Jennifer A. Mattera

BACKGROUND: Small studies suggest that telemonitoring may improve heart-failure outcomes, but its effect in a large trial has not been established. METHODS: We randomly assigned 1653 patients who had recently been hospitalized for heart failure to undergo either telemonitoring (826 patients) or usual care (827 patients). Telemonitoring was accomplished by means of a telephone-based interactive voice-response system that collected daily information about symptoms and weight that was reviewed by the patients' clinicians. The primary end point was readmission for any reason or death from any cause within 180 days after enrollment. Secondary end points included hospitalization for heart failure, number of days in the hospital, and number of hospitalizations. RESULTS: The median age of the patients was 61 years; 42.0% were female, and 39.0% were black. The telemonitoring group and the usual-care group did not differ significantly with respect to the primary end point, which occurred in 52.3% and 51.5% of patients, respectively (difference, 0.8 percentage points; 95% confidence interval [CI], -4.0 to 5.6; P=0.75 by the chi-square test). Readmission for any reason occurred in 49.3% of patients in the telemonitoring group and 47.4% of patients in the usual-care group (difference, 1.9 percentage points; 95% CI, -3.0 to 6.7; P=0.45 by the chi-square test). Death occurred in 11.1% of the telemonitoring group and 11.4% of the usual care group (difference, -0.2 percentage points; 95% CI, -3.3 to 2.8; P=0.88 by the chi-square test). There were no significant differences between the two groups with respect to the secondary end points or the time to the primary end point or its components. No adverse events were reported. CONCLUSIONS: Among patients recently hospitalized for heart failure, telemonitoring did not improve outcomes. The results indicate the importance of a thorough, independent evaluation of disease-management strategies before their adoption. (Funded by the National Heart, Lung, and Blood Institute; ClinicalTrials.gov number, NCT00303212.).

BACKGROUND: Prompt reperfusion treatment is essential for patients who have myocardial infarction with ST-segment elevation. Guidelines recommend that the interval between arrival at the hospital and intracoronary balloon inflation (door-to-balloon time) during primary percutaneous coronary intervention should be 90 minutes or less. However, few hospitals meet this objective. We sought to identify hospital strategies that were significantly associated with a faster door-to-balloon time. METHODS: We surveyed 365 hospitals to determine whether each of 28 specific strategies was in use. We used hierarchical generalized linear models and data on patients from the Centers for Medicare and Medicaid Services to determine the association between hospital strategies and the door-to-balloon time. RESULTS: In multivariate analysis, six strategies were significantly associated with a faster door-to-balloon time. These strategies included having emergency medicine physicians activate the catheterization laboratory (mean reduction in door-to-balloon time, 8.2 minutes), having a single call to a central page operator activate the laboratory (13.8 minutes), having the emergency department activate the catheterization laboratory while the patient is en route to the hospital (15.4 minutes), expecting staff to arrive in the catheterization laboratory within 20 minutes after being paged (vs. >30 minutes) (19.3 minutes), having an attending cardiologist always on site (14.6 minutes), and having staff in the emergency department and the catheterization laboratory use real-time data feedback (8.6 minutes). Despite the effectiveness of these strategies, only a minority of hospitals surveyed were using them. CONCLUSIONS: 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: 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.

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 (SE=0.003). The slope of the weighted regression line was 0.95 (SE=0.007), and the intercept was 0.008 (SE=0.001), 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 <0.001 (25th and 75th percentiles, -0.003 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.