Patterns of Hospital Performance in Acute Myocardial Infarction and Heart Failure 30-Day Mortality and Readmission

Krumholz, Harlan M.; Merrill, Angela; Schöne, Eric; Schreiner, Geoffrey C.; Chen, Jersey; Bradley, Elizabeth H.; Wang, Yun; Wang, Yongfei; Lin, Zhenqiu; Straube, Barry M.; Rapp, Michael; Normand, Sharon‐Lise T.; Drye, Elizabeth E.

doi:10.1161/circoutcomes.109.883256

Cited by 511 publications

(371 citation statements)

References 12 publications

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“…First, in this overall cohort of >700 000 STEMI patients, the 30‐day readmission rate was 12.3%, lower than the previously reported rate of ≈20% 8. The median length of index hospitalization was short (2.5 days) and median length of readmission stay was 2.6 days, while index admission in‐hospital mortality was 8.7% and readmission in‐hospital mortality was 4.6%.…”

Section: Discussioncontrasting

confidence: 56%

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Thirty‐Day Readmission Rates, Timing, Causes, and Costs after ST‐Segment–Elevation Myocardial Infarction in the United States: A National Readmission Database Analysis 2010–2014

Kim

Yeo

Cheung

et al. 2018

JAHA

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BackgroundReadmission after ST‐segment–elevation myocardial infarction (STEMI) poses an enormous economic burden to the US healthcare system. Efforts to prevent readmissions should be based on understanding the timing and causes of these readmissions. This study aimed to investigate contemporary causes, timing, and cost of 30‐day readmissions after STEMI.Methods and ResultsAll STEMI hospitalizations were selected in the Nationwide Readmissions Database (NRD) from 2010 to 2014. The 30‐day readmission rate as well as the primary cause and cost of readmission were examined. Multivariate regression analysis was performed to identify the predictors of 30‐day readmission and increased cumulative cost. From 2010 to 2014, the 30‐day readmission rate after STEMI was 12.3%. Within 7 days of discharge, 43.9% were readmitted, and 67.3% were readmitted within 14 days. The annual rate of 30‐day readmission decreased by 19% from 2010 to 2014 (P<0.001). Female sex, AIDS, anemia, chronic kidney disease, collagen vascular disease, diabetes mellitus, hypertension, pulmonary hypertension, congestive heart failure, atrial fibrillation, and increased length of stay were independent predictors of 30‐day readmission. A large proportion of patients (41.6%) were readmitted for noncardiac reasons. After multivariate adjustment, 30‐day readmission was associated with a 47.9% increase in cumulative cost (P<0.001).ConclusionsTwo thirds of patients were readmitted within the first 14 days after STEMI, and a large proportion of patients were readmitted for noncardiac reasons. Thirty‐day readmission was associated with an ≈50% increase in cumulative hospitalization costs. These findings highlight the importance of closer surveillance of both cardiac and general medical conditions in the first several weeks after STEMI discharge.

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Section: Discussioncontrasting

confidence: 56%

“…In July 2009, the Centers for Medicare & Medicaid Services began reporting 30‐day readmission for 3 common medical conditions, one of which was acute myocardial infarction 8. These measures have become part of a federal strategy to provide incentives to improve quality of care by reducing preventable readmissions 9.…”

Section: Introductionmentioning

confidence: 99%

Thirty‐Day Readmission Rates, Timing, Causes, and Costs after ST‐Segment–Elevation Myocardial Infarction in the United States: A National Readmission Database Analysis 2010–2014

Kim

Yeo

Cheung

et al. 2018

JAHA

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“…In comparison, greater variation has been observed for currently penalized conditions, such as heart failure 25. As such, programs designed to penalize institutional outliers, similar to the mandate of the Hospital Readmissions Reduction Program, may have less of an impact on improving outcomes for this disease process.…”

Section: Discussionmentioning

confidence: 99%

High Burden of 30‐Day Readmissions After Acute Venous Thromboembolism in the United States

Secemsky

Rosenfield

Kennedy

et al. 2018

JAHA

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BackgroundVenous thromboembolism (VTE) is the third leading cause of vascular disease and accounts for $10 billion in annual US healthcare costs. The nationwide burden of 30‐day readmissions after such events has not been comprehensively assessed.Methods and ResultsWe analyzed adults ≥18 years of age with hospitalizations associated with acute VTE between January 1, 2010, and December 31, 2014, in the Nationwide Readmissions Database. International Classification of Disease, Ninth Revision, Clinical Modification (ICD‐9‐ CM) codes were used to identify hospitalizations associated with acute pulmonary embolism or deep vein thrombosis. The primary outcome was the rate of unplanned 30‐day readmission. Hierarchical logistic regression was used to calculate hospital‐specific 30‐day risk‐standardized readmission rates, a marker of healthcare quality. Among 1 176 335 hospitalizations with acute VTE, in‐hospital death occurred in 6.2%. VTE was associated with malignancy in 19.7%, recent surgery in 19.3%, recent trauma in 4.6%, hypercoagulability in 3.3%, and pregnancy in 1.0%. Among survivors to discharge, the 30‐day readmission rate was 17.5%, with no significant difference in rates across study years (17.4%–17.7%; P=0.10 for trend). Major predictors of readmission were malignancy (relative risk, 1.49, 95% confidence interval 1.47‐1.50), Medicaid insurance (relative risk, 1.48, 95% confidence interval 1.46‐1.50), and nonelective index admission (relative risk, 1.31, 95% confidence interval 1.29‐1.33). Top causes of readmission included sepsis (9.6%) and procedural complications (8.1%). Median rehospitalization costs were $9781.7 (interquartile range, $5430.7–$18 784.1), and 8.1% died during readmission. The interquartile range in risk‐standardized readmission rates was 16.6% to 18.3%, suggesting modest interhospital heterogeneity in readmission risk.ConclusionsNearly 1 in 5 patients with acute VTE were readmitted within 30 days. Predictors and causes of readmission were primarily related to patient characteristics and complications from comorbid conditions, whereas healthcare quality had a moderate impact on readmission risk.

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“…Krumholz et al developed and validated an administrative claims–based risk‐adjustment model for HF with the purpose of characterizing hospital quality. This model, which is used by the Centers for Medicare and Medicaid Services (CMS) to compare mortality rates across hospitals for public reporting purposes, produces results that are very similar to medical records–based models 10, 11, 19, 20. There are several key differences between our model and the CMS model.…”

Section: Discussionmentioning

confidence: 90%

Derivation and Validation of an In‐Hospital Mortality Prediction Model Suitable for Profiling Hospital Performance in Heart Failure

Lagu

Pekow

Stefan

et al. 2018

JAHA

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BackgroundComparing heart failure (HF) outcomes across hospitals requires adequate risk adjustment. We aimed to develop and validate a model that can be used to compare quality of HF care across hospitals.Methods and ResultsWe included patients with HF aged ≥18 years admitted to one of 433 hospitals that participated in the Premier Inc Data Warehouse. This model (Premier) contained patient demographics, comorbidities, and acute conditions present on admission, derived from administrative and billing records. In a separate data set derived from electronic health records, we validated the Premier model by comparing hospital risk‐standardized mortality rates calculated with the Premier model to those calculated with a validated clinical model containing laboratory data (LAPS [Laboratory‐Based Acute Physiology Score]). Among the 200 832 admissions in the Premier Inc Data Warehouse, inpatient mortality was 4.0%. The model showed acceptable discrimination in the warehouse data (C statistic 0.75; 95% confidence interval, 0.74–0.76). In the validation data set, both the Premier model and the LAPS models showed acceptable discrimination (C statistic: Premier: 0.76 [95% confidence interval, 0.74–0.77]; LAPS: 0.78 [95% confidence interval, 0.76–0.80]). Risk‐standardized mortality rates for both models ranged from 2% to 7%. A linear regression equation describing the association between Premier‐ and LAPS‐specific mortality rates revealed a regression line with a slope of 0.71 (SE: 0.07). The correlation coefficient of the standardized mortality rates from the 2 models was 0.82.ConclusionsCompared with a validated model derived from clinical data, an HF mortality model derived from administrative data showed highly correlated risk‐standardized mortality rate estimates, suggesting it could be used to identify high‐ and low‐performing hospitals for HF care.

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Patterns of Hospital Performance in Acute Myocardial Infarction and Heart Failure 30-Day Mortality and Readmission

Abstract: Background-In 2009, the Centers for Medicare & Medicaid Services is publicly reporting hospital-level risk-standardized 30-day mortality and readmission rates after acute myocardial infarction (AMI) and heart failure (HF

Cited by 511 publications

References 12 publications

Thirty‐Day Readmission Rates, Timing, Causes, and Costs after ST‐Segment–Elevation Myocardial Infarction in the United States: A National Readmission Database Analysis 2010–2014

Thirty‐Day Readmission Rates, Timing, Causes, and Costs after ST‐Segment–Elevation Myocardial Infarction in the United States: A National Readmission Database Analysis 2010–2014

High Burden of 30‐Day Readmissions After Acute Venous Thromboembolism in the United States

Derivation and Validation of an In‐Hospital Mortality Prediction Model Suitable for Profiling Hospital Performance in Heart Failure

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