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Background: Poor adherence to oral bisphosphonates is a challenge to treatment and prevention of osteoporosis. The Veterans Health Administration (VA) operates the largest integrated health care system in the United States and offers certain advantages to possibly improve medication adherence. We aimed to determine adherence to weekly alendronate for osteoporosis in Veterans, and investigate predictors and outcomes related to adherence. Methods: A retrospective study cohort was generated from VA databases selecting Veterans who were treated with weekly alendronate. Adherence was measured by medication possession ratio (MPR) and persistence. Two groups were defined as low and high adherence based on MPR <80% or ≥80%, respectively. Regression models were used to investigate predictors of adherence and included clinically relevant covariates. Further regressions were used to investigate the impact of adherence on change in bone mineral density measured by dual energy X-ray absorptiometry and incident fracture. Results: In a cohort of 913 (female/male, 207/706) Veterans, 48% had high adherence in year 1. Distribution for gender, race, and age were similar between the 2 groups, MPR <80% or MPR ≥80%. Baseline fracture [odds ratio OR: 0.64, 95%CI: (0.41, 0.98)], alcohol abuse [0.40 (0.21, 0.74)] and tobacco use [0.44 (0.31, 0.63)] were associated with low adherence in the unadjusted analyses, but only tobacco use [0.45 (0.30, 0.67)] was associated with low adherence after adjustment. Among males, tobacco use was associated with low adherence while prostate cancer predicted high adherence in adjusted models. High adherence was associated with a 30% [hazard ratio HR: 0.70, 95% CI: (0.47, 1.03)] decreased risk of incident fracture in the whole cohort, and a 40% [0.60 (0.38, 0.95)] decrease risk in males. Conclusion: Year one adherence to weekly alendronate was a relevant determinant to long-term clinical outcomes including changes in bone mineral density and incident fracture in Veterans.

Background: Left atrial hypertension results in pulmonary hypertension which is a frequent finding in the setting of left ventricular failure. We hypothesized that noninvasive measurement of systolic pulmonary artery pressure (SPAP) by echocardiography could be used to estimate the risk of hospital readmission in patients with congestive heart failure (CHF). Methods: We conducted a retrospective cohort study on patients discharged from a major urban hospital between June 2009 and October 2009 with a primary diagnosis of CHF. Noninvasive assessment of SPAP by 2D echocardiography performed within 6 months preceding hospital discharge was among the inclusion criteria. A SPAP of 40 mmHg or greater in addition to other factors potentially predictive of re-hospitalization were included in a univariable cox-regression analysis for predicting all-cause readmission. Variables with p-values less than 0.25 were allowed to participate in a stepwise cox-regression model which identified all statistically significant predictors within a mutlivariable setting. Results: 145 patients participated in the stepwise cox-regression analysis. The mean SPAP (mmHg) was 47 with a median of 46. The following statistically significant predictors were identified: hemoglobin nadir < 10 g/dl (HR 1.63; 95% CI 1.07-2.48; P = 0.02), requirement of skilled nursing care after discharge (HR 1.73; 95% CI 1.14-2.63; P = 0.011), diagnosis of COPD (HR 1.62; 95% CI 1.04-2.51; P = 0.03), diagnosis of ESRD (HR 2.05; 95% CI 1.18-3.55; P = 0.01), use of cardiac resynchronization therapy (HR 4.71; 95% CI 1.95-11.35; P < 0.001) and SPAP of 40 or above (HR 2.03; 95% CI 1.30-3.18; P = 0.002). Conclusions: Previous limited studies have suggested that pulmonary hypertension is a prognostic marker in patients with cardiomyopathy. Our data suggests that noninvasive measurement of SPAP by echocardiography can help to predict the risk of hospital readmission in patients hospitalized for CHF. A systolic PAP of 40 mmHg or above appears to be a stronger predictor of re-hospitalization when compared to traditional risk factors including COPD, ESRD and anemia.

COVID-19 was declared a pandemic on 03/11/2020 by the WHO and the State of Minnesota issued a stay-at-home mandate on 03/27/2020. While individuals with diabetes mellitus (DM) and severe obesity were identified as more likely to be infected and at higher risk of complications from COVID-19, access to outpatient clinics was limited during this time, with a shift towards telemedicine. In June 2020, the WHO reported that in 49% of 155 surveyed countries, diabetes treatment was disrupted. However, the impact on diabetes care in the US was poorly characterized, particularly among underserved groups. Aim To study the impact of the pandemic and telemedicine on medical treatment of patients with DM at Hennepin County Medical Center (HCMC), a teaching hospital in Minneapolis, Minnesota serving a diverse inner-city population. Methods Retrospective cohort study design with 710 participants. We compared A1C and weight changes before and after the pandemic. The study group (n=344) included patients, 18 years of age or older, with DM Type 1 or Type 2 who presented for an HCMC diabetes clinic visit between 12/2019-02/2020 and a follow up visit between 6/2020 -10/2020. The control group (n=366) included patients with DM who presented for an HCMC diabetes clinic visit between 12/2018 - 02/2019 and a follow up visit between 6/2019 - 10/2019. Results There was a significantly lower A1C reduction in the cases as compared to the control group (-0.34 [CI -0.59, -0. 09] p value 0. 007), adjusted for inpatient admission, continuous glucose monitor use, diabetes type 1, insurance status, insulin use, GLP-1 agonist use, smoking status and hypertension. Weight reduction during the pandemic was lower as compared to the control but did not achieve statistical significance (-0.96 [CI -3. 02, 1. 09] p value 0.36). Telemedicine use among the study group resulted in an insignificant A1c reduction of -0.24 (p value 0.23). There was statistically significant weight gain of 0.42 among the group who used telemedicine, while nonusers had weight loss of -3.24 (p value 0. 04). Telemedicine users had higher insulin utilization (92.6% vs 83.3%, p value 0. 017) and lower self-pay and Medicaid enrollment (1.7% vs 5.9% and 33.8% vs 40.6% respectively, p value 0. 011) as compared to nonusers. Conclusion The COVID-19 pandemic led to a significant deterioration of glycemic control but had no effect on weight. Telemedicine use led to A1c reduction but did not reach statistical significance potentially due to the small sample size and short duration of follow up. Significant weight gain was observed in telemedicine users influenced by greater insulin use and insurance coverage as compared to weight loss amongst nonusers which may be attributed to socioeconomic barriers such as food scarcity among those without the means to access telemedicine. Presentation: Sunday, June 12, 2022 12:30 p.m. - 2:30 p.m.

Background: Readmission within 30 days of a prior hospitalization for congestive heart failure (CHF) has emerged as a major marker of quality improvement and payment reform. Whereas a number of interventions are available to improve outcomes and reduce readmission rates, there remains an urgent need for accurate risk stratification tools that could be used to build cost-effective and personalized CHF management programs. Methods: We conducted a retrospective cohort study on patients discharged from a major urban hospital between June 2009 and October 2009 with a primary diagnosis of CHF. Patients were followed until their first all-cause hospital readmission. We made minor modifications to 4 different readmission risk scores in order to assess and compare their ability to estimate the risk of hospital readmission at 30 days from discharge. Three of the scores (Chin and Goldman 11 point scoring, Philbin and DiSalvo 15 point scoring and Krumholz et al 4 point scoring) were retrieved from reviewing the literature. The fourth tool (Henry Ford score) is an internal non-validated computed calculator based on 8 predictive factors, previously derived within our institution. The scores were compared by logistic regression and area under the ROC curve. Results: 285 patients were included in the study. The readmission rate at 30 days was 23.5% (67 of 285). A logistic regression revealed that both Chin and Goldman and Philbin scores were poor and statistically non-significant predictors of 30-day all-cause readmission (p-values = 0.784 and 0.165 respectively). When compared by logistic regression and area under the ROC curve, Henry Ford tool (p-value = 0.003; AUC = 0.64) performed the best followed by the Krumholz score (p-value = 0.017; AUC = 0.60). Conclusions: In an era where there is an urgent need to improve and personalize heart failure management programs, simple scores can be used to stratify patients according to their risk of re-hospitalization. Krumholz and Henry Ford scores are both accurate tools for the prediction of 30-day all-cause readmission. Krumholz score relies on 4 easily retrievable risk factors while the Henry Ford tool is a computed model that integrates 8 different factors.