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Importance: Present-on-admission (POA) indicators in administrative claims data allow researchers to distinguish between preexisting conditions and those acquired during a hospital stay. The impact of adding POA information to claims-based measures of hospital quality has not yet been investigated to better understand patient underlying risk factors in the International Statistical Classification of Diseases and Related Health Problems, Tenth Revision setting., Objective: To assess POA indicator use on Medicare claims and to assess the hospital- and patient-level outcomes associated with incorporating POA indicators in identifying risk factors for publicly reported outcome measures used by the Centers for Medicare & Medicaid Services (CMS)., Design, Setting, and Participants: This comparative effectiveness study used national CMS claims data between July 1, 2015, and June 30, 2018. Six hospital quality measures assessing readmission and mortality outcomes were modified to include POA indicators in risk adjustment models. The models using POA were then compared with models using the existing complications-of-care algorithm to evaluate changes in risk model performance. Patient claims data were included for all Medicare fee-for-service and Veterans Administration beneficiaries aged 65 years or older with inpatient hospitalizations for acute myocardial infarction, heart failure, or pneumonia within the measurement period. Data were analyzed between September 2019 and March 2020., Main Outcomes and Measures: Changes in patient-level (C statistics) and hospital-level (quintile shifts in risk-standardized outcome rates) model performance after including POA indicators in risk adjustment., Results: Data from a total of 6 027 988 index admissions were included for analysis, ranging from 491 366 admissions (269 209 [54.8%] men; mean [SD] age, 78.2 [8.3] years) for the acute myocardial infarction mortality outcome measure to 1 395 870 admissions (677 158 [48.5%] men; mean [SD] age, 80.3 [8.7] years) for the pneumonia readmission measure. Use of POA indicators was associated with improvements in risk adjustment model performance, particularly for mortality measures (eg, the C statistic increased from 0.728 [95% CI, 0.726-0.730] to 0.774 [95% CI, 0.773-0.776] when incorporating POA indicators into the acute myocardial infarction mortality measure)., Conclusions and Relevance: The findings of this quality improvement study suggest that leveraging POA indicators in the risk adjustment methodology for hospital quality outcome measures may help to more fully capture patients' risk factors and improve overall model performance. Incorporating POA indicators does not require extra effort on the part of hospitals and would be easy to implement in publicly reported quality outcome measures.

Objectives: To characterize hospital phenotypes by their combined utilization pattern of percutaneous coronary interventions (PCI), coronary artery bypass grafting (CABG) procedures, and intensive care unit (ICU) admissions for patients hospitalized for acute myocardial infarction (AMI)., Research Design: Using the Premier Analytical Database, we identified 129,138 hospitalizations for AMI from 246 hospitals with the capacity for performing open-heart surgery during 2010-2013. We calculated year-specific, risk-standardized estimates of PCI procedure rates, CABG procedure rates, and ICU admission rates for each hospital, adjusting for patient clinical characteristics and within-hospital correlation of patients. We used a mixture modeling approach to identify groups of hospitals (ie, hospital phenotypes) that exhibit distinct longitudinal patterns of risk-standardized PCI, CABG, and ICU admission rates., Results: We identified 3 distinct phenotypes among the 246 hospitals: (1) high PCI-low CABG-high ICU admission (39.2% of the hospitals), (2) high PCI-low CABG-low ICU admission (30.5%), and (3) low PCI-high CABG-moderate ICU admission (30.4%). Hospitals in the high PCI-low CABG-high ICU admission phenotype had significantly higher risk-standardized in-hospital costs and 30-day risk-standardized payment yet similar risk-standardized mortality and readmission rates compared with hospitals in the low PCI-high CABG-moderate ICU admission phenotype. Hospitals in these phenotypes differed by geographic region., Conclusions: Hospitals differ in how they manage patients hospitalized for AMI. Their distinctive practice patterns suggest that some hospital phenotypes may be more successful in producing good outcomes at lower cost.

Background: During a myocardial infarction, no single best approach of systemic anticoagulation is recommended, likely due to a lack of comparative effectiveness studies and trade-offs between treatments., Methods and Results: We investigated the patterns of use and site-level variability in anticoagulant strategies (unfractionated heparin [UFH] only, low-molecular-weight heparin [LMWH] only, UFH+LMWH, any bivalirudin) of 63 796 patients with a principal diagnosis of myocardial infarction treated with an early invasive strategy with percutaneous coronary intervention at 257 hospitals. About half (47%) of patients received UFH only, 6% UFH+LMWH, 7% LMWH only, and 40% bivalirudin. Compared with UFH, the median odds ratio was 2.90 for LMWH+UFH, 4.70 for LMWH only, and 3.09 for bivalirudin, indicating that 2 "identical" patients would have a 3- to 4-fold greater likelihood of being treated with anticoagulants other than UFH at one hospital compared with another. We then categorized hospitals as low- or high-users of LMWH and bivalirudin. Using hierarchical, multivariate regression models, we found that low bivalirudin-using hospitals had higher unadjusted bleeding rates, but the risk-adjusted and anticoagulant-adjusted bleeding rates did not differ across the hospital anticoagulation phenotypes. Risk-standardized mortality and risk-standardized length of stay also did not differ across hospital phenotypes., Conclusions: We found substantial site-level variability in the choice of anticoagulants for invasively managed acute myocardial infarction patients, even after accounting for patient factors. No single hospital-use pattern was found to be clinically superior. More studies are needed to determine which patients would derive the greatest benefit from various anticoagulants and to support consistent treatment of patients with the optimal anticoagulant strategy., (© 2015 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley Blackwell.)

Objective: To characterize hospitals based on patterns of their combined financial and clinical outcomes for heart failure hospitalizations longitudinally., Data Source: Detailed cost and administrative data on hospitalizations for heart failure from 424 hospitals in the 2005-2011 Premier database., Study Design: Using a mixture modeling approach, we identified groups of hospitals with distinct joint trajectories of risk-standardized cost (RSC) per hospitalization and risk-standardized in-hospital mortality rate (RSMR), and assessed hospital characteristics associated with the distinct patterns using multinomial logistic regression., Principal Findings: During 2005-2011, mean hospital RSC decreased from $12,003 to $10,782, while mean hospital RSMR declined from 3.9 to 3.2 percent. We identified five distinct hospital patterns: highest cost and low mortality (3.2 percent of the hospitals), high cost and low mortality (20.4 percent), medium cost and low mortality (34.6 percent), medium cost and high mortality (6.2 percent), and low cost and low mortality (35.6 percent). Longer hospital stay and greater use of intensive care unit and surgical procedures were associated with phenotypes with higher costs or greater mortality., Conclusions: Hospitals vary substantially in the joint longitudinal patterns of cost and mortality, suggesting marked difference in value of care. Understanding determinants of the variation will inform strategies for improving the value of hospital care., (© Health Research and Educational Trust.)

Background: Prior claims analyses suggest that the use of intravenous inotropic therapy for patients hospitalized with heart failure varies substantially by hospital. Whether differences in the clinical characteristics of the patients explain observed differences in the use of inotropic therapy is not known., Methods and Results: We sought to characterize institutional variation in inotrope use among patients hospitalized with heart failure before and after accounting for clinical factors of patients. Hierarchical generalized linear regression models estimated risk-standardized hospital-level rates of inotrope use within 209 hospitals participating in Get With The Guidelines-Heart Failure (GWTG-HF) registry between 2005 and 2011. The association between risk-standardized rates of inotrope use and clinical outcomes was determined. Overall, an inotropic agent was administered in 7691 of 126 564 (6.1%) heart failure hospitalizations: dobutamine 43%, dopamine 24%, milrinone 17%, or a combination 16%. Patterns of inotrope use were stable during the 7-year study period. Use of inotropes varied significantly between hospitals even after accounting for patient and hospital characteristics (median risk-standardized hospital rate, 5.9%; interquartile range, 3.7%-8.6%; range, 1.3%-32.9%). After adjusting for case-mix and hospital structural differences, model intraclass correlation indicated that 21% of the observed variation in inotrope use was potentially attributable to random hospital effects (ie, institutional preferences). Hospitals with higher risk-standardized inotrope use had modestly longer risk-standardized length of stay (P=0.005) but had no difference in risk-standardized inpatient mortality (P=0.12)., Conclusions: Use of intravenous inotropic agents during hospitalization for heart failure varies significantly among US hospitals even after accounting for patient and hospital factors.

Background: Despite increasing attention on reducing relatively costly hospital practices while maintaining the quality of care, few studies have examined how hospitals use the intensive care unit (ICU), a high-cost setting, for patients admitted with heart failure (HF). We characterized hospital patterns of ICU admission for patients with HF and determined their association with the use of ICU-level therapies and patient outcomes., Methods and Results: We identified 166 224 HF discharges from 341 hospitals in the 2009-2010 Premier Perspective database. We excluded hospitals with <25 HF admissions, patients <18 years old, and transfers. We defined ICU as including medical ICU, coronary ICU, and surgical ICU. We calculated the percent of patients admitted directly to an ICU. We compared hospitals in the top quartile (high ICU admission) with the remaining quartiles. The median percentage of ICU admission was 10% (interquartile range, 6%-16%; range, 0%-88%). In top-quartile hospitals, treatments requiring an ICU were used less often; the percentage of ICU days receiving mechanical ventilation was 6% for the top quartile versus 15% for the others; noninvasive positive pressure ventilation, 8% versus 19%; vasopressors and/or inotropes, 9% versus 16%; vasodilators, 6% versus 12%; and any of these interventions, 26% versus 51%. Overall HF in-hospital risk-standardized mortality was similar (3.4% versus 3.5%; P=0.2)., Conclusions: ICU admission rates for HF varied markedly across hospitals and lacked association with in-hospital risk-standardized mortality. Greater ICU use correlated with fewer patients receiving ICU interventions. Judicious ICU use could reduce resource consumption without diminishing patient outcomes.

Objectives: This study sought to determine hospital variation in the use of positive inotropic agents in patients with heart failure., Background: Clinical guidelines recommend targeted use of positive inotropic agents in highly selected patients, but data are limited and the recommendations are not specific., Methods: We analyzed data from 376 hospitals including 189,948 hospitalizations for heart failure from 2009 through 2010. We used hierarchical logistic regression models to estimate hospital-level risk-standardized rates of inotrope use and risk-standardized in-hospital mortality rates., Results: The risk-standardized rates of inotrope use ranged across hospitals from 0.9% to 44.6% (median: 6.3%, interquartile range: 4.3% to 9.2%). We identified various hospital patterns based on the type of agents: dobutamine-predominant (29% of hospitals), dopamine-predominant (25%), milrinone-predominant (1%), mixed dobutamine and dopamine pattern (32%), and mixed pattern including all 3 agents (13%). When studying the factors associated with interhospital variation, the best model performance was with the hierarchical generalized linear models that adjusted for patient case mix and an individual hospital effect (receiver operating characteristic curves from 0.77 to 0.88). The intraclass correlation coefficients of the hierarchical generalized linear models (0.113 for any inotrope) indicated that a noteworthy proportion of the observed variation was related to an individual institutional effect. Hospital rates or patterns of use were not associated with differences in length of stay or risk-standardized mortality rates., Conclusions: We found marked differences in the use of inotropic agents for heart failure patients among a diverse group of hospitals. This variability, occurring in the context of little clinical evidence, indicates an urgent need to define the appropriate use of these medications., (Copyright © 2012 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.)

Identifying temporal variation in hospitalization rates may provide insights about disease patterns and thereby inform research, policy, and clinical care. However, the majority of medical conditions have not been studied for their potential seasonal variation. The objective of this study was to apply a data-driven approach to characterize temporal variation in condition-specific hospitalizations. Using a dataset of 34 million inpatient discharges gathered from hospitals in New York State from 2008–2011, we grouped all discharges into 263 clinical conditions based on the principal discharge diagnosis using Clinical Classification Software in order to mitigate the limitation that administrative claims data reflect clinical conditions to varying specificity. After applying Seasonal-Trend Decomposition by LOESS, we estimated the periodicity of the seasonal component using spectral analysis and applied harmonic regression to calculate the amplitude and phase of the condition’s seasonal utilization pattern. We also introduced four new indices of temporal variation: mean oscillation width, seasonal coefficient, trend coefficient, and linearity of the trend. Finally, K-means clustering was used to group conditions across these four indices to identify common temporal variation patterns. Of all 263 clinical conditions considered, 164 demonstrated statistically significant seasonality. Notably, we identified conditions for which seasonal variation has not been previously described such as ovarian cancer, tuberculosis, and schizophrenia. Clustering analysis yielded three distinct groups of conditions based on multiple measures of seasonal variation. Our study was limited to New York State and results may not directly apply to other regions with distinct climates and health burden. A substantial proportion of medical conditions, larger than previously described, exhibit seasonal variation in hospital utilization. Moreover, the application of clustering tools yields groups of clinically heterogeneous conditions with similar seasonal phenotypes. Further investigation is necessary to uncover common etiologies underlying these shared seasonal phenotypes. [ABSTRACT FROM AUTHOR]

Objective Data Sources and Study Setting Study Design Data Collection/Extraction Methods Principal Findings Conclusions To determine the feasibility of integrating Medicare Advantage (MA) admissions into the Centers for Medicare & Medicaid Services (CMS) hospital outcome measures through combining Medicare Advantage Organization (MAO) encounter‐ and hospital‐submitted inpatient claims.Beneficiary enrollment data and inpatient claims from the Integrated Data Repository for 2018 Medicare discharges.We examined timeliness of MA claims, compared diagnosis and procedure codes for admissions with claims submitted both by the hospital and the MAO (overlapping claims), and compared demographic characteristics and principal diagnosis codes for admissions with overlapping claims versus admissions with a single claim.We combined hospital‐ and MAO‐submitted claims to capture MA admissions from all hospitals and identified overlapping claims. For admissions with only an MAO‐submitted claim, we used provider history data to match the National Provider Identifier on the claim to the CMS Certification Number used for reporting purposes in CMS outcome measures.After removing void and duplicate claims, identifying overlapped claims between the hospital‐ and MAO‐submitted datasets, restricting claims to acute care and critical access hospitals, and bundling same admission claims, we identified 5,078,611 MA admissions. Of these, 76.1% were submitted by both the hospital and MAO, 14.2% were submitted only by MAOs, and 9.7% were submitted only by hospitals. Nearly all (96.6%) hospital‐submitted claims were submitted within 3 months after a one‐year performance period, versus 85.2% of MAO‐submitted claims. Among the 3,864,524 admissions with overlapping claims, 98.9% shared the same principal diagnosis code between the two datasets, and 97.5% shared the same first procedure code.Inpatient MA data are feasible for use in CMS claims‐based hospital outcome measures. We recommend prioritizing hospital‐submitted over MAO‐submitted claims for analyses. Monitoring, data audits, and ongoing policies to improve the quality of MA data are important approaches to address potential missing data and errors. [ABSTRACT FROM AUTHOR]