Dobutamine stress echocardiography predicts cardiac events or death in asymptomatic patients long-term after heart transplantation: 4-year prospective evaluation.

BACKGROUND: Cardiac allograft vasculopathy (CAV) remains the major cause of death after cardiac transplantation during long-term follow-up. Nevertheless, annual angiographic evaluation is difficult to perform routinely. We evaluated the value of clinical risk factors and non-invasive testing for cardiac allograft vasculopathy in predicting cardiac events or death in asymptomatic patients with normal ventricular function during long-term follow-up after heart transplantation. METHODS: We studied 39 patients, mean aged 48 +/- 13 years, at 86 +/- 31 months after heart transplantation. Patients underwent thallium scintigraphy, treadmill stress testing, dobutamine stress echocardiography, and angiography to detect CAV. We prospectively observed all patients an additional 4 years for acute myocardial infarction, congestive heart failure, or death. RESULTS: Angiography detected CAV in 15 patients (38%). Three patients had acute myocardial infarction and another 7 had congestive heart failure, representing 25% of cardiac events during the study period. Nine deaths (23%) occurred during the same observation time. Univariate analysis showed that increased body mass index, positive dobutamine stress echocardiography results, and positive angiography results were associated significantly with cardiac events or death during follow-up. In the absence of coronary angiography, stepwise logistic regression identified positive dobutamine echocardiography results as the unique independent predictor of cardiac events (p = 0.001) or death (p = 0.002). CONCLUSION: Cardiac events and death after heart transplantation increased during long-term follow-up of this population. However, dobutamine stress echocardiography is well tolerated and, in the absence of routine angiographic evaluation, may be a strong predictor of these events.     

Advances in managing the noninfected open chest after cardiac surgery: Negative-pressure wound therapy

Objective

The objective of this study was to compare safety and clinical effectiveness of negative-pressure wound therapy (NPWT) with traditional wound therapy for managing noninfected open chests with delayed sternal closure after cardiac surgery.

Influence of Platelet Reactivity on Clinical Outcome of Patients with Stable Coronary Artery Disease

Single antiplatelet therapy with aspirin is actually recommended for cardiovascular prevention in patients with stable coronary disease, whereas dual antiplatelet therapy (aspirin and clopidogrel) represents the established treatment in patients with acute coronary syndromes or stable angina undergoing percutaneous coronary intervention. However, recurrent ischemic events occur in patients on treatment with clopidogrel; this may be due to low responsiveness to this agent, a phenomenon influenced by environmental, clinical, and genetic factors. Different strategies have been tested to overcome this phenomenon, such as increase in clopidogrel loading and maintenance doses and use of newer P2Y12 inhibitors (prasugrel and ticagrelor), which are by now indicated for patients with acute coronary syndromes; the latter agents have been associated with stronger antiplatelet effect than clopidogrel even in patients with stable coronary disease, but further studies are needed to test their net clinical benefit in this setting (reduction of ischemic events without increase in bleeding).     

Using machine learning methods to predict prolonged operative time in elective total shoulder arthroplasty

BackgroundMachine learning (ML) is a form of artificial intelligence in which computer algorithms improve automatically with experience. Recently, ML has been utilized to predict operative characteristics and patient outcomes for orthopedic procedures, thereby allowing for better patient selection and preoperative planning. This study sought to develop ML models to aid in predicting operative time and 30-day postoperative complications for elective total shoulder arthroplasty and to compare them to regression models.MethodsThis cross-sectional national database study identified patients who underwent elective total shoulder arthroplasty from 2012 to 2018 in the American College of Surgeons National Surgical Quality Improvement Program registry. Boosted decision tree and artificial neural network (ANN) ML models were developed to predict prolonged operative time and 30-day postoperative complication rates. Model performance was measured using the area under the receiver operating characteristic curve and overall accuracy. Multivariate binary logistic regression analyses were also used to identify variables that predicted prolonged operative time and 30-day postoperative complication rates. ML model performance was then compared to the regression models in predicting outcomes.ResultsIn total, 21,544 elective total shoulder arthroplasty procedures met inclusion criteria. Variables associated with greater odds of prolonged operative time included male sex (odds ratio [OR] = 0.66; 95% confidence interval [CI] = 0.61-0.71; P < .001), obesity (OR = 1.19; 95% CI = 1.09-1.29; P < .001), age under 70 years (OR = 0.77; 95% CI = 0.71-0.85; P < .001), smoking history (OR = 1.16; 95% CI = 1.03-1.32; P = .022), and history of cancer (OR = 2.91; 95% CI = 1.52-5.54; P = .001). The boosted decision tree model yielded an area under the curve (AUC) of 0.642, with an overall accuracy of 85.6% for predicting prolonged operative time. The ANN model had an AUC of 0.906 and overall accuracy of 84.7%, while the regression model had an AUC of 0.590 with overall accuracy of 85.6%. Thirty-day complication rate (7.7% vs. 3.9%, respectively; P < .001) and reoperation rate (1.8% vs. 1.2%, respectively; P = .006) also differed significantly between the prolonged operative duration and normal operative duration cohorts.ConclusionThis is the first study to successfully develop ML models for predicting operative time in total shoulder arthroplasty and compare them to existing methods of data analysis. The ANN model was superior to the other models in predicting prolonged operative time. With regard to 30-day postoperative complications, both ML models displayed fair predictive capacity, compared to the regression model, which had poor predictive performance.Level of evidenceLevel III; Database Retrospective Comparative Cohort Study