BACKGROUND-Small studies suggest that telemonitoring may improve heart-failure outcomes, but its effect in a large trial has not been established.

Background-Readmission soon after hospital discharge is an expensive and often preventable event for patients with heart failure. We present a model approved by the National Quality Forum for the purpose of public reporting of hospital-level readmission rates by the Centers for Medicare & Medicaid Services. Methods and Results-We developed a hierarchical logistic regression model to calculate hospital risk-standardized 30-day all-cause readmission rates for patients hospitalized with heart failure. The model was derived with the use of Medicare claims data for a 2004 cohort and validated with the use of claims and medical record data. The unadjusted readmission rate was 23.6%. The final model included 37 variables, had discrimination ranging from 15% observed 30-day readmission rate in the lowest predictive decile to 37% in the upper decile, and had a c statistic of 0.60. The 25th and 75th percentiles of the risk-standardized readmission rates across 4669 hospitals were 23.1% and 24.0%, with 5th and 95th percentiles of 22.2% and 25.1%, respectively. The odds of all-cause readmission for a hospital 1 standard deviation above average was 1.30 times that of a hospital 1 standard deviation below average. State-level adjusted readmission rates developed with the use of the claims model are similar to rates produced for the same cohort with the use of a medical record model (correlation, 0.97; median difference, 0.06 percentage points). Conclusions-This claims-based model of hospital risk-standardized readmission rates for heart failure patients produces estimates that may serve as surrogates for those derived from a medical record model. (Circ Cardiovasc Qual Outcomes.

Several specific hospital strategies are associated with a significant reduction in the door-to-balloon time in the management of myocardial infarction with ST-segment elevation.

Background-A model using administrative claims data that is suitable for profiling hospital performance for acute myocardial infarction would be useful in quality assessment and improvement efforts. We sought to develop a hierarchical regression model using Medicare claims data that produces hospital risk-standardized 30-day mortality rates and to validate the hospital estimates against those derived from a medical record model. Methods and Results-For hospital estimates derived from claims data, we developed a derivation model using 140 120 cases discharged from 4664 hospitals in 1998. For the comparison of models from claims data and medical record data, we used the Cooperative Cardiovascular Project database. To determine the stability of the model over time, we used annual Medicare cohorts discharged in 1995, 1997, and 1999 -2001. The final model included 27 variables and had an area under the receiver operating characteristic curve of 0.71. In a comparison of the risk-standardized hospital mortality rates from the claims model with those of the medical record model, the correlation coefficient was 0.90 (SEϭ0.003).The slope of the weighted regression line was 0.95 (SEϭ0.007), and the intercept was 0.008 (SEϭ0.001), both indicating strong agreement of the hospital estimates between the 2 data sources. The median difference between the claims-based hospital risk-standardized mortality rates and the chart-based rates was Ͻ0.001 (25th and 75th percentiles, Ϫ0.003 and 0.003). The performance of the model was stable over time. Conclusions-This administrative claims-based model for profiling hospitals performs consistently over several years and produces estimates of risk-standardized mortality that are good surrogates for estimates from a medical record model.

Background-A model using administrative claims data that is suitable for profiling hospital performance for heart failure would be useful in quality assessment and improvement efforts. Methods and Results-We developed a hierarchical regression model using Medicare claims data from 1998 that produces hospital risk-standardized 30-day mortality rates. We validated the model by comparing state-level standardized estimates with state-level standardized estimates calculated from a medical record model. To determine the stability of the model over time, we used annual Medicare cohorts discharged in 1999 -2001. The final model included 24 variables and had an area under the receiver operating characteristic curve of 0.70. In the derivation set from 1998, the 25th and 75th percentiles of the risk-standardized mortality rates across hospitals were 11.6% and 12.8%, respectively. The 95th percentile was 14.2%, and the 5th percentile was 10.5%. In the validation samples, the 5th and 95th percentiles of risk-standardized mortality rates across states were 9.9% and 13.9%, respectively. Correlation between risk-standardized state mortality rates from claims data and rates derived from medical record data was 0.95 (SEϭ0.015). The slope of the weighted regression line from the 2 data sources was 0.76 (SEϭ0.04) with intercept of 0.03 (SEϭ0.004). The median difference between the claims-based state risk-standardized estimates and the chart-based rates was Ͻ0.001 (25th percentileϭϪ0.003; 75th percentileϭ0.002).

Background National attention has increasingly focused on readmission as a target for quality improvement. We present the development and validation of a model approved by the National Quality Forum and used by the Centers for Medicare & Medicaid Services for hospital-level public reporting of risk-standardized readmission rates for patients discharged from the hospital after an acute myocardial infarction. Methods and Results We developed a hierarchical logistic regression model to calculate hospital risk-standardized 30-day all-cause readmission rates for patients hospitalized with acute myocardial infarction. The model was derived using Medicare claims data for a 2006 cohort and validated using claims and medical record data. The unadjusted readmission rate was 18.9%. The final model included 31 variables and had discrimination ranging from 8% observed 30-day readmission rate in the lowest predictive decile to 32% in the highest decile and a C statistic of 0.63. The 25th and 75th percentiles of the risk-standardized readmission rates across 3890 hospitals were 18.6% and 19.1%, with fifth and 95th percentiles of 18.0% and 19.9%, respectively. The odds of all-cause readmission for a hospital 1 SD above average were 1.35 times that of a hospital 1 SD below average. Hospital-level adjusted readmission rates developed using the claims model were similar to rates produced for the same cohort using a medical record model (correlation, 0.98; median difference, 0.02 percentage points). Conclusions This claims-based model of hospital risk-standardized readmission rates for patients with acute myocardial infarction produces estimates that are excellent surrogates for those produced from a medical record model.

Whole-genome sequencing to characterize monogenic and polygenic contributions in patients hospitalized with early-onset myocardial infarction The MIT Faculty has made this article openly available. Please share how this access benefits you. Your story matters. Citation Khera, Amit V., et al., "Whole-genome sequencing to characterize monogenic and polygenic contributions in patients hospitalized with early-onset myocardial infarction."

Background-Depression is an established independent prognostic factor for mortality, readmission, and cardiac events after CABG surgery. However, limited data exist on whether depression influences functional outcomes after CABG. Methods and Results-We followed 963 patients who underwent first CABG between February 1999 and February 2001. At baseline and at 6 months after CABG, we interviewed patients to assess depressive symptoms using the Geriatric Depression Scale (GDS) and physical function using the Short Form-36 Physical Component Scale (PCS). The patient's physical function was considered improved if the PCS score increased Ն5 points at 6 months. Patients with high GDS scores were younger, were more often female, and had worse physical function and higher comorbidity than patients with low GDS scores. Rates of improvement in physical function were 60.1% for a GDS score Ͻ5 (below 75th percentile), 49.8% for a GDS score between 5 and 9 (75th to 90th percentile), and 39.7% for a GDS score Ն10 (Ն90th percentile; Pϭ0.002 for the trend). Depressive symptoms remained a significant independent predictor of lack of functional improvement after adjustment for severity of coronary artery disease, angina class, baseline PCS score, and medical history. A GDS score Ն10 was a stronger inverse risk factor for functional improvement after CABG than such traditional measures of disease severity as previous myocardial infarction, heart failure on admission, history of diabetes, and left ventricular ejection fraction. Conclusions-Higher levels of depressive symptoms at the time of CABG are a strong risk factor for lack of functional benefits 6 months after CABG.