30-day Risk-standardized Mortality Research Articles

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.

Association of short-term hospital-level outcome metrics with 1-year mortality and recurrence for US Medicare beneficiaries with ischemic stroke.

Whether stroke patients treated at hospitals with better short-term outcome metrics have better long-term outcomes is unknown. We investigated whether treatment at US hospitals with better 30-day hospital-level stroke outcome metrics was associated with better 1-year outcomes, including reduced mortality and recurrent stroke, for patients after ischemic stroke. This cohort study included Medicare fee-for-service beneficiaries aged ≥65 years discharged alive from US hospitals with a principal diagnosis of ischemic stroke from 07/01/2015 to 12/31/2018. We categorized patients by the treating hospital's performance on the CMS hospital-specific 30-day risk-standardized all-cause mortality and readmission measures for ischemic stroke from 07/01/2012 to 06/30/2015: Low-Low (both CMS mortality and readmission rates for the hospital were <25th percentile of national rates), High-High (both >75th percentile), and Intermediate (all other hospitals). We balanced characteristics between hospital performance categories using stabilized inverse probability weights (IPW) based on patient demographic and clinical factors. We fit Cox models assessing patient risks of 1-year all-cause mortality and ischemic stroke recurrence across hospital performance categories, weighted by the IPW and accounting for competing risks. There were 595,929 stroke patients (mean age 78.9±8.8 years, 54.4% women) discharged from 2,563 hospitals (134 Low-Low, 2288 Intermediate, 141 High-High). For Low-Low, Intermediate, and High-High hospitals, respectively, 1-year mortality rates were 23.8% (95% confidence interval [CI] 23.3%-24.3%), 25.2% (25.1%-25.3%), and 26.5% (26.1%-26.9%), and recurrence rates were 8.0% (7.6%-8.3%), 7.9% (7.8%-8.0%), and 8.0% (7.7%-8.3%). Compared with patients treated at High-High hospitals, those treated at Low-Low and Intermediate hospitals, respectively, had 15% (hazard ratio 0.85; 95% CI 0.82-0.87) and 9% (0.91; 0.89-0.93) lower risks of 1-year mortality but no difference in recurrence. Ischemic stroke patients treated at hospitals with better CMS short-term outcome metrics had lower risks of post-discharge 1-year mortality, but similar recurrent stroke rates, compared with patients treated at other hospitals.

Relationship Between In-Hospital Adverse Events and Hospital Performance on 30-Day All-cause Mortality and Readmission for Patients With Heart Failure.

Hospitals with high mortality and readmission rates for patients with heart failure (HF) might also perform poorly in other quality concepts. We sought to evaluate the association between hospital performance on mortality and readmission with hospital performance rates of safety adverse events. This cross-sectional study linked the 2009 to 2019 patient-level adverse events data from the Medicare Patient Safety Monitoring System, a randomly selected medical records-abstracted patient safety database, to the 2005 to 2016 hospital-level HF-specific 30-day all-cause mortality and readmissions data from the United States Centers for Medicare & Medicaid Services. Hospitals were classified to one of 3 performance categories based on their risk-standardized 30-day all-cause mortality and readmission rates: better (both in <25th percentile), worse (both >75th percentile), and average (otherwise). Our main outcome was the occurrence (yes/no) of one or more adverse events during hospitalization. A mixed-effect model was fit to assess the relationship between a patient's risk of having adverse events and hospital performance categories, adjusted for patient and hospital characteristics. The study included 39 597 patients with HF from 3108 hospitals, of which 252 hospitals (8.1%) and 215 (6.9%) were in the better and worse categories, respectively. The rate of patients with one or more adverse events during a hospitalization was 12.5% (95% CI, 12.1-12.8). Compared with patients admitted to better hospitals, patients admitted to worse hospitals had a higher risk of one or more hospital-acquired adverse events (adjusted risk ratio, 1.24 [95% CI, 1.06-1.44]). Patients admitted with HF to hospitals with high 30-day all-cause mortality and readmission rates had a higher risk of in-hospital adverse events. There may be common quality issues among these 3 measure concepts in these hospitals that produce poor performance for patients with HF.

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

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. 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. 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. 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.

Abstract 157: Association Of Hospital Performance With 1-Year Mortality And Recurrence After Ischemic Stroke Among US Medicare Beneficiaries

Background: Whether stroke patients treated at hospitals with better short-term quality performance metrics have better long-term outcomes is unknown. We determined if ischemic stroke patients treated at US hospitals with better past performance on the Centers for Medicare &amp; Medicaid Services (CMS) 30-day mortality and readmission outcome measures for stroke had better 1-year outcomes compared to those treated at other hospitals. Methods: We included all Medicare fee-for-service beneficiaries aged ≥65 years discharged alive from US hospitals with a principal diagnosis of ischemic stroke from 07/01/2015 to 12/31/2018. We categorized patients by the treating hospital’s performance on the CMS hospital-specific 30-day risk-standardized all-cause mortality and readmission rate measures for stroke from 07/01/2012 to 06/30/2015: Low-Low (both CMS mortality and readmission rates for the hospital were &lt;25 th percentile of national rates), High-High (both &gt;75 th percentile), and Intermediate (all others). We balanced characteristics between hospital performance categories using stabilized inverse probability weights (IPW) based on patient demographic and clinical factors. We fit Cox models to assess patient risks of 1-year mortality and recurrent stroke across hospital performance categories, weighted by the IPW and accounting for competing risks. Results: There were 595,929 stroke patients (mean age 78.9y, 54.4% women, 82.2% White) discharged from 134 Low-Low, 2288 Intermediate, and 141 High-High hospitals. For patients treated at Low-Low, Intermediate, and High-High hospitals, respectively, 1-year mortality rates were 23.8% (95% CI 23.3-24.3%), 25.2% (25.1-25.3%), and 26.5% (26.1-26.9%), and recurrence rates were 8.0% (7.6-8.3%), 7.9% (7.8-8.0%), and 8.0% (7.7-8.3%). Compared with patients at High-High hospitals, those at Low-Low and Intermediate hospitals, respectively, had 15% (hazard ratio 0.85; 95% CI 0.82-0.87) and 9% (0.91; 0.89-0.93) lower risks of 1-year mortality but no difference in 1-year recurrence. Conclusions: Ischemic stroke patients treated at better-performing hospitals had lower risks of post-discharge 1-year mortality but similar risks of recurrent stroke compared with patients treated at other hospitals.

Risk-standardized mortality metric to monitor hospital performance for chest pain presentations.

Risk-standardized mortality rates (RSMR) have been used to monitor hospital performance in procedural and disease-based registries, but limitations include the potential to promote risk-averse clinician decisions and a lack of assessment of the whole patient journey. We aimed to determine whether it is feasible to use RSMR at the symptom-level to monitor hospital performance using routinely collected, linked, clinical and administrative data of chest pain presentations. We included 192 978 consecutive adult patients (mean age 62 years; 51% female) with acute chest pain without ST-elevation brought via emergency medical services (EMS) to 53 emergency departments in Victoria, Australia (1/1/2015-30/6/2019). From 32 candidate variables, a risk-adjusted logistic regression model for 30-day mortality (C-statistic 0.899) was developed, with excellent calibration in the full cohort and with optimism-adjusted bootstrap internal validation. Annual 30-day RSMR was calculated by dividing each hospital's observed mortality by the expected mortality rate and multiplying it by the annual mean 30-day mortality rate. Hospital performance according to annual 30-day RSMR was lower for outer regional or remote locations and at hospitals without revascularisation capabilities. Hospital rates of angiography or transfer for patients diagnosed with non-ST elevation myocardial infarction (NSTEMI) correlated with annual 30-day RSMR, but no correlations were observed with other existing key performance indicators. Annual hospital 30-day RSMR can be feasibly calculated at the symptom-level using routinely collected, linked clinical, and administrative data. This outcome-based metric appears to provide additional information for monitoring hospital performance in comparison with existing process of care key performance measures.

Improving and Promoting Social Determinants of Health at a System Level.

Social determinants of health (SDoH), such as low socioeconomic status, race and ethnicity, poor environmental conditions, food insecurity, and inadequate access to health care services, are known to adversely affect health outcomes. The aim of this project is to address SDoH needs with clinical care to further improve patient outcomes and assist safe discharge. Prime Healthcare Service's model to address SDoH needs focused on supplementing its existing care coordination process with digital innovation while leveraging its highly skilled multidisciplinary team of physicians and mid-level providers, care coordinators, case managers, and social workers and strengthening its relationships with community partners. Prime extended the scope of its services by growing its providers and care navigators' presence in the community to assess needs and foster hospital-community relationships to meet the needs of individual patients. The 2021 Lown Hospital Index, which includes matrices Equity, Value, and Outcomes, has ranked Prime Healthcare Services, as a system, #1 overall in New Jersey and Nevada states. It also ranked Saint Michael's Medical Center number 2 in New Jersey for social responsibility, Centinela Hospital Medical Center number 9 in California for clinical outcomes, and St. Joseph Medical Center number 3 in Missouri for outcomes. The Index also ranked North Vista Hospital as the number 1 most racially inclusive hospital in Nevada. The acute myocardial infarction (AMI) 30-day risk-standardized mortality rate was reduced by an average 1% across the system, while the risk-standardized AMI complication rate was at 0%. Development of an SDoH screening tool, prioritizing high-risk factors, involving a multidisciplinary team, and strengthening the post-acute network have helped address and link the role of SDoH factors in patient outcomes and has resulted in reduced readmission rate, lower mortality and complications rate, and improved cost alignment with the Centers for Medicare & Medicaid Services payment reforms initiative and value-based care.

Risk-standardized sepsis mortality map of the United States.

ObjectiveSepsis is the leading cause of in-hospital mortality in the United States (US). Quality improvement initiatives for improving sepsis care depend on accurate estimates of sepsis mortality. While hospital 30-day risk-standardized mortality rates have been published for patients hospitalized with acute myocardial infarction, heart failure, and pneumonia, risk-standardized mortality rates for sepsis have not been well characterized. We aimed to construct a sepsis risk-standardized mortality rate map for the United States, to illustrate disparities in sepsis care across the country.MethodsThis cross-sectional study included adults from the US Nationwide Inpatient Sample who were hospitalized with sepsis between 1 January 2010 and 30 December 2011. Hospital-level risk-standardized mortality rates were calculated using hierarchical logistic modelling, and were risk-adjusted with predicted mortality derived from (1) the Sepsis Risk Prediction Score, a logistic regression model, and (2) gradient-boosted decision trees, a supervised machine learning (ML) algorithm.ResultsAmong 1,739,033 adults hospitalized with sepsis, 50% were female, and the median age was 71 years (interquartile range: 58–81). The national median risk-standardized mortality rate for sepsis was 18.4% (interquartile range: 17.0, 21.0) by the boosted tree model, which had better discrimination than the Sepsis Risk Prediction Score model (C-statistic 0.87 and 0.78, respectively). The highest risk-standardized mortality rates were found in Wyoming, North Dakota, and Mississippi, while the lowest were found in Arizona, Colorado, and Michigan.ConclusionsWide variation exists in sepsis risk-standardized mortality rates across states, representing opportunities for improvement in sepsis care. This represents the first map of state-level variation of risk-standardized mortality rates in sepsis.

Association of readmission penalty amount with subsequent 30-day risk standardized readmission and mortality rates among patients hospitalized with heart failure: An analysis of get with the guidelines – heart failure participating centers

The Hospital Readmissions Reduction Program penalizes hospitals with excess 30-day risk-standardized readmission rates (RSRR) for heart failure (HF). The association of financial penalty amount with subsequent short-term clinical outcomes is unknown. Patients admitted to American Heart Association Get With The Guidelines-HF registry participating centers from October 1, 2012 through December 1, 2015 who had Medicare-linked data were included. October 2012 hospital-specific penalty amounts were calculated based on diagnosis-related group payments and excess readmission ratios. Adjusted Cox models were created to evaluate the association of penalty amount categories (non-penalized: 0%; low-penalized: >0%-<0.50%; mid-penalized ≥0.50%-<0.99%; high-penalized ≥0.99%) with subsequent 30-day RSRR and risk-standardized mortality rates (RSMR). Trends in post-discharge 30-day RSRR and RSMR from 2012 to 2015 were analyzed across hospitals stratified by penalty amount categories. The present study included 61,329 patients who were admitted across 262 hospitals. Compared with patients admitted to non-penalized hospitals (36.3%), those admitted to increasingly penalized hospitals were more likely to have higher 30-day RSRR (low-penalized [43.9%]: HR, 1.10 [95% CI, 1.04-1.16]; mid-penalized [12.0%]: HR, 1.07 [95% CI, 0.99-1.16]; high-penalized [7.9%]: HR, 1.23 [95% CI, 1.12-1.35]) but not 30-day RSMR. Over time, 30-day RSRR and RSMR did not meaningfully change across penalized versus non-penalized hospitals. Financial penalties based on 30-day RSRR are not associated with declines in 30-day RSRR or RSMR from 2012 to 2015 among patients hospitalized with HF. Financially penalizing hospitals based on current Hospital Readmissions Reduction Program metrics may not incentivize improvements in short-term clinical outcomes for HF.

County-Level Socioeconomic Status Adjustment of Acute Myocardial Infarction Mortality Hospital Performance Measure in the U.S.

The U.S. Centers for Medicare and Medicaid Services’ (CMS’s) Hospital Compare (HC) data provides a collection of risk-adjusted hospital performance metrics intended to allow comparison of hospital-provided care. However, CMS does not adjust for socioeconomic status (SES) factors, which have been found to be associated with disparate health outcomes. Associations between county-level SES factors and CMS’s risk-adjusted 30-day acute myocardial infarction (AMI) mortality rates are explored for n = 2462 hospitals using a variety of sources for county-level SES information. Upon performing multiple imputation, a stepwise backward elimination model selection approach using Akaike’s information criteria was used to identify the optimal model. The resulting model, comprised of 14 predictors mostly at the county level, provides an additional 8% explanatory power to capture the variability in 30-day risk-standardized AMI mortality rates, which already account for patient-level clinical differences. SES factors may be an important feature for inclusion in future risk-adjustment models, which will have system and policy implications for distributing resources to hospitals, such as reimbursements. It also serves as a stepping stone to identify and address long-standing SES-related inequities.

828. Evaluation of Home Time as a Patient-Centered Metric for Pneumonia Hospitalizations: A Retrospective Cohort Study of Medicare Fee-For-Service Beneficiaries

BackgroundThe Centers for Medicare & Medicaid Services (CMS) uses hospital readmission to incentivize hospital care delivery for acute conditions including pneumonia. However, current CMS performance metrics do not account for the competing risk of mortality in the post-discharge period or during the hospital stay. Our objective was to assess home time within 30 days after discharge among pneumonia hospitalizations, as a patient-centered metric.MethodsA retrospective observational study was conducted in a cohort of Medicare fee-for-service beneficiaries admitted between 01/01/2015 and 11/30/2017. Home time was the number of days spent alive, out of an acute care setting, skilled nursing facility, or a rehabilitation facility within 30 days of discharge. If a patient spends any part of a day in a care facility or died after discharge, then that day was not included in the calculation for home time. Hospital-level rates of risk-adjusted home time were calculated using multilevel regression models. We compared hospital performance on 30-day risk-standardized home time with its performance on 30-day risk standardized readmission rate (RSRR) and mortality rate (RSMR). Characteristics of hospitals with high and low risk-adjusted home-time were compared.ResultsAmong 1.7 million pneumonia admissions admitted to 3,116 hospitals, the median 30-day risk-standardized home time was 20.5 days (interquartile range: 18.9-21.9 days). Hospital-level characteristics such as case volume, bed size, for-profit ownership, rural location of hospital, teaching status, and participation in the bundle payment program were significantly associated with home-time. RSRR (rho: -0.233, p< 0.0001) and RSMR (rho: -0.223, p< 0.0001) had weak, inverse correlations with home time. Using the home time metric, 35.5% of hospitals were reclassified as high performers compared with their average or poor performance on the RSRR or RSMR metric.ConclusionHome time is a novel, patient-centered, hospital-level metric that can be easily calculated using claims data, accounts for differences in post-discharge mortality and can be intuitively interpreted. Utilization of this metric could potentially have policy implications in assessing hospital performance on delivery of healthcare to pneumonia patients.DisclosuresRajeshwari Nair, PhD, Merck and Company, Inc. (Research Grant or Support)

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

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. 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. 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. 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. 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. 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.

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

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. 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. 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. 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. 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. 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. 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.

Magnet Hospitals and 30-Day Readmission and Mortality Rates for Medicare Beneficiaries.

US hospitals are penalized for excess 30-day readmissions and mortality for select conditions. Under the Centers for Medicare and Medicaid Services policy, readmission prevention is incentivized to a greater extent than mortality reduction. A strategy to potentially improve hospital performance on either measure is by improving nursing care, as nurses provide the largest amount of direct patient care. However, little is known as to whether achieving nursing excellence, such as Magnet status, is associated with improved hospital performance on readmissions and mortality. The purpose of this study was to examine the relationship between hospitals' Magnet status and performance on readmission and mortality rates for Medicare beneficiaries. This is a cross-sectional analysis of Medicare readmissions and mortality reduction programs from 2013 to 2016. A propensity score-matching approach was used to take into account differences in baseline characteristics when comparing Magnet and non-Magnet hospitals. The sample was comprised of 3877 hospitals. The outcome measures were 30-day risk-standardized readmission and mortality rates. Following propensity score matching on hospital characteristics, we found that Magnet hospitals outperformed non-Magnet hospitals in reducing mortality; however, Magnet hospitals performed worse in reducing readmissions for acute myocardial infarction, coronary artery bypass grafting, and stroke. Magnet hospitals performed better on the Hospital Value-Based Purchasing Mortality Program than the Hospital Readmissions Reduction Program. The results of this study suggest the need for The Magnet Recognition Program to examine the role of nurses in postdischarge activities as a component of its evaluation criteria.

Readmissions Reduction Program: Mortality and Readmissions for Chronic Obstructive Pulmonary Disease.

Rationale: Implementation of the Hospital Readmissions Reduction Program (HRRP) following discharge of patients with chronic obstructive pulmonary disease (COPD) has led to a reduction in 30-day readmissions with unknown effects on postdischarge mortality.Objectives: To examine the association of HRRP with 30-day hospital readmission and 30-day postdischarge mortality rate in patients after discharge from COPD hospitalization.Methods: Retrospective cohort study of readmission and mortality rates in a national cohort (N = 4,587,542) of admissions of Medicare fee-for-service beneficiaries 65 years or older with COPD from 2006 to 2017.Measurements and Main Results: Data were analyzed for three nonoverlapping periods based on implementation of the HRRP specific to COPD: 1) preannouncement (December 2006 to March 2010), 2) announcement (April 2010 to August 2014), and 3) implementation (October 2014 to November 2017). The 30-day readmission rate decreased from 20.54% in the preannouncement period (December 2006 to July 2008) to 18.74% in the implementation period (May 2016 to November 2017). The 30-day risk-standardized postdischarge mortality rates were 6.91%, 6.59%, and 7.30% for the preannouncement, announcement, and implementation periods, respectively. Generalized estimating equations analyses estimated an additional 1,196 deaths (October 2014 to April 2016) and 3,858 deaths (May 2016 to November 2017) during the HRRP implementation period.Conclusions: We found a reduction in 30-day all-cause readmission rate during the implementation period compared with the preannouncement phase. HRRP implementation was also associated with a significant increase in 30-day mortality after discharge from COPD hospitalization. Additional research is necessary to confirm our findings and understand the factors contributing to increased mortality in patients with COPD in the HRRP implementation period.

Association of Do-Not-Resuscitate Patient Case Mix With Publicly Reported Risk-Standardized Hospital Mortality and Readmission Rates

The Centers for Medicare and Medicaid Services's (CMS's) 30-day risk-standardized mortality rate (RSMR) and risk-standardized readmission rate (RSRR) models do not adjust for do-not-resuscitate (DNR) status of hospitalized patients and may bias Hospital Readmissions Reduction Program (HRRP) financial penalties and Overall Hospital Quality Star Ratings. To identify the association between hospital-level DNR prevalence and condition-specific 30-day RSMR and RSRR and the implications of this association for HRRP financial penalty. This cross-sectional study obtained patient-level data from the Medicare Limited Data Set Inpatient Standard Analytical File and hospital-level data from the CMS Hospital Compare website for all consecutive Medicare inpatient encounters from July 1, 2015, to June 30, 2018, in 4484 US hospitals. Hospitalized patients had a principal diagnosis of acute myocardial infarction (AMI), heart failure (HF), stroke, pneumonia, or chronic obstructive pulmonary disease (COPD). Incoming acute care transfers, discharges against medical advice, and patients coming from or discharged to hospice were among those excluded from the analysis. Present-on-admission (POA) DNR status was defined as an International Classification of Diseases, Ninth Revision diagnosis code of V49.86 (before October 1, 2015) or as an International Statistical Classification of Diseases and Related Health Problems, Tenth Revision diagnosis code of Z66 (beginning October 1, 2015). Hospital-level prevalence of POA DNR status was calculated for each of the 5 conditions. Hospital-level 30-day RSMRs and RSRRs for 5 condition-specific cohorts (mortality cohorts: AMI, HF, stroke, pneumonia, and COPD; readmission cohorts: AMI, HF, pneumonia, and COPD) and HRRP financial penalty status (yes or no). Included in the study were 4 884 237 inpatient encounters across condition-specific 30-day mortality cohorts (patient mean [SD] age, 78.8 [8.5] years; 2 608 182 women [53.4%]) and 4 450 378 inpatient encounters across condition-specific 30-day readmission cohorts (patient mean [SD] age, 78.6 [8.5] years; 2 349 799 women [52.8%]). Hospital-level median (interquartile range [IQR]) prevalence of POA DNR status in the mortality cohorts varied: 11% (7%-16%) for AMI, 13% (7%-23%) for HF, 14% (9%-22%) for stroke, 17% (9%-26%) for pneumonia, and 10% (5%-18%) for COPD. For the readmission cohorts, the hospital-level median (IQR) POA DNR prevalence was 9% (6%-15%) for AMI, 12% (6%-22%) for HF, 16% (8%-24%) for pneumonia, and 9% (4%-17%) for COPD. The 30-day RSMRs were significantly higher for hospitals in the highest quintiles vs the lowest quintiles of DNR prevalence (eg, AMI: 12.9 [95% CI, 12.8-13.1] vs 12.5 [95% CI, 12.4-12.7]; P < .001). The inverse was true among the readmission cohorts, with the highest quintiles of DNR prevalence exhibiting the lowest RSRRs (eg, AMI: 15.3 [95% CI, 15.1-15.5] vs 15.9 [95% CI, 15.7-16.0]; P < .001). A 1% absolute increase in risk-adjusted hospital-level DNR prevalence was associated with greater odds of avoiding HRRP financial penalty (odds ratio, 1.06; 95% CI, 1.04-1.08; P < .001). This cross-sectional study found that the lack of adjustment in CMS 30-day RSMR and RSRR models for POA DNR status of hospitalized patients may be associated with biased readmission penalization and hospital-level performance.

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

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. 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. 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. 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.

Evaluation of Risk-Adjusted Home Time After Acute Myocardial Infarction as a Novel Hospital-Level Performance Metric for Medicare Beneficiaries.

The utility of 30-day risk-standardized readmission rate (RSRR) as a hospital performance metric has been a matter of debate. Home time is a patient-centered outcome measure that accounts for rehospitalization, mortality, and postdischarge care. We aim to characterize risk-adjusted 30-day home time in patients with acute myocardial infarction (AMI) as a hospital-level performance metric and to evaluate associations with 30-day RSRR, 30-day risk-standardized mortality rate (RSMR), and 1-year RSMR. The study included 984 612 patients with AMI hospitalization across 2379 hospitals between 2009 and 2015 derived from 100% Medicare claims data. Home time was defined as the number of days alive and spent outside of a hospital, skilled nursing facility, or intermediate-/long-term acute care facility 30 days after discharge. Correlations between hospital-level risk-adjusted 30-day home time and 30-day RSRR, 30-day RSMR, and 1-year RSMR were estimated with the Pearson correlation. Reclassification in hospital performance using 30-day home time versus 30-day RSRR and 30-day RSMR was also evaluated. Median hospital-level risk-adjusted 30-day home time was 24.0 days (range, 15.3-29.0 days). Hospitals with higher home time were more commonly academic centers, had available cardiac surgery and rehabilitation services, and had higher AMI volume and percutaneous coronary intervention use during the AMI hospitalization. Of the mean 30-day home time days lost, 58% were to intermediate-/long-term care or skilled nursing facility stays (4.7 days), 30% to death (2.5 days), and 12% to readmission (1.0 days). Hospital-level risk-adjusted 30-day home time was inversely correlated with 30-day RSMR (r=-0.22, P<0.0001) and 30-day RSRR (r=-0.25, P<0.0001). Patients admitted to hospitals with higher risk-adjusted 30-day home time had lower 30-day readmission (quartile 1 versus 4, 21% versus 17%), 30-day mortality rate (5% versus 3%), and 1-year mortality rate (18% versus 12%). Furthermore, 30-day home time reclassified hospital performance status in ≈30% of hospitals versus 30-day RSRR and 30-day RSMR. Thirty-day home time for patients with AMI can be assessed as a hospital-level performance metric with the use of Medicare claims data. It varies across hospitals, is associated with postdischarge readmission and mortality outcomes, and meaningfully reclassifies hospital performance compared with the 30-day RSRR and 30-day RSMR metrics.

Use of 90-day mortality does not change assessment of hospital quality after coronary artery bypass grafting in New York State

ObjectivesPublicly reported postoperative 30-day mortality rates are commonly used to compare hospital quality after coronary artery bypass grafting. We sought to determine whether 90-day mortality rates, which are not publicly reported but better capture postdischarge mortality, are a better determinant of hospital performance. MethodsWe performed a retrospective cohort analysis of 30- versus 90-day risk-standardized mortality rates at adult cardiac surgical centers in New York State from 2008 to 2014. Hospitals were classified as good or poor performing outliers at each time point based on the bounds of the 95% confidence interval around each hospital's predicted risk-standardized mortality rates determined via hierarchical models. The primary outcome was change in institutional performance via outlier classification from 30 to 90 days. ResultsDuring the study period, 72,398 adults underwent a coronary artery bypass grafting procedure at 1 of 42 institutions. The risk-standardized mortality rates increased from 30 to 90 days at all institutions, with a median 30-day risk-standardized mortality rate of 2.16% (interquartile range, 0.69%) and median 90-day risk-standardized mortality rate of 3.69% (interquartile range, 1.00%). In using a 90-day instead of a 30-day metric, 3 hospitals changed outlier status. One hospital improved to a good from as expected performer, and 2 worsened to as expected from good performers. ConclusionsIn a cohort of patients who underwent coronary artery bypass grafting surgery from 2008 to 2014 in New York State, use of a 90-day mortality metric resulted in a change in hospital quality assessment for a minority of hospitals. The use of 90-day mortality may not provide additional value when evaluating institutional performance for this population.

Impact of left ventricular assist devices and heart transplants on acute myocardial infarction and heart failure mortality and readmission measures.

Concern has been raised about consequences of including patients with left ventricular assist device (LVAD) or heart transplantation in readmission and mortality measures. We calculated unadjusted and hospital-specific 30-day risk-standardized mortality (RSMR) and readmission (RSRR) rates for all Medicare fee-for-service beneficiaries with a primary diagnosis of AMI or HF discharged between July 2010 and June 2013. Hospitals were compared before and after excluding LVAD and heart transplantation patients. LVAD indication was measured. In the AMI mortality (n = 506,543) and readmission (n = 526,309) cohorts, 1,166 and 1,016 patients received an LVAD while 3 and 2 had a heart transplantation, respectively. In the HF mortality (n = 1,015,335) and readmission (n = 1,254,124) cohorts, 789 and 931 received an LVAD, while 212 and 202 received a heart transplantation, respectively. Less than 2% of hospitals had either ≥6 patients who received an LVAD or, independently, had ≥1 heart transplantation. The AMI mortality and readmission cohorts used 1.8% and 2.8% of LVADs for semi-permanent/permanent indications, versus 73.8% and 78.0% for HF patients, respectively. The rest were for temporary/external indications. In the AMI cohort, RSMR for hospitals without LVAD patients versus hospitals with ≥6 LVADs was 14.8% and 14.3%, and RSRR was 17.8% and 18.3%, respectively; the HF cohort RSMR was 11.9% and 9.7% and RSRR was 22.6% and 23.4%, respectively. In the AMI cohort, RSMR for hospitals without versus with heart transplantation patients was 14.7% and 13.9% and RSRR was 17.8% and 17.7%, respectively; in the HF cohort, RSMR was 11.9% and 11.0%, and RSRR was 22.6% and 22.6%, respectively. Estimations changed ≤0.1% after excluding LVAD or heart transplantation patients. Hospitals caring for ≥6 patients with LVAD or ≥1 heart transplantation typically had a trend toward lower RSMRs but higher RSRRs. Rates were insignificantly changed when these patients were excluded. LVADs were primarily for acute-care in the AMI cohort and chronic support in the HF cohort. LVAD and heart transplantation patients are a distinct group with differential care requirements and outcomes, thus should be considered separately from the rest of the HF cohort.