Context Depressive symptoms predict adverse cardiovascular outcomes in patients with coronary heart disease, but the mechanisms responsible for this association are unknown. Objective To determine why depressive symptoms are associated with an increased risk of cardiovascular events. Design and Participants The Heart and Soul Study is a prospective cohort study of 1017 outpatients with stable coronary heart disease followed up for a mean (SD) of 4.8 (1.4) years. Setting Participants were recruited between September 11, 2000, and December 20, 2002, from 12 outpatient clinics in the San Francisco Bay Area and were followed up to January 12, 2008. Main Outcome Measures Baseline depressive symptoms were assessed using the Patient Health Questionnaire (PHQ). We used proportional hazards models to evaluate the extent to which the association of depressive symptoms with subsequent cardiovascular events (heart failure, myocardial infarction, stroke, transient ischemic attack, or death) was explained by baseline disease severity and potential biological or behavioral mediators. Results A total of 341 cardiovascular events occurred during 4876 person-years of follow-up. The age-adjusted annual rate of cardiovascular events was 10.0% among the 199 participants with depressive symptoms (PHQ score ≥10) and 6.7% among the 818 participants without depressive symptoms (hazard ratio [HR], 1.50; 95% confidence interval, [CI], 1.16–1.95; P=.002). After adjustment for comorbid conditions and disease severity, depressive symptoms were associated with a 31% higher rate of cardiovascular events (HR, 1.31; 95% CI, 1.00–1.71; P=.04). Additional adjustment for potential biological mediators attenuated this association (HR, 1.24; 95% CI, 0.94–1.63; P=.12). After further adjustment for potential behavioral mediators, including physical inactivity, there was no significant association (HR, 1.05; 95% CI, 0.79–1.40; P=.75). Conclusion In this sample of outpatients with coronary heart disease, the association between depressive symptoms and adverse cardiovascular events was largely explained by behavioral factors, particularly physical inactivity.

consumer (DTC) advertising of prescription drugs in the United States totaled $3.2 billion in 2003. 1 Although expenditures may be leveling off, 2 DTC advertisements have become a stable, if controversial, feature of the media landscape. [3][4][5][6] Critics charge that DTC advertisements lead to overprescribing of unnecessary, expensive, and potentially harmful medications, while proponents counter that they can serve a useful educational function and help avert underuse of effective treatments for conditions that may be poorly recognized, highly stigmatized, or both. 7 Antidepressant medications consistently rank among the top DTC advertising categories. 8,9 Major depressive disorder (defined in the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition as Ն5 depressive symptoms lasting at least 2 weeks and accompanied by functional impairment) 10 carries stigma, [11][12][13] is frequentlyFor editorial comment see p 2030.

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IntroductionSeveral methods have been developed to electronically monitor patients for severe sepsis, but few provide predictive capabilities to enable early intervention; furthermore, no severe sepsis prediction systems have been previously validated in a randomised study. We tested the use of a machine learning-based severe sepsis prediction system for reductions in average length of stay and in-hospital mortality rate.MethodsWe conducted a randomised controlled clinical trial at two medical-surgical intensive care units at the University of California, San Francisco Medical Center, evaluating the primary outcome of average length of stay, and secondary outcome of in-hospital mortality rate from December 2016 to February 2017. Adult patients (18+) admitted to participating units were eligible for this factorial, open-label study. Enrolled patients were assigned to a trial arm by a random allocation sequence. In the control group, only the current severe sepsis detector was used; in the experimental group, the machine learning algorithm (MLA) was also used. On receiving an alert, the care team evaluated the patient and initiated the severe sepsis bundle, if appropriate. Although participants were randomly assigned to a trial arm, group assignments were automatically revealed for any patients who received MLA alerts.ResultsOutcomes from 75 patients in the control and 67 patients in the experimental group were analysed. Average length of stay decreased from 13.0 days in the control to 10.3 days in the experimental group (p=0.042). In-hospital mortality decreased by 12.4 percentage points when using the MLA (p=0.018), a relative reduction of 58.0%. No adverse events were reported during this trial.ConclusionThe MLA was associated with improved patient outcomes. This is the first randomised controlled trial of a sepsis surveillance system to demonstrate statistically significant differences in length of stay and in-hospital mortality.Trial registrationNCT03015454.

The majority of primary care patients surveyed reported one or more perceived barriers that would interfere with or prevent initiation or regular attendance of psychotherapy. Perceived barriers were more common among depressed than nondepressed patients making depression both an indicator for psychotherapy and a barrier to receiving it.

Purpose To explore the mentor–mentee relationship with a focus on determining the characteristics of effective mentors and mentees and understanding the factors influencing successful and failed mentoring relationships. Method The authors completed a qualitative study through the Departments of Medicine at the University of Toronto Faculty of Medicine and the University of California, San Francisco, School of Medicine between March 2010 and January 2011. They conducted individual, semistructured interviews with faculty members from different career streams and ranks and analyzed transcripts of the interviews, drawing on grounded theory. Results The authors completed interviews with 54 faculty members and identified a number of themes, including the characteristics of effective mentors and mentees, actions of effective mentors, characteristics of successful and failed mentoring relationships, and tactics for successful mentoring relationships. Successful mentoring relationships were characterized by reciprocity, mutual respect, clear expectations, personal connection, and shared values. Failed mentoring relationships were characterized by poor communication, lack of commitment, personality differences, perceived (or real) competition, conflicts of interest, and the mentor’s lack of experience. Conclusions Successful mentorship is vital to career success and satisfaction for both mentors and mentees. Yet challenges continue to inhibit faculty members from receiving effective mentorship. Given the importance of mentorship on faculty members’ careers, future studies must address the association between a failed mentoring relationship and a faculty member’s career success, how to assess different approaches to mediating failed mentoring relationships, and how to evaluate strategies for effective mentorship throughout a faculty member’s career.

BackgroundTo determine the characteristics associated with having a mentor, the association of mentoring with self-efficacy, and the content of mentor–mentee interactions at the University of California, San Francisco (UCSF), we conducted a baseline assessment prior to implementing a comprehensive faculty mentoring program.MethodWe surveyed all prospective junior faculty mentees at UCSF. Mentees completed a web-based, 38-item survey including an assessment of self-efficacy and a needs assessment. We used descriptive and inferential statistics to determine the association between having a mentor and gender, ethnicity, faculty series, and self-efficacy.ResultsOur respondents (n=464, 56%) were 53% female, 62% white, and 7% from underrepresented minority groups. More than half of respondents (n=319) reported having a mentor. There were no differences in having a mentor based on gender or ethnicity (p≥0.05). Clinician educator faculty with more teaching and patient care responsibilities were statistically significantly less likely to have a mentor compared with faculty in research intensive series (p<0.001). Having a mentor was associated with greater satisfaction with time allocation at work (p<0.05) and with higher academic self-efficacy scores, 6.07 (sd = 1.36) compared with those without a mentor, 5.33 (sd = 1.35, p<0.001). Mentees reported that they most often discussed funding with the mentors, but rated highest requiring mentoring assistance with issues of promotion and tenure.ConclusionFindings from the UCSF faculty mentoring program may assist other health science institutions plan similar programs. Mentoring needs for junior faculty with greater teaching and patient care responsibilities must be addressed.

Sepsis can be predicted at least three hours in advance of onset of the first five hour SIRS episode, using only nine commonly available vital signs, with better performance than methods in standard practice today. High-order correlations of vital sign measurements are key to this prediction, which improves the likelihood of early identification of at-risk patients.