Relation between depression after coronary artery bypass surgery and 12-month outcome: a prospective study.

BACKGROUND: The association of depression with cardiac events has been investigated mainly in community cohorts, in patients undergoing catheterisation, or in patients who have had myocardial infarction. We have assessed the effect of depression on outcomes after coronary artery bypass graft (CABG) surgery. METHODS: In a prospective study, we followed up for 1 year 207 men and 102 women, who had undergone coronary artery bypass graft surgery. We assessed depression with a structured psychiatric interview (diagnostic interview schedule) and a questionnaire (Beck depression inventory) before discharge. Cardiac events included angina or heart failure that needed admission to hospital, myocardial infarction, cardiac arrest, percutaneous transluminal coronary angioplasty, repeat CABG, and cardiac mortality. Non-cardiac events consisted of all other reasons for mortality or readmission. FINDINGS: 63 patients (20%) met modified diagnostic statistical manual IV criteria for major depressive disorder. At 12 months, 17 (27%) of these patients had a cardiac event compared with 25 of 246 (10%) who were not depressed (p<0.0008). Five variables had significant univariate associations with cardiac events: sex, living alone, low ejection fraction (<0.35), length of hospital stay, and depression. In a Cox proportional-hazard model with these five and two other variables of cardiac severity, major depressive disorder (risk ratio 2.3 [95% CI 1.17-4.56]), low ejection fraction (2.3 [1.07-5.03]), and female sex (2.4 [1.24-4.44]) were associated with adverse outcomes. Depression did not predict deaths or admissions for non-cardiac events. INTERPRETATION: Depression is an important independent risk factor for cardiac events after CABG surgery.

Outcomes management: from concepts to application.

This article provides an introduction to the definition of and rationale for outcomes management and includes a brief review of the outcomes management literature. A model for outcomes management, which links processes that can be changed in care delivery to outcomes that can be measured in a patient population, is reviewed. Guidelines for application of the outcomes management model and practical examples of application to two surgical patient populations are presented. Finally, issues important to outcomes management as a tool for performance improvement are discussed.

A neural network model for predicting pancreas transplant graft outcome.

OBJECTIVE: To compare the results of a neural network versus a logistic regression model for predicting early (0-3 months) pancreas transplant graft survival or loss. RESEARCH DESIGN AND METHODS: This study was a cross-sectional, secondary analysis of demographic and clinical data from 117 simultaneous pancreas-kidney (SPK), 35 pancreas-after-kidney (PAK), and 8 pancreas-transplant-alone (PTA) patients (n = 160). The majority of patients were men (57%) and were white (90.1%), with a mean age of 39 +/- 8.09 years. Of the patients, 23 (14.4%) experienced early graft loss, which included any loss owing to technical or immunological causes, and death with a functional graft. Data were analyzed with a logistic regression model for multivariate analysis and a backpropagation neural network (BPNN) model. RESULTS: A total of 12 predictor variables were chosen from literature and transplant surgeon recommendations. A logistic model with all predictor variables included correctly classified 93.53% of cases. Model sensitivity was 35.71%; specificity was 100% (pseudo-R2 0.24). Of the predictors, history of alcohol abuse (odds ratio [OR] 32.39; 95% CI 1.67-626.89), having a PAK or PTA (OR 13.6; 95% CI 2.20-84.01), and use of a nonlocal organ procurement center (OPO) (OR 4.51; 95% CI 0.78-25.96) were most closely associated with early graft loss. The BPNN model with the same 12 predictor variables correctly predicted 92.50% of cases (R2 0.71). Model sensitivity was 68%; specificity was 96%. Of the predictors, the three variables most closely associated with graft outcome in this model were recipient/donor weight difference >50 lb, having a PAK or PTA, and use of a nonlocal OPO. CONCLUSIONS: First, the BPNN model correctly predicted 92.5% of graft outcomes versus the logistic model (93.53%). Second, the BPNN model rendered more accurate predictions (>0.70 = loss; <0.30 = survival) versus the logistic model (>0.50 = loss; <0.50 = survival). Third, the BPNN model was more sensitive (68%) than the logistic model (35.71%) to graft failures and demonstrated an almost threefold increase in explained variance (R2 = 0.71 vs. 0.24). These results suggest that the BPNN model is a more powerful tool for predicting early pancreas graft loss than traditional multivariate statistical models.

A protocol to switch high-dose, methadone-maintained subjects to buprenorphine.

Buprenorphine (BUP) is an alternative to methadone (METH) maintenance. However, there are few studies on the switching of patients from METH to BUP. Eighteen volunteers who had been maintained on METH for 1-19 years were recruited for a residential cocaine self-administration study. All subjects were maintained on 60 mg METH for up to 1 1/2 weeks before the 7-day changeover (60, 40, 30, 30, 0 mg METH; 4, 8 mg BUP). Fifteen subjects successfully completed the transfer from METH to BUP, experiencing moderate withdrawal symptoms, as measured by the Subjective Opiate Withdrawal Scale (SOWS). Withdrawal symptoms were the highest during the first assessment of the day, at the time of BUP administration. SOWS scores returned to baseline 4 days after the switchover. This study demonstrates that within a supportive inpatient setting, research volunteers can be rapidly switched from high-maintenance doses of METH to BUP with an acceptable degree of tolerability.