Body mass index and all-cause readmissions following acute heart failure hospitalization.

BACKGROUND/OBJECTIVES: Obesity is associated with a lower mortality risk among patients with heart failure (HF). Whether this obesity paradox applies to all-cause hospitalizations is unknown. We aimed to investigate the association between body mass index (BMI) and 30-day all-cause readmissions following HF hospitalization. SUBJECTS/METHODS: We retrospectively evaluated 2252 HF hospital admissions of Centers of Medicare Services beneficiaries from an academic medical center. We classified obesity using established BMI categories. All 30-day postdischarge readmission to all hospitals and mortality events were documented. We evaluated 30-day postdischarge unplanned, all-cause readmission and death in the total cohort, propensity-matched cohort, and by ejection fraction (EF). RESULTS: An Overweight-Obese BMI (BMI ≥ 25 kg/m2) was paradoxically associated with a lower mortality rate than a Normal BMI (18.5-24.9 kg/m2) (5.0% vs 8.5%, p = 0.0018). In contrast, an Overweight-Obese BMI was associated with a 29% (95% CI: 1.03-1.63) increased relative risk of all-cause readmission compared with a Normal BMI (23.2% vs 18.9%, p = 0.0288), which was consistent across obesity severity subgroups. Among 966 matched admissions, an Overweight-Obese BMI retained higher readmission risk compared with a Normal BMI (25.1% vs 17.2%, p = 0.003). After matching, readmissions remained higher for Overweight-Obese vs Normal BMI in admissions with reduced EF (25.7% vs 17.8%, p = 0.032) and preserved EF (23.0% vs 15.0%, p = 0.048). No difference in the percentage of readmissions for HF (40%) or noncardiovascular causes (45%) existed between Overweight-Obese and Normal BMI groups. CONCLUSIONS: Despite a lower mortality risk, increased BMI is associated with increased all-cause hospital readmission rates in an elderly HF population which persists after propensity matching.

Validation of an automated electronic algorithm and "dashboard" to identify and characterize decompensated heart failure admissions across a medical center.

BACKGROUND: We aim to validate the diagnostic performance of the first fully automatic, electronic heart failure (HF) identification algorithm and evaluate the implementation of an HF Dashboard system with 2 components: real-time identification of decompensated HF admissions and accurate characterization of disease characteristics and medical therapy. METHODS: We constructed an HF identification algorithm requiring 3 of 4 identifiers: B-type natriuretic peptide >400 pg/mL; admitting HF diagnosis; history of HF International Classification of Disease, Ninth Revision, diagnosis codes; and intravenous diuretic administration. We validated the diagnostic accuracy of the components individually (n = 366) and combined in the HF algorithm (n = 150) compared with a blinded provider panel in 2 separate cohorts. We built an HF Dashboard within the electronic medical record characterizing the disease and medical therapies of HF admissions identified by the HF algorithm. We evaluated the HF Dashboard's performance over 26 months of clinical use. RESULTS: Individually, the algorithm components displayed variable sensitivity and specificity, respectively: B-type natriuretic peptide >400 pg/mL (89% and 87%); diuretic (80% and 92%); and International Classification of Disease, Ninth Revision, code (56% and 95%). The HF algorithm achieved a high specificity (95%), positive predictive value (82%), and negative predictive value (85%) but achieved limited sensitivity (56%) secondary to missing provider-generated identification data. The HF Dashboard identified and characterized 3147 HF admissions over 26 months. CONCLUSIONS: Automated identification and characterization systems can be developed and used with a substantial degree of specificity for the diagnosis of decompensated HF, although sensitivity is limited by clinical data input.

Utilization of galectin-3 in case management across the spectrum of heart failure.

In patients with heart failure as a result of mechanical and neurohormonal derangements, macrophages secrete galectin-3, which is a paracrine and endocrine factor that stimulates additional macrophages, pericytes, myofibroblasts, and fibroblasts to proliferate and secrete procollagen I, which is irreversibly crosslinked to form fibrotic collagen. Normal plasma concentrations of galectin-3 are < 11.0 ng/mL. Galectin-3 measured in blood has been shown to predict the development of all-cause mortality and heart failure in the general population, identify increased risk for de novo heart failure and progressive loss of renal filtration function in healthy middle-aged adults, predict cardiac failure in patients after acute coronary syndromes, help establish the diagnosis of heart failure with preserved ejection fraction in patients presenting with effort intolerance, and aid in the prognosis of both systolic and nonsystolic heart failure for the outcomes of hospitalization and death. This article presents case discussions of these applications to highlight the importance of galectin-3 measurement across the spectrum of patients at risk for cardiorenal disease.

Change in admission blood glucose from chronic glycemic status in acute heart failure hospitalization and 30-day outcomes: A retrospective analysis.

BACKGROUND: Admission blood glucose (BG) has demonstrated contradictory association with 30-day mortality in acute heart failure (AHF) hospitalization. To explore these contradictory findings, we aimed to determine if admission BG reflects an acute change from chronic glucose control and investigate the association between the admission and chronic BG change (ΔBG) with 30-day mortality in AHF. METHODS: We analyzed patients (n = 1045) age ≥ 65 with Centers of Medicare Services benefits and known 30-day all-cause mortality hospitalized with AHF at an academic medical center from 2009 to 2016. We included diabetic (n = 736) and non-diabetic (n = 309) patients with recent Hemoglobin A1c (HbA1c). We defined ΔBG as the difference in the admission and chronic BG, calculated from HbA1c. RESULTS: Admission BG was 126 (101, 167) mg/dl and was moderately elevated (≥170 mg/dl) in 25% of admissions. The median (IQR) ΔBG was -7 (-29, 26) mg/dl, with 74% of all admissions (66% diabetic, 92% non-diabetic) presenting within ±50 mg/dl of the chronic BG. Admission BG was not associated with mortality. ΔBG > +100 mg/dl displayed increased 30-day mortality (18.6% vs 6.9%, p < 0.001) compared to -26 to +25 mg/dl. When admission BG was >200 mg/dl (n = 166), a ΔBG > +100 mg/dl was present in 77% of those deceased vs 31% of those alive at 30 days (p = 0.003; positive likelihood ratio = 5.7). ΔBG > +100 mg/dl was strongly associated with 30-day mortality risk (OR 6.4, 95% CI 2.3-18.9; p = 0.0005) after multivariate adjustment. CONCLUSIONS: Admission BG predominantly reflects chronic glycemic status. Increased change in admission from chronic BG was associated with increased 30-day mortality.

Customizing national models for a medical center's population to rapidly identify patients at high risk of 30-day all-cause hospital readmission following a heart failure hospitalization.

BACKGROUND: Nationally-derived models predicting 30-day readmissions following heart failure (HF) hospitalizations yield insufficient discrimination for institutional use. OBJECTIVE: Develop a customized readmission risk model from Medicare-employed and institutionally-customized risk factors and compare the performance against national models in a medical center. METHODS: Medicare patients age ≥ 65 years hospitalized for HF (n = 1,454) were studied in a derivation cohort and in a separate validation cohort (n = 243). All 30-day hospital readmissions were documented. The primary outcome was risk discrimination (c-statistic) compared to national models. RESULTS: A customized model demonstrated improved discrimination (c-statistic 0.72; 95% CI 0.69 - 0.74) compared to national models (c-statistics of 0.60 and 0.61) with a c-statistic of 0.63 in the validation cohort. Compared to national models, a customized model demonstrated superior readmission risk profiling by distinguishing a high-risk (38.3%) from a low-risk (9.4%) quartile. CONCLUSIONS: A customized model improved readmission risk discrimination from HF hospitalizations compared to national models.

Centers for Medicare and Medicaid Services' readmission reports inaccurately describe an institution's decompensated heart failure admissions.

Hospitals typically use Center for Medicare and Medicaid Services' (CMS) Hospital Readmission Reduction Program (HRRP) administrative reports as the standard of heart failure (HF) admission quantification. We aimed to evaluate the HF admission population identified by CMS HRRP definition of HF hospital admissions compared with a clinically based HF definition. We evaluated all hospital admissions at an academic medical center over 16 months in patients with Medicare fee-for service benefits and age ≥65 years. We compared the CMS HRRP HF definition against an electronic HF identification algorithm. Admissions identified solely by the CMS HF definition were manually reviewed by HF providers. Admissions confirmed with having decompensated HF as the primary problem by manual review or by the HF ID algorithm were deemed "HF positive," whereas those refuted were "HF negative." Of the 1672 all-cause admissions evaluated, 708 (42%) were HF positive. The CMS HF definition identified 440 admissions: sensitivity (54%), specificity (94%), positive predictive value (87%), negative predictive value (74%). The CMS HF definition missed 324 HF admissions because of inclusion/exclusion criteria (15%) and decompensated HF being a secondary diagnosis (85%). The CMS HF definition falsely identified 56 admissions as HF. The most common admission reasons in this cohort included elective pacemaker or defibrillator implantations (n = 13), noncardiac dyspnea (n = 9), left ventricular assist device complications (n = 8), and acute coronary syndrome (n = 6). The CMS HRRP HF report is a poor representation of an institution's HF admissions because of limitations in administrative coding and the HRRP HF report inclusion/exclusion criteria.