How can hospitals change practice to better implement smoking cessation interventions? A systematic review.

Smoking cessation reduces the risk of death, improves recovery, and reduces the risk of hospital readmission. Evidence and policy support hospital admission as an ideal time to deliver smoking-cessation interventions. However, this is not well implemented in practice. In this systematic review, the authors summarize the literature on smoking-cessation implementation strategies and evaluate their success to guide the implementation of best-practice smoking interventions into hospital settings. The CINAHL Complete, Embase, MEDLINE Complete, and PsycInfo databases were searched using terms associated with the following topics: smoking cessation, hospitals, and implementation. In total, 14,287 original records were identified and screened, resulting in 63 eligible articles from 56 studies. Data were extracted on the study characteristics, implementation strategies, and implementation outcomes. Implementation outcomes were guided by Proctor and colleagues' framework and included acceptability, adoption, appropriateness, cost, feasibility, fidelity, penetration, and sustainability. The findings demonstrate that studies predominantly focused on the training of staff to achieve implementation. Brief implementation approaches using a small number of implementation strategies were less successful and poorly sustained compared with well resourced and multicomponent approaches. Although brief implementation approaches may be viewed as advantageous because they are less resource-intensive, their capacity to change practice in a sustained way lacks evidence. Attempts to change clinician behavior or introduce new models of care are challenging in a short time frame, and implementation efforts should be designed for long-term success. There is a need to embrace strategic, well planned implementation approaches to embed smoking-cessation interventions into hospitals and to reap and sustain the benefits for people who smoke.

The burden and dynamics of hospital-acquired SARS-CoV-2 in England.

Hospital-based transmission had a dominant role in Middle East respiratory syndrome coronavirus (MERS-CoV) and severe acute respiratory syndrome coronavirus (SARS-CoV) epidemics1,2, but large-scale studies of its role in the SARS-CoV-2 pandemic are lacking. Such transmission risks spreading the virus to the most vulnerable individuals and can have wider-scale impacts through hospital-community interactions. Using data from acute hospitals in England, we quantify within-hospital transmission, evaluate likely pathways of spread and factors associated with heightened transmission risk, and explore the wider dynamical consequences. We estimate that between June 2020 and March 2021 between 95,000 and 167,000 inpatients acquired SARS-CoV-2 in hospitals (1% to 2% of all hospital admissions in this period). Analysis of time series data provided evidence that patients who themselves acquired SARS-CoV-2 infection in hospital were the main sources of transmission to other patients. Increased transmission to inpatients was associated with hospitals having fewer single rooms and lower heated volume per bed. Moreover, we show that reducing hospital transmission could substantially enhance the efficiency of punctuated lockdown measures in suppressing community transmission. These findings reveal the previously unrecognized scale of hospital transmission, have direct implications for targeting of hospital control measures and highlight the need to design hospitals better equipped to limit the transmission of future high-consequence pathogens.

Hospital Prices for Physician-Administered Drugs for Patients with Private Insurance.

BACKGROUND: Hospitals can leverage their position between the ultimate buyers and sellers of drugs to retain a substantial share of insurer pharmaceutical expenditures. METHODS: In this study, we used 2020-2021 national Blue Cross Blue Shield claims data regarding patients in the United States who had drug-infusion visits for oncologic conditions, inflammatory conditions, or blood-cell deficiency disorders. Markups of the reimbursement prices were measured in terms of amounts paid by Blue Cross Blue Shield plans to hospitals and physician practices relative to the amounts paid by these providers to drug manufacturers. Acquisition-price reductions in hospital payments to drug manufacturers were measured in terms of discounts under the federal 340B Drug Pricing Program. We estimated the percentage of Blue Cross Blue Shield drug spending that was received by drug manufacturers and the percentage retained by provider organizations. RESULTS: The study included 404,443 patients in the United States who had 4,727,189 drug-infusion visits. The median price markup (defined as the ratio of the reimbursement price to the acquisition price) for hospitals eligible for 340B discounts was 3.08 (interquartile range, 1.87 to 6.38). After adjustment for drug, patient, and geographic factors, price markups at hospitals eligible for 340B discounts were 6.59 times (95% confidence interval [CI], 6.02 to 7.16) as high as those in independent physician practices, and price markups at noneligible hospitals were 4.34 times (95% CI, 3.77 to 4.90) as high as those in physician practices. Hospitals eligible for 340B discounts retained 64.3% of insurer drug expenditures, whereas hospitals not eligible for 340B discounts retained 44.8% and independent physician practices retained 19.1%. CONCLUSIONS: This study showed that hospitals imposed large price markups and retained a substantial share of total insurer spending on physician-administered drugs for patients with private insurance. The effects were especially large for hospitals eligible for discounts under the federal 340B Drug Pricing Program on acquisition costs paid to manufacturers. (Funded by Arnold Ventures and the National Institute for Health Care Management.).

The Evolving Practice of Hospital at Home in the United States.

Hospital at Home (HaH) provides hospital-level services in the home to eligible patients who would otherwise require facility-based hospitalization. In the last two decades, studies have shown that HaH can improve patient outcomes and satisfaction and reduce hospital readmissions. Improved technology and greater experience with the model have led to expansion in the scope of patients served and services provided by the model, but dissemination in the United States has been hampered by lack of insurance coverage until recently. HaH is likely at the tipping point for wide adoption in the United States. To realize its full benefits, HaH will need to continue volume expansion to achieve culture change in clinical practice as facilitated by increased insurance coverage, technological advancements, and improved workforce expertise. It is also essential that HaH programs maintain high-quality acute hospital care, ensure that their benefits can be accessed by hard-to-reach rural populations, and continue to advance health equity.

The many faces of Candida auris: Phenotypic and strain variation in an emerging pathogen.

Candida auris is an emerging fungal pathogen with unusual evolutionary history-there are multiple distinct phylogeographic clades showing a near simultaneous transition from a currently unknown reservoir to nosocomial pathogen. Each of these clades has experienced different selective pressures over time, likely resulting in selection for genotypes with differential fitness or phenotypic consequences when introduced to new environments. We also observe diversification within clades, providing additional opportunities for phenotypic differences. These differences can have large impacts on pathogenic potential, drug resistance profile, evolutionary trajectory, and transmissibility. In recent years, there have been significant advances in our understanding of strain-specific behavior in other microbes, including bacterial and fungal pathogens, and we have an opportunity to take this strain variation into account when describing aspects of C. auris biology. Here, we critically review the literature to gain insight into differences at both the strain and clade levels in C. auris, focusing on phenotypes associated with clinical disease or transmission. Our goal is to integrate clinical and epidemiological perspectives with molecular perspectives in a way that would be valuable for both audiences. Identifying differences between strains and understanding which phenotypes are strain specific will be crucial for understanding this emerging pathogen, and an important caveat when describing the analysis of a singular isolate.

Video-based AI for beat-to-beat assessment of cardiac function.

Accurate assessment of cardiac function is crucial for the diagnosis of cardiovascular disease1, screening for cardiotoxicity2 and decisions regarding the clinical management of patients with a critical illness3. However, human assessment of cardiac function focuses on a limited sampling of cardiac cycles and has considerable inter-observer variability despite years of training4,5. Here, to overcome this challenge, we present a video-based deep learning algorithm-EchoNet-Dynamic-that surpasses the performance of human experts in the critical tasks of segmenting the left ventricle, estimating ejection fraction and assessing cardiomyopathy. Trained on echocardiogram videos, our model accurately segments the left ventricle with a Dice similarity coefficient of 0.92, predicts ejection fraction with a mean absolute error of 4.1% and reliably classifies heart failure with reduced ejection fraction (area under the curve of 0.97). In an external dataset from another healthcare system, EchoNet-Dynamic predicts the ejection fraction with a mean absolute error of 6.0% and classifies heart failure with reduced ejection fraction with an area under the curve of 0.96. Prospective evaluation with repeated human measurements confirms that the model has variance that is comparable to or less than that of human experts. By leveraging information across multiple cardiac cycles, our model can rapidly identify subtle changes in ejection fraction, is more reproducible than human evaluation and lays the foundation for precise diagnosis of cardiovascular disease in real time. As a resource to promote further innovation, we also make publicly available a large dataset of 10,030 annotated echocardiogram videos.

Aerodynamic analysis of SARS-CoV-2 in two Wuhan hospitals.

The ongoing outbreak of coronavirus disease 2019 (COVID-19) has spread rapidly on a global scale. Although it is clear that severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is transmitted through human respiratory droplets and direct contact, the potential for aerosol transmission is poorly understood1-3. Here we investigated the aerodynamic nature of SARS-CoV-2 by measuring viral RNA in aerosols in different areas of two Wuhan hospitals during the outbreak of COVID-19 in February and March 2020. The concentration of SARS-CoV-2 RNA in aerosols that was detected in isolation wards and ventilated patient rooms was very low, but it was higher in the toilet areas used by the patients. Levels of airborne SARS-CoV-2 RNA in the most public areas was undetectable, except in two areas that were prone to crowding; this increase was possibly due to individuals infected with SARS-CoV-2 in the crowd. We found that some medical staff areas initially had high concentrations of viral RNA with aerosol size distributions that showed peaks in the submicrometre and/or supermicrometre regions; however, these levels were reduced to undetectable levels after implementation of rigorous sanitization procedures. Although we have not established the infectivity of the virus detected in these hospital areas, we propose that SARS-CoV-2 may have the potential to be transmitted through aerosols. Our results indicate that room ventilation, open space, sanitization of protective apparel, and proper use and disinfection of toilet areas can effectively limit the concentration of SARS-CoV-2 RNA in aerosols. Future work should explore the infectivity of aerosolized virus.

Feature architecture aware phylogenetic profiling indicates a functional diversification of type IVa pili in the nosocomial pathogen Acinetobacter baumannii.

The Gram-negative bacterial pathogen Acinetobacter baumannii is a major cause of hospital-acquired opportunistic infections. The increasing spread of pan-drug resistant strains makes A. baumannii top-ranking among the ESKAPE pathogens for which novel routes of treatment are urgently needed. Comparative genomics approaches have successfully identified genetic changes coinciding with the emergence of pathogenicity in Acinetobacter. Genes that are prevalent both in pathogenic and a-pathogenic Acinetobacter species were not considered ignoring that virulence factors may emerge by the modification of evolutionarily old and widespread proteins. Here, we increased the resolution of comparative genomics analyses to also include lineage-specific changes in protein feature architectures. Using type IVa pili (T4aP) as an example, we show that three pilus components, among them the pilus tip adhesin ComC, vary in their Pfam domain annotation within the genus Acinetobacter. In most pathogenic Acinetobacter isolates, ComC displays a von Willebrand Factor type A domain harboring a finger-like protrusion, and we provide experimental evidence that this finger conveys virulence-related functions in A. baumannii. All three genes are part of an evolutionary cassette, which has been replaced at least twice during A. baumannii diversification. The resulting strain-specific differences in T4aP layout suggests differences in the way how individual strains interact with their host. Our study underpins the hypothesis that A. baumannii uses T4aP for host infection as it was shown previously for other pathogens. It also indicates that many more functional complexes may exist whose precise functions have been adjusted by modifying individual components on the domain level.

Multidimensional analysis of immune cells from COVID-19 patients identified cell subsets associated with the severity at hospital admission.

BACKGROUND: SARS-CoV-2 emerged as a new coronavirus causing COVID-19, and it has been responsible for more than 760 million cases and 6.8 million deaths worldwide until March 2023. Although infected individuals could be asymptomatic, other patients presented heterogeneity and a wide range of symptoms. Therefore, identifying those infected individuals and being able to classify them according to their expected severity could help target health efforts more effectively. METHODOLOGY/PRINCIPAL FINDINGS: Therefore, we wanted to develop a machine learning model to predict those who will develop severe disease at the moment of hospital admission. We recruited 75 individuals and analysed innate and adaptive immune system subsets by flow cytometry. Also, we collected clinical and biochemical information. The objective of the study was to leverage machine learning techniques to identify clinical features associated with disease severity progression. Additionally, the study sought to elucidate the specific cellular subsets involved in the disease following the onset of symptoms. Among the several machine learning models tested, we found that the Elastic Net model was the better to predict the severity score according to a modified WHO classification. This model was able to predict the severity score of 72 out of 75 individuals. Besides, all the machine learning models revealed that CD38+ Treg and CD16+ CD56neg HLA-DR+ NK cells were highly correlated with the severity. CONCLUSIONS/SIGNIFICANCE: The Elastic Net model could stratify the uninfected individuals and the COVID-19 patients from asymptomatic to severe COVID-19 patients. On the other hand, these cellular subsets presented here could help to understand better the induction and progression of the symptoms in COVID-19 individuals.

Employing Multicolor Melting Curve Analysis to Rapidly Identify Non-Tuberculous Mycobacteria in Patients with Bronchiectasis: A Study from a Pulmonary Hospital in the Fuzhou District of China, 2018-2022.

Non-tuberculous mycobacteria (NTM) infection is common in bronchiectasis, with rising incidence globally. However, investigation into NTM in bronchiectasis patients in China remains relatively limited. This work aimed to identify and understand the features of NTM in bronchiectasis patient in Fuzhou district of China. The pulmonary samples were collected from 281 bronchiectasis patients with suspected NTM infection in Fuzhou, 2018-2022. MPB64 antigen detection was employed for the preliminary evaluation of NTM. Further NTM identification was realized using gene chip and gene sequencing. Among 281 patients, 172 (61.21%) patients were NTM-positive (58.72%) according to MPB64 antigen detection, with females (58.72%) outnumbering males (41.28%) and the highest prevalence in the age group of 46-65 years. In total, 47 NTM single infections and 3 mixed infections (1 Mycobacterium tuberculosis complex-M. intracellulare, 1 M. avium-M. intracellulare, and 1 M. abscessus-M. intracellulare) were identified through multicolor melting curve analysis (MMCA), which was compared with gene sequencing results. Both methods suggested Mycobacterium (M.) intracellulare, M. abscessus, and M. avium as the primary NTM species affecting bronchiectasis patients. M. intracellulare and M. abscessus were more frequent in females than males with the highest prevalence in the age group of 46-65 years according to MMCA. This research provides novel insights into the epidemiological and clinical features of NTM in bronchiectasis patients in Southeastern China. Significantly, M. intracellulare, M. abscessus, and M. avium were identified as the major NTM species, contributing to a better understanding and management of bronchiectasis accompanied by NTM infection.

Real-time forecasting of COVID-19-related hospital strain in France using a non-Markovian mechanistic model.

Projects such as the European Covid-19 Forecast Hub publish forecasts on the national level for new deaths, new cases, and hospital admissions, but not direct measurements of hospital strain like critical care bed occupancy at the sub-national level, which is of particular interest to health professionals for planning purposes. We present a sub-national French framework for forecasting hospital strain based on a non-Markovian compartmental model, its associated online visualisation tool and a retrospective evaluation of the real-time forecasts it provided from January to December 2021 by comparing to three baselines derived from standard statistical forecasting methods (a naive model, auto-regression, and an ensemble of exponential smoothing and ARIMA). In terms of median absolute error for forecasting critical care unit occupancy at the two-week horizon, our model only outperformed the naive baseline for 4 out of 14 geographical units and underperformed compared to the ensemble baseline for 5 of them at the 90% confidence level (n = 38). However, for the same level at the 4 week horizon, our model was never statistically outperformed for any unit despite outperforming the baselines 10 times spanning 7 out of 14 geographical units. This implies modest forecasting utility for longer horizons which may justify the application of non-Markovian compartmental models in the context of hospital-strain surveillance for future pandemics.

In medical inpatients, the IMPROVE risk score had moderate predictive power for 14-d in-hospital bleeding.

SOURCE CITATION: Villiger R, Juillard P, Darbellay Farhoumand P, et al. Prediction of in-hospital bleeding in acutely ill medical patients: external validation of the IMPROVE bleeding risk score. Thromb Res. 2023;230:37-44. 37634309.

In nursing homes, universal decolonization vs. routine care reduced transfers to hospital for infection.

SOURCE CITATION: Miller LG, McKinnell JA, Singh RD, et al. Decolonization in nursing homes to prevent infection and hospitalization. N Engl J Med. 2023;389:1766-1777. 37815935.

Health Care Contact Days Among Older Adults in Traditional Medicare : A Cross-Sectional Study.

BACKGROUND: Days spent obtaining health care outside the home can represent not only access to needed care but also substantial time, effort, and cost, especially for older adults and their care partners. Yet, these "health care contact days" have not been characterized. OBJECTIVE: To assess composition of, variation and patterns in, and factors associated with contact days among older adults. DESIGN: Cross-sectional study. SETTING: Nationally representative 2019 Medicare Current Beneficiary Survey data linked to claims. PARTICIPANTS: Community-dwelling adults aged 65 years and older in traditional Medicare. MEASUREMENTS: Ambulatory contact days (days with a primary care or specialty care office visit, test, imaging, procedure, or treatment) and total contact days (ambulatory days plus institutional days in a hospital, emergency department, skilled-nursing facility, or hospice facility); multivariable mixed-effects Poisson regression to identify patient factors associated with contact days. RESULTS: In weighted results, 6619 older adults (weighted: 29 694 084) had means of 17.3 ambulatory contact days (SD, 22.1) and 20.7 total contact days (SD, 27.5) in the year; 11.1% had 50 or more total contact days. Older adults spent most contact days on ambulatory care, including primary care visits (mean [SD], 3.5 [5.0]), specialty care visits (5.7 [9.6]), tests (5.3 [7.2]), imaging (2.6 [3.9]), procedures (2.5 [6.4]), and treatments (5.7 [13.3]). Half of the test and imaging days were not on the same days as office visits (48.6% and 50.1%, respectively). Factors associated with more ambulatory contact days included younger age, female sex, White race, non-Hispanic ethnicity, higher income, higher educational attainment, urban residence, more chronic conditions, and care-seeking behaviors (for example, "go to the doctor…as soon as (I)…feel bad"). LIMITATION: Study population limited to those in traditional Medicare. CONCLUSION: On average, older adults spent 3 weeks in the year getting care outside the home. These contact days were mostly ambulatory and varied widely not only by number of chronic conditions but also by sociodemographic factors, geography, and care-seeking behaviors. These results show factors beyond clinical need that may drive overuse and underuse of contact days and opportunities to optimize this person-centered measure to reduce patient burdens, for example, via care coordination. PRIMARY FUNDING SOURCE: National Institute on Aging.

Real-time pandemic surveillance using hospital admissions and mobility data.

Forecasting the burden of COVID-19 has been impeded by limitations in data, with case reporting biased by testing practices, death counts lagging far behind infections, and hospital census reflecting time-varying patient access, admission criteria, and demographics. Here, we show that hospital admissions coupled with mobility data can reliably predict severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission rates and healthcare demand. Using a forecasting model that has guided mitigation policies in Austin, TX, we estimate that the local reproduction number had an initial 7-d average of 5.8 (95% credible interval [CrI]: 3.6 to 7.9) and reached a low of 0.65 (95% CrI: 0.52 to 0.77) after the summer 2020 surge. Estimated case detection rates ranged from 17.2% (95% CrI: 11.8 to 22.1%) at the outset to a high of 70% (95% CrI: 64 to 80%) in January 2021, and infection prevalence remained above 0.1% between April 2020 and March 1, 2021, peaking at 0.8% (0.7-0.9%) in early January 2021. As precautionary behaviors increased safety in public spaces, the relationship between mobility and transmission weakened. We estimate that mobility-associated transmission was 62% (95% CrI: 52 to 68%) lower in February 2021 compared to March 2020. In a retrospective comparison, the 95% CrIs of our 1, 2, and 3 wk ahead forecasts contained 93.6%, 89.9%, and 87.7% of reported data, respectively. Developed by a task force including scientists, public health officials, policy makers, and hospital executives, this model can reliably project COVID-19 healthcare needs in US cities.

Predictors of Severity of Influenza-Related Hospitalizations: Results From the Global Influenza Hospital Surveillance Network (GIHSN).

BACKGROUND: The Global Influenza Hospital Surveillance Network (GIHSN) has since 2012 provided patient-level data on severe influenza-like-illnesses from >100 participating clinical sites worldwide based on a core protocol and consistent case definitions. METHODS: We used multivariable logistic regression to assess the risk of intensive care unit admission, mechanical ventilation, and in-hospital death among hospitalized patients with influenza and explored the role of patient-level covariates and country income level. RESULTS: The data set included 73 121 patients hospitalized with respiratory illness in 22 countries, including 15 660 with laboratory-confirmed influenza. After adjusting for patient-level covariates we found a 7-fold increase in the risk of influenza-related intensive care unit admission in lower middle-income countries (LMICs), compared with high-income countries (P = .01). The risk of mechanical ventilation and in-hospital death also increased by 4-fold in LMICs, though these differences were not statistically significant. We also find that influenza mortality increased significantly with older age and number of comorbid conditions. Across all severity outcomes studied and after controlling for patient characteristics, infection with influenza A/H1N1pdm09 was more severe than with A/H3N2. CONCLUSIONS: Our study provides new information on influenza severity in underresourced populations, particularly those in LMICs.

Respiratory Syncytial Virus-Associated Hospital Admissions by Deprivation Levels Among Children and Adults in Scotland.

BACKGROUND: Socioeconomic deprivation may predispose individuals to respiratory tract infections. We estimated RSV-associated hospitalizations by socioeconomic deprivation in Scotland. METHODS: Using national routine health care records and virological surveillance from 2010 to 2016, we used a time-series linear regression model and a direct measurement based on ICD-10 coded diagnoses to estimate RSV-associated hospitalizations by Scottish Index of Multiple Deprivation (SIMD) quintile and age in comparison to influenza-associated hospitalizations. RESULTS: We estimated an annual average rate per 1000 people of 0.76 (95% CI: 0.43-0.90) in the least deprived group to 1.51 (1.03-1.79) for the most deprived group using model-based approach. The rate ratio (RR) was 1.96 (1.23-3.25), 1.60 (1.0-2.66), 1.35 (0.85-2.25), and 1.12 (0.7-1.85) in the 1st to 4th quintile versus the least deprived group. The pattern of RSV-associated hospitalization rates variation with SIMD was most pronounced in children 0-2y. The ICD-10 approach provided much lower rates than the model-based approach but yielded similar RR estimates between SIMD. Influenza-associated hospitalization rate generally increased with higher deprivation levels among individuals 1y+. CONCLUSIONS: Higher RSV and influenza hospitalization rates are related to higher deprivation levels. Differences between deprivation levels are most pronounced in infants and young children for RSV, and are more apparent among individuals 1y+ for influenza.

Changes in Cardiovascular Spending, Care Utilization, and Clinical Outcomes Associated With Participation in Bundled Payments for Care Improvement - Advanced.

BACKGROUND: Bundled Payments for Care Improvement - Advanced (BPCI-A) is a Medicare initiative that aims to incentivize reductions in spending for episodes of care that start with a hospitalization and end 90 days after discharge. Cardiovascular disease, an important driver of Medicare spending, is one of the areas of focus BPCI-A. It is unknown whether BPCI-A is associated with spending reductions or quality improvements for the 3 cardiovascular medical events or 5 cardiovascular procedures in the model. METHODS: In this retrospective cohort study, we conducted difference-in-differences analyses using Medicare claims for patients discharged between January 1, 2017, and September 30, 2019, to assess differences between BPCI-A hospitals and matched nonparticipating control hospitals. Our primary outcomes were the differential changes in spending, before versus after implementation of BPCI-A, for cardiac medical and procedural conditions at BPCI-A hospitals compared with controls. Secondary outcomes included changes in patient complexity, care utilization, healthy days at home, readmissions, and mortality. RESULTS: Baseline spending for cardiac medical episodes at BPCI-A hospitals was $25 606. The differential change in spending for cardiac medical episodes at BPCI-A versus control hospitals was $16 (95% CI, -$228 to $261; P=0.90). Baseline spending for cardiac procedural episodes at BPCI-A hospitals was $37 961. The differential change in spending for cardiac procedural episodes was $171 (95% CI, -$429 to $772; P=0.58). There were minimal differential changes in physicians' care patterns such as the complexity of treated patients or in their care utilization. At BPCI-A versus control hospitals, there were no significant differential changes in rates of 90-day readmissions (differential change, 0.27% [95% CI, -0.25% to 0.80%] for medical episodes; differential change, 0.31% [95% CI, -0.98% to 1.60%] for procedural episodes) or mortality (differential change, -0.14% [95% CI, -0.50% to 0.23%] for medical episodes; differential change, -0.36% [95% CI, -1.25% to 0.54%] for procedural episodes). CONCLUSIONS: Participation in BPCI-A was not associated with spending reductions, changes in care utilization, or quality improvements for the cardiovascular medical events or procedures offered in the model.

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

BACKGROUND: The contemporary measures of hospital performance for heart failure hospitalization and 30-day risk-standardized readmission rate (RSRR) and risk-standardized mortality rate (RSMR) are estimated using the same risk adjustment model and overall event rate for all patients. Thus, these measures are mainly driven by the care quality and outcomes for the majority racial and ethnic group, and may not adequately represent the hospital performance for patients of Black and other races. METHODS: Fee-for-service Medicare beneficiaries from January 2014 to December 2019 hospitalized with heart failure were identified. Hospital-level 30-day RSRR and RSMR were estimated using the traditional race-agnostic models and the race-specific approach. The composite race-specific performance metric was calculated as the average of the RSRR/RMSR measures derived separately for each race and ethnicity group. Correlation and concordance in hospital performance for all patients and patients of Black and other races were assessed using the composite race-specific and race-agnostic metrics. RESULTS: The study included 1 903 232 patients (75.7% White [n=1 439 958]; 14.5% Black [n=276 684]; and 9.8% other races [n=186 590]) with heart failure from 1860 hospitals. There was a modest correlation between hospital-level 30-day performance metrics for patients of White versus Black race (Pearson correlation coefficient: RSRR=0.42; RSMR=0.26). Compared with the race-agnostic RSRR and RSMR, composite race-specific metrics for all patients demonstrated stronger correlation with RSRR (correlation coefficient: 0.60 versus 0.74) and RSMR (correlation coefficient: 0.44 versus 0.51) for Black patients. Concordance in hospital performance for all patients and patients of Black race was also higher with race-specific (versus race-agnostic) metrics (RSRR=64% versus 53% concordantly high-performing; 61% versus 51% concordantly low-performing). Race-specific RSRR and RSMR metrics (versus race-agnostic) led to reclassification in performance ranking of 35.8% and 39.2% of hospitals, respectively, with better 30-day and 1-year outcomes for patients of all race groups at hospitals reclassified as high-performing. CONCLUSIONS: Among patients hospitalized with heart failure, race-specific 30-day RSMR and RSRR are more equitable in representing hospital performance for patients of Black and other races.

Heterogeneity of State Stroke Center Certification and Designation Processes.

BACKGROUND: Stroke centers are critical for the timely diagnosis and treatment of acute stroke and have been associated with improved treatment and outcomes; however, variability exists in the definitions and processes used to certify and designate these centers. Our study categorizes state stroke center certification and designation processes and provides examples of state processes across the United States, specifically in states with independent designation processes that do not rely on national certification. METHODS: In this cross-sectional study from September 2022 to April 2023, we used peer-reviewed literature, primary source documents from states, and communication with state officials in all 50 states to capture each state's process for stroke center certification and designation. We categorized this information and outlined examples of processes in each category. RESULTS: Our cross-sectional study of state-level stroke center certification and designation processes across states reveals significant heterogeneity in the terminology used to describe state processes and the processes themselves. We identify 3 main categories of state processes: No State Certification or Designation Process (category A; n=12), State Designation Reliant on National Certification Only (category B; n=24), and State Has Option for Self-Certification or Independent Designation (category C; n=14). Furthermore, we describe 3 subcategories of self-certification or independent state designation processes: State Relies on Self-Certification or Independent Designation for Acute Stroke Ready Hospital or Equivalent (category C1; n=3), State Has Hybrid Model for Acute Stroke Ready Hospital or Equivalent (category C2; n=5), and State Has Hybrid Model for Primary Stroke Center and Above (category C3; n=6). CONCLUSIONS: Our study found significant heterogeneity in state-level processes. A better understanding of how these differences may impact the rigor of each process and clinical performance of stroke centers is worthy of further investigation.