Faculty Bibliography

BACKGROUND: The prevalence and significance of right ventricular dysfunction (RVD) in patients with cardiogenic shock due to acute myocardial infarction (AMI-CS) has not been well characterized. We hypothesized that RVD is common in AMI-CS and associated with worse clinical outcomes. METHODS AND RESULTS: We retrospectively analyzed patients with available hemodynamics enrolled in the SHould we emergently revascularize Occluded coronaries for Cardiogenic shocK (SHOCK) Trial (n=139) and Registry (n=258) to identify RVD in AMI-CS. RVD was defined by an elevated central venous pressure (CVP), elevated CVP/ pulmonary capillary wedge pressure (PCWP) ratio, decreased pulmonary artery pulsatility index (PAPi), and decreased right ventricular stroke work index (RVSWI). A p value of less than 0.01 was used to infer significance. In both the SHOCK trial and registry, 38% and 37% of patients had RVD, however RVD was not associated with 30-day or 6-month survival (HR 1.51 (0.92, 2.49) p=0.10). RV failure using inclusion criteria from the Recover Right Trial for RV failure (RR-RVF) requiring percutaneous mechanical circulatory support included an elevated CVP, CVP/PCWP ratio, and a low cardiac index despite >/=1 inotrope or vasopressor. In both the SHOCK trial and registry, 45% (n=63/139) and 38% (n=98/258) of patients met RR-RVF criteria respectively. The RR-RVF criteria were not significantly associated with 30-day mortality in the registry cohort (HR 1.44 (1.01,2.04), p=0.04), or in the trial cohort (HR 1.51(0.92,2.49), p=0.10). CONCLUSIONS: Hemodynamically defined RVD is common in AMI-CS. Routine assessment with PA catherization allows detection of RVD; however, further work is needed to identify interventions that will result in improved outcomes for these patients.

BACKGROUND: Interventions to reduce readmissions following acute heart failure hospitalization require early identification of patients. The purpose of this study was to develop and test accuracies of various approaches to identify patients with acute decompensated heart failure (ADHF) using data derived from the electronic health record. METHODS AND RESULTS: We included 37,229 hospitalizations of adult patients at a single hospital in 2013-2015. We developed four algorithms to identify hospitalization with a principal discharge diagnosis of ADHF: 1) presence of one of three clinical characteristics; 2) logistic regression of 31 structured data elements; 3) machine learning with unstructured data; 4) machine learning with both structured and unstructured data. In data validation, Algorithm 1 had a sensitivity of 0.98 and positive predictive value (PPV) of 0.14 for ADHF. Algorithm 2 had an area under the receiver operating characteristic curve (AUC) of 0.96, while both machine learning algorithms had AUCs of 0.99. Based on a brief survey of three providers who perform chart review for ADHF, we estimated providers spent 8.6 minutes per chart review; using this this parameter, we estimated providers would spend 61.4, 57.3, 28.7, and 25.3 minutes on secondary chart review for each case of ADHF if initial screening was done with algorithms 1, 2, 3, and 4, respectively. CONCLUSION: Machine learning algorithms with unstructured notes had best performance for identification of ADHF and can improve provider efficiency for delivery of quality improvement interventions.

Patients with complex chronic diseases usually must make multiple lifestyle changes to limit and manage their conditions. Numerous studies have shown that education alone is insufficient for engaging people in lifestyle behavior change, and that theory-based behavioral approaches also are necessary. However, even the most motivated individual may have difficulty with making lifestyle changes because of the information complexity associated with multiple behavior changes. The goal of the current Healthy Hearts and Kidneys study was to evaluate, different mobile health (mHealth)-delivered intervention approaches for engaging individuals with type 2 diabetes (T2D) and concurrent chronic kidney disease (CKD) in behavior changes. Participants were randomized to 1 of 4 groups, receiving: (1) a behavioral counseling, (2) technology-based self-monitoring to reduce information complexity, (3) combined behavioral counseling and technology-based self-monitoring, or (4) baseline advice. We will determine the impact of randomization assignment on weight loss success and 24-hour urinary excretion of sodium and phosphorus. With this report we describe the study design, methods, and approaches used to assure information security for this ongoing clinical trial. Clinical Trials.gov Identifier: NCT02276742.

BACKGROUND: Insulin resistance and endothelial dysfunction share a reciprocal relationship that links the metabolic and cardiovascular sequelae of obesity. We characterized the brachial artery reactivity testing (BART) and carotid artery-intima media thickness (CIMT) in adolescents categorized as obese insulin resistant (OIR) and obese not insulin resistant (ONIR). Lipoprotein particle (p) analysis and inflammatory cytokines in OIR and ONIR groups were also analyzed. METHODS: Obese adolescents (n=40; mean body mass index [BMI] 35.6) were categorized as ONIR and OIR based on their homeostatic model assessment of insulin resistance (HOMA-IR) calculation ( than 3.4). Ultrasound measured conduit arterial function BART, microvascular function (post-ischemic hyperemia) and conduit artery structure CIMT. RESULTS: BART did not differ according to IR status (mean+/-SD: 7.0+/-4.3% vs. 5.9+/-3.4% in ONIR and OIR, respectively, p=0.3, but post-ischemic hyperemia was significantly greater in the ONIR group (4.5+/-2.2 vs. 3.5+/-3, p=0.04). Atherogenic lipoprotein particles; large VLDL particles and small LDL particles were higher in the OIR compared to ONIR group. CONCLUSIONS: OIR adolescents demonstrate an inflamed atherogenic milieu compared to the ONIR adolescents. Microvascular function, but not conduit vessel structure or function, was impaired in association with IR.