separated into those that lost less than 15 mmHg arterial pressure (compensators), and those that lost more (decompensators). The populations were parametrically analyzed to determine baseline conditions correlating with compensation and decompensation. Analysis included single variable correlation, graphical time series analysis, and support vector machine (SVM) classification. Most variables were seen to correlate with propensity for circulatory collapse, but not sufficiently to effect reasonable classification by any single variable. Time series analysis indicated a single significant measure, the stressed blood volume, as predicting collapse in situ, but measurement of this quantity is clinically impossible. SVM uncovered a collection of variables and parameters that, when taken together, provided useful rubrics for classification. Due to the probabilistic origins of the method, multiple classifications were attempted, resulting in an average of 3.5 variables necessary to construct classification. The most common variables used were systemic compliance, baseline baroreceptor signal strength and total peripheral resistance, providing predictive ability exceeding 90%. The methods presented are suitable for use in any deterministic mathematical model.

Funding Acknowledgements Type of funding sources: None. Introduction Social determinants of health (SDOH) are increasingly being recognized as critical, independent prognosticators in cardiovascular disease. Despite this, little is known about the role of SDOH in predicting outcomes following transcatheter aortic valve implantation (TAVI). Purpose To assess the value of adding census-derived SDOH in developing machine learning (ML) models for prediction of all-cause mortality in patients following TAVI. Methods A total of 398 patients, who underwent TAVI in 2019, were studied. Clinical, demographic, echocardiographic (echo) and census-derived SDOH data were collected. All-cause mortality at 1 year was the endpoint. A general linear ML model was fit with 100 iterations and a 70:30 training-test split. We compared the predictive performance of the model with and without adding SDOH. The SDOH included in the ML model were race (white vs. non-white), % zip code population as female, and zip code average yearly income less than $45,000. Results Baseline SDOH, demographic, clinical, and echo data are shown in Table 1. Following univariate and multivariate predictor analysis, the following input data were used for the ML model without the SDOH: post TAVI all-cause hospitalizations, history of outpatient hemodialysis, atrial fibrillation, heart failure with reduced ejection fraction, myocardial infarction, coronary artery disease and beta-blockers. The ML model with SDOH used the same input as well as the SDOH variables. The model with vs. without SDOH had a median AUC of 0.75 vs. 0.73 (p = 0.9957). Conclusions Despite not reaching statistical significance, our ML model provides a holistic picture of mortality predictors. Larger studies are needed to more assess the predictive value of SDOH post TAVI. Abstract Figure. Baseline patient characteristics  Abstract Figure. ML Model: Area Under Curve