Citations

Background Current American Heart Association/American College of Cardiology guidelines recommend the GRACE (Global Registry of Acute Coronary Events) and TIMI (Thrombolysis in Myocardial Infarction) scores to assess myocardial infarction ( MI ) prognosis. Changes in the epidemiological characteristics of MI and the availability of new biomarkers warrant an assessment of the performance of these scores in contemporary practice. We assessed the following: (1) the performance of GRACE and TIMI to predict 1‐year mortality in a cohort of patients stratified by ST ‐segment elevation MI (STEMI) and non‐STEMI ( NSTEMI ) and (2) the incremental discriminatory power of soluble suppression of tumorigenicity‐2, a myocardial fibrosis biomarker. Methods and Results Olmsted County, Minnesota, residents with incident MI (N=1401) were recruited prospectively from November 1, 2002 to December 31, 2012 (mean age, 67 years; 61% men; 79% with NSTEMI ). Baseline data were used to calculate risk scores; soluble suppression of tumorigenicity‐2 was measured in stored plasma samples obtained at index MI . C ‐statistics adapted to survival data were used to assess the discriminatory power of the risk scores and the improvement gained by adding other markers. During the first year of follow‐up, 190 patients (14%) died. The discriminatory performance to predict death was reasonable for GRACE and poor for TIMI , and was generally worse in those with NSTEMI versus those with STEMI. In people with NSTEMI , sequential addition of comorbidities and soluble suppression of tumorigenicity‐2 substantially improved the c ‐statistic over GRACE (from 0.78 to 0.80 to 0.84) and TIMI (from 0.61 to 0.73 to 0.81), respectively (all P ≤0.05). Conclusions Guideline‐recommended scores for risk assessment after MI underperform in contemporary community patients, particularly those with NSTEMI, which now represents most infarcts. Incorporating comorbidities and soluble suppression of tumorigenicity‐2 substantially improves risk prediction, thereby delineating opportunities to improve clinical care.

Background: A diagnosis of heart failure (HF) often requires a comprehensive lifestyle change to maintain disease stability. When patients with HF are married, the spouse frequently assumes the caregiving role. Our objectives were to describe the health of spouses of married patients with HF, and examine whether the health of a spouse impacts patient outcomes. Methods and Results: We identified 905 patients that were married at the time of incident HF diagnosis in Olmsted County, MN, from 2000 to 2012. Using Rochester Epidemiology Project resources, the patient and their spouse’s comprehensive longitudinal health histories were linked. Spousal health at patient HF diagnosis was assessed by comorbidity burden, self-reported difficulty with activities of daily living and prior hospitalizations. The associations of spousal health with patient outcomes and patient death with spousal outcomes were examined using Cox and Andersen–Gill models. Spouses of patients with HF were elderly (mean age, 71 years), often had comorbid conditions, and 16% had difficulty with ≥1 activities of daily living. After adjustment for patient age, sex, and comorbidity, there were no independent associations of spousal health and patient risk of death or hospitalization after HF diagnosis. However, the risk of hospitalization (adjusted hazard ratio, 1.34; 95% confidence interval, 1.11–1.60; P =0.002) and death (hazard ratio, 2.10; 95% confidence interval, 1.60–2.75; P <0.001) increased in the surviving spouse after patient death. Conclusions: We found no evidence that the health of a spouse impacts patient outcomes after HF diagnosis. However, after a patient with HF dies, their surviving spouse’s risk of hospitalization and death increases.

Advancements in computational musculoskeletal biomechanics are constrained by a lack of experimental measurement under real-time physiological loading conditions. This paper presents the design, configuration, capabilities, accuracy, and repeatability of The University of Texas at El Paso Joint Load Simulator (UTJLS) by testing four cadaver knee specimens with 47 real-time tests including heel and toe squat maneuvers with and without musculotendon forces. The UTJLS is a musculoskeletal simulator consisting of two robotic manipulators and eight musculotendon actuators. Sensors include eight tension load cells, two force/torque systems, nine absolute encoders, and eight incremental encoders. A custom control system determines command output for position, force, and hybrid control and collects data at 2000 Hz. Controller configuration performed forward-dynamic control for all knee degrees-of-freedom (DOFs) except knee flexion. Actuator placement and specimen potting techniques uniquely replicate muscle paths. Accuracy and repeatability standard deviations across specimen during squat simulations were equal or less than 8 N and 5 N for musculotendon actuators, 30 N and 13 N for ground reaction forces (GRFs), and 4.4 N·m and 1.9 N·m for ground reaction moments. The UTJLS is the first of its design type. Controller flexibility and physical design support axis constraints to match traditional testing rigs, absolute motion, and synchronous real-time simulation of multiplanar kinematics, GRFs, and musculotendon forces. System DOFs, range of motion, and speed support future testing of faster maneuvers, various joints, and kinetic chains of two connected joints.