An administrative claims model suitable for profiling hospital performance based on 30-day mortality rates among patients with heart failure

Testing the Feasibility of a Cross-Setting Measure to Address the Rising Trend in Hospital Outpatient TJA Procedures

BACKGROUND

Elective primary total hip and total knee arthroplasty (collectively, total joint arthroplasties [TJAs]) are commonly performed procedures that can reduce pain and improve function. TJAs are generally safe, but complications can occur. Although historically performed as inpatient procedures, TJAs are increasingly being performed in the outpatient setting. We sought to develop a scientifically acceptable cross-setting measure for evaluating care quality across inpatient and outpatient settings.

METHODS

Using Medicare administrative claims and enrollment data for qualifying TJA patients, we respecified the Centers for Medicare & Medicaid Services (CMS) inpatient-only risk-standardized TJA complications measure to assess complication rates following elective primary TJAs performed in an inpatient or outpatient setting. We aligned inpatient and outpatient coding practices and used hierarchical logistic regression to calculate hospital-specific, risk-standardized complication rates (RSCRs). Lower rates correspond to better quality. Using accepted approaches for CMS measures, we tested measure reliability and vetted key measure decisions with patient and provider input.

RESULTS

A single combined model including the procedure setting as a risk variable produced the highest discrimination (C-statistic for a single combined model with a setting indicator: 0.664, C-statistic for the inpatient-only model: 0.651, C-statistic for the outpatient-only model: 0.638). Among the 2,747 hospitals with at least 25 TJAs, the mean RSCR (using the combined model with a setting indicator) was 2.91% (median RSCR: 2.85%; interquartile range: 2.59% to 3.18%). The median odds ratio for complication occurrence at a higher-risk hospital compared with a lower-risk hospital was 1.33.

CONCLUSIONS

We respecified a measure to assess hospital inpatient or outpatient TJA performance and evaluated the reliability and validity of the measure. The findings showed variation in hospital-level complication rates across settings as indicated by this measure, supporting the feasibility of evaluating hospital performance using a more representative population than inpatient TJAs alone.

LEVEL OF EVIDENCE

Prognostic Level III . See Instructions for Authors for a complete description of levels of evidence.

Development and Application of New Risk-Adjustment Models to Improve the Current Model for Hospital Standardized Mortality Ratio in South Korea

PURPOSE

This study assessed the validity of the hospital standardized mortality ratio (HSMR) risk-adjusted model by comparing models that include clinical information and the current model based on administrative information in South Korea.

MATERIALS AND METHODS

The data of 53976 inpatients were analyzed. The current HSMR risk-adjusted model (Model 1) adjusts for sex, age, health coverage, emergency hospitalization status, main diagnosis, surgery status, and Charlson Comorbidity Index (CCI) using administrative data. As candidate variables, among clinical information, the American Society of Anesthesiologists score, Acute Physiology and Chronic Health Evaluation (APACHE) II, Simplified Acute Physiology Score (SAPS) 3, present on admission CCI, and cancer stage were collected. Surgery status, intensive care in the intensive care unit, and CCI were selected as proxy variables among administrative data. In-hospital death was defined as the dependent variable, and a logistic regression analysis was performed. The statistical performance of each model was compared using C-index values.

RESULTS

There was a strong correlation between variables in the administrative data and those in the medical records. The C-index of the existing model (Model 1) was 0.785; Model 2, which included all clinical data, had a higher C-index of 0.857. In Model 4, in which APACHE II and SAPS 3 were replaced with variables recorded in the administrative data from Model 2, the C-index further increased to 0.863.

CONCLUSION

The HSMR assessment model improved when clinical data were adjusted. Simultaneously, the validity of the evaluation method could be secured even if some of the clinical information was replaced with the information in the administrative data.

Is 70% Achievable? Hospital-Level Variation in Rates of Cardiac Rehabilitation Use Among Medicare Beneficiaries

Background

Despite national goals to enroll 70% of cardiac rehabilitation (CR)-eligible patients, enrollment remains low.

Objectives

The purpose of this study was to evaluate how the treating hospital influences CR enrollment nationally.

Methods

We included Fee-for-Service Medicare beneficiaries aged ≥66 years who were hospitalized for acute myocardial infarction, coronary artery bypass grafting, percutaneous coronary intervention, or heart valve repair/replacement. We examined: 1) a risk-standardized model to assess comparative hospital rates; 2) a linear regression model to identify hospital factors associated with rates of risk-standardized CR; and 3) a hierarchical generalized linear model to calculate the hospital median OR.

Results

At 3,420 hospitals, we identified 264,970 eligible patients. A minority of hospitals (n = 1,446; 38%) performed cardiac surgery, but these hospitals cared for the majority (n = 242,875; 92%) of all eligible patients. Subsequent analyses were limited to these hospitals. The median risk-standardized CR enrollment rate was low (22%) and varied 10-fold across hospitals (10th, 90th percentile: 3%, 42%). Factors associated with higher hospital performance were Midwest location, higher number of hospital beds, directly affiliated CR program, and <1 mile distance between the hospital and closest CR facility. The national hospital median OR was 2.1.

Conclusions

The treating hospital plays a key role in facilitating CR enrollment after discharge. Fewer than 1% of U.S. hospitals achieved a risk-standardized CR enrollment rate of >70%. Hospitals with cardiac surgery capability care for more than 90% of all CR-eligible patients and may be a logical place to focus improvement efforts.

Change in Hospital Risk-Standardized Stroke Mortality Performance With and Without the Passive Surveillance Stroke Severity Score

BACKGROUND

Adjustment for baseline stroke severity is necessary for accurate assessment of hospital performance. We evaluated whether adjusting for the Passive Surveillance Stroke SeVerity (PaSSV) score, a measure of stroke severity derived using administrative data, changed hospital-specific estimated 30-day risk-standardized mortality rate (RSMR) after stroke.

METHODS

We used linked administrative data to identify adults who were hospitalized with ischemic stroke or intracerebral hemorrhage across 157 hospitals in Ontario, Canada between 2014 and 2019. We fitted a random effects logistic regression model using Markov Chain Monte Carlo methods to estimate hospital-specific 30-day RSMR and 95% credible intervals with adjustment for age, sex, Charlson comorbidity index, and stroke type. In a separate model, we additionally adjusted for stroke severity using PaSSV. Hospitals were defined as low-performing, average-performing, or high-performing depending on whether the RSMR and 95% credible interval were above, overlapping, or below the cohort's crude mortality rate.

RESULTS

We identified 65,082 patients [48.0% were female, the median age (25th,75th percentiles) was 76 years (65,84), and 86.4% had an ischemic stroke]. The crude 30-day all-cause mortality rate was 14.1%. The inclusion of PaSSV in the model reclassified 18.5% (n=29) of the hospitals. Of the 143 hospitals initially classified as average-performing, after adjustment for PaSSV, 20 were reclassified as high-performing and 8 were reclassified as low-performing. Of the 4 hospitals initially classified as low-performing, 1 was reclassified as high-performing. All 10 hospitals initially classified as high-performing remained unchanged.

CONCLUSION

PaSSV may be useful for risk-adjusting mortality when comparing hospital performance. External validation of our findings in other jurisdictions is needed.

Racial/Ethnic Disparities in Aortic Valve Replacement Among Medicare Beneficiaries in the United States, 2012-2019

PURPOSE

There are concerns that transcatheter or surgical aortic valve replacement (TAVR/SAVR) procedures are preferentially available to White patients. Our objective was to examine differences in utilization of aortic valve replacement and outcomes by race/ethnicity in the US for patients with aortic stenosis.

METHODS

We performed a serial cross-sectional cohort study of 299,976 Medicare beneficiaries hospitalized with principal diagnosis of aortic stenosis between 2012 and 2019 stratified by self-reported race/ethnicity (Black, Hispanic, Asian, Native American, and White). Outcomes included aortic valve replacement rates within 6 months of index hospitalization and associated procedural outcomes, including 30-day readmission, 30-day and 1-year mortality.

RESULTS

Within 6 months of an index admission for aortic stenosis, 86.8% (122,457 SAVR; 138,026 TAVR) patients underwent aortic valve replacement. Overall, compared with White people, Black (HR 0.87 [0.85-0.89]), Hispanic (0.92 [0.88-0.96]), and Asian (0.95 [0.91-0.99]) people were less likely to receive aortic valve replacement. Among patients who were admitted emergently/urgently, White patients (41.1%, 95% CI, 40.7-41.4) had a significantly higher aortic valve replacement rate compared with Black (29.6%, 95% CI, 28.3-30.9), Hispanic (36.6%, 95% CI, 34.0-39.3), and Asian patients (35.4%, 95% CI, 32.3-38.9). Aortic valve replacement rates increased annually for all race/ethnicities. There were no significant differences in 30-day or 1-year mortality by race/ethnicity.

CONCLUSIONS

Aortic valve replacement rates within 6 months of aortic stenosis admission are lower for Black, Hispanic, and Asian people compared to White people. These race-related differences in aortic stenosis treatment reflect complex issues in diagnosis and management, warranting a comprehensive reassessment of the entire care spectrum for disadvantaged populations.

Pre-COVID-19 hospital quality and hospital response to COVID-19: examining associations between risk-adjusted mortality for patients hospitalised with COVID-19 and pre-COVID-19 hospital quality

OBJECTIVES

The extent to which care quality influenced outcomes for patients hospitalised with COVID-19 is unknown. Our objective was to determine if prepandemic hospital quality is associated with mortality among Medicare patients hospitalised with COVID-19.

DESIGN

This is a retrospective observational study. We calculated hospital-level risk-standardised in-hospital and 30-day mortality rates (risk-standardised mortality rates, RSMRs) for patients hospitalised with COVID-19, and correlation coefficients between RSMRs and pre-COVID-19 hospital quality, overall and stratified by hospital characteristics.

SETTING

Short-term acute care hospitals and critical access hospitals in the USA.

PARTICIPANTS

Hospitalised Medicare beneficiaries (Fee-For-Service and Medicare Advantage) age 65 and older hospitalised with COVID-19, discharged between 1 April 2020 and 30 September 2021.

INTERVENTION/EXPOSURE

Pre-COVID-19 hospital quality.

OUTCOMES

Risk-standardised COVID-19 in-hospital and 30-day mortality rates (RSMRs).

RESULTS

In-hospital (n=4256) RSMRs for Medicare patients hospitalised with COVID-19 (April 2020-September 2021) ranged from 4.5% to 59.9% (median 18.2%; IQR 14.7%-23.7%); 30-day RSMRs ranged from 12.9% to 56.2% (IQR 24.6%-30.6%). COVID-19 RSMRs were negatively correlated with star rating summary scores (in-hospital correlation coefficient -0.41, p<0.0001; 30 days -0.38, p<0.0001). Correlations with in-hospital RSMRs were strongest for patient experience (-0.39, p<0.0001) and timely and effective care (-0.30, p<0.0001) group scores; 30-day RSMRs were strongest for patient experience (-0.34, p<0.0001) and mortality (-0.33, p<0.0001) groups. Patients admitted to 1-star hospitals had higher odds of mortality (in-hospital OR 1.87, 95% CI 1.83 to 1.91; 30-day OR 1.46, 95% CI 1.43 to 1.48) compared with 5-star hospitals. If all hospitals performed like an average 5-star hospital, we estimate 38 000 fewer COVID-19-related deaths would have occurred between April 2020 and September 2021.

CONCLUSIONS

Hospitals with better prepandemic quality may have care structures and processes that allowed for better care delivery and outcomes during the COVID-19 pandemic. Understanding the relationship between pre-COVID-19 hospital quality and COVID-19 outcomes will allow policy-makers and hospitals better prepare for future public health emergencies.

[Hospital admissions and outcomes for systolic and diastolic heart failure in Spain between 2016 and 2019: A population-based study]

BACKGROUND AND PURPOSE

In Spain there is a lack of population data that specifically compare hospitalization for systolic and diastolic heart failure (HF). We assessed clinical characteristics, in-hospital mortality and 30-day cardiovascular readmission rates differentiating by HF type.

METHODS

We conducted a retrospective observational study of patients discharged with the principal diagnosis of HF from The National Health System' acute hospital during 2016-2019, distinguishing between systolic and diastolic HF. The source of the data was the Minimum Basic Data Set. The risk-standardized in-hospital mortality ratio and risk-standardized 30-day cardiovascular readmission ratio were calculated using multilevel risk adjustment models.

RESULTS

The 190,200 episodes of HF were selected. Of these, 163,727 (86.1%) were classified as diastolic HF and were characterized by older age, higher proportion of women, diabetes mellitus, dementia and renal failure than those with systolic HF. In the multilevel risk adjustment models, diastolic HF was a protective factor for both in-hospital mortality (odds ratio [OR]: 0.79; 95% confidence interval [CI]: 0.75-0.83; P<.001) and 30-day cardiovascular readmission versus systolic HF (OR: 0.93; 95% CI: 0.88-0.97; P=.002).

CONCLUSIONS

In Spain, between 2016 and 2019, hospitalization episodes for HF were mostly due to diastolic HF. According to the multilevel risk adjustment models, diastolic HF compared to systolic HF was a protective factor for both in-hospital mortality and 30-day cardiovascular readmission.

Administrative Model for Profiling Hospital Performance on Coronary Artery Bypass Graft Surgery: Based on the Chinese Hospital Quality Monitoring System

BACKGROUND

We aimed to develop an administrative model to profile the performance on the outcomes of coronary artery bypass grafting across hospitals in China.

METHODS AND RESULTS

This retrospective study was based on the Chinese Hospital Quality Monitoring System (HQMS) from 2016 to 2020. The coronary artery bypass grafting cases were identified by procedure code, and those of 2016 to 2017 were randomly divided into modeling and validation cohorts, while those in other years were used to ensure the model stability across years. The outcome was discharge status as "death or withdrawal," and that withdrawal referred to discharge without medical advice when patients were in the terminal stage but reluctant to die in the hospital. Candidate covariates were mainly identified by diagnoses or procedures codes. Patient-level logistic models and hospital-level hierarchical models were established. A total of 203 010 coronary artery bypass grafts in 699 hospitals were included, with 60 704 and 20 233 cases in the modeling and validation cohorts and 40 423, 42 698, and 38 952 in the years 2018, 2019, and 2020, respectively. The death or withdrawal rate was 3.4%. The areas under the curve were 0.746 and 0.729 in the patient-level models of modeling and validation cohorts, respectively, with good calibration and stability across years. Hospital-specific risk-standardized death or withdrawal rates were 2.61% (interquartile range, 1.87%-3.99%) and 2.63% (interquartile range, 1.97%-3.44%) in the modeling and validation cohorts, which were highly correlated (correlation coefficient, 0.96; P<0.001). Between-hospital variations were distinguished among hospitals of different volumes and across years.

CONCLUSIONS

The administrative model based on Hospital Quality Monitoring System could profile hospital performance on coronary artery bypass grafting in China.

Defining Multimorbidity in Older Patients Hospitalized with Medical Conditions

BACKGROUND

The term "multimorbidity" identifies high-risk, complex patients and is conventionally defined as ≥2 comorbidities. However, this labels almost all older patients as multimorbid, making this definition less useful for physicians, hospitals, and policymakers.

OBJECTIVE

Develop new medical condition-specific multimorbidity definitions for patients admitted with acute myocardial infarction (AMI), heart failure (HF), and pneumonia patients. We developed three medical condition-specific multimorbidity definitions as the presence of single, double, or triple combinations of comorbidities - called Qualifying Comorbidity Sets (QCSs) - associated with at least doubling the risk of 30-day mortality for AMI and pneumonia, or one-and-a-half times for HF patients, compared to typical patients with these conditions.

DESIGN

Cohort-based matching study PARTICIPANTS: One hundred percent Medicare Fee-for-Service beneficiaries with inpatient admissions between 2016 and 2019 for AMI, HF, and pneumonia.

MAIN MEASURES

Thirty-day all-location mortality KEY RESULTS: We defined multimorbidity as the presence of ≥1 QCS. The new definitions labeled fewer patients as multimorbid with a much higher risk of death compared to the conventional definition (≥2 comorbidities). The proportions of patients labeled as multimorbid using the new definition versus the conventional definition were: for AMI 47% versus 87% (p value<0.0001), HF 53% versus 98% (p value<0.0001), and pneumonia 57% versus 91% (p value<0.0001). Thirty-day mortality was higher among patients with ≥1 QCS compared to ≥2 comorbidities: for AMI 15.0% versus 9.5% (p<0.0001), HF 9.9% versus 7.0% (p <0.0001), and pneumonia 18.4% versus 13.2% (p <0.0001).

CONCLUSION

The presence of ≥2 comorbidities identified almost all patients as multimorbid. In contrast, our new QCS-based definitions selected more specific combinations of comorbidities associated with substantial excess risk in older patients admitted for AMI, HF, and pneumonia. Thus, our new definitions offer a better approach to identifying multimorbid patients, allowing physicians, hospitals, and policymakers to more effectively use such information to consider focused interventions for these vulnerable patients.

Achieving Equity in Hospital Performance Assessments Using Composite Race-Specific Measures of Risk-Standardized Readmission and Mortality Rates for Heart Failure

BACKGROUND

The contemporary measures of hospital performance for heart failure hospitalization and 30-day risk-standardized readmission rate (RSRR) and risk-standardized mortality rate (RSMR) are estimated using the same risk adjustment model and overall event rate for all patients. Thus, these measures are mainly driven by the care quality and outcomes for the majority racial and ethnic group, and may not adequately represent the hospital performance for patients of Black and other races.

METHODS

Fee-for-service Medicare beneficiaries from January 2014 to December 2019 hospitalized with heart failure were identified. Hospital-level 30-day RSRR and RSMR were estimated using the traditional race-agnostic models and the race-specific approach. The composite race-specific performance metric was calculated as the average of the RSRR/RMSR measures derived separately for each race and ethnicity group. Correlation and concordance in hospital performance for all patients and patients of Black and other races were assessed using the composite race-specific and race-agnostic metrics.

RESULTS

The study included 1 903 232 patients (75.7% White [n=1 439 958]; 14.5% Black [n=276 684]; and 9.8% other races [n=186 590]) with heart failure from 1860 hospitals. There was a modest correlation between hospital-level 30-day performance metrics for patients of White versus Black race (Pearson correlation coefficient: RSRR=0.42; RSMR=0.26). Compared with the race-agnostic RSRR and RSMR, composite race-specific metrics for all patients demonstrated stronger correlation with RSRR (correlation coefficient: 0.60 versus 0.74) and RSMR (correlation coefficient: 0.44 versus 0.51) for Black patients. Concordance in hospital performance for all patients and patients of Black race was also higher with race-specific (versus race-agnostic) metrics (RSRR=64% versus 53% concordantly high-performing; 61% versus 51% concordantly low-performing). Race-specific RSRR and RSMR metrics (versus race-agnostic) led to reclassification in performance ranking of 35.8% and 39.2% of hospitals, respectively, with better 30-day and 1-year outcomes for patients of all race groups at hospitals reclassified as high-performing.

CONCLUSIONS

Among patients hospitalized with heart failure, race-specific 30-day RSMR and RSRR are more equitable in representing hospital performance for patients of Black and other races.

Sample size calculations for indirect standardization

Indirect standardization, and its associated parameter the standardized incidence ratio, is a commonly-used tool in hospital profiling for comparing the incidence of negative outcomes between an index hospital and a larger population of reference hospitals, while adjusting for confounding covariates. In statistical inference of the standardized incidence ratio, traditional methods often assume the covariate distribution of the index hospital to be known. This assumption severely compromises one's ability to compute required sample sizes for high-powered indirect standardization, as in contexts where sample size calculation is desired, there are usually no means of knowing this distribution. This paper presents novel statistical methodology to perform sample size calculation for the standardized incidence ratio without knowing the covariate distribution of the index hospital and without collecting information from the index hospital to estimate this covariate distribution. We apply our methods to simulation studies and to real hospitals, to assess both its capabilities in a vacuum and in comparison to traditional assumptions of indirect standardization.

A Prognostic Model Based on Clinical Biomarkers for Heart Failure in Adult Patients Following Allogeneic Hematopoietic Stem Cell Transplantation

Heart failure (HF) is an uncommon but serious cardiovascular complication after allogeneic hematopoietic stem cell transplantation (allo-HSCT). Unfortunately, knowledge about early mortality prognostic factors in patients with HF after allo-HSCT is limited, and an easy-to-use prognostic model is not available. This study aimed to develop and validate a clinical-biomarker prognostic model capable of predicting HF mortality following allo-HSCT that uses a combination of variables readily available in clinical practice. To investigate this issue, we conducted a retrospective analysis at our center with 154 HF patients who underwent allo-HSCT between 2008 and 2021. The patients were separated according to the time of transplantation, with 100 patients composing the derivation cohort and the other 54 patients composing the external validation cohort. We first calculated the univariable association for each variable with 2-month mortality in the derivation cohort. We then included the variables with a P value <.1 in univariate analysis as candidate predictors in the multivariate analysis using a backward stepwise logistic regression model. Variables remaining in the final model were identified as independent prognostic factors. To predict the prognosis of HF, a scoring system was established, and scores were assigned to the prognostic factors based on the regression coefficient. Finally, 4 strongly significant independent prognostic factors for 2-month mortality from HF were identified using multivariable logistic regression methods with stepwise variable selection: pulmonary infection (P = .005), grade III to IV acute graft-versus-host disease (severe aGVHD; P = .033), lactate dehydrogenase (LDH) >426 U/L (P = .049), and brain natriuretic peptide (BNP) >1799 pg/mL (P = .026). A risk grading model termed the BLIPS score (for BNP, LDH, cardiac troponin I, pulmonary infection, and severe aGVHD) was constructed according to the regression coefficients. The validated internal C-statistic was .870 (95% confidence interval [CI], .798 to .942), and the external C-statistic was .882 (95% CI, .791-.973). According to the calibration plots, the model-predicted probability correlated well with the actual observed frequencies. The clinical use of the prognostic model, according to decision curve analysis, could benefit HF patients. The BLIPS model in our study can serve to identify HF patients at higher risk for mortality early, which might aid designing timely targeted therapies and eventually improving patients' survival and prognosis.

Variation in Emergency Department Use of Racemic Epinephrine and Associated Outcomes for Croup

OBJECTIVES

Racemic epinephrine (RE) is commonly administered for croup in the emergency department (ED). Our objectives were to examine variation in RE use between EDs, to determine whether ED variation in RE use is associated with hospital or patient factors, and to evaluate the associations between the rates of hospital-specific ED RE use and patient outcomes.

METHODS

We performed a retrospective cohort study using the Pediatric Heath Information System of children aged 3 months to 10 years with croup in the ED. We used mixed-effects regression to calculate risk-standardized proportions of patients receiving RE in each ED and to analyze the relationship between risk-standardized institutional RE use and individual patient odds of hospital admission, ICU admission, and ED revisits.

RESULTS

We analyzed 231 683 patient visits from 39 hospitals. ED administration of RE varied from 14% to 48% of visits (median, 24.5%; interquartile range, 20.0%-27.8%). A total of 8.6% of patients were hospitalized and 1% were admitted to the ICU. After standardizing for case mix and site effects, increasing ED use of RE per site was associated with increasing patient odds of hospital admission (odds ratio [OR], 1.39-95%; confidence interval [CI], 1.01-1.91), but not ICU admission (OR, 1.39; 95% CI, 0.99-1.97) or ED revisit (OR, 1.00; 95% CI, 0.92-1.09).

CONCLUSIONS

In this large, observational study, RE administration varied widely across EDs. Increased RE use by site was associated with increased odds of hospital admission for individual patients when controlling for patient factors. These results suggest further standardization of RE use in children with croup is warranted.

Reconceptualizing high-quality emergency general surgery care: Non-mortality-based quality metrics enable meaningful and consistent assessment

BACKGROUND

Ongoing efforts to promote quality-improvement in emergency general surgery (EGS) have made substantial strides but lack clear definitions of what constitutes "high-quality" EGS care. To address this concern, we developed a novel set of five non-mortality-based quality metrics broadly applicable to the care of all EGS patients and sought to discern whether (1) they can be used to identify groups of best-performing EGS hospitals, (2) results are similar for simple versus complex EGS severity in both adult (18-64 years) and older adult (≥65 years) populations, and (3) best performance is associated with differences in hospital-level factors.

METHODS

Patients hospitalized with 1-of-16 American Association for the Surgery of Trauma-defined EGS conditions were identified in the 2019 Nationwide Readmissions Database. They were stratified by age/severity into four cohorts: simple adults, complex adults, simple older adults, complex older adults. Within each cohort, risk-adjusted hierarchical models were used to calculate condition-specific risk-standardized quality metrics. K-means cluster analysis identified hospitals with similar performance, and multinomial regression identified predictors of resultant "best/average/worst" EGS care.

RESULTS

A total of 1,130,496 admissions from 984 hospitals were included (40.6% simple adults, 13.5% complex adults, 39.5% simple older adults, and 6.4% complex older adults). Within each cohort, K-means cluster analysis identified three groups ("best/average/worst"). Cluster assignment was highly conserved with 95.3% of hospitals assigned to the same cluster in each cohort. It was associated with consistently best/average/worst performance across differences in outcomes (5×) and EGS conditions (16×). When examined for associations with hospital-level factors, best-performing hospitals were those with the largest EGS volume, greatest extent of patient frailty, and most complicated underlying patient case-mix.

CONCLUSION

Use of non-mortality-based quality metrics appears to offer a needed promising means of evaluating high-quality EGS care. The results underscore the importance of accounting for outcomes applicable to all EGS patients when designing quality-improvement initiatives and suggest that, given the consistency of best-performing hospitals, natural EGS centers-of-excellence could exist.

LEVEL OF EVIDENCE

Prognostic and Epidemiological; Level III.

Characteristics and outcomes of patients with heart failure discharged from different speciality units in Australia: an observational study

BACKGROUND

Previous studies have reported differing clinical outcomes among hospitalized heart failure (HF) patients admitted under cardiology and general medicine (GM) without consideration of patients' frailty.

AIMS

To explore outcomes in patients admitted under the two specialities after taking into account their frailty and other characteristics.

METHODS

This retrospective study included all HF patients ≥18 years admitted between 1 January 2013 and 31 December 2019 at two Australian tertiary hospitals. Frailty was determined by use of the Hospital Frailty Risk Score (HFRS) and patients with HFRS ≥ 5 were classified as frail. Propensity score matching (PSM) was used to match 11 variables between the two specialities. The primary outcomes included the days-alive-and-out-of-hospital (DAOH90) at 90 days of discharge, 30-day mortality and readmissions.

RESULTS

Of 4913 HF patients, mean age 76.2 (14.1) years, 51% males, 2653 (54%) were admitted under cardiology compared to 2260 (46%) under GM. Patients admitted under GM were more likely to be older females, with a higher Charlson index and poor renal function than those admitted under cardiology. Overall, 23.8% patients were frail and frail patients were more likely to be admitted under GM than cardiology (33.6% vs. 15.3%, P < 0.001). PSM created 1532 well-matched patients in each group. After PSM, the DAOH90 was not significantly different among patients admitted in GM when compared to cardiology (coefficient -5.36, 95% confidence interval -11.73 to 1.01, P = 0.099). Other clinical outcomes were also similar between the two specialities.

CONCLUSIONS

Clinical characteristics of HF patients differ between GM and cardiology; however, clinical outcomes were not significantly different after taking into account frailty and other variables.

Machine learning-derived prediction of in-hospital mortality in patients with severe acute respiratory infection: analysis of claims data from the German-wide Helios hospital network

BACKGROUND

Severe acute respiratory infections (SARI) are the most common infectious causes of death. Previous work regarding mortality prediction models for SARI using machine learning (ML) algorithms that can be useful for both individual risk stratification and quality of care assessment is scarce. We aimed to develop reliable models for mortality prediction in SARI patients utilizing ML algorithms and compare its performances with a classic regression analysis approach.

METHODS

Administrative data (dataset randomly split 75%/25% for model training/testing) from years 2016-2019 of 86 German Helios hospitals was retrospectively analyzed. Inpatient SARI cases were defined by ICD-codes J09-J22. Three ML algorithms were evaluated and its performance compared to generalized linear models (GLM) by computing receiver operating characteristic area under the curve (AUC) and area under the precision-recall curve (AUPRC).

RESULTS

The dataset contained 241,988 inpatient SARI cases (75 years or older: 49%; male 56.2%). In-hospital mortality was 11.6%. AUC and AUPRC in the testing dataset were 0.83 and 0.372 for GLM, 0.831 and 0.384 for random forest (RF), 0.834 and 0.382 for single layer neural network (NNET) and 0.834 and 0.389 for extreme gradient boosting (XGBoost). Statistical comparison of ROC AUCs revealed a better performance of NNET and XGBoost as compared to GLM.

CONCLUSION

ML algorithms for predicting in-hospital mortality were trained and tested on a large real-world administrative dataset of SARI patients and showed good discriminatory performances. Broad application of our models in clinical routine practice can contribute to patients' risk assessment and quality management.

A systematic examination of quality-adjusted price index alternatives for medical care using claims data

We investigate alternative methods for constructing quality-adjusted medical price indexes both theoretically and empirically using medical claims data. The methodology and assumptions applied in the formation of the index have substantive effects on the magnitude of the quality-adjusted price changes. A method based on utility theory produces the most robust and accurate results, while alternative methods used in recent work overstate inflation. Based on Medicare claims data for three medical conditions, we find declining prices across each condition when properly adjusted for quality.

Institutional Variation in 30‐Day Complications Following Catheter Ablation of Atrial Fibrillation

Background

Complications are a measure of procedural quality, yet variation in complication rates following catheter ablation of atrial fibrillation (AF) among hospitals has not been systematically examined. We examined institutional variation in the risk‐standardized 30‐day complication rates (RSCRs) following AF ablation which may suggest variation in care quality.

Methods and Results

This cohort study included all patients >18 years old undergoing AF ablations from 2012 to 2017 in Australia and New Zealand. The primary outcome was procedure‐related complications occurring during the hospital stay and within 30 days of hospital discharge. We estimated the hospital‐specific risk‐standardized complication rates using a hierarchical generalized linear model. A total of 25 237 patients (mean age, 62.5±11.4 years; 30.2% women; median length of stay 1 day [interquartile range, 1–2 days]) were included. Overall, a complication occurred in 1400 (5.55%) patients (4.34% in hospital, 1.46% following discharge, and 0.25% experienced both). Bleeding (3.31%), pericardial effusion (0.74%), and infection (0.44%) were the most common complications while stroke/transient ischemic attack (0.24%), cardiorespiratory failure and shock (0.19%), and death (0.08%) occurred less frequently. Among 46 hospitals that performed ≥25 ablations during the study period, the crude complication rate varied from 0.00% to 21.43% (median, 5.74%). After adjustment for differences in patient and procedural characteristics, the median risk‐standardized complication rate was 5.50% (range, 2.89%–10.31%), with 10 hospitals being significantly different from the national average.

Conclusions

Procedure‐related complications occur in 5.55% of patients undergoing AF ablations, although the risk of complications varies 3‐fold among hospitals, which suggests potential disparities in care quality and the need for efforts to standardize AF ablation practices among hospitals.

A baseline profile of the Queensland Cardiac Record Linkage Cohort (QCard) study

BACKGROUND

Cardiovascular disease (CVD) is one of the leading causes of death in Australia. Longitudinal record linkage studies have the potency to influence clinical decision making to improve cardiac health. This paper describes the baseline characteristics of the Queensland Cardiac Record Linkage Cohort study (QCard).

METHODS

International Classification of Disease, 10th Revision Australian Modification (ICD-10-AM) diagnosis codes were used to identify CVD and comorbidities. Cost and adverse health outcomes (e.g., comorbidities, hospital-acquired complications) were compared between first-time and recurrent admissions. Descriptive statistics and standard tests were used to analyse the baseline data.

RESULTS

There were 132,343 patients with hospitalisations in 2010, of which 47% were recurrent admissions, and 53% were males. There were systematic differences between characteristics of recurrent and first-time hospitalisations. Patients with recurrent episodes were nine years older (70 vs. 61; p < 0.001) and experienced a twice higher risk of multiple comorbidities (3.17 vs. 1.59; p < 0.001). CVD index hospitalisations were concentrated in large metropolitan hospitals.

CONCLUSIONS

Our study demonstrates that linked administrative health data provide an effective tool to investigate factors determining the progress of heart disease. Our main finding suggests that recurrent admissions were associated with higher hospital costs and a higher risk of having adverse outcomes.

Associations of continuity and coordination of care with outcomes and costs after discharge among patients with heart failure: A nationwide population-based study

BACKGROUND

Although continuity and coordination of care have received increased attention as important ways to improve outcomes and decrease costs, limited information is available concerning the effects of "care continuity" and "care coordination" on mortality and costs. We used nationwide population-based data from Taiwan to explore the effects of care continuity and coordination on mortality and costs for heart failure.

METHODS

We analyzed all 18,991 heart failure patients 18 years of age or older and discharged from hospitals in 2016 using Taiwan's National Health Insurance claims data. Cox proportional hazard and multiple linear regression models were used, after adjustment for patient characteristics, to explore the relative impacts of the continuity of care (COC) index and care density on 1-year mortality and costs.

RESULTS

Higher COC index was associated with lower mortality (low vs. medium: hazard ratio [HR], 1.59; 95% confidence interval [CI], 1.47-1.71; high vs. medium: HR, 0.66; 95% CI, 0.61-0.72) and costs (low vs. medium: cost ratio [CR], 1.11; 95% CI, 1.07-1.16; high vs. medium: CR, 0.84; 95% CI, 0.81-0.88). Low care density was associated with higher mortality (low vs. medium: HR, 1.12; 95% CI, 1.04-1.20). Higher care density was associated with lower costs (low vs. medium: CR, 1.14; 95% CI, 1.10-1.18; high vs. medium: CR, 0.76; 95% CI, 0.73-0.79).

CONCLUSIONS

Low care continuity and coordination are associated with higher 1-year post-discharge mortality and costs. Facilitating care continuity and coordination may be an important strategy for improving value-based care for heart failure.

Risk-Adjusted Mortality Rates as a Quality Proxy Outperform Volume in Surgical Oncology-A New Perspective on Hospital Centralization Using National Population-Based Data

PURPOSE

Despite a long-known association between annual hospital volume and outcome, little progress has been made in shifting high-risk surgery to safer hospitals. This study investigates whether the risk-standardized mortality rate (RSMR) could serve as a stronger proxy for surgical quality than volume.

METHODS

We included all patients who underwent complex oncologic surgeries in Germany between 2010 and 2018 for any of five major cancer types, splitting the data into training (2010-2015) and validation sets (2016-2018). For each surgical group, we calculated annual volume and RSMR quintiles in the training set and applied these thresholds to the validation set. We studied the overlap between the two systems, modeled a market exit of low-performing hospitals, and compared effectiveness and efficiency of volume- and RSMR-based rankings. We compared travel distance or time that would be required to reallocate patients to the nearest hospital with low-mortality ranking for the specific procedure.

RESULTS

Between 2016 and 2018, 158,079 patients were treated in 974 hospitals. At least 50% of high-volume hospitals were not ranked in the low-mortality group according to RSMR grouping. In an RSMR centralization model, an average of 32 patients undergoing complex oncologic surgery would need to relocate to a low-mortality hospital to save one life, whereas 47 would need to relocate to a high-volume hospital. Mean difference in travel times between the nearest hospital to the hospital that performed surgery ranged from 10 minutes for colorectal cancer to 24 minutes for pancreatic cancer. Centralization on the basis of RSMR compared with volume would ensure lower median travel times for all cancer types, and these times would be lower than those observed.

CONCLUSION

RSMR is a promising proxy for measuring surgical quality. It outperforms volume in effectiveness, efficiency, and hospital availability for patients.

Marginal indirect standardization using latent clustering on multiple hospitals

A method was introduced in 2018 of performing indirect standardization for hospital profiling when only the marginal distributions of confounding variables are observed for the index hospital but the full joint covariate distribution is available for the reference hospitals (Wang et al, J Am Stat Assoc 2018; 114:662-630). The method constructs a synthetic comparison hospital using a weighted combination of reference hospitals, with weights assumed to follow a Dirichlet distribution with equal concentration parameters. In this article, we propose a novel method that improves upon the approach in a previous study (Wang et al, J Am Stat Assoc 2018; 114:662-630), by assuming the existence of latent classes among reference hospitals to allow for unequal Dirichlet concentration parameters. The latent class memberships, and thus the hospital weights, are informed by hospital-level characteristics. Our new method results in less biased point estimates and narrower uncertainty intervals for the standardized incidence ratio compared with the existing approach. We show the superiority of our novel methods in an application to a study on prevalence of high-radiation computed tomography exams, as well as in a simulation of the same medical context.

Assessing and Augmenting Predictive Models for Hospital Readmissions With Novel Variables in an Urban Safety-net Population

BACKGROUND

The performance of existing predictive models of readmissions, such as the LACE, LACE+, and Epic models, is not established in urban safety-net populations. We assessed previously validated predictive models of readmission performance in a socially complex, urban safety-net population, and if augmentation with additional variables such as the Area Deprivation Index, mental health diagnoses, and housing access improves prediction. Through the addition of new variables, we introduce the LACE-social determinants of health (SDH) model.

METHODS

This retrospective cohort study included adult admissions from July 1, 2016, to June 30, 2018, at a single urban safety-net health system, assessing the performance of the LACE, LACE+, and Epic models in predicting 30-day, unplanned rehospitalization. The LACE-SDH development is presented through logistic regression. Predictive model performance was compared using C-statistics.

RESULTS

A total of 16,540 patients met the inclusion criteria. Within the validation cohort (n=8314), the Epic model performed the best (C-statistic=0.71, P<0.05), compared with LACE-SDH (0.67), LACE (0.65), and LACE+ (0.61). The variables most associated with readmissions were (odds ratio, 95% confidence interval) against medical advice discharge (3.19, 2.28-4.45), mental health diagnosis (2.06, 1.72-2.47), and health care utilization (1.94, 1.47-2.55).

CONCLUSIONS

The Epic model performed the best in our sample but requires the use of the Epic Electronic Health Record. The LACE-SDH performed significantly better than the LACE and LACE+ models when applied to a safety-net population, demonstrating the importance of accounting for socioeconomic stressors, mental health, and health care utilization in assessing readmission risk in urban safety-net patients.

Racial/Ethnic Differences in 30-Day Mortality for Heart Failure and Pneumonia in the Veterans Health Administration Using Claims-based, Clinical, and Social Risk-adjustment Variables

BACKGROUND

Prior studies have identified lower mortality in Black Veterans compared with White Veterans after hospitalization for common medical conditions, but these studies adjusted for comorbid conditions identified in administrative claims.

OBJECTIVES

The objectives of this study were to compare mortality for non-Hispanic White (hereafter, "White"), non-Hispanic Black (hereafter, "Black"), and Hispanic Veterans hospitalized for heart failure (HF) and pneumonia and determine whether observed mortality differences varied according to whether claims-based comorbid conditions and/or clinical variables were included in risk-adjustment models.

RESEARCH DESIGN

This was an observational study.

SUBJECTS

The study cohort included 143,520 admissions for HF and 127,782 admissions for pneumonia for Veterans hospitalized in 132 Veterans Health Administration (VA) Medical Centers between January 2009 and September 2015.

MEASURES

The primary independent variable was racial/ethnic group (ie, Black, Hispanic, and non-Hispanic White), and the outcome was all-cause mortality 30 days following admission. To compare mortality by race/ethnicity, we used logistic regression models that included different combinations of claims-based, clinical, and sociodemographic variables. For each model, we estimated the average marginal effect (AME) for Black and Hispanic Veterans relative to White Veterans.

RESULTS

Among the 143,520 (127,782) hospitalizations for HF (pneumonia), the average patient age was 71.6 (70.9) years and 98.4% (97.1%) were male. The unadjusted 30-day mortality rates for HF (pneumonia) were 7.2% (11.0%) for White, 4.1% (10.4%) for Black and 8.4% (16.9%) for Hispanic Veterans. Relative to White Veterans, when only claims-based variables were used for risk adjustment, the AME (95% confidence interval) for the HF [pneumonia] cohort was -2.17 (-2.45, -1.89) [0.08 (-0.41, 0.58)] for Black Veterans and 1.32 (0.49, 2.15) [4.51 (3.65, 5.38)] for Hispanic Veterans. When clinical variables were incorporated in addition to claims-based ones, the AME, relative to White Veterans, for the HF [pneumonia] cohort was -1.57 (-1.88, -1.27) [-0.83 (-1.31, -0.36)] for Black Veterans and 1.50 (0.71, 2.30) [3.30 (2.49, 4.11)] for Hispanic Veterans.

CONCLUSIONS

Compared with White Veterans, Black Veterans had lower mortality, and Hispanic Veterans had higher mortality for HF and pneumonia. The inclusion of clinical variables into risk-adjustment models impacted the magnitude of racial/ethnic differences in mortality following hospitalization. Future studies examining racial/ethnic disparities should consider including clinical variables for risk adjustment.

Trends in Transcatheter and Surgical Aortic Valve Replacement Among Older Adults in the United States

BACKGROUND

Recent trends, including survival beyond 30 days, in aortic valve replacement (AVR) following the expansion of indications for transcatheter aortic valve replacement (TAVR) are not well-understood.

OBJECTIVES

The authors sought to characterize the trends in characteristics and outcomes of patients undergoing AVR.

METHODS

The authors analyzed Medicare beneficiaries who underwent TAVR and SAVR in 2012 to 2019. They evaluated case volume, demographics, comorbidities, 1-year mortality, and discharge disposition. Cox proportional hazard models were used to assess the annual change in outcomes.

RESULTS

Per 100,000 beneficiary-years, AVR increased from 107 to 156, TAVR increased from 19 to 101, whereas SAVR declined from 88 to 54. The median [interquartile range] age remained similar from 77 [71-83] years to 78 [72-84] years for overall AVR, decreased from 84 [79-88] years to 81 [75-86] years for TAVR, and decreased from 76 [71-81] years to 72 [68-77] years for SAVR. For all AVR patients, the prevalence of comorbidities remained relatively stable. The 1-year mortality for all AVR decreased from 11.9% to 9.4%. Annual change in the adjusted odds of 1-year mortality was 0.93 (95% CI: 0.92-0.94) for TAVR and 0.98 (95% CI: 0.97-0.99) for SAVR, and 0.94 (95% CI: 0.93-0.95) for all AVR. Patients discharged to home after AVR increased from 24.2% to 54.7%, primarily driven by increasing home discharge after TAVR.

CONCLUSIONS

The advent of TAVR has led to about a 60% increase in overall AVR in older adults. Improving outcomes in AVR as a whole following the advent of TAVR with increased access is a reassuring trend.

National Trends and Disparities in Hospitalization for Acute Hypertension Among Medicare Beneficiaries (1999-2019)

BACKGROUND

In the past 2 decades, hypertension control in the US population has not improved and there are widening disparities. Little is known about progress in reducing hospitalizations for acute hypertension.

METHODS

We conducted serial cross-sectional analysis of Medicare fee-for-service beneficiaries age 65 years or older between 1999 and 2019 using Medicare denominator and inpatient files. We evaluated trends in national hospitalization rates for acute hypertension overall and by demographic and geographical subgroups. We identified all beneficiaries admitted with a primary discharge diagnosis of acute hypertension on the basis of International Classification of Diseases codes. We then used a mixed effects model with a Poisson link function and state-specific random intercepts, adjusting for age, sex, race and ethnicity, and dual-eligible status, to evaluate trends in hospitalizations.

RESULTS

The sample consisted of 397 238 individual Medicare fee-for-service beneficiaries. From 1999 through 2019, the annual hospitalization rates for acute hypertension increased significantly, from 51.5 to 125.9 per 100 000 beneficiary-years; the absolute increase was most pronounced among the following subgroups: adults ≥85 years (66.8-274.1), females (64.9-160.1), Black people (144.4-369.5), and Medicare/Medicaid insured (dual-eligible, 93.1-270.0). Across all subgroups, Black adults had the highest hospitalization rate in 2019, and there was a significant increase in the differences in hospitalizations between Black and White people from 1999 to 2019. Marked geographic variation was also present, with the highest hospitalization rates in the South. Among patients hospitalized for acute hypertension, the observed 30-day and 90-day all-cause mortality rates (95% CI) decreased from 2.6% (2.27-2.83) and 5.6% (5.18-5.99) to 1.7% (1.53-1.80) and 3.7% (3.45-3.84) and 30-day and 90-day all-cause readmission rates decreased from 15.7% (15.1-16.4) and 29.4% (28.6-30.2) to 11.8% (11.5-12.1) and 24.0% (23.5-24.6).

CONCLUSIONS

Among Medicare fee-for-service beneficiaries age 65 years or older, hospitalization rates for acute hypertension increased substantially and significantly from 1999 to 2019. Black adults had the highest hospitalization rate in 2019 across age, sex, race and ethnicity, and dual-eligible strata. There was significant national variation, with the highest rates generally in the South.

Thirteen-year trends in hospitalization and outcomes of patients with heart failure in Spain

BACKGROUND

Heart failure is one of the most pressing current public health concerns. However, in Spain there is a lack of population data. We aimed to examine thirteen-year nationwide trends in heart failure hospitalization, in-hospital mortality and 30-day readmission rates in Spain.

METHODS

We conducted a retrospective observational study of patients discharged with the principal diagnosis of heart failure from The National Health System' acute hospitals during 2003-2015. The source of the data was the Minimum Basic Data Set. Temporal trends were modelled using Poisson regression analysis. The risk-standardized in-hospital mortality ratio was calculated using a multilevel risk adjustment logistic regression model.

RESULTS

A total of 1 254 830 episodes of heart failure were selected. Throughout 2003-2015, the number of hospital discharges with principal diagnosis of heart failure increased by 61%. Discharge rates weighted by age and sex increased during the period [incidence rate ratio (IRR): 1.03; 95% confidence interval (95% CI): 1.03-1.03; P < .001)], although this increase was motivated by the increase in older age groups (≥75 years old). The crude mortality rate diminished (IRR: 0.99; 95% CI: 0.98-1, P < .001), but 30-day readmission rate increased (IRR: 1.05; 95% CI: 1.04-1.06; P < .001). The risk-standardized in-hospital mortality ratio did not change throughout the study period (IRR: 0.997; 95% CI: 0.992-1; P = .32).

CONCLUSIONS

From 2003 to 2015, heart failure admission rates increased significantly in Spain as a consequence of the sustained increase of hospitalization in the population ≥75 years. 30-day readmission rates increased, but the risk-standardized in-hospital mortality ratio did not significantly change for the same period.

Development of Acute Myocardial Infarction Mortality and Readmission Models for Public Reporting on Hospital Performance in Canada

Background

Given changes in the care and outcomes of acute myocardial infarction (AMI) patients over the past several decades, we sought to develop prediction models that could be used to generate accurate risk-adjusted mortality and readmission outcomes for hospitals in current practice across Canada.

Methods

A Canadian national expert panel was convened to define appropriate AMI patients for reporting and develop prediction models. Preliminary candidate variable evaluation was conducted using Ontario patients hospitalized with a most responsible diagnosis of AMI from April 1, 2015 to March 31, 2018. National data from the Canadian Institute for Health Information was used to develop AMI prediction models. The main outcomes were 30-day all-cause in-hospital mortality and 30-day urgent all-cause readmission. Discrimination of these models (measured by c-statistics) was compared with that of existing Canadian Institute for Health Information models in the same study cohort.

Results

The AMI mortality model was assessed in 54,240 Ontario AMI patients and 153,523 AMI patients across Canada. We observed a 30-day in-hospital mortality rate of 6.3%, and a 30-day all-cause urgent readmission rate of 10.7% in Canada. The final Canadian AMI mortality model included 12 variables and had a c-statistic of 0.834. For readmission, the model had 13 variables and a c-statistic of 0.679. Discrimination of the new AMI models had higher c-statistics compared with existing models (c-statistic 0.814 for mortality; 0.673 for readmission).

Conclusions

In this national collaboration, we developed mortality and readmission models that are suitable for profiling performance of hospitals treating AMI patients in Canada.

Application of the Risk Stratification Index to Multilevel Models of All-condition 30-Day Mortality in Hospitalized Populations Over the Age of 65

BACKGROUND

The Risk Stratification Index (RSI) is superior to Hierarchical Conditions Categories (HCC) in patient-level regressions but has not been applied to assess hospital effects.

OBJECTIVE

The objective of this study was to measure the accuracy of RSI in modeling 30-day hospital mortality across all conditions using multilevel logistic regression.

SUBJECTS AND DATA SOURCES

A 100% sample of Medicare inpatient stays from 2009 to 2014, restricted to patients greater than 65 years of age in general hospitals, resulting in 64 million stays at 3504 hospitals.

RESEARCH DESIGN

We calculated RSI and HCC scores for patient stays using multilevel logistic regression in 3 populations: all inpatients, surgical, and nonsurgical. Correlations of risk-standardized mortality rates with rates of specific case types assessed case-mix balance. Patient stay volume was included to assess smaller hospitals.

RESULTS

We found a negligible correlation of all-conditions risk-standardized mortality rates with hospitals' proportions of orthopedic, cardiac, or pneumonia cases. RSI outperformed HCC in multilevel regressions containing both patient and hospital-level effects. C-statistics using RSI were 0.87 for the all-inpatients group, 0.87 for surgical, and 0.86 for nonsurgical stays. With HCC they were 0.82, 0.82, and 0.81. Akaike Information Criteria and Bayesian Information Criteria values were higher with HCC. RSI shifted 41% of hospitals' rankings by >1 decile. Hospitals with smaller volumes had higher 30-day observed and standardized mortality: 11.2% in the lowest volume quintile versus 8.5% in the highest volume quintile.

CONCLUSION

RSI has superior accuracy and results in a significant shift in rankings compared with HCC in multilevel models of 30-day hospital mortality across all conditions.

Prediction Model Using Machine Learning for Mortality in Patients with Heart Failure

Heart Failure (HF) is a major cause of morbidity and mortality in the US. With aging of the US population, the public health burden of HF is enormous. We aimed to develop an ensemble prediction model for 30-day mortality after discharge using machine learning. Using an electronic medical records (EMR) database, all patients with a non-elective HF admission over 10 years (January 2001 - December 2010) within the Montefiore Medical Center (MMC) health system, in the Bronx, New York, were included. We developed an ensemble model for 30-day mortality after discharge and employed discrimination, range of prediction, Brier index and explained variance as metrics in assessing model performance. A total of 7,516 patients were included. The discrimination achieved by the ensemble model was higher 0.83 (95% CI: 0.80 to 0.87) compared to the benchmark model 0.79 (95% CI: 0.75 to 0.84). The ensemble model also exhibited a better range of prediction as well as a favorable profile with respect to the other metrics employed. In conclusion, an ensemble machine learning approach exhibited an improvement in performance compared to the benchmark logistic model in predicting all-cause mortality among HF patients within 30-days of discharge. Machine learning is a promising alternative approach for risk profiling of HF patients, and it enhances individualized patient management.

Variation in propofol induction doses administered to surgical patients over age 65

BACKGROUND/OBJECTIVES

Advanced age is associated with increased susceptibility to acute adverse effects of propofol. The present study aimed to describe patterns of propofol dosing for induction of general anesthesia before endotracheal intubation in a nationwide sample of older adults presenting for surgery.

DESIGN

Retrospective observational study using the Multicenter Perioperative Outcomes Group data set.

SETTING

Thirty-six institutions across the United States.

PARTICIPANTS

A total of 350,766 patients aged over 65 years who received propofol for general anesthetic induction and endotracheal intubation between 2014 and 2018.

INTERVENTION

None.

MEASUREMENTS

Total induction bolus dose of propofol administered.

RESULTS

The mean (SD) weight-adjusted propofol dose was 1.7 (0.6) mg/kg. The mean prevalent propofol induction dose exceeded the upper bound of what has been described as the typical geriatric dose requirement across every age category examined. The percent of patients receiving propofol induction doses above the described typical geriatric range was 64.8% (95% CI 64.6-65.0), varying from 73.8% among patients aged 65-69 to 45.8% among patients aged 80 and older.

CONCLUSION

The present study of a large multicenter cohort demonstrates that prevalent propofol dosing commonly falls above the published typically required dose range for patients aged ≥65 in nationwide anesthetic practice. Widespread variability in induction dose administration remains incompletely explained by known patient variables. The nature and clinical consequences of these unexplained dosing decisions remain important topics for further study. Observed discordance between expected and actual induction dosing raises the question of whether there should be reconsideration of widespread provider practice or, alternatively, whether what is published as the typical propofol induction dose range should be revisited.

[Opportunities and limitations of risk adjustment of quality indicators based on inpatient administrative health data - a workshop report]

INTRODUCTION

The quality indicators of the Initiative Qualitätsmedizin e. V. (IQM) have been developed as triggers to examine treatment processes for opportunities for improvement. Published quality results have partly been used for external quality comparisons in the media. Therefore, member hospitals of IQM demanded to investigate if methods of risk adjustment should be applied in the calculation of the quality indicators. After a hearing of experts had been held, a task force was founded to conduct test calculations on risk adjustment methods.

METHODS

Specific risk adjustment models for mortality in myocardial infarction, heart failure, stroke, pneumonia, and colectomy in colorectal cancer were developed in the database of national German DRG data of the year 2016. These models were used to calculate standardized mortality ratios (SMR) per indicator in a sample of 172 member hospitals of IQM based on the data of the year 2018. Median SMR per indicator were compared to median SMR based on a standardization by age and gender, which is the standard procedure in IQM. Correlations between the different SMR were calculated. Quality of care was judged by two different approaches: a) a descriptive discrepancy of |0.1| from the SMR value of 1, and b) a significant discrepancy from 1 using the 95% confidence limits. The effect of using the specific risk adjustment in relation to the standard procedure was investigated for both approaches (a and b).

RESULTS

The specific risk adjustment methods showed an area under the curve between 0.72 and 0.84. The median differences between the SMR based on standardization by age and gender and the SMR based on specific risk adjustment were small (between 0 and 0.4); Spearman's correlations were between 0.90 and 0.99. Changes in the judgement of quality of care in comparison to the national average occurred in 3.9% (mortality from pneumonia) to 20.6% of the hospitals (mortality from heart failure) in descriptive comparisons. When the judgement was based on confidence limits changes were observed in 1.6% (mortality after colectomy) to 17.4% of the hospitals (mortality from heart failure).

DISCUSSION

Implementing specific risk adjustment models had only minor effects on the distribution of risk-adjusted mortality compared to the standard procedure, but the judgement of quality of care could change for a fifth of the hospitals in individual indicators. Concerning methodological and practical reasons, the task force recommends further development of risk adjustment methods for selected indicators. This should be accompanied by studies on the validity of inpatient administrative data for quality management as well as by efforts to improve the usefulness of these data for such purposes.

Interhospital variability in health care-associated infections and payments after durable ventricular assist device implant among Medicare beneficiaries

OBJECTIVE

The objective of this study was to investigate variations across hospitals in infection rates and associated costs, the latter reflected in 90-day Medicare payments. Despite high rates and expenditures of health care--associated infections associated with durable ventricular assist device implantation, few studies have examined interhospital variation and associated costs.

METHODS

Clinical data on 8688 patients who received primary durable ventricular assist devices from July 2008 to July 2017 from the Society of Thoracic Surgeons Interagency Registry for Mechanically Assisted Circulatory Support (Intermacs) hospitals (n = 120) were merged with postimplantation 90-day Medicare claims. Terciles of hospital-specific, risk-adjusted infection rates per 100 patient-months were estimated using Intermacs and associated with Medicare payments (among 5440 Medicare beneficiaries). Primary outcomes included infections within 90 days of implantation and Medicare payments.

RESULTS

There were 3982 infections identified among 27.8% (2417/8688) of patients developing an infection. The median (25th, 75th percentile) adjusted incidence of infections (per 100 patient-months) across hospitals was 14.3 (9.3, 19.5) and varied according to hospital (range, 0.0-35.6). Total Medicare payments from implantation to 90 days were 9.0% (absolute difference: $13,652) greater in high versus low infection tercile hospitals (P < .0001). The period between implantation to discharge accounted for 73.1% of the difference in payments during the implantation to 90-day period across terciles.

CONCLUSIONS

Health care--associated infection rates post durable ventricular assist device implantation varied according to hospital and were associated with increased 90-day Medicare expenditures. Interventions targeting preventing infections could improve the value of durable ventricular assist device support from the societal and hospital perspectives.

Readmission after hospitalization for heart failure in elderly patients in Chapidze Emergency Cardiology Center, Georgia

Purpose

Heart Failure (HF) is one of the leading mortality causes in elderly people. The purpose of this study is to assess readmission rates and reasons in elderly patients with HF.

Design/methodology/approach

The authors explored medical records of elderly patients with HF (75 years and more) at Chapidze Emergency Cardiology Center (Georgia) from 2015 to 2019. The authors analyzed the structure of the cardiovascular diseases and readmission rates of hospitalized patients with HF (I50, I50.0 I50.1). A multivariate logistic regression model was used to identify factors, associated with readmission for any reason during 6–9 months after the initial hospitalization for HF.

Findings

The major complication of cardiovascular diseases in elderly patients is HF (68.6%). Hospitalization rates due to HF in elderly patients have increased in recent years, which is associated with the population aging process. This trend will be most likely continue. Despite significant improvements in HF treatment, readmission rates are still high. HF is the most commonly revealed cause of readmission (48% of all readmissions). About 6–9 months after the primary hospitalization due to HF, readmission for any reason was 60%. Patients had concomitant diseases, including hypertension (43%), myocardial infarction (14%), diabetes (36%) and stroke (8%), affecting the readmission rate.

Originality/value

HF remains an important problem in public health. During HF-associated hospitalizations, both cardiac and non-cardiac conditions should be addressed, which has the potential for health problems and disease progression. Some readmissions may be prevented by the proper selection of medicines and monitoring.

Comparison of Major Clinical Outcomes between Accredited and Nonaccredited Hospitals for Inpatient Care of Acute Myocardial Infarction

Hospital accreditation programs are used worldwide to improve the quality of care and improve patient safety. It is of great help in improving the structure of hospitals, but there are mixed research results on improving the clinical outcome of patients. The purpose of this study was to compare the levels of core clinical outcome indicators after receiving inpatient services from accredited and nonaccredited hospitals in patients with acute myocardial infarction (AMI). For all patients with AMI admitted to general hospitals in Korea from 2010 to 2017, their 30-day mortality and readmissions and length of stay were compared according to accreditation status. In addition, through a multivariate model that controls various patients’ and hospitals’ factors, the differences in those indicators were analyzed more accurately. The 30-day mortality of patients admitted to accredited hospitals was statistically significantly lower than that of patients admitted to nonaccredited hospitals. However, for 30-day readmission and length of stay, accreditation did not appear to yield more desirable results. This study shows that when evaluating the clinical impact of hospital accreditation programs, not only the mortality but also various clinical indicators need to be included, and a more comprehensive review is needed.

The associations between direct and delayed critical care unit admission with mortality and readmissions among patients with heart failure

BACKGROUND

Although greater than 20% of patients hospitalized with heart failure (HF) are admitted to a critical care unit, associated outcomes, and costs have not been delineated. We determined 30-day mortality, 30-day readmissions, and hospital costs associated with direct or delayed critical care unit admission.

METHODS

In a population-based analysis, we compared HF patients who were admitted to critical care directly from the emergency department (direct), after initial ward admission (delayed), or never admitted to critical care during their hospital stay (ward-only).

RESULTS

Among 178,997 HF patients (median age 80 [IQR 71-86] years, 49.6% men) 36,175 (20.2%) were admitted to critical care during their hospitalization (April 2003 to March 2018). Critical care patients were admitted directly from the emergency department (direct, 81.9%) or after initial ward admission (delayed, 18.1%). Multivariable-adjusted hazard ratios (HR) for all-cause 30-day mortality were: 1.69 for direct (95% confidence interval [CI]; 1.55, 1.84) and 4.92 for delayed (95% CI; 4.26, 5.68) critical care-admitted compared to ward-only patients. Multivariable-adjusted repeated events analysis demonstrated increased risk for all-cause 30-day readmission with both direct (HR 1.04, 95% CI; 1.01, 1.08, P = .013) and delayed critical care unit admissions (HR 1.20, 95% CI; 1.13, 1.28, P < .001). Median 30-day costs were $12,163 for direct admissions, $20,173 for delayed admissions, and $9,575 for ward-only patients (P < .001).

CONCLUSIONS

While critical care unit admission indicates increased risk of mortality and readmission at 30 days, those who experienced delayed critical care unit admission exhibited the highest risk of death and highest costs of care.

Psychopharmaceutical Prescription Monitoring for Children in the Child Welfare System

OBJECTIVE

Children in the child welfare system are more likely to receive psychotropic medication prescriptions than children in the general population. The authors used prescription- and administrative-level data to quantify variability in prescribing practices among prescribers for the child welfare population in a southern U.S. state.

METHODS

Using administrative- and prescription-level child data and Administration on Children, Youth and Families guidelines, the authors defined the primary outcome, potentially inappropriate psychotropic prescriptions (i.e., red-flagged prescriptions). A hierarchical-logistic regression model was fit to account for case complexity and estimate the adjusted probability of a prescription being red-flagged. A funnel plot was used to visualize standardized prescribing rates for every prescriber and identify outlying prescribers.

RESULTS

From May 2016 to September 2017, 506 prescribers issued 64,923 prescriptions for 4,093 children with a median (interquartile range) age of 14 (10-16) years. Most prescribers (76.9%) issued at least one red-flagged prescription, 1,263 (30.9%) children received at least one red-flagged prescription, and 14,806 (22.8%) prescriptions were red-flagged. The standardized prescribing rate for each prescriber was compared with a benchmark of 22.8%, defined a priori as the proportion of red-flagged prescriptions in the overall sample. Forty-seven prescribers (9%) prescribed red-flagged prescriptions between two and three standard deviations above the benchmark, and 72 prescribers (14%) more than three standard deviations above the benchmark.

CONCLUSIONS

It is vital to monitor psychotropic prescriptions for children in the child welfare system. Quantifying variability in prescribing practices among prescribers for these children might be used to guide oversight.

Impact of Global Budget Payments on Cardiovascular Care in Maryland: An Interrupted Time Series Analysis

Background Global budget payments (GBP) are considered effective in containing health care expenditures; however, information on their impact on quality of cardiovascular care is limited. We aimed to evaluate the effects of GBP on utilization, outcomes, and costs for 3 major cardiovascular conditions. Methods We analyzed claims data of hospital admissions in Maryland from fiscal year 2013 to 2018. Using segmented regression, we evaluated temporal trends in hospitalizations, length of stay, percutaneous coronary intervention and coronary artery bypass grafting volumes, case mix-adjusted 30-day readmission rates, risk-standardized mortality rates, and hospitalization charges in patients with principal diagnosis of heart failure, acute ischemic stroke, and acute myocardial infarction (AMI) in relation to GBP implementation. Trends in global cardiovascular procedure charges/volumes were also studied. Results Hospitalization rates for congestive heart failure and AMI remained unaffected by GBP, while the gradient of ischemic stroke admissions decreased (P_trend <0.0001). Length of stay slightly increased for patients with congestive heart failure (P_trend=0.03). Inpatient coronary artery bypass grafting surgeries decreased (P_trend <0.0001). We observed a significant decrease in casemix-adjusted 30-day readmission rate in the AMI cohort beyond the prepolicy trend (P_trend=0.0069). There were no significant changes in mortality for any of the 3 conditions. Hospitalization charges increased for ischemic stroke (P_trend <0.0001), remained constant for congestive heart failure (P_trend=0.1), and decreased for AMI (P_trend=0.0005). We observed a significant increase in electrocardiography rate charges (P_trend <0.0001), coincidentally with a reduction in volumes (P_trend=0.0003). Conclusions Introducing GBP in Maryland had no perceivable adverse effects on inpatient outcomes and quality indicators for 3 major cardiovascular conditions. Savings were observed in the AMI cohort, possibly due to reduced unnecessary readmissions, efficiency improvements, or shifts to outpatient care. Reduced cardiovascular procedure volumes were counterbalanced by a proportional rise in charges. State-level adoption of GBP with pay-for-performance incentives may be effective for cost containment without adversely impacting quality of cardiovascular care.

Hospital-level variation in follow-up strategies after percutaneous coronary intervention, revealed in health claims data of Korea

This study sought to determine hospital variation in the use of follow-up stress testing (FUST) and invasive coronary angiography (FUCAG) after percutaneous coronary intervention (PCI). The claims records of 150,580 Korean patients who received PCI in 128 hospitals between 2008 and 2015 were analyzed. Patient were considered to have undergone FUST and FUCAG, when these testings were performed within two years after discharge from the index hospitalization. Hierarchical generalized linear and frailty models were used to evaluate binary and time-to-event outcomes. Hospital-level risk-standardized FUCAG and FUST rates were highly variable across the hospitals (median, 0.41; interquartile range [IQR], 0.27-0.59; median, 0.22; IQR, 0.08-0.39, respectively). The performances of various models predicting the likelihood of FUCAG and FUST were compared, and the best performance was observed with the models adjusted for patient case mix and individual hospital effects as random effects (receiver operating characteristic curves, 0.72 for FUCAG; 0.82 for FUST). The intraclass correlation coefficients of the models (0.41 and 0.68, respectively) indicated that a considerable proportion of the observed variation was related to individual institutional effects. Higher hospital-level FUCAG and FUST rates were not preventive of death or myocardial infarction. Increased repeat revascularizations were observed in hospitals with higher FUCAG rates.

Association of Inclusion of Medicare Advantage Patients in Hospitals' Risk-Standardized Readmission Rates, Performance, and Penalty Status

Importance

The Hospital Readmissions Reduction Program publicly reports and financially penalizes hospitals according to 30-day risk-standardized readmission rates (RSRRs) exclusively among traditional Medicare (TM) beneficiaries but not persons with Medicare Advantage (MA) coverage. Exclusively reporting readmission rates for the TM population may not accurately reflect hospitals' readmission rates for older adults.

Objective

To examine how inclusion of MA patients in hospitals' performance is associated with readmission measures and eligibility for financial penalties.

Design, Setting, and Participants

This is a retrospective cohort study linking the Medicare Provider Analysis and Review file with the Healthcare Effectiveness Data and Information Set at 4070 US acute care hospitals admitting both TM and MA patients. Participants included patients admitted and discharged alive with a diagnosis of acute myocardial infarction (AMI), congestive heart failure (CHF), or pneumonia between 2011 and 2015. Data analyses were conducted between April 1, 2018, and November 20, 2020.

Exposures

Admission to an acute care hospital.

Main Outcomes and Measures

The outcome was readmission for any reason occurring within 30 days after discharge. Each hospital's 30-day RSRR was computed on the basis of TM, MA, and all patients and estimated changes in hospitals' performance and eligibility for financial penalties after including MA beneficiaries for calculating 30-day RSRRs.

Results

There were 748 033 TM patients (mean [SD] age, 76.8 [83] years; 360 692 [48.2%] women) and 295 928 MA patients (mean [SD] age, 77.5 [7.9] years; 137 422 [46.4%] women) hospitalized and discharged alive for AMI; 1 327 551 TM patients (mean [SD] age, 81 [8.3] years; 735 855 [55.4%] women) and 457 341 MA patients (mean [SD] age, 79.8 [8.1] years; 243 503 [53.2%] women) for CHF; and 2 017 020 TM patients (mean [SD] age, 80.7 [8.5] years; 1 097 151 [54.4%] women) and 610 790 MA patients (mean [SD] age, 79.6 [8.2] years; 321 350 [52.6%] women) for pneumonia. The 30-day RSRRs for TM and MA patients were correlated (correlation coefficients, 0.31 for AMI, 0.40 for CHF, and 0.41 for pneumonia) and the TM-based RSRR systematically underestimated the RSRR for all Medicare patients for each condition. Of the 2820 hospitals with 25 or more admissions for at least 1 of the outcomes of AMI, CHF, and pneumonia, 635 (23%) had a change in their penalty status for at least 1 of these conditions after including MA data. Changes in hospital performance and penalty status with the inclusion of MA patients were greater for hospitals in the highest quartile of MA admissions.

Conclusions and Relevance

In this cohort study, the inclusion of data from MA patients changed the penalty status of a substantial fraction of US hospitals for at least 1 of 3 reported conditions. This suggests that policy makers should consider including all hospital patients, regardless of insurance status, when assessing hospital quality measures.

Evaluation of Risk-Adjusted Home Time After Hospitalization for Heart Failure as a Potential Hospital Performance Metric

Importance

Thirty-day home time, defined as time spent alive and out of a hospital or facility, is a novel, patient-centered performance metric that incorporates readmission and mortality.

Objectives

To characterize risk-adjusted 30-day home time in patients discharged with heart failure (HF) as a hospital-level quality metric and evaluate its association with the 30-day risk-standardized readmission rate (RSRR), 30-day risk-standardized mortality rate (RSMR), and 1-year RSMR.

Design, Setting, and Participants

This hospital-level cohort study retrospectively analyzed 100% of Medicare claims data from 2 968 341 patients from 3134 facilities from January 1, 2012, to November 30, 2017.

Exposures

Home time, defined as time spent alive and out of a short-term hospital, skilled nursing facility, or intermediate/long-term facility 30 days after discharge.

Main Outcomes and Measures

For each hospital, a risk-adjusted 30-day home time for HF was calculated similar to the Centers for Medicare & Medicaid Services risk-adjustment models for 30-day RSRR and RSMR. Hospitals were categorized into quartiles (lowest to highest risk-adjusted home time). The correlations between hospital rates of risk-adjusted 30-day home time and 30-day RSRR, 30-day RSMR, and 1-year RSMR were estimated using the Pearson correlation coefficient. Distribution of days lost from a perfect 30-day home time were calculated. Reclassification of hospital performance using 30-day home time vs 30-day RSRR was also evaluated.

Results

Overall, 2 968 341 patients (mean [SD] age, 81.0 [8.3] years; 53.6% female) from 3134 hospitals were included in this study. The median hospital risk-adjusted 30-day home time for patients with HF was 21.77 days (range, 8.22-28.41 days). Hospitals in the highest quartile of risk-adjusted 30-day home time (best-performing hospitals) were larger (mean [SD] number of beds, 285 [275]), with a higher volume of patients with HF (median, 797 patients; interquartile range, 395-1484) and were more likely academic hospitals (59.9%) with availability of cardiac surgery (51.1%) and cardiac rehabilitation (68.8%). A total of 72% of home time lost was attributable to stays in an intermediate- or long-term care facility (mean [SD], 2.65 [6.44] days) or skilled nursing facility (mean [SD], 3.96 [9.04] days), 13% was attributable to short-term readmissions (mean [SD], 1.25 [3.25] days), and 15% was attributable to death (mean [SD], 1.37 [6.04] days). Among 30-day outcomes, the 30-day RSRR and 30-day RSMR decreased in a graded fashion across increasing 30-day home time categories (correlation coefficients: 30-day RSRR and 30-day home time, -0.23, P < .001; 30-day RSMR and 30-day home time, -0.31, P < .001). Similar patterns of association were also noted for 1-year RSMR and 30-day home time (correlation coefficient, -0.35, P < .001). Thirty-day home time meaningfully reclassified hospital performance in 30% of the hospitals compared with 30-day RSRR and in 25% of hospitals compared with 30-day RSMR.

Conclusions and Relevance

In this study, 30-day home time among patients discharged after a hospitalization for HF was objectively assessed as a hospital-level quality metric using Medicare claims data and was associated with readmission and mortality outcomes and with reclassification of hospital performance compared with 30-day RSRR and 30-day RSMR.

The LENT index predicts 30 day outcomes following hospitalization for heart failure

Aims

The LE index (Length of hospitalization plus number of Emergent visits ≤6 months) predicts 30 day all‐cause readmission or death following hospitalization for heart failure (HF). We combined N‐terminal pro‐B type natriuretic peptide (NT‐proBNP) levels with the LE index to derive and validate the LENT index for risk prediction at the point of care on the day of hospital discharge.

Methods and results

In this prospective cohort sub‐study of the Patient‐centred Care Transitions in HF clinical trial, we used log‐binomial regression models with LE index and either admission or discharge NT‐proBNP as the predictors and 30 day composite all‐cause readmission or death as the primary outcome. No other variables were added to the model. We used regression coefficients to derive the LENT index and bootstrapping analysis for internal validation. There were 772 patients (mean [SD] age 77.0 [12.4] years, 49.9% female). Each increment in the LE index was associated with a 25% increased risk of the primary outcome (RR 1.25, 95% CI 1.16–1.35; C‐statistic 0.63). Adjusted for the LE index, every 10‐fold increase in admission and discharge NT‐proBNP was associated with a 48% (RR 1.48; 95% CI 1.10, 1.99; C‐statistic 0.64; net reclassification index [NRI] 0.19) and 56% (RR 1.56; 95% CI 1.08, 2.25; C‐statistic 0.64; NRI 0.21) increased risk of the primary outcome, respectively. The predicted probability of the primary outcome increased to a similar extent with incremental LENT, regardless of whether admission or discharge NT‐proBNP level was used.

Conclusions

The point‐of‐care LENT index predicts 30 day composite all‐cause readmission or death among patients hospitalized with HF, with improved risk reclassification compared with the LE index. The performance of this simple, 3‐variable index ‐ without adjustment for comorbidities ‐ is comparable to complex risk prediction models in HF.

Evaluation of Changes in Veterans Affairs Medical Centers' Mortality Rates After Risk Adjustment for Socioeconomic Status

IMPORTANCE

Socioeconomic factors are associated with worse outcomes after hospitalization, but neither the Centers for Medicare & Medicaid Services (CMS) nor the Veterans Affairs (VA) health care system adjust for socioeconomic factors in profiling hospital mortality.

OBJECTIVE

To evaluate changes in Veterans Affairs medical centers' (VAMCs') risk-standardized mortality rates among veterans hospitalized for heart failure and pneumonia after adjusting for socioeconomic factors.

DESIGN, SETTING, AND PARTICIPANTS

In this cross-sectional study, retrospective data were used to assess 131 VAMCs' risk-standardized 30-day mortality rates with or without adjustment for socioeconomic covariates. The study population included 42 892 veterans hospitalized with heart failure and 39 062 veterans hospitalized with pneumonia from January 1, 2012, to December 31, 2014. Data were analyzed from March 1, 2019, to April 1, 2020.

MAIN OUTCOMES AND MEASURES

The primary outcome was 30-day mortality after admission. Socioeconomic covariates included neighborhood disadvantage, race/ethnicity, homelessness, rurality, nursing home residence, reason for Medicare eligibility, Medicaid and Medicare dual eligibility, and VA priority.

RESULTS

The study population included 42 892 veterans hospitalized with heart failure (98.2% male; mean [SD] age, 71.9 [11.4] years) and 39 062 veterans hospitalized with pneumonia (96.8% male; mean [SD] age, 71.0 [12.4] years). The addition of socioeconomic factors to the CMS models modestly increased the C statistic from 0.77 (95% CI, 0.77-0.78) to 0.78 (95% CI, 0.78-0.78) for 30-day mortality after heart failure and from 0.73 (95% CI, 0.72-0.73) to 0.74 (95% CI, 0.73-0.74) for 30-day mortality after pneumonia. Mortality rates were highly correlated (Spearman correlations of ≥0.98) in models that included or did not include socioeconomic factors. With the use of the CMS model for heart failure, VAMCs in the lowest quintile had a mean (SD) mortality rate of 6.0% (0.4%), those in the middle 3 quintiles had a mean (SD) mortality rate of 7.2% (0.4%), and those in the highest quintile had a mean (SD) mortality rate of 8.8% (0.6%). After the inclusion of socioeconomic covariates, the adjusted mean (SD) mortality was 6.1% (0.4%) for hospitals in the lowest quintile, 7.2% (0.4%) for those in the middle 3 quintiles, and 8.6% (0.5%) for those in the highest quintile. The mean absolute change in rank after socioeconomic adjustment was 3.0 ranking positions (interquartile range, 1.0-4.0) among hospitals in the highest quintile of mortality after heart failure and 4.4 ranking positions (interquartile range, 1.0-6.0) among VAMCs in the lowest quintile. Similar findings were observed for mortality rankings in pneumonia and after inclusion of clinical covariates.

CONCLUSIONS AND RELEVANCE

This study suggests that adjustments for socioeconomic factors did not meaningfully change VAMCs' risk-adjusted 30-day mortality rates for veterans hospitalized for heart failure and pneumonia. The implications of such adjustments should be examined for other quality measures and health systems.

Frequency, trends and institutional variation in 30-day all-cause mortality and unplanned readmissions following hospitalisation for heart failure in Australia and New Zealand

AIMS

National 30-day mortality and readmission rates after heart failure (HF) hospitalisations are a focus of US policy intervention and yet have rarely been assessed in other comparable countries. We examined the frequency, trends and institutional variation in 30-day mortality and unplanned readmission rates after HF hospitalisations in Australia and New Zealand.

METHODS AND RESULTS

We included patients >18 years hospitalised with HF at all public and most private hospitals from 2010-15. The primary outcomes were the frequencies of 30-day mortality and unplanned readmissions, and the institutional risk-standardised mortality rate (RSMR) and readmission rate (RSRR) evaluated using separate cohorts. The mortality cohort included 153 592 patients (mean age 78.9 ± 11.8 years, 51.5% male) with 16 442 (10.7%) deaths within 30 days. The readmission cohort included 148 704 patients (mean age 78.6 ± 11.9 years, 51.7% male) with 33 158 (22.3%) unplanned readmission within 30 days. In 392 hospitals with at least 25 HF hospitalisations, the median RSMR was 10.7% (range 6.1-17.3%) with 59 hospitals significantly different from the national average. Similarly, in 391 hospitals with at least 25 HF hospitalisations, the median RSRR was 22.3% (range 17.7-27.1%) with 24 hospitals significantly different from the average. From 2010-15, the adjusted 30-day mortality [odds ratio (OR) 0.991/month, 95% confidence interval (CI) 0.990-0.992, P < 0.01] and unplanned readmission (OR 0.998/month, 95% CI 0.998-0.999, P < 0.01) rates declined.

CONCLUSION

Within 30 days of a HF hospitalisation, one in 10 patients died and almost a quarter of those surviving experienced an unplanned readmission. The risk of these outcomes varied widely among hospitals suggesting disparities in HF care quality. Nevertheless, a substantial decline in 30-day mortality and a modest decline in readmissions occurred over the study period.

Association between coronary artery bypass graft center volume and year-to-year outcome variability: New York and California statewide analysis

OBJECTIVE

We evaluated whether volume-based, rather than time-based, annual reporting of center outcomes for coronary artery bypass grafting may improve inference of quality, assuming that large center-level year-to-year outcome variability is related to statistical noise.

METHODS

We analyzed 2012 to 2016 data on isolated coronary artery bypass grafting using statewide outcome reports from New York and California. Annual changes in center-level observed-to-expected mortality ratio represented stability of year-to-year outcomes. Cubic spline fit related the annual observed-to-expected ratio change and center volume. Volume above the inflection point of the spline curve indicated centers with low year-to-year change in outcome. We compared observed-to-expected ratio changes between centers below and above the volume threshold and observed-to-expected ratio changes between consecutive annual and biennial measurements.

RESULTS

There were 155 centers with median annual volume of 89 (interquartile range, 55-160) for isolated coronary artery bypass grafting. The inflection point of observed-to-expected ratio variability was observed at 111 cases/year. Median year-to-year observed-to-expected ratio change for centers performing less than 111 cases (62 centers) was greater at 0.83 (0.26-1.59) compared with centers performing 111 cases or more (93 centers) at 0.49 (022-0.87) (P < .001). By aggregating the outcome over 2 years, centers above the 111-case threshold increased from 93 centers (60%) to 118 centers (76%), but the median observed-to-expected change for all centers was similar between annual aggregates at 0.70 (0.26-1.22) compared with observed-to-expected change between biennial aggregates at 0.54 (0.23-1.02) (P = .095).

CONCLUSIONS

Center-level, risk-adjusted coronary artery bypass grafting mortality varies significantly from one year to the next. Reporting outcomes by specific case volume may complement annual reports.

Trends in Reoperative Coronary Artery Bypass Graft Surgery for Older Adults in the United States, 1998 to 2017

Background The likelihood of undergoing reoperative coronary artery bypass graft surgery (CABG) is important for older patients who are considering first-time CABG. Trends in the reoperative CABG for these patients are unknown. Methods and Results We used the Medicare fee-for-service inpatient claims data of adults undergoing isolated first-time CABG between 1998 and 2017. The primary outcome was time to first reoperative CABG within 5 years of discharge from the index surgery, treating death as a competing risk. We fitted a Cox regression to model the likelihood of reoperative CABG as a function of patient baseline characteristics. There were 1 666 875 unique patients undergoing first-time isolated CABG and surviving to hospital discharge. The median (interquartile range) age of patients did not change significantly over time (from 74 [69-78] in 1998 to 73 [69-78] in 2017); the proportion of women decreased from 34.8% to 26.1%. The 5-year rate of reoperative CABG declined from 0.77% (95% CI, 0.72%-0.82%) in 1998 to 0.23% (95% CI, 0.19%-0.28%) in 2013. The annual proportional decline in the 5-year rate of reoperative CABG overall was 6.6% (95% CI, 6.0%-7.1%) nationwide, which did not differ across subgroups, except the non-white non-black race group that had an annual decline of 8.5% (95% CI, 6.2%-10.7%). Conclusions Over a recent 20-year period, the Medicare fee-for-service patients experienced a significant decline in the rate of reoperative CABG. In this cohort of older adults, the rate of declining differed across demographic subgroups.

Association between Accreditation and In-Hospital Mortality in Patients with Major Cardiovascular Diseases in South Korean Hospitals: Pre-Post Accreditation Comparison

The direct impact of hospital accreditation on patients' clinical outcomes is unclear. The purpose of this study was to evaluate whether mortality within 30 days of hospitalization for acute myocardial infarction (AMI), ischemic stroke (IS), and hemorrhagic stroke (HS) differed before and after hospital accreditation. This study targeted patients who had been hospitalized for the three diseases at the general hospitals newly accredited by the government in 2014. Thirty-day mortality rates of three years before and after accreditation were compared. Mortality within 30 days of hospitalization for the three diseases was lower after accreditation than before (7.34% vs. 6.15% for AMI; 4.64% vs. 3.80% for IS; and 18.52% vs. 15.81% for HS). In addition, hospitals that meet the criteria of the patient care process domain have a statistically lower mortality rate than hospitals that do not. In the newly accredited Korean general hospital, it was confirmed that in-hospital mortality rates of major cardiovascular diseases were lower than before the accreditation.

Medicare Shared Savings Program and readmission rate among patients with ischemic stroke

OBJECTIVE

Hospitals participating in the Medicare Shared Savings Program (MSSP) share with the Centers for Medicare and Medicaid Services (CMS) the savings generated by reduced cost of care. Our aim was to determine whether MSSP is associated with changes in readmissions and mortality for Medicare patients hospitalized with ischemic stroke, and whether MSSP has a different impact on safety net hospitals (SNHs) compared to non-SNHs.

METHODS

This study was based on the CMS Hospital Compare data for risk-standardized 30-day readmission and mortality rates for Medicare patients hospitalized with ischemic strokes between 2010 and 2017. With a propensity score-matched sample, hospital-level difference-in-difference analysis was used to determine whether MSSP was associated with changes in hospital readmission and mortality as well as to examine the impact of MSSP on SNHs compared to non-SNHs.

RESULTS

MSSP-participating hospitals had slightly greater reductions in readmission rates compared to matched nonparticipating hospitals (difference, 0.25 percentage points; 95% confidence interval [CI], -0.42 to -0.08). Mortality rates decreased among all hospitals, but mortality reduction was not significantly different between MSSP-participating hospitals and matched hospitals (difference, 0.06 percentage points; 95% CI, -0.28 to 0.17). Prior to MSSP, readmission rates in SNHs were higher compared to non-SNHs, but MSSP did not have significantly different impact on hospital readmission and mortality rates for SNHs and non-SNHs.

CONCLUSION

MSSP led to slightly fewer readmissions without increases in mortality for Medicare patients hospitalized with ischemic stroke. Similar reductions in readmission rates were observed in SNHs and non-SNHs participating in MSSP, indicating persistent gaps between SNHs and non-SNHs.