Public Reporting of 30-Day Mortality for Patients Hospitalized With Acute Myocardial Infarction and Heart Failure

30-day in-hospital stroke case fatality and significant risk factors in sub-Saharan-Africa: A systematic review and meta-analysis

Existing studies investigating 30-day in-hospital stroke case fatality rates in sub-Saharan Africa have produced varying results, underscoring the significance of obtaining precise and reliable estimations for this indicator. Consequently, this study aimed to conduct a systematic review and update of the current scientific evidence regarding 30-day in-hospital stroke case fatality and associated risk factors in sub-Saharan Africa. Medline/PubMed, Cumulative Index to Nursing and Allied Health Literature (CINAHL), APA PsycNet (encompassing PsycINFO and PsychArticle), Google Scholar, and Africa Journal Online (AJOL) were systematically searched to identify potentially relevant articles. Two independent assessors extracted the data from the eligible studies using a pre-tested and standardized excel spreadsheet. Outcomes were 30-day in-hospital stroke case fatality and associated risk factors. Data was pooled using random effects model. Ninety-three (93) studies involving 42,057 participants were included. The overall stroke case fatality rate was 27% [25%-29%]. Subgroup analysis revealed 24% [21%-28%], 25% [21%-28%], 29% [25%-32%] and 31% [20%-43%] stroke case fatality rates in East Africa, Southern Africa, West Africa, and Central Africa respectively. Stroke severity, stroke type, untyped stroke, and post-stroke complications were identified as risk factors. The most prevalent risk factors were low (<8) Glasgow Coma Scale score, high (≥10) National Institute Health Stroke Scale score, aspiration pneumonia, hemorrhagic stroke, brain edema/intra-cranial pressure, hyperglycemia, untyped stroke (stroke diagnosis not confirmed by neuroimaging), recurrent stroke and fever. The findings indicate that one in every four in-hospital people with stroke in sub-Saharan Africa dies within 30 days of admission. Importantly, the identified risk factors are mostly modifiable and preventable, highlighting the need for context-driven health policies, clinical guidelines, and treatments targeting these factors.

No short-term mortality from benzodiazepine use post-acute ischemic stroke after accounting for bias

BACKGROUND AND OBJECTIVES

Older adults receive benzodiazepines for agitation, anxiety, and insomnia after acute ischemic stroke (AIS). No trials have been conducted to determine if benzodiazepine use affects poststroke mortality in the elderly.

METHODS

We examined the association between initiating benzodiazepines within 1 week after AIS and 30-day mortality. We included patients ≥65 years, admitted for new nonsevere AIS (NIH-Stroke-Severity[NIHSS]≤ 20), 2014-2020, with no recorded benzodiazepine use in the previous 3 months and no contraindication for use. We linked a stroke registry to electronic health records, used inverse-probability weighting to address confounding, and estimated the risk difference (RD). A process of cloning, weighting, and censoring was used to avoid immortal time bias.

RESULTS

Among 2,584 patients, 389 received benzodiazepines. The crude 30-day mortality risk from treatment initiation was 212/1,000 among patients who received benzodiazepines, while the 30-day mortality was 34/1,000 among those who did not. When follow-up was aligned on day of AIS admission and immortal time was assigned to the two groups, the estimated risks were 27/1,000 and 22/1,000, respectively. Upon further adjustment for confounders, the RD was 5 (-12 to 19) deaths/1,000 patients.

CONCLUSION

The observed higher 30-day mortality associated with benzodiazepine initiation within 7 days was largely due to bias.

Texas Hospitals' Perspectives About NICU Performance Measures: A Mixed-Methods Study

BACKGROUND AND OBJECTIVES

This study was conducted to assess Texas hospital leaders' perspectives about neonatal intensive care (NICU) performance measures.

METHODS

We conducted an explanatory mixed-methods study. First, we sent a survey and a copy of the Dartmouth Atlas of Neonatal Intensive Care to clinical and administrative leaders of 150 NICUs in Texas. We asked respondents to review the chapter that reported Texas-specific results and respond to a variety of open and closed-ended questions about the overall usefulness of the report. Second, we conducted semistructured qualitative interviews with a subset of survey respondents to better understand their perspectives.

RESULTS

The survey had a 50% hospital response rate. Respondents generally found the report to be interesting and useful, and 87.7% of all respondents reported being in favor of receiving future reports with their own hospital's data benchmarked against anonymous peers. All of the specific measures in the Atlas were found to be of interest and valuable, with NICU admissions and special care days rating among the most interesting and useful. In the semistructured interviews, respondents expressed that a report with performance data would serve as a mechanism to drive change by identifying opportunities for improvement.

CONCLUSION

Texas hospital NICU leaders are interested in routinely receiving more information about their own NICU's performance anonymously benchmarked against their peers. This would facilitate a greater understanding of a unit's functionality, as well as accelerate clinically appropriate quality improvement initiatives, which together have the potential to deliver better newborn care at lower costs for all Texans.

Clinical profile and predictors of 30-day all-cause mortality of ST-elevation myocardial infarction (STEMI) patients receiving fibrinolytic therapy in an Asian population

BACKGROUND

Fibrinolysis remains the primary reperfusion strategy for ST-elevation myocardial infarction (STEMI) in many Asian countries. The outcomes and factors affecting mortality in STEMI fibrinolysis in the Asian population are lacking despite being widely used.

OBJECTIVES

This study aimed to assess the clinical profile of patients and predictors affecting STEMI mortality in an Asian population.

METHODS

This single-center retrospective study analyzed data from STEMI patients who received fibrinolytic therapy from 2016 to 2020 in a tertiary hospital. Logistic regression analysis was performed to identify the significant predictors of the 30-day all-cause mortality, the primary outcome.

RESULTS

A total of 859 patients were included. Their mean age was 53.6 ±12.1 years and they were predominantly male (n=769, 89.4%). The majority of them had anterior involvement STEMI (n = 477, 55.5%) and presented with Killip ≥ II (n = 424, 49.4%). The 30-day all-cause mortality was 12.0% (n =  103). The final model found six predictors for 30-day mortality: age ≥75 (aOR 4.784, p < 0.001), female gender (aOR 2.869, p = 0.001), pre-existing hypertension (aOR 1.623, p = 0.046), anterior myocardial infarction (MI) (aOR 1.947, p < 0.001), Killip class (p < 0.001) and heart rate ≥100 at presentation (aOR 1.823, p =  0.016). Following fibrinolytic therapy, five predictors were found to affect 30-day mortality, i.e. failed fibrinolysis (aOR 2.094, p = 0.041), bleeding events, congestive heart failure (aOR 3.554, p = 0.046), ventricular fibrillation/ tachycardia (aOR 5.920, p < 0.001), and atrial fibrillation/ flutter (aOR 2.968, p = 0.016).

CONCLUSION

Our STEMI patients were younger and more ill at presentation. The risk predictors on 30-day all-cause mortality identified in our Asian population allow the clinicians to better triage and manage STEMI patients.

You cannot be good at everything: tradeoff and returns in healthcare services

Purpose

In the face of growing demand for care and tightening resource constraints, hospitals need to ensure access to care that is affordable and effective. Yet, the multiplicity of objectives is a key challenge in this industry. An understanding of the interrelationships (tradeoffs) between the multiple outcome objectives of care (throughput, experiential and financial performance) and returns to operational inputs (diversification of care) is fundamental to improving access to care that is effective and affordable. This study serves to address this need.

Design/methodology/approach

The empirical analysis in the study builds on an output-oriented distance function model and uses a longitudinal panel dataset from 153 hospitals in California.

Findings

This study results point to key insights related to output–output tradeoffs along the production frontier. Specifically, the authors find that higher throughput rates may lead to significantly lower levels of experiential quality, and net revenue from operations, accounting for the clinical quality of care. Similarly, the authors’ findings highlight the resource intensity and operational challenges of improving experiential quality of care. In regards to input–output relationships, this study finds diversification of care is associated with increased throughput, improvements in service satisfaction and a corresponding increase in the net revenue from operations.

Originality/value

Highlighting the tradeoffs along the production frontier among the various outcomes of interest (throughput, experiential quality and net revenue from operations), and highlighting the link between diversification of care and care delivery outcomes at the hospital level are key contributions of this study. An understanding of the tradeoffs and returns in healthcare delivery serves to inform policy-making with key managerial implications in the delivery of care.

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.

A global point prevalence survey of antimicrobial use in neonatal intensive care units: The no-more-antibiotics and resistance (NO-MAS-R) study

BACKGROUND

Global assessment of antimicrobial agents prescribed to infants in the neonatal intensive care unit (NICU) may inform antimicrobial stewardship efforts.

METHODS

We conducted a one-day global point prevalence study of all antimicrobials provided to NICU infants. Demographic, clinical, and microbiologic data were obtained including NICU level, census, birth weight, gestational/chronologic age, diagnoses, antimicrobial therapy (reason for use; length of therapy), antimicrobial stewardship program (ASP), and 30-day in-hospital mortality.

FINDINGS

On July 1, 2019, 26% of infants (580/2,265; range, 0-100%; median gestational age, 33 weeks; median birth weight, 1800 g) in 84 NICUs (51, high-income; 33, low-to-middle income) from 29 countries (14, high-income; 15, low-to-middle income) in five continents received ≥1 antimicrobial agent (92%, antibacterial; 19%, antifungal; 4%, antiviral). The most common reasons for antibiotic therapy were "rule-out" sepsis (32%) and "culture-negative" sepsis (16%) with ampicillin (40%), gentamicin (35%), amikacin (19%), vancomycin (15%), and meropenem (9%) used most frequently. For definitive treatment of presumed/confirmed infection, vancomycin (26%), amikacin (20%), and meropenem (16%) were the most prescribed agents. Length of therapy for culture-positive and "culture-negative" infections was 12 days (median; IQR, 8-14) and 7 days (median; IQR, 5-10), respectively. Mortality was 6% (42%, infection-related). An NICU ASP was associated with lower rate of antibiotic utilization (p = 0·02).

INTERPRETATION

Global NICU antibiotic use was frequent and prolonged regardless of culture results. NICU-specific ASPs were associated with lower antibiotic utilization rates, suggesting the need for their implementation worldwide.

FUNDING

Merck & Co.; The Ohio State University College of Medicine Barnes Medical Student Research Scholarship.

Validity of National Institutes of Health Stroke Scale for Severity of Stroke to Predict Mortality Among Patients Presenting With Symptoms of Stroke

Introduction

Cerebrovascular accident (CVA), also termed as stroke, is the third leading cause of mortality and the most common cause of disability globally. The National Institutes of Health Stroke Scale (NIHSS) is a valid assessment tool utilized to determine the severity of the stroke and can be used to prioritize patients to design treatment plans, rehabilitation, and better clinical outcomes. The primary objective of this study was to determine the validity of the NIHSS to predict mortality among patients presenting with symptoms of a stroke.

Material and methods

This was a descriptive case-series conducted over a period of six months between September 2019 and February 2020 at a tertiary care hospital in Nawabshah, Pakistan. The sample population included 141 patients admitted within 24 hours of the onset of symptoms of a stroke. A neurological examination of the patients was performed. On admission, stroke severity was evaluated with the NIHSS. After an initial clinical evaluation, patients underwent a non-enhanced computed tomography (CT) scan of the brain. The score of NIHSS and mortality at 72 hours were recorded on the pre-defined proforma by the investigators. All statistical analysis was performed using Statistical Package for Social Sciences (SPSS) version 23.0 (Armonk, NY: IBM Corp).

Results

The mean age of the participants was 52.37±8.61 years. 68.1% of patients were hypertensive, 29.1% were diabetic, and 36.9% of patients were found with hyperlipidemia. The mortality rate was 41.1%. The mean NIHSS score was 16.68±6.72 points. The findings of this study demonstrated that the score of 14.9% cases was good (0-6 points), the score of 29.1% cases was moderate (7-15 points), and the score of 56% cases was poor (≥16 points). There was a significant association of NIHSS score with mortality (p<0.001).

Conclusions

Baseline NIHSS score has a profound association with mortality after acute stroke. It can help clinicians decide whether to provide thrombolytic treatment, rehabilitation or a combination of both in these patients and decrease the mortality rate. However, more studies are needed to potentiate these conclusions.

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.

Association of adoption of transradial access for percutaneous coronary intervention in ST elevation myocardial infarction with door-to-balloon time

Objectives

We aimed to study adoption of transradial primary percutaneous coronary intervention (TR‐PPCI) for ST elevation myocardial infarction (STEMI) (“radial first” approach) and its association with door‐to‐balloon time (D2BT).

Background

TR‐PPCI for STEMI is underutilized in the United States due to concerns about prolonging D2BT. Whether operators and hospitals adopting a radial first approach in STEMI incur prolonged D2BT is unknown.

Methods

In 1,272 consecutive cases of STEMI with PPCI at our hospital from January 1, 2011, to December 31, 2016, we studied TR‐PPCI adoption and its association with D2BT including a propensity matched analysis of similar risk TR‐PPCI and trans‐femoral primary PCI (TF‐PPCI) patients.

Results

With major increases in hospital‐level TR‐PPCI (hospital TR‐PPCI rate: 2.6% in 2011 to 79.4% in 2016, p‐trend<.001) and operator‐level TR‐PPCI (mean operator TR‐PPCI rate: 2.9% in 2011 to 81.1% in 2016, p‐trend = .005), median hospital level D2BT decreased from 102 min [81, 142] in 2011 to 84 min [60, 105] in 2016 (p‐trend<.001). TF crossover (10.3%; n = 57) was not associated with unadjusted D2BT (TR‐PPCI success 91 min [72, 112] vs. TF crossover 99 min [70, 115], p = .432) or D2BT adjusted for study year and presenting location (7.2% longer D2BT with TF crossover, 95% CI: −4.0% to +18.5%, p = .208). Among 273 propensity‐matched pairs, unadjusted D2BT (TR‐PPCI 98 [78, 117] min vs. TF‐PPCI 101 [76, 132] min, p = .304), and D2BT adjusted for study year and presenting location (5.0% shorter D2BT with TR‐PPCI, 95% CI: −12.4% to +2.4%, p = .188) were similar.

Conclusions

TR‐PPCI can be successfully implemented without compromising D2BT performance.

Clinical Model to Predict 90-Day Risk of Readmission After Acute Myocardial Infarction

BACKGROUND

Readmissions within 30 days after acute myocardial infarction have been used as a performance metric for hospitals. However, evolving concepts of value-based reimbursement have shifted the focus to 90 days after hospital discharge. Tools are needed to determine risk for 90-day readmission to identify patients who might benefit from enhanced transitional healthcare resources.

METHODS AND RESULTS

In this cohort study, we identified all Medicare beneficiaries with a primary diagnosis of acute myocardial infarction who were discharged from hospitals participating in National Cardiovascular Data Registry ACTION registry between 2008 and 2014. Among a random 70% sample (derivation cohort), we performed hierarchical proportional hazards regression, accounting for death as a competing risk, to assess predictors of all-cause readmission within 90 days. Models were validated in the remaining 30%. Among 86 849 unique patients, 23 912 (27.5%) were readmitted within 90 days. Of the readmissions, 55% occurred within 30 days and 81% occurred within 60 days. Predictors of readmission included older age and a history of diabetes mellitus or heart failure. Coronary revascularization was associated with a lower risk of readmission. A simple risk score incorporating patient demographic and clinical characteristics known before discharge identified groups of patients with readmission risks ranging from 13.1% to 42.9%. Model discrimination was moderate (C statistic=0.662), and calibration was excellent (slope=0.97, intercept=-0.04).

CONCLUSIONS

Readmission within 90 days of hospitalization for acute myocardial infarction can be predicted by variables known before discharge and offers the potential to prospectively design transitional care to the risks of individual patients.

Hospital Variation in 30-Day Mortality for Patients With Stroke; The Impact of Individual and Municipal Socio-Demographic Status

Background

Thirty‐day mortality after hospitalization for stroke is commonly reported as a quality indicator. However, the impact of adjustment for individual and/or neighborhood sociodemographic status (SDS) has not been well documented. This study aims to evaluate the role of individual and contextual sociodemographic determinants in explaining the variation across hospitals in Norway and determine the impact when testing for hospitals with low or high mortality.

Methods and Results

Patient Administrative System data on all 45 448 patients admitted to hospitals in Norway with an incident stroke diagnosis from 2005 to 2009 were included. The data were merged with data from several databases to obtain information on vital status (dead/alive) and individual SDS variables. Logistic regression models were compared to estimate the predictive effect of individual and neighborhood SDS on 30‐day mortality and to determine outlier hospitals. All individual SDS factors, except travel time, were statistically significant predictors of 30‐day mortality. Of the municipal variables, only the municipal variable proportion of low income was statistically significant as a predictor of 30‐day mortality. Including sociodemographic characteristics of the individual and other characteristics of the municipality improved the model fit. However, performance classification was only changed for 1 (out of 56) hospital, from “significantly high mortality” to “nonoutlier.”

Conclusions

Our study showed that those stroke patients with a lower SDS have higher odds of dying after 30 days compared with those with a higher SDS, although this did not have a substantial impact when classifying providers as performing as expected, better than expected, or worse than expected.

Short-term effects of ambient fine particulate air pollution on inpatient visits for myocardial infarction in Beijing, China

The effects of ambient fine particulate matter (PM_2.5) on the incidence of myocardial infarction have been reported, but little is known about this association in China. We conducted a time-series study of ambient PM_2.5 concentrations and inpatient visits for myocardial infarction in Beijing. A generalized additive model with a Poisson link was applied to estimate the percentage change in inpatient visits for myocardial infarction following a 10-μg/m³ increase in PM_2.5 concentrations. A total of 15,432 inpatient visits for myocardial infarction were identified between January 1, 2010, and June 30, 2012. A 10-μg/m³ increase in PM_2.5 concentrations was associated with a 0.46% (P ≤ 0.001) increase in daily inpatient visits for myocardial infarction. Males were more sensitive to the adverse effects, and the association was more significant during the warm season (May through October). Short-term exposure to PM_2.5 was associated with increased risk of inpatient visits for myocardial infarction in Beijing. The findings may be useful in developing more accurate targeted interventions.

Short-term effects of ambient air pollutants and myocardial infarction in Changzhou, China

Ambient air pollution had been shown strongly associated with cardiovascular diseases. However, the association between air pollution and myocardial infarction (MI) is inconsistent. In the present study, we conducted a time-series study to investigate the association between air pollution and MI. Daily air pollutants, weather data, and MI data were collected from January 2015 to December 2016 in Changzhou, China. Generalized linear model (GLM) was used to assess the immediate effects of air pollutants (PM_2.5, PM₁₀, NO₂, SO₂, and O₃) on MI. We identified a total of 5545 cases for MI, and a 10-μg/m³ increment in concentrations of PM_2.5 and PM₁₀ was associated with respective increases of 1.636% (95% confidence interval [CI] 0.537-2.740%) and 0.805% (95% CI 0.037-1.574%) for daily MI with 2-day cumulative effects. The associations were more robust among males and in the warm season versus the cold one. No significant effect was found in SO₂, NO₂, or O₃. This study suggested that short-term exposure to PM_2.5 and PM₁₀ was associated with the increased MI risks. Our results might be useful for the primary prevention of MI exacerbated by air pollutants.

Hospital Variation in Risk-Adjusted Pediatric Sepsis Mortality

OBJECTIVES

With continued attention to pediatric sepsis at both the clinical and policy levels, it is important to understand the quality of hospitals in terms of their pediatric sepsis mortality. We sought to develop a method to evaluate hospital pediatric sepsis performance using 30-day risk-adjusted mortality and to assess hospital variation in risk-adjusted sepsis mortality in a large state-wide sample.

DESIGN

Retrospective cohort study using administrative claims data.

SETTINGS

Acute care hospitals in the state of Pennsylvania from 2011 to 2013.

PATIENTS

Patients between the ages of 0-19 years admitted to a hospital with sepsis defined using validated International Classification of Diseases, Ninth revision, Clinical Modification, diagnosis and procedure codes.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

During the study period, there were 9,013 pediatric sepsis encounters in 153 hospitals. After excluding repeat visits and hospitals with annual patient volumes too small to reliably assess hospital performance, there were 6,468 unique encounters in 24 hospitals. The overall unadjusted mortality rate was 6.5% (range across all hospitals: 1.5-11.9%). The median number of pediatric sepsis cases per hospital was 67 (range across all hospitals: 30-1,858). A hierarchical logistic regression model for 30-day risk-adjusted mortality controlling for patient age, gender, emergency department admission, infection source, presence of organ dysfunction at admission, and presence of chronic complex conditions showed good discrimination (C-statistic = 0.80) and calibration (slope and intercept of calibration plot: 0.95 and -0.01, respectively). The hospital-specific risk-adjusted mortality rates calculated from this model varied minimally, ranging from 6.0% to 7.4%.

CONCLUSIONS

Although a risk-adjustment model for 30-day pediatric sepsis mortality had good performance characteristics, the use of risk-adjusted mortality rates as a hospital quality measure in pediatric sepsis is not useful due to the low volume of cases at most hospitals. Novel metrics to evaluate the quality of pediatric sepsis care are needed.

Chronic Obstructive Pulmonary Disease Readmissions and Other Measures of Hospital Quality

RATIONALE

The Centers for Medicare and Medicaid Services recently implemented financial penalties to reduce hospital readmissions for select conditions, including chronic obstructive pulmonary disease (COPD). Despite growing pressure to reduce COPD readmissions, it is unclear how COPD readmission rates are related to other measures of quality, which could inform efforts on common organizational factors that affect high-quality care.

OBJECTIVES

To examine the association between COPD readmissions and other quality measures.

METHODS

We analyzed data from the 2015 Centers for Medicare and Medicaid Services annual files, downloaded from the Hospital Compare website. We included 3,705 hospitals nationwide that had publically reported data on COPD readmissions. We compared COPD readmission rates to other risk-adjusted measures of quality, including readmission and mortality rates for other conditions, and patient reports about care experiences.

MEASUREMENTS AND MAIN RESULTS

There were modest correlations between COPD readmission rates and readmission rates for other medical conditions, including heart failure (r = 0.39; P < 0.01), acute myocardial infarction (r = 0.30; P < 0.01), pneumonia (r = 0.38; P < 0.01), and stroke (r = 0.29; P < 0.01). In contrast, we found low correlations between COPD readmission rates and readmission rates for surgical conditions, as well as mortality rates for all measured conditions. There were significant correlations between COPD readmission rates and all patient experience measures.

CONCLUSIONS

These findings suggest there may be common organizational factors that influence multiple disease-specific outcomes. As pay-for-performance programs focus attention on individual disease outcomes, hospitals may benefit from in-depth assessments of organizational factors that affect multiple aspects of hospital quality.

Vital Signs Are Still Vital: Instability on Discharge and the Risk of Post-Discharge Adverse Outcomes

BACKGROUND

Vital sign instability on discharge could be a clinically objective means of assessing readiness and safety for discharge; however, the association between vital sign instability on discharge and post-hospital outcomes is unclear.

OBJECTIVE

To assess the association between vital sign instability at hospital discharge and post-discharge adverse outcomes.

DESIGN

Multi-center observational cohort study using electronic health record data. Abnormalities in temperature, heart rate, blood pressure, respiratory rate, and oxygen saturation were assessed within 24 hours of discharge. We used logistic regression adjusted for predictors of 30-day death and readmission.

PARTICIPANTS

Adults (≥18 years) with a hospitalization to any medicine service in 2009-2010 at six hospitals (safety-net, community, teaching, and non-teaching) in north Texas.

MAIN MEASURES

Death or non-elective readmission within 30 days after discharge.

KEY RESULTS

Of 32,835 individuals, 18.7 % were discharged with one or more vital sign instabilities. Overall, 12.8 % of individuals with no instabilities on discharge died or were readmitted, compared to 16.9 % with one instability, 21.2 % with two instabilities, and 26.0 % with three or more instabilities (p < 0.001). The presence of any (≥1) instability was associated with higher risk-adjusted odds of either death or readmission (AOR 1.36, 95 % CI 1.26-1.48), and was more strongly associated with death (AOR 2.31, 95 % CI 1.91-2.79). Individuals with three or more instabilities had nearly fourfold increased odds of death (AOR 3.91, 95 % CI 1.69-9.06) and increased odds of 30-day readmission (AOR 1.36, 95 % 0.81-2.30) compared to individuals with no instabilities. Having two or more vital sign instabilities at discharge had a positive predictive value of 22 % and positive likelihood ratio of 1.8 for 30-day death or readmission.

CONCLUSIONS

Vital sign instability on discharge is associated with increased risk-adjusted rates of 30-day mortality and readmission. These simple vital sign criteria could be used to assess safety for discharge, and to reduce 30-day mortality and readmissions.

Predicting readmission risk following percutaneous coronary intervention at the time of admission

OBJECTIVE

To investigate whether a prediction model based on data available early in percutaneous coronary intervention (PCI) admission can predict the risk of readmission.

BACKGROUND

Reducing readmissions following hospitalization is a national priority. Identifying patients at high risk for readmission after PCI early in a hospitalization would enable hospitals to enhance discharge planning.

METHODS

We developed 3 different models to predict 30-day inpatient readmission to our institution for patients who underwent PCI between January 2010 and April 2013. These models used data available: 1) at admission, 2) at discharge 3) from CathPCI Registry data. We used logistic regression and assessed the discrimination of each model using the c-index. The models were validated with testing on a different patient cohort who underwent PCI between May 2013 and September 2015.

RESULTS

Our cohort included 6717 PCI patients; 3739 in the derivation cohort and 2978 in the validation cohort. The discriminative ability of the admission model was good (C-index of 0.727). The c-indices for the discharge and cath PCI models were slightly better. (C-index of 0.751 and 0.752 respectively). Internal validation of the models showed a reasonable discriminative admission model with slight improvement with adding discharge and registry data (C-index of 0.720, 0.739 and 0.741 respectively). Similarly validation of the models on the validation cohort showed similar results (C-index of 0.703, 0.725 and 0.719 respectively).

CONCLUSION

Simple models based on available demographic and clinical data may be sufficient to identify patients at highest risk of readmission following PCI early in their hospitalization.

Predicting readmission risk following coronary artery bypass surgery at the time of admission

BACKGROUND

Reducing readmissions following hospitalization is a national priority. Identifying patients at high risk for readmission after coronary artery bypass graft surgery (CABG) early in a hospitalization would enable hospitals to enhance discharge planning.

METHODS

We developed different models to predict 30-day inpatient readmission to our institution in patients who underwent CABG between January 2010 and April 2013. These models used data available: 1) at admission, 2) at discharge 3) from STS Registry data. We used logistic regression and assessed the discrimination of each model using the c-index. The models were validated with testing on a different patient cohort who underwent CABG between May 2013 and September 2015. Our cohort included 1277 CABG patients: 1159 in the derivation cohort and 1018 in the validation cohort.

RESULTS

The discriminative ability of the admission model was reasonable (C-index of 0.673). The c-indices for the discharge and STS models were slightly better. (C-index of 0.700 and 0.714 respectively). Internal validation of the models showed a reasonable discriminative admission model with slight improvement with adding discharge and registry data (C-index of 0.641, 0.659 and 0.670 respectively). Similarly validation of the models on the validation cohort showed similar results (C-index of 0.573, 0.605 and 0.595 respectively).

CONCLUSIONS

Risk prediction models based on data available early on admission are predictive for readmission risk. Adding registry data did not improved the performance of these models. These simplified models may be sufficient to identify patients at highest risk of readmission following coronary revascularization early in the hospitalization.

Life Expectancy after Myocardial Infarction, According to Hospital Performance

BACKGROUND

Thirty-day risk-standardized mortality rates after acute myocardial infarction are commonly used to evaluate and compare hospital performance. However, it is not known whether differences among hospitals in the early survival of patients with acute myocardial infarction are associated with differences in long-term survival.

METHODS

We analyzed data from the Cooperative Cardiovascular Project, a study of Medicare beneficiaries who were hospitalized for acute myocardial infarction between 1994 and 1996 and who had 17 years of follow-up. We grouped hospitals into five strata that were based on case-mix severity. Within each case-mix stratum, we compared life expectancy among patients admitted to high-performing hospitals with life expectancy among patients admitted to low-performing hospitals. Hospital performance was defined by quintiles of 30-day risk-standardized mortality rates. Cox proportional-hazards models were used to calculate life expectancy.

RESULTS

The study sample included 119,735 patients with acute myocardial infarction who were admitted to 1824 hospitals. Within each case-mix stratum, survival curves of the patients admitted to hospitals in each risk-standardized mortality rate quintile separated within the first 30 days and then remained parallel over 17 years of follow-up. Estimated life expectancy declined as hospital risk-standardized mortality rate quintile increased. On average, patients treated at high-performing hospitals lived between 0.74 and 1.14 years longer, depending on hospital case mix, than patients treated at low-performing hospitals. When 30-day survivors were examined separately, there was no significant difference in unadjusted or adjusted life expectancy across hospital risk-standardized mortality rate quintiles.

CONCLUSIONS

In this study, patients admitted to high-performing hospitals after acute myocardial infarction had longer life expectancies than patients treated in low-performing hospitals. This survival benefit occurred in the first 30 days and persisted over the long term. (Funded by the National Heart, Lung, and Blood Institute and the National Institute of General Medical Sciences Medical Scientist Training Program.).

Predictors of 30-day mortality following hip/pelvis fractures

INTRODUCTION

With the cost of healthcare in the United States reaching $2.9 trillion in 2013 and expected to increase with a growing geriatric population, the Center for Medicare and Medicaid Services (CMS) and Hospital Quality Alliance (HQA) began publicly reporting 30-day mortality rates so that hospitals and physicians may begin to confront clinical problems and promote high-quality and patient-centered care. Though the 30-day mortality is considered a highly effective tool in measuring hospital performance, little data actually exists that explores the rate and risk factors for trauma-related hip and pelvis fractures. Therefore, in this study, we sought to explore the risk factors associated with 30-day mortality in trauma-related hip and pelvic fractures.

MATERIALS AND METHODS

Utilizing the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) database, 341,062 patients undergoing orthopaedic procedures from 2005 to 2013 were identified through a Current Procedural Terminology (CPT) code search. A second CPT code search identified 24,805 patients who sustained a hip/pelvis fracture. Patient demographics, preoperative comorbidities, operative characteristics and postoperative complications were collected and compared using Chi-squared test, Wilcoxon-Mann-Whitney test and multivariate logistic regression analysis.

RESULTS

Preoperative and postoperative risk factors for 30-day mortality following a hip/pelvis fracture were found: ASA classification, ascites, disseminated cancer, dyspnea, functional status, history of congestive heart failure (CHF), history of chronic obstructive pulmonary disease (COPD), a recent blood transfusion, and the postoperative complications: pneumonia, myocardial infarction, stroke, and septic shock.

DISCUSSION

Several preoperative patient risk factors and postoperative complications greatly increased the odds for patient mortality following 30-days after initial surgery. Orthopaedic surgeons can utilize these predictive risk factors to better improve patient care.

LEVEL OF EVIDENCE

Retrospective study. Level IV.

Association Between Hospital Performance on Patient Safety and 30-Day Mortality and Unplanned Readmission for Medicare Fee-for-Service Patients With Acute Myocardial Infarction

Background

Little is known regarding the relationship between hospital performance on adverse event rates and hospital performance on 30‐day mortality and unplanned readmission rates for Medicare fee‐for‐service patients hospitalized for acute myocardial infarction (AMI).

Methods and Results

Using 2009–2013 medical record‐abstracted patient safety data from the Agency for Healthcare Research and Quality's Medicare Patient Safety Monitoring System and hospital mortality and readmission data from the Centers for Medicare & Medicaid Services, we fitted a mixed‐effects model, adjusting for hospital characteristics, to evaluate whether hospital performance on patient safety, as measured by the hospital‐specific risk‐standardized occurrence rate of 21 common adverse event measures for which patients were at risk, is associated with hospital‐specific 30‐day all‐cause risk‐standardized mortality and unplanned readmission rates for Medicare patients with AMI. The unit of analysis was at the hospital level. The final sample included 793 acute care hospitals that treated 30 or more Medicare patients hospitalized for AMI and had 40 or more adverse events for which patients were at risk. The occurrence rate of adverse events for which patients were at risk was 3.8%. A 1% point change in the risk‐standardized occurrence rate of adverse events was associated with average changes in the same direction of 4.86% points (95% CI, 0.79–8.94) and 3.44% points (95% CI, 0.19–6.68) for the risk‐standardized mortality and unplanned readmission rates, respectively.

Conclusions

For Medicare fee‐for‐service patients discharged with AMI, hospitals with poorer patient safety performance were also more likely to have poorer performance on 30‐day all‐cause mortality and on unplanned readmissions.

Impact of renal impairment on cardiovascular disease mortality after liver transplantation for nonalcoholic steatohepatitis cirrhosis

BACKGROUND & AIMS

Non-alcoholic steatohepatitis (NASH) is an independent risk factor for cardiovascular disease (CVD) morbidity after liver transplantation, but its impact on CVD mortality is unknown. We sought to assess the impact of NASH on CVD mortality after liver transplantation and to predict which NASH recipients are at highest risk of a CVD-related death following a liver transplant.

METHODS

Using the Organ Procurement and Transplantation Network database, we examined associations between NASH and post-liver transplant CVD mortality, defined as primary cause of death from thromboembolism, arrhythmia, heart failure, myocardial infarction or stroke. A physician panel reviewed cause of death.

RESULTS

Of 48 360 liver transplants (2/2002-12/2011), 5057 (10.5%) were performed for NASH cirrhosis. NASH recipients were more likely to be older, female, obese, diabetic and have history of renal failure or prior CVD vs. non-NASH (P < 0.001 for all). Although there was no difference in overall all-cause mortality (log-rank P = 0.96), both early (30-day) and long-term CVD-specific mortality was increased among NASH recipients (Odds ratio = 1.30, 95% Confidence interval (CI): 1.02-1.66; Hazard ratio = 1.42, 95% CI: 1.07-1.41 respectively). These associations were no longer significant after adjustment for pre-transplant diabetes, renal impairment or CVD. A risk score comprising age ≥55, male sex, diabetes and renal impairment was developed for prediction of post-liver transplant CVD mortality (c-statistic 0.60).

CONCLUSION

NASH recipients have an increased risk of CVD mortality after liver transplantation explained by a high prevalence of comorbid cardiometabolic risk factors that in aggregate identify those at highest risk of post-transplant CVD mortality.

The Correlation of Media Ranking's "Best" Hospitals and Surgical Outcomes Following Radical Cystectomy for Urothelial Cancer

OBJECTIVE

To evaluate whether there is a correlation between publicized health ranking systems and surgical outcomes after radical cystectomy (RC) in New York State (NYS).

MATERIALS AND METHODS

Using the Statewide Planning and Research Cooperative System, data were collected in an aggregated fashion per hospital for the 20 hospitals with the highest RC volume in NYS from 2009 to 2012. Hospital characteristics were obtained from the publicly available sources such as the Centers for Medicare and Medicaid Services. Publicized ranking systems evaluated included the US News & World Health Report for Urology ranking (USHR), Healthgrades (HG) score, and Consumer Reports (CR) safety ranking. Outcomes measured included mortality, readmissions, and causes of readmissions.

RESULTS

CR safety scores were inversely associated with overall death at 90 days after surgery (R = -0.527, P = .030), number of readmissions (R = -0.608, P = .030), and readmissions because of surgical complications (R = -0.523, P = .031) on a Pearson correlation test. On Kendall rank tau test, USHR and HG were not associated with any outcome of interest, although the scores correlated with increasing RC volume.

CONCLUSION

In our analysis of 20 hospitals with the highest RC volume in NYS, USHR and HG scores were not strongly associated with any clinical outcome after RC. CR performed well in comparison with USHR and HG. Nevertheless, better metrics are needed to compare hospitals and to incorporate curative rates for morbid surgeries.

Risk-Adjusted In-Hospital Mortality Models for Congestive Heart Failure and Acute Myocardial Infarction: Value of Clinical Laboratory Data and Race/Ethnicity

OBJECTIVE

To examine the impact of key laboratory and race/ethnicity data on the prediction of in-hospital mortality for congestive heart failure (CHF) and acute myocardial infarction (AMI).

DATA SOURCES

Hawaii adult hospitalizations database between 2009 and 2011, linked to laboratory database.

STUDY DESIGN

Cross-sectional design was employed to develop risk-adjusted in-hospital mortality models among patients with CHF (n = 5,718) and AMI (n = 5,703).

DATA COLLECTION/EXTRACTION METHODS

Results of 25 selected laboratory tests were requested from hospitals and laboratories across the state and mapped according to Logical Observation Identifiers Names and Codes standards. The laboratory data were linked to administrative data for each discharge of interest from an all-payer database, and a Master Patient Identifier was used to link patient-level encounter data across hospitals statewide.

PRINCIPAL FINDINGS

Adding a simple three-level summary measure based on the number of abnormal laboratory data observed to hospital administrative claims data significantly improved the model prediction for inpatient mortality compared with a baseline risk model using administrative data that adjusted only for age, gender, and risk of mortality (determined using 3M's All Patient Refined Diagnosis Related Groups classification). The addition of race/ethnicity also improved the model.

CONCLUSIONS

The results of this study support the incorporation of a simple summary measure of laboratory data and race/ethnicity information to improve predictions of in-hospital mortality from CHF and AMI. Laboratory data provide objective evidence of a patient's condition and therefore are accurate determinants of a patient's risk of mortality. Adding race/ethnicity information helps further explain the differences in in-hospital mortality.

A case-cohort study of postoperative myocardial infarction: impact of anemia and cardioprotective medications

OBJECTIVE

Postoperative myocardial infarction (poMI) is a serious and costly complication. Multiple risk factors for poMI are known, but the effect of anemia and cardioprotective medications have not been defined in real-world surgical practice.

METHODS

Patients undergoing inpatient elective surgery were assessed at 17 hospitals from 2008 to 2011 for the occurrence of poMI (American Heart Association definition). Non-MI control patients were chosen randomly on the basis of case type. Descriptive, univariable, and multivariable statistical analysis were performed for primary outcomes of poMI and death at 30 days.

RESULTS

Compared with controls (N = 304), patients with poMI (N = 222) were older (72 ± 11 vs 60 ± 17 years, P < .0001), had a lesser preoperative hematocrit (37 ± 6 vs 39 ± 5, P < .0001), more often were smokers, had a preoperative T-wave abnormality (21% vs 9%, P < .0001), and had a preoperative stress test with a fixed deficit (26% vs 3%; P < .001). Preoperative factors associated with poMI included peripheral vascular disease (odds ratio 2.6; 95% confidence interval 1.3-5.3), tobacco use (1.7; 1.01-2.9), history of percutaneous coronary angioplasty (2.8; 1.6-5.0), and age (1.05; 1.03-1.07), whereas hematocrit >35 (0.51; 0.32-0.82) and preoperative acetylsalicylic acid, ie, aspirin (0.59; 0.4-0.97) were protective. Preoperative β-blockade, statin, and use of angiotensin-converting enzyme inhibitors were not associated with lesser rates of poMI. Non-MI complication rates were 23-fold greater in the poMI group compared with the control group (P < .0001). Mortality with poMI within 30 days was 11% compared with 0.3% in non-MI control patients (P < .0001). In patients with poMI, factors independently associated with death included use of epidurals (3.5; 1.07-11.4) and bleeding (4.2; 1.1-16), whereas preoperative use of aspirin (0.29; 0.1-0.88), and postoperative β-blockade (0.18; 0.05-0.63) were protective. Cardiac catheterization, percutaneous coronary intervention, or coronary artery bypass grafting after poMI was performed in 34% of those alive and 20% of those who died (P = .16).

CONCLUSION

In the current era, poMI patients have a markedly increased risk of death. This risk is decreased with preoperative use of acetylsalicylic acid and post MI β-blockade. Further study is warranted to explore the role of anemia and cardiac interventions after poMI.

High early cardiovascular mortality after liver transplantation

Cardiovascular disease (CVD) contributes to excessive long-term mortality after liver transplantation (LT); however, little is known about early postoperative CVD mortality in the current era. In addition, there is no model for predicting early postoperative CVD mortality across centers. We analyzed adult recipients of primary LT in the Organ Procurement and Transplantation Network (OPTN) database between February 2002 and December 2012 to assess the prevalence and predictors of early (30-day) CVD mortality, which was defined as death from arrhythmia, heart failure, myocardial infarction, cardiac arrest, thromboembolism, and/or stroke. We performed logistic regression with stepwise selection to develop a predictive model of early CVD mortality. Sex and center volume were forced into the final model, which was validated with bootstrapping techniques. Among 54,697 LT recipients, there were 1576 deaths (2.9%) within 30 days. CVD death was the leading cause of 30-day mortality (40.2%), and it was followed by infection (27.9%) and graft failure (12.2%). In a multivariate analysis, 9 significant covariates (6 recipient covariates, 2 donor covariates, and 1 operative covariate) were identified: age, preoperative hospitalization, intensive care unit status, ventilator status, calculated Model for End-Stage Liver Disease score, portal vein thrombosis, national organ sharing, donor body mass index, and cold ischemia time. The model showed moderate discrimination (C statistic = 0.66, 95% confidence interval = 0.63-0.68). In conclusion, we provide the first multicenter prognostic model for the prediction of early post-LT CVD death, the most common cause of early post-LT mortality in the current transplant era. However, evaluations of additional CVD-related variables not collected by the OPTN are needed in order to improve the model's accuracy and potential clinical utility.

A comparison of clinical outcomes from carotid artery stenting among US hospitals

BACKGROUND

The Centers for Medicare and Medicaid Services require hospitals performing carotid artery stenting (CAS) to recertify the quality of their programs every 2 years, but currently this involves no explicit comparisons of postprocedure mortality across hospitals. Hence, the current recertification process may fail to identify hospitals that are performing poorly in relation to peer institutions. Our objective was to compare risk-standardized procedural outcomes across US hospitals that performed CAS and to identify hospitals with statistically high postprocedure mortality rates.

METHODS AND RESULTS

We conducted a retrospective cohort study of Medicare beneficiaries who underwent CAS from July 2009 to June 2011 at 927 US hospitals. Thirty-day risk-standardized mortality rates were calculated using the Hospital Compare statistical method, a well-validated hierarchical generalized linear model that included both patient-level and hospital-level predictors. Claims were examined from 22 708 patients undergoing CAS, with a crude 30-day mortality rate of 2.0%. Risk-standardized 30-day mortality rates after CAS varied from 1.1% to 5.1% (P<0.001 for the difference). Thirteen hospitals had risk-standardized mortality rates that were statistically (P<0.05) higher than the national mean. Conversely, 5 hospitals had risk-standardized mortality rates that were statistically (P<0.05) lower than the national mean.

CONCLUSIONS

We used administrative claims to identify several CAS hospitals with excessively high 30-day mortality after carotid stenting. When combined with information currently used by Medicare for CAS recertification, such as clinical registry data and program reports, clinical outcomes comparisons could enhance Medicare's ability to identify hospitals that are questionable candidates for recertification.

Stroke outcomes measures must be appropriately risk adjusted to ensure quality care of patients: a presidential advisory from the American Heart Association/American Stroke Association

Because stroke is among the leading causes of death, disability, hospitalizations, and healthcare expenditures in the United States, there is interest in reporting outcomes for patients hospitalized with acute ischemic stroke. The American Heart Association/American Stroke Association, as part of its commitment to promote high-quality, evidence-based care for cardiovascular and stroke patients, fully supports the development of properly risk-adjusted outcome measures for stroke. To accurately assess and report hospital-level outcomes, adequate risk adjustment for case mix is essential. During the development of the Centers for Medicare & Medicaid Services 30-day stroke mortality and 30-day stroke readmission measures, concerns were expressed that these measures were not adequately designed because they do not include a valid initial stroke severity measure, such as the National Institutes of Health Stroke Scale. These outcome measures, as currently constructed, may be prone to mischaracterizing the quality of stroke care being delivered by hospitals and may ultimately harm acute ischemic stroke patients. This article details (1) why the Centers for Medicare & Medicaid Services acute ischemic stroke outcome measures in their present form may not provide adequate risk adjustment, (2) why the measures as currently designed may lead to inaccurate representation of hospital performance and have the potential for serious unintended consequences, (3) what activities the American Heart Association/American Stroke Association has engaged in to highlight these concerns to the Centers for Medicare & Medicaid Services and other interested parties, and (4) alternative approaches and opportunities that should be considered for more accurately risk-adjusting 30-day outcomes measures in patients with ischemic stroke.

Association of hospitalist presence and hospital-level outcome measures among Medicare patients

BACKGROUND

Hospitalists have been shown to lower patient costs through better resource utilization and decreased length of stay, but it is unclear whether hospitalists are associated with quality of care. We examined the association between the presence of hospitalists and 30-day predicted excess all-cause hospital mortality and readmissions among Medicare patients admitted to a hospital with any of 3 conditions: heart failure, acute myocardial infarction, and pneumonia.

METHODS

Using national hospital-level, case mix-adjusted, risk-standardized, 30-day all-cause excess mortality and readmission data from the Centers for Medicare and Medicaid Services, we used descriptive and bivariate statistics to illustrate trends across hospitals. Using multivariable ordinary least squares regression to control for hospital-level characteristics, we then estimated the association between the presence of hospitalists and predicted hospital mortality and readmission.

RESULTS

After multivariable adjustment, the presence of hospitalists was associated with lower probability of readmission for all 3 target conditions. No significant associations for any of the target conditions were found in all-cause mortality models.

CONCLUSIONS

Hospitalists are already integral to the delivery of inpatient care at most institutions. This study, however, showed an association at the national level of the presence of hospitalists with an important and timely quality measure: reduction of readmission rates. Future research is indicated to explore specific causation pathways for the impact of hospitalists on quality of care.

Red blood cell distribution width is a predictor of readmission in cardiac patients

BACKGROUND

Three-quarters of rehospitalizations ($44 billion yearly estimated cost) may be avoidable. A screening tool for the detection of potential readmission may facilitate more efficient case management.

HYPOTHESIS

An elevated red blood cell distribution width (RDW) is an independent predictor of hospital readmission in patients with unstable angina (UA) or non-ST-elevation myocardial infarction (NSTEMI).

METHODS

The study is a retrospective observational cohort analysis of adults admitted in 2007 with UA or NSTEMI. Data were gathered by review of inpatient medical records. The rate of 30-day nonelective readmission and time to nonelective readmission were recorded until November 1, 2011, and compared by RDW group using the 95th percentile (16.3%) as a cutoff.

RESULTS

The median follow-up time of the 503 subjects (average age, 65 ± 13 years; 56% male) was 3.8 years (interquartile range: 0.3-4.3 years). Those readmitted within 30 days were older, had more comorbidities and higher RDW and creatinine levels, and were more likely to have had an intervention. At 3.8 years of follow-up, subjects with high RDW (>16.3%) were more likely to be readmitted compared to those with normal RDW (≤16.3%) (72.28% vs 59.95%, P = 0.003). In multivariable analyses, high RDW was a statistically significant predictor of readmission in general (hazard ratio: 1.35 (95% confidence interval [CI]:1.02-1.79), P = 0.033) but not of 30-day rehospitalization (odds ratio: 1.34 (95% CI: 0.78-2.31), P = 0.292). Its area under the receiver operating characteristic curve was 0.54 (sensitivity 23% and specificity 85%).

CONCLUSIONS

An elevated RDW is an independent predictor of hospital readmission in patients with UA or NSTEMI.

30-day in-hospital mortality after acute myocardial infarction in Tuscany (Italy): an observational study using hospital discharge data

Background

Coronary heart disease is the leading cause of mortality in the world. One of the outcome indicators recently used to measure hospital performance is 30-day mortality after acute myocardial infarction (AMI). This indicator has proven to be a valid and reproducible indicator of the appropriateness and effectiveness of the diagnostic and therapeutic process for AMI patients after hospital admission. The aim of this study was to examine the determinants of inter-hospital variability on 30-day in-hospital mortality after AMI in Tuscany. This indicator is a proxy of 30-day mortality that includes only deaths occurred during the index or subsequent hospitalizations.

Methods

The study population was identified from hospital discharge records (HDRs) and included all patients with primary or secondary ICD-9-CM codes of AMI (ICD-9 codes 410.xx) that were discharged between January 1, 2009 and November 30, 2009 from any hospital in Tuscany. The outcome of interest was 30-day all-cause in-hospital mortality, defined as a death occurring for any reason in the hospital within 30 days of the admission date. Because of the hierarchical structure of the data, with patients clustered into hospitals, random-effects (multilevel) logistic regression models were used. The models included patient risk factors and random intercepts for each hospital.

Results

The study included 5,832 patients, 61.90% male, with a mean age of 72.38 years. During the study period, 7.99% of patients died within 30 days of admission. The 30-day in-hospital mortality rate was significantly higher among patients with ST segment elevation myocardial infarction (STEMI) compared with those with non-ST segment elevation myocardial infarction (NSTEMI). The multilevel analysis which included only the hospital variance showed a significant inter-hospital variation in 30-day in-hospital mortality. When patient characteristics were added to the model, the hospital variance decreased. The multilevel analysis was then carried out separately in the two strata of patients with STEMI and NSTEMI. In the STEMI group, after adjusting for patient characteristics, some residual inter-hospital variation was found, and was related to the presence of a cardiac catheterisation laboratory.

Conclusion

We have shown that it is possible to use routinely collected administrative data to predict mortality risk and to highlight inter-hospital differences. The distinction between STEMI and NSTEMI proved to be useful to detect organisational characteristics, which affected only the STEMI subgroup.

The incomplete infrastructure for interhospital patient transfer

OBJECTIVE

Interhospital transfer of critically ill patients is a common part of their care. This article sought to review the data on the current patterns of use of interhospital transfer and identify systematic barriers to optimal integration of transfer as a mechanism for improving patient outcomes and value of care.

DATA SOURCE

Narrative review of medical and organizational literature.

SUMMARY

Interhospital transfer of patients is common, but not optimized to improve patient outcomes. Although there is a wide variability in quality among hospitals of nominally the same capability, patients are not consistently transferred to the highest quality nearby hospital. Instead, transfer destinations are selected by organizational routines or non-patient-centered organizational priorities. Accomplishing a transfer is often quite difficult for sending hospitals. But once a transfer destination is successfully found, the mechanics of interhospital transfer now appear quite safe.

CONCLUSION

Important technological advances now make it possible to identify nearby hospitals best able to help critically ill patients, and to successfully transfer patients to those hospitals. However, organizational structures have not yet developed to insure that patients are optimally routed, resulting in potentially significant excess mortality.

A hybrid Centers for Medicaid and Medicare service mortality model in 3 diagnoses

INTRODUCTION

Reliance on administrative data sources and a cohort with restricted age range (Medicare 65 y and above) may limit conclusions drawn from public reporting of 30-day mortality rates in 3 diagnoses [acute myocardial infarction (AMI), congestive heart failure (CHF), pneumonia (PNA)] from Center for Medicaid and Medicare Services.

METHODS

We categorized patients with diagnostic codes for AMI, CHF, and PNA admitted to 138 Veterans Administration hospitals (2006-2009) into 2 groups (less than 65 y or ALL), then applied 3 different models that predicted 30-day mortality [Center for Medicaid and Medicare Services administrative (ADM), ADM+laboratory data (PLUS), and clinical (CLIN)] to each age/diagnosis group. C statistic (CSTAT) and Hosmer Lemeshow Goodness of Fit measured discrimination and calibration. Pearson correlation coefficient (r) compared relationship between the hospitals' risk-standardized mortality rates (RSMRs) calculated with different models. Hospitals were rated as significantly different (SD) when confidence intervals (bootstrapping) omitted National RSMR.

RESULTS

The ≥ 65-year models included 57%-67% of all patients (78%-82% deaths). The PLUS models improved discrimination and calibration across diagnoses and age groups (CSTAT-CHF/65 y and above: 0.67 vs. 0. 773 vs. 0.761; ADM/PLUS/CLIN; Hosmer Lemeshow Goodness of Fit significant 4/6 ADM vs. 2/6 PLUS). Correlation of RSMR was good between ADM and PLUS (r-AMI 0.859; CHF 0.821; PNA 0.750), and 65 years and above and ALL (r>0.90). SD ratings changed in 1%-12% of hospitals (greatest change in PNA).

CONCLUSIONS

Performance measurement systems should include laboratory data, which improve model performance. Changes in SD ratings suggest caution in using a single metric to label hospital performance.

Guided transfer of critically ill patients: where patients are transferred can be an informed choice

PURPOSE OF REVIEW

Given increasingly scarce healthcare resources and highly differentiated hospitals, with growing demand for critical care, interhospital transfer is an essential part of the care of many patients. The purpose of this review is to examine the extent to which hospital quality is considered when transferring critically ill patients, and to examine the potential benefits to patients of a strategy that incorporates objective quality data into referral patterns.

RECENT FINDINGS

Interhospital transfer of critically ill patients is now common and safe. Although extensive research has focused on which patients should be transferred and when they should be transferred, recent study has focused on where patients should be transferred. Yet, the choice of destination hospital is rarely recognized as a therapeutic choice with implications for patient outcomes. The recent public release of high-quality, risk-adjusted and reliability-adjusted outcome data for most hospitals now offers physicians an informed basis on which to choose to which destination hospital a patient should be transferred. A strategy of 'guided transfer' that integrates public quality information into critical care transfer decisions is now feasible.

SUMMARY

Although hospitals often transfer patients, there may be substantial room for improvement in transfer patterns. Guiding transfers on the basis of objective quality information may offer substantial benefits to patients, and could be incorporated into quality improvement initiatives.

Relationship of national institutes of health stroke scale to 30-day mortality in medicare beneficiaries with acute ischemic stroke

Background

The National Institutes of Health Stroke Scale (NIHSS), a well-validated tool for assessing initial stroke severity, has previously been shown to be associated with mortality in acute ischemic stroke. However, the relationship, optimal categorization, and risk discrimination with the NIHSS for predicting 30-day mortality among Medicare beneficiaries with acute ischemic stroke has not been well studied.

Methods and Results

We analyzed data from 33102 fee-for-service Medicare beneficiaries treated at 404 Get With The Guidelines-Stroke hospitals between April 2003 and December 2006 with NIHSS documented. The 30-day mortality rate by NIHSS as a continuous variable and by risk-tree determined or prespecified categories were analyzed, with discrimination of risk quantified by the c-statistic. In this cohort, mean age was 79.0 years and 58% were female. The median NIHSS score was 5 (25th to 75th percentile 2 to 12). There were 4496 deaths in the first 30 days (13.6%). There was a strong graded relation between increasing NIHSS score and higher 30-day mortality. The 30-day mortality rates for acute ischemic stroke by NIHSS categories were as follows: 0 to 7, 4.2%; 8 to 13, 13.9%; 14 to 21, 31.6%; 22 to 42, 53.5%. A model with NIHSS alone provided excellent discrimination whether included as a continuous variable (c-statistic 0.82 [0.81 to 0.83]), 4 categories (c-statistic 0.80 [0.79 to 0.80]), or 3 categories (c-statistic 0.79 [0.78 to 0.79]).

Conclusions

The NIHSS provides substantial prognostic information regarding 30-day mortality risk in Medicare beneficiaries with acute ischemic stroke. This index of stroke severity is a very strong discriminator of mortality risk, even in the absence of other clinical information, whether used as a continuous or categorical risk determinant. (J Am Heart Assoc. 2012;1:42-50.)

Factors associated with 30-day readmission rates after percutaneous coronary intervention

BACKGROUND

Thirty-day readmission rates have become a publicly reported quality performance measure for congestive heart failure, acute myocardial infarction, and percutaneous coronary intervention (PCI). However, little is known regarding the factors associated with 30-day readmission after PCI.

METHODS

To assess the demographic, clinical, and procedural factors associated with 30-day readmission rates after PCI, we identified 15, 498 PCI hospitalizations (elective or for acute coronary syndromes) from January 1998 through June 2008 at Saint Marys Hospital, Rochester, Minnesota. All were included in this analysis. Multivariate logistic regression models were used to estimate the adjusted association between demographic, clinical, and procedural variables and 30-day readmission. The association between 30-day readmission and 1-year mortality was estimated using Cox proportional hazards models with readmission as a time-dependent covariate and by using landmark analysis. The main outcome measures were all-cause 30-day readmission to any hospital following PCI and 1-year mortality.

RESULTS

Overall, 9.4% of PCIs (n = 1459) were readmitted, and 0.68% of PCIs (n = 106) resulted in death within 30 days after discharge. After multivariate analysis, female sex, Medicare insurance, having less than a high school education, unstable angina, cerebrovascular accident or transient ischemic attack, moderate to severe renal disease, chronic obstructive pulmonary disease, peptic ulcer disease, metastatic cancer, and a length of stay of more than 3 days were associated with an increased risk of 30-day readmission after PCI. Thirty-day readmission after PCI was associated with a higher risk of 1-year mortality (adjusted hazard ratio, 1.38; 95% CI, 1.08-1.75; P = .009).

CONCLUSIONS

Nearly 1 in 10 patients undergoing PCI were readmitted within 30 days. Thirty-day readmission after PCI was associated with a higher risk of 1-year mortality.

The impact of paradoxical comorbidities on risk-adjusted mortality of Medicare beneficiaries with cardiovascular disease

BACKGROUND

Persistent uncertainty remains regarding assessments of patient comorbidity based on administrative data for mortality risk adjustment. Some models include comorbid conditions that are associated with improved mortality while other models exclude these so-called paradoxical conditions. The impact of these conditions on patient risk assessments is unknown.

OBJECTIVE

To examine trends in the prevalence of conditions with a paradoxical (protective) relationship with mortality, and the impact of including these conditions on assessments of risk adjusted mortality.

METHODS

Patients age 65 and older admitted for acute myocardial infarction (AMI) or coronary artery bypass graft (CABG) surgery during 1994 through 2005 were identified in Medicare Part A files. Comorbid conditions defined using a common algorithm were categorized as having a paradoxical or non-paradoxical relationship with 30-day mortality, based upon regression coefficients in multivariable logistic regression models.

RESULTS

For AMI, the proportion of patients with one or more paradoxical condition and one or more non-paradoxical condition increased by 24% and 3% respectively between 1994 and 2005. The odds of death for patients with one-or-more paradoxical comorbidities, relative to patients with no paradoxical comorbidity, declined from 0.69 to 0.54 over the study period. In contrast, the risk associated with having one or more non-paradoxical comorbidities increased from 2.66 to 4.62 for AMI. This pattern was even stronger for CABG. Risk adjustment models that included paradoxical comorbidities found larger improvements, in risk-adjusted mortality for AMI and CABG, over time than models that did not include paradoxical comorbidities.

CONCLUSION

The relationship between individual comorbid conditions and mortality is changing over time, with potential impact on estimates of hospital performance and trends in mortality. Development of a standard approach for handling conditions with a paradoxical relationship to mortality is needed.

Race and timeliness of transfer for revascularization in patients with acute myocardial infarction

OBJECTIVES

Patients with acute myocardial infarction (AMI) who are admitted to hospitals without coronary revascularization are frequently transferred to hospitals with this capability. We sought to determine whether the timeliness of hospital transfer and quality of destination hospitals differed between black and white patients.

METHODS

We evaluated all white and black Medicare beneficiaries admitted with AMI at nonrevascularization hospitals in 2006 who were transferred to a revascularization hospital. We compared hospital length of stay before transfer and the transfer destination's 30-day risk-standardized mortality rate for AMI between black and white patients. We used hierarchical regression to adjust for patient characteristics and examine within and across-hospital effects of race on 30-day mortality and length of stay before transfer.

RESULTS

A total of 25,947 (42%) white and 2345 (37%) black patients with AMI were transferred from 857 urban and 774 rural nonrevascularization hospitals to 928 revascularization hospitals. Median (interquartile range) length of stay before transfer was 1 day (1 to 3 d) for white patients and 2 days (1 to 4 d) for black patients (P<0.001). In adjusted models, black patients tended to be transferred more slowly than white patients, a finding because of both across and within-hospital effects. For example, within a given urban hospital, black patients were transferred an additional 0.24 days (95% confidence interval 0.03-0.44 d) later than white patients. In addition, the lengths of stay before transfer for all patients at urban hospitals increased by 0.37 days (95% confidence interval 0.28-0.47 d) for every 20% increase in the proportion of AMI patients who were black. These results were attenuated in rural hospitals. The risk-standardized mortality rate of the revascularization hospital to which patients were ultimately sent did not differ between black and white patients.

CONCLUSIONS

Black patients are transferred more slowly to revascularization hospitals after AMI than white patients, resulting from both less timely transfers within hospitals and admission to hospitals with greater delays in transfer; however, 30-day mortality of the revascularization hospital to which both groups were sent to appeared similar. Race-based delays in transfer may contribute to known racial disparities in outcomes of AMI.

ACCF/AHA/HFSA 2011 survey results: current staffing profile of heart failure programs, including programs that perform heart transplant and mechanical circulatory support device implantation: a report of the ACCF Heart Failure and Transplant Committee, AHA Heart Failure and Transplantation Committee, and Heart Failure Society of America

OBJECTIVES

There have been no published recommendations about staffing needs for a heart failure (HF) clinic or an office setting focused on heart transplant. The goal of this survey was to understand the current staffing environment of HF, transplant, and mechanical circulatory support device (MCSD) programs in the United States and abroad. This report identifies current staffing patterns but does not endorse a particular staffing model.

METHODS

An online survey, jointly sponsored by the American College of Cardiology Foundation (ACCF), American Heart Association (AHA), and the Heart Failure Society of America (HFSA), was sent to the members of all 3 organizations who had identified themselves as interested in HF, heart transplant, or both, between March 12, 2009, and May 12, 2009.

RESULTS

The overall response rate to the 1823 e-mail surveys was 23%. There were 257 unique practices in the United States (81% of total sites) and 58 international sites (19%); approximately 30% of centers were in a cardiovascular group practice and 30% in a medical school hospital setting. The large majority of practices delivered HF care in both an inpatient and outpatient environment, and slightly more centers were implanting MCSDs (47%) than performing cardiac transplantation (39%). Most practices (43%) were small, with <4 staff members, or small- to medium-sized (34%), with 4 to 10 staff members, with only 23% being medium (11-20 staff) or large programs (>20 staff). On average, a US HF practice cared for 1641 outpatients annually. An average HF program with transplant performed 10 transplants. Although larger programs were able to perform more transplants and see more outpatient HF visits, their clinician staffing volume tended to double for approximately every 500 to 700 additional HF visits annually. The average staffing utilization was 2.65 physician full-time equivalents (FTEs), 2.21 nonphysician practitioner (nurse practitioner or physician assistant) FTEs, and 2.61 nurse coordinator FTEs annually.

CONCLUSIONS

The HF patient population is growing in number in the United States and internationally, and the clinicians who provide the highly skilled and time-consuming care to this population are under intense scrutiny as a result of focused quality improvement initiatives and reduced financial resources. Staffing guidelines should be developed to ensure that an adequate number of qualified professionals are hired for a given practice volume. These survey results are an initial step in developing such standards.

Hospital mortality rates: how is palliative care taken into account?

CONTEXT

Using mortality rates to measure hospital quality presumes that hospital deaths are medical failures. To be a fair measure of hospital quality, hospital mortality measures must take patient-level factors, such as goals of care, into account.

OBJECTIVES

To answer questions about how hospital mortality rates are computed and how the involvement of hospice or palliative care (PC) are recognized and handled.

METHODS

We analyzed the methods of four entities: Centers for Medicare & Medicaid Services "Hospital Compare;" U.S. News & World Report "Best Hospitals;" Thomson-Reuters "100 TopHospitals;" and HealthGrades.

RESULTS

All entities reviewed rely on Medicare data, compute risk-adjusted mortality rates, and use "all-cause" mortality. They vary considerably in their recognition and handling of cases that involved hospice care or PC. One entity excludes cases with prior hospice care and another excludes those discharged to hospice at the end of the index hospitalization. Two entities exclude some or all cases that were coded with the V66.7 "Palliative Care Encounter" International Classification of Disease, Ninth Revision, Clinical Modification diagnosis code.

CONCLUSION

Proliferation of, and variability among, hospital mortality measures creates a challenge for hospital administrators. PC and hospice leaders need to educate themselves and their hospital administrators about the extent to which these mortality rates take end-of-life care into account. At the national level, PC and hospice leaders should take advantage of opportunities to engage these mortality raters in conversation about possible changes in their methods and to conduct further research on this topic.

Interhospital transfers among Medicare beneficiaries admitted for acute myocardial infarction at nonrevascularization hospitals

BACKGROUND

Patients with acute myocardial infarction (AMI) who are admitted to hospitals without coronary revascularization are frequently transferred to hospitals with this capability, yet we know little about the basis for how such revascularization hospitals are selected.

METHODS AND RESULTS

We examined interhospital transfer patterns in 71 336 AMI patients admitted to hospitals without revascularization capabilities in the 2006 Medicare claims using network analysis and regression models. A total of 31 607 (44.3%) AMI patients were transferred from 1684 nonrevascularization hospitals to 1104 revascularization hospitals. Median time to transfer was 2 days. Median transfer distance was 26.7 miles, with 96.1% within 100 miles. In 45.8% of cases, patients bypassed a closer hospital to go to a farther hospital that had a better 30-day risk standardized mortality rates. However, in 36.8% of cases, another revascularization hospital with lower 30-day risk-standardized mortality was actually closer to the original admitting nonrevascularization hospital than the observed transfer destination. Adjusted regression models demonstrated that shorter transfer distances were more common than transfers to the hospitals with lowest 30-day mortality rates. Simulations suggest that an optimized system that prioritized the transfer of AMI patients to a nearby hospital with the lowest 30-day mortality rate might produce clinically meaningful reductions in mortality.

CONCLUSIONS

More than 40% of AMI patients admitted to nonrevascularization hospitals are transferred to revascularization hospitals. Many patients are not directed to nearby hospitals with the lowest 30-day risk-standardized mortality, and this may represent an opportunity for improvement.

Do popular media and internet-based hospital quality ratings identify hospitals with better cardiovascular surgery outcomes?

BACKGROUND

Several popular media and Internet-based hospital quality rankings have become increasingly publicized as a method for patients to choose better hospitals. It is unclear whether selecting highly rated hospitals will improve outcomes after cardiovascular surgery procedures.

STUDY DESIGN

Using 2005 to 2006 Medicare data, we studied all patients undergoing abdominal aortic aneurysm repair, coronary artery bypass, aortic valve repair, and mitral valve repair (n = 312,813). Primary outcomes included risk-adjusted mortality, adjusting for patient characteristics and surgical acuity. We compared mortality at "Best Hospitals," according to US News and World Report and HealthGrades, with all other hospitals. We adjusted for hospital volume to determine whether hospital experience accounts for differences in mortality.

RESULTS

Risk-adjusted mortality was considerably lower in US News and World Report's "Best Hospitals" for abdominal aortic aneurysm repair only (odds ratio [OR] = 0.76; 95% CI, 0.61 to 0.94). Risk-adjusted mortality was considerably lower in HealthGrades' "Best Hospitals" after all 4 procedures: abdominal aortic aneurysm repair (OR = 0.75; 95% CI, 0.58 to 0.97), coronary artery bypass (OR = 0.78; 95% CI, 0.68 to 0.89), aortic valve repair (OR = 0.71; 95% CI, 0.59 to 0.85), and mitral valve repair (OR = 0.77; 95% CI, 0.61 to 0.99). Accounting for hospital volume, risk-adjusted mortality was not substantially lower at the US News and World Report's "Best Hospitals," while risk-adjusted mortality was lower at HealthGrades' "Best Hospitals" after coronary artery bypass and aortic valve repair mortality rates were adjusted for hospital volume (OR = 0.77; 95% CI, 0.64 to 0.92 and OR = 0.81; 95% CI, 0.71 to 0.94).

CONCLUSIONS

Popular hospital rating systems identify high-quality hospitals for cardiovascular operations. However, patients can experience equivalent outcomes by seeking care at high-volume hospitals.

Relationships between emerging measures of heart failure processes of care and clinical outcomes

BACKGROUND

Previous studies have not confirmed associations between some current performance measures for inpatient heart failure processes of care and postdischarge outcomes. It is unknown if alternative measures are associated with outcomes.

METHODS

Using data for 20,441 Medicare beneficiaries in OPTIMIZE-HF from March 2003 through December 2004, which we linked to Medicare claims data, we examined associations between hospital-level processes of care and patient outcomes. Performance measures included any beta-blocker for patients with left ventricular systolic dysfunction (LVSD); evidence-based beta-blocker for patients with LVSD; warfarin for patients with atrial fibrillation; aldosterone antagonist for patients with LVSD; implantable cardioverter-defibrillator for patients with ejection fraction < or =35%; and referral to disease management. Outcome measures were unadjusted and adjusted associations of each process measure with 60-day and 1-year mortality and cardiovascular readmission at the hospital level.

RESULTS

Adjusted hazard ratios for 1-year mortality with a 10% increase in hospital- level adherence were 0.94 for any beta-blocker (95% CI, 0.90-0.98; P = .004), 0.95 for evidence-based beta-blocker (95% CI, 0.92-0.98; P = .004); 0.97 for warfarin (95% CI, 0.92-1.03; P = .33); 0.94 for aldosterone antagonists (95% CI, 0.91-0.98; P = .006); 0.92 for implantable cardioverter-defibrillator (95% CI, 0.87-0.98; P = .007); and 1.01 for referral to disease management (95% CI, 0.99-1.03; P = .21).

CONCLUSIONS

Several evidence-based processes of care are associated with improved outcomes, can discriminate hospital-level quality of care, and could be considered as clinical performance measures.