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Background: Multimorbidity and functional limitation are associated with poor outcomes in heart failure (HF). However, the individual and combined effect of these on health‐related quality of life in patients with HF is not well understood. Methods: Patients aged ≥30 years with two or more HF diagnostic codes and one or more HF‐related prescription drugs from four U.S. institutions were mailed a survey to measure patient‐centric factors including functional status (activities of daily living [ADLs]) and health‐related quality of life (PROMIS‐29 Health Profile). Patients with HF from January 1, 2013 to February 1, 2018 were included. Multimorbidity was defined as ≥2 non‐cardiovascular comorbidities; functional limitation as any limitation in at least one of eight ADLs. Patients were categorized into four groups by multimorbidity (Yes/No) and functional limitation (Yes/No). We dichotomized the PROMIS‐29 sub‐scale scores at the median and calculated odd ratios for the four multimorbidity/functional limitation groups. Results: A total of 3330 patients with HF returned the survey (response rate 31%); 3020 completed the questions of interest and were retained. Among these patients (45% female; mean age 73 [standard deviation: 12] years), 29% had neither multimorbidity nor functional limitation, 24% had multimorbidity only, 22% had functional limitation only, and 25% had both. After adjustment, having functional limitation only was associated with higher anxiety (odds ratio [OR]: 3.44, 95% confidence interval [CI]: 2.66–4.45), depression (OR: 3.11, 95% CI: 2.39–4.06), and fatigue (OR: 4.19, 95% CI: 3.25–5.40); worse sleep (OR: 2.14, 95% CI: 1.69–2.72) and pain (OR: 6.73, 95% CI: 5.15–8.78); and greater difficulty with social activities (OR: 9.40, 95% CI: 7.19–12.28) compared with having neither. Results were similar for having both multimorbidity and functional limitation. Conclusion: Patients with only functional limitation have similar poor health‐related quality of life scores as those with both multimorbidity and functional limitation, underscoring the important role that physical functioning plays in the well‐being of patients with HF. [ABSTRACT FROM AUTHOR]

Context: Metformin is the first-line drug for treating diabetes but has a high failure rate. Objective: To identify demographic and clinical factors available in the electronic health record (EHR) that predict metformin failure. Methods: A cohort of patients with at least 1 abnormal diabetes screening test that initiated metformin was identified at 3 sites (Arizona, Mississippi, and Minnesota). We identified 22 047 metformin initiators (48% female, mean age of 57± 14 years) including 2141 African Americans, 440 Asians, 962 Other/Multiracial, 1539 Hispanics, and 16 764 non-Hispanic White people. We defined metformin failure as either the lack of a target glycated hemoglobin (HbA1c) (<7%) within 18 months of index or the start of dual therapy. We used tree-based extreme gradient boosting (XGBoost) models to assess overall risk prediction performance and relative contribution of individual factors when using EHR data for risk of metformin failure. Results: In this large diverse population, we observed a high rate of metformin failure (43%). The XGBoost model that included baseline HbA1c, age, sex, and race/ethnicity corresponded to high discrimination performance (C-index of 0.731; 95% CI 0.722, 0.740) for risk of metformin failure. Baseline HbA1c corresponded to the largest feature performance with higher levels associated with metformin failure. The addition of other clinical factors improved model performance (0.745; 95% CI 0.737, 0.754, P<.0001). Conclusion: Baseline HbA1c was the strongest predictor of metformin failure and additional factors substantially improved performance suggesting that routinely available clinical data could be used to identify patients at high risk of metformin failure who might benefit from closer monitoring and earlier treatment intensification. [ABSTRACT FROM AUTHOR]

Background: To evaluate associations between attention-deficit/hyperactivity disorder (ADHD) and comorbid psychiatric disorders using research-identified incident cases of ADHD and population-based controls. Methods: Subjects included a birth cohort of all children born 1976-1982 remaining in Rochester, MN after age five (n = 5,718). Among them we identified 379 ADHD incident cases and 758 age-gender matched non-ADHD controls, passively followed to age 19 years. All psychiatric diagnoses were identified and abstracted, but only those confirmed by qualified medical professionals were included in the analysis. For each psychiatric disorder, cumulative incidence rates for subjects with and without ADHD were estimated using the Kaplan-Meier method. Corresponding hazard ratios (HR) were estimated using Cox models adjusted for gender and mother's age and education at the subject's birth. The association between ADHD and the likelihood of having an internalizing or externalizing disorder was summarized by estimating odds ratios (OR). Results: Attention-deficit/hyperactivity disorder was associated with a significantly increased risk of adjustment disorders (HR = 3.88), conduct/oppositional defiant disorder (HR = 9.54), mood disorders (HR = 3.67), anxiety disorders (HR = 2.94), tic disorders (HR = 6.53), eating disorders (HR = 5.68), personality disorders (HR = 5.80), and substance-related disorders (HR = 4.03). When psychiatric comorbidities were classified on the internalization-externalization dimension, ADHD was strongly associated with coexisting internalizing/externalizing (OR = 10.6), or externalizing-only (OR = 10.0) disorders. Conclusion: This population-based study confirms that children with ADHD are at significantly increased risk for a wide range of psychiatric disorders. Besides treating the ADHD, clinicians should identify and provide appropriate treatment for psychiatric comorbidities. (Contains 1 figure and 4 tables.)