Pikki Lai

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 institutions HF admissions because of limitations in administrative coding and the HRRP HF report inclusion/exclusion criteria.

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 Dashboards 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.

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.

HEART FAILURE READMISSION RISK PREDICTION: EVALUATION ON DIFFERENT APPROACHES FOR PATIENT LEVEL PROFILING OF READMISSION

Two heart failure (HF) readmission models utilize a hierarchy generalized linear model (HGLM) to predicted a patient’s readmission risk. An administrative claims based model is employed by the Center for Medicare and Medicaid Services (CMS) in the Hospital Readmission Reduction Program (HRRP),