Cognitive impairment predicts mortality in outpatient veterans with heart failure.

BACKGROUND: In our prior study of 250 outpatient veterans with heart failure (HF), 58% had unrecognized cognitive impairment (CI) which was linked to worsened medication adherence. Literature suggests HF patients with CI have poorer clinical outcomes including higher mortality. OBJECTIVE: The study is to examine mortality rates in outpatients with HF and undiagnosed CI compared to their cognitively intact peers. METHODS: This is a retrospective study for all-cause mortality. RESULTS: During the 3-year follow up, 64/250 (25.6%) patients died: 20/106 (18.9%) with no CI, 29/104 (27.9%) with mild CI, and 15/40 (37.5%) with severe CI. Patients with CI were at increased risk for mortality (hazard ratio 1.82, p = 0.038). Those with severe CI had the worst outcome (hazard ratio 2.710, p = 0.011). CONCLUSIONS: CI was an independent risk factor for mortality in patients with heart failure when controlling for age and markers of disease severity. Cognitive screening should be performed routinely to identify patients at greater risk for adverse outcomes.

Right Ventricular Ejection Fraction in Ischemic Versus Nonischemic Cardiomyopathy.

The relation between the etiology of cardiomyopathy and the function of the right ventricle (RV) has not been well described in the current era of 3-dimensional cardiac imaging. New advances in cardiac imaging with computed tomography (CT) have allowed accurate measurements of ejection fraction (EF), often a challenging task considering the unique RV shape. We evaluated 130 patients at the Loma Linda Veterans Affairs Healthcare System with cardiomyopathy and a left ventricular (LV) EF ≤ 40%. Etiology of cardiomyopathy was determined by CT angiography as ischemic (n = 56) or nonischemic (n = 74). RV volumes and RVEF were calculated based on 3-dimensional data set from CT images. Baseline LVEF was similar with a mean LVEF of 28% (± 6%) in the ischemic group and 28% (± 9%) in the nonischemic group (p = 0.46). RV function and volumes were moderately decreased in both cohorts, without significant difference between the groups (mean RVEF 34 ± 11% in ischemic group and 32 ± 10% in nonischemic group, p = 0.26). In conclusion, most patients with LV dysfunction also have RV dysfunction. The degree of RV dysfunction is not dependent on the etiology of cardiomyopathy.

Comparative effectiveness of fecal immunochemical test outreach, colonoscopy outreach, and usual care for boosting colorectal cancer screening among the underserved: a randomized clinical trial.

IMPORTANCE: Colorectal cancer (CRC) screening saves lives, but participation rates are low among underserved populations. Knowledge on effective approaches for screening the underserved, including best test type to offer, is limited. OBJECTIVE: To determine (1) if organized mailed outreach boosts CRC screening compared with usual care and (2) if FIT is superior to colonoscopy outreach for CRC screening participation in an underserved population. DESIGN, SETTING, AND PARTICIPANTS: We identified uninsured patients, not up to date with CRC screening, age 54 to 64 years, served by the John Peter Smith Health Network, Fort Worth and Tarrant County, Texas, a safety net health system. INTERVENTIONS: Patients were assigned randomly to 1 of 3 groups. One group was assigned to fecal immunochemical test (FIT) outreach, consisting of mailed invitation to use and return an enclosed no-cost FIT (n = 1593). A second was assigned to colonoscopy outreach, consisting of mailed invitation to schedule a no-cost colonoscopy (n = 479). The third group was assigned to usual care, consisting of opportunistic primary care visit­based screening (n = 3898). In addition, FIT and colonoscopy outreach groups received telephone follow-up to promote test completion. MAIN OUTCOME MEASURES: Screening participation in any CRC test within 1 year after randomization. RESULTS: Mean patient age was 59 years; 64% of patients were women. The sample was 41% white, 24% black, 29% Hispanic, and 7% other race/ethnicity. Screening participation was significantly higher for both FIT (40.7%) and colonoscopy outreach (24.6%) than for usual care (12.1%) (P < .001 for both comparisons with usual care). Screening was significantly higher for FIT than for colonoscopy outreach (P < .001). In stratified analyses, screening was higher for FIT and colonoscopy outreach than for usual care, and higher for FIT than for colonoscopy outreach among whites, blacks, and Hispanics (P < .005 for all comparisons). Rates of CRC identification and advanced adenoma detection were 0.4% and 0.8% for FIT outreach, 0.4% and 1.3% for colonoscopy outreach, and 0.2% and 0.4% for usual care, respectively (P < .05 for colonoscopy vs usual care advanced adenoma comparison; P > .05 for all other comparisons). Eleven of 60 patients with abnormal FIT results did not complete colonoscopy. CONCLUSIONS AND REVELANCE: Among underserved patients whose CRC screening was not up to date, mailed outreach invitations resulted in markedly higher CRC screening compared with usual care. Outreach was more effective with FIT than with colonoscopy invitation. TRIAL REGISTRATION: clinicaltrials.gov Identifier: NCT01191411.

Measuring the intensity of resident supervision in the department of veterans affairs: the resident supervision index.

PURPOSE: To develop a survey instrument designed to quantify supervision by attending physicians in nonprocedural care and to assess the instrument's feasibility and reliability. METHOD: In 2008, the Department of Veterans Affairs (VA) Office of Academic Affiliations convened an expert panel to adopt a working definition of attending supervision in nonprocedural patient care and to construct a survey to quantify it. Feasibility was field-tested on residents and their supervising attending physicians at primary care internal medicine clinics at the VA Loma Linda Healthcare System in their encounters with randomly selected outpatients diagnosed with either major depressive disorder or diabetes. The authors assessed both interrater concurrent reliability and test-retest reliability. RESULTS: The expert panel adopted the VA's definition of resident supervision and developed the Resident Supervision Index (RSI) to measure supervision in terms of residents' case understanding, attending physicians' contributions to patient care through feedback to the resident, and attending physicians' time (minutes). The RSI was field-tested on 60 residents and 37 attending physicians for 148 supervision episodes from 143 patient encounters. Consent rates were 94% for residents and 97% for attending physicians; test-retest reliability intraclass correlations (ICCs) were 0.93 and 0.88, respectively. Concurrent reliability between residents' and attending physicians' reported time was an ICC of 0.69. CONCLUSIONS: The RSI is a feasible and reliable measure of resident supervision that is intended for research studies in graduate medical education focusing on education outcomes, as well as studies assessing quality of care, patient health outcomes, care costs, and clinical workload.

Validity of electronically monitored medication adherence and conventional adherence measures in schizophrenia.

OBJECTIVE: This study evaluated the validity of prescriber, patient, and research assistant ratings of adherence to prescribed oral antipsychotic medication among outpatients with schizophrenia or schizoaffective disorder in comparison with electronic monitoring. METHODS: Adult outpatients with schizophrenia (N=35) or schizoaffective disorder (N=26) received adherence assessments via electronically monitored medication vial caps as well as by monthly prescriber, patient, and research assistant report for up to six months. RESULTS: Electronic monitoring detected greater nonadherence rates (57%) than either prescribers (7%) or patients (5%), though the research assistant ratings were 54%. No directional bias was found between electronic monitoring and assignment of adherence by research assistants, although disagreement occurred in 36% of cases. CONCLUSIONS: Both patients and prescribers grossly overestimated medication adherence, which may interfere with or reduce the effectiveness of diligent medication management.

Measuring costs of guideline-driven mental health care: the Texas Medication Algorithm Project.

BACKGROUND: Algorithms describe clinical choices to treat a specific disorder. To many, algorithms serve as important tools helping practitioners make informed choices about how best to treat patients, achieving better outcomes more quickly and at a lower cost. Appearing as flow charts and decision trees, algorithms are developed during consensus conferences by leading experts who explore the latest scientific evidence to describe optimal treatment for each disorder. Despite a focus on "optimal" care, there has been little discussion in the literature concerning how costs should be defined and measured in the context of algorithm-based practices. AIMS OF THE STUDY: This paper describes the strategy to measure costs for the Texas Medication Algorithm project, or TMAP. Launched by the Texas Department of Mental Health and Mental Retardation and the University of Texas Southwestern Medical Center at Dallas, this multi-site study investigates outcomes and costs of medication algorithms for bipolar disorder, schizophrenia and depression. METHODS: To balance costs with outcomes, we turned to cost-effectiveness analyses as a framework to define and measure costs. Alternative strategies (cost-benefit, cost-utility, cost-of-illness) were inappropriate since algorithms are not intended to guide resource allocation across different diseases or between health- and non-health-related commodities. "Costs" are operationalized consistent with the framework presented by the United States Public Health Service Panel on Cost Effectiveness in Medicine. Patient specific costs are calculated by multiplying patient units of use by a unit cost, and summing over all service categories. Outpatient services are counted by procedures. Inpatient services are counted by days classified into diagnosis groups. Utilization information is derived from patient self-reports, medical charts and administrative file sources. Unit costs are computed by payer source. Finally, hierarchical modeling is used to describe how costs and effectiveness differ between algorithm-based and treatment-as-usual practices. DISCUSSION: Cost estimates of algorithm-based practices should (i) measure opportunity costs, (ii) employ structured data collection methods, (iii) profile patient use of both mental health and general medical providers and (iv) reflect costs by payer status in different economic environments. IMPLICATION FOR HEALTH CARE PROVISION AND USE: Algorithms may help guide clinicians, their patients and third party payers to rely on the latest scientific evidence to make treatment choices that balance costs with outcomes. IMPLICATION FOR HEALTH POLICIES: Planners should consider consumer wants and economic costs when developing and testing new clinical algorithms. IMPLICATIONS FOR FURTHER RESEARCH: Future studies may wish to consider similar methods to estimate costs in evaluating algorithm-based practices.