Disparities in Hospital Length of Stay Across Race and Ethnicity Among Patients With Heart Failure

Temporal Trends, Outcomes, and Predictors of Next-Day Discharge and Readmission Following Uncomplicated Evolut Transcatheter Aortic Valve Replacement: A Propensity Score-Matched Analysis

BACKGROUND

Next-day discharge (NDD) outcomes following uncomplicated self-expanding transcatheter aortic valve replacement have not been studied. Here, we compare readmission rates and clinical outcomes in NDD versus non-NDD transcatheter aortic valve replacement with Evolut.

METHODS AND RESULTS

Society of Thoracic Surgeons/American College of Cardiology TVT (Transcatheter Valve Therapy) Registry patients (n=29 597) undergoing elective transcatheter aortic valve replacement with self-expanding supra-annular valves (Evolut R, PRO, and PRO+) from July 2019 to June 2021 were stratified by postprocedure length of stay: ≤1 day (NDD) versus >1 day (non-NDD). Propensity score matching was used to compare risk adjusted 30-day readmission rates and 1-year outcomes in NDD versus non-NDD, and multivariable regression to determine predictors of NDD and readmission. Between the first and last calendar quarter, the rate of NDD increased from 45.4% to 62.1% and median length of stay decreased from 2 days to 1. Propensity score matching produced relatively well-matched NDD and non-NDD cohorts (n=10 549 each). After matching, NDD was associated with lower 30-day readmission rates (6.3% versus 8.4%; P<0.001) and 1-year adverse outcomes (death, 7.0% versus 9.3%; life threatening/major bleeding, 1.6% versus 3.4%; new permanent pacemaker implantation/implantable cardioverter-defibrillator, 3.6 versus 11.0%; [all P<0.001]). Predictors of NDD included non-Hispanic ethnicity, preexisting permanent pacemaker implantation/implantable cardioverter-defibrillator, and previous surgical aortic valve replacement.

CONCLUSIONS

Most patients undergoing uncomplicated self-expanding Evolut transcatheter aortic valve replacement are discharged the next day. This study found that NDD can be predicted from baseline patient characteristics and was associated with favorable 30-day and 1-year outcomes, including low rates of permanent pacemaker implantation and readmission.

Contemporary Decongestion Strategies in Patients Hospitalized for Heart Failure: A National Community-Based Cohort Study

BACKGROUND

Heart failure (HF) is a leading cause of hospitalization in the United States. Decongestion remains a central goal of inpatient management, but contemporary decongestion practices and associated weight loss have not been well characterized nationally.

OBJECTIVES

This study aimed to describe contemporary inpatient diuretic practices and clinical predictors of weight loss in patients hospitalized for HF.

METHODS

The authors identified HF hospitalizations from 2015 to 2022 in a U.S. national database aggregating deidentified patient-level electronic health record data across 31 geographically diverse community-based health systems. The authors report patient characteristics and inpatient weight change as a primary indicator of decongestion. Predictors of weight loss were evaluated using multivariable models. Temporal trends in inpatient diuretic practices, including augmented diuresis strategies such as adjunctive thiazides and continuous diuretic infusions, were assessed.

RESULTS

The study cohort included 262,673 HF admissions across 165,482 unique patients. The median inpatient weight loss was 5.3 pounds (Q1-Q3: 0.0-12.8 pounds) or 2.4 kg (Q1-Q3: 0.0-5.8 kg). Discharge weight was higher than admission weight in 20% of encounters. An increase of ≥0.3 mg/dL in serum creatinine from admission to inpatient peak occurred in >30% of hospitalizations and was associated with less weight loss. Adjunctive diuretic agents were utilized in <20% of encounters but were associated with greater weight loss.

CONCLUSIONS

In a large-scale U.S. community-based cohort study of HF hospitalizations, estimated weight loss from inpatient decongestion remains highly variable, with weight gain observed across many admissions. Augmented diuresis strategies were infrequently used. Comparative effectiveness trials are needed to establish optimal strategies for inpatient decongestion for acute HF.

Determinants of Health and Outcomes in Medicare Recipients With Heart Disease: A Population Study

CONTEXT

Heart disease (HD) is a primary cause of mortality and morbidity in the United States. While there is a growing body of evidence demonstrating the contribution of social determinants of health (SDoH) to HD outcomes, the impact of combined or individual SDoH on health-related quality of life (HRQoL) in patients with HD is not well understood.

OBJECTIVES

To analyze the National Health and Aging Trends Study (NHATS) to explore the relationship of SDoH with HRQoL, advance care planning, and treatment preferences in Medicare beneficiaries with HD.

METHODS

The study design was a secondary data analysis using latent class analysis (LCA) and multivariable analysis of NHATS participants with HD, Round 8, that included End of Life Plans and Care questions.

RESULTS

1202 participants, median age 81 years, 57% female, 70% non-Hispanic White, 20% non-Hispanic Black, 10% Other. LCA identified two SDoH risk profiles (low/high), using 12 measures within the NHATS Economic and Social Consequences key concept area. The high-risk SDoH profile participants were more likely to have fair/poor HRQoL, and identify as female, non-White (P < 0.0001); and less likely to have completed advance care planning (P < 0.0001). High-risk SDoH participants were more likely to want life-prolonging treatments (P < 0.0001), however, this association was not significant after adjusting for age, sex, and race (P = 0.344).

CONCLUSION

Higher risk SDoH profiles are associated with reduced HRQoL, reduced advance care planning completion, female sex, and non-White race in a cohort of Medicare beneficiaries. These findings provide opportunities to improve SDoH-related care practices in older patients with HD.

Hospital characteristics and perioperative complications of Hispanic patients following reverse shoulder arthroplasty-a large database study

BACKGROUND

Hispanic patients are the youngest and fastest-growing ethnic group in the USA. Many of these patients are increasingly met with orthopedic issues, often electing to undergo corrective procedures such as reverse shoulder arthroplasty (RSA). This patient population has unique medical needs and has been reported to have higher incidences of perioperative complications following major procedures. Unfortunately, there is a lack of information on the hospitalization data and perioperative complications in Hispanic patients following procedures such as RSA. This project aimed to query the Nationwide Inpatient Sample (NIS) database to assess patient hospitalization information, demographics, and the prevalence of perioperative complications among Hispanic patients who received RSA.

METHODS

Information from 2016-2019 was queried from the NIS database. Demographic information, incidences of perioperative complications, length of stay, and costs of care among Hispanic patients undergoing RSA were compared to non-Hispanic patients undergoing RSA. A subsequent propensity matching was conducted to consider preoperative comorbidities.

RESULTS

The query of NIS identified 59,916 patients who underwent RSA. Of this sample, 2,656 patients (4.4%) were identified to be Hispanic, while the remaining 57,260 patients (95.6%) were found to belong to other races (control). After propensity matching, Hispanic patients had a significantly longer LOS (median = 1.4 days) than the patients in the control group (median = 1.0, P < 0.001). The Hispanic patients (89,168.5 USD) had a significantly higher cost of care than those in the control group (67,396.1 USD, P < 0.001). In looking at postoperative complications, Hispanic patients had increased incidences of acute renal failure (Hispanics: 3.1%, control group: 1.1%, P = 0.03) and blood loss anemia (Hispanics: 12.7%, control group: 10.9%, P = 0.03).

CONCLUSIONS

Hispanic patients had significantly longer lengths of stay, higher costs of care, and higher rates of perioperative complications compared to the control group. For patients who are Hispanic and undergoing RSA, this information will aid doctors in making comprehensive decisions regarding patient care and resource allocation.

A systematic comparison of machine learning algorithms to develop and validate prediction model to predict heart failure risk in middle-aged and elderly patients with periodontitis (NHANES 2009 to 2014)

Periodontitis is increasingly associated with heart failure, and the goal of this study was to develop and validate a prediction model based on machine learning algorithms for the risk of heart failure in middle-aged and elderly participants with periodontitis. We analyzed data from a total of 2876 participants with a history of periodontitis from the National Health and Nutrition Examination Survey (NHANES) 2009 to 2014, with a training set of 1980 subjects with periodontitis from the NHANES 2009 to 2012 and an external validation set of 896 subjects from the NHANES 2013 to 2014. The independent risk factors for heart failure were identified using univariate and multivariate logistic regression analysis. Machine learning algorithms such as logistic regression, k-nearest neighbor, support vector machine, random forest, gradient boosting machine, and multilayer perceptron were used on the training set to construct the models. The performance of the machine learning models was evaluated using 10-fold cross-validation on the training set and receiver operating characteristic curve (ROC) analysis in the validation set. Based on the results of univariate logistic regression and multivariate logistic regression, it was found that age, race, myocardial infarction, and diabetes mellitus status were independent predictors of the risk of heart failure in participants with periodontitis. Six machine learning models, including logistic regression, K-nearest neighbor, support vector machine, random forest, gradient boosting machine, and multilayer perceptron, were built on the training set, respectively. The area under the ROC for the 6 models was obtained using 10-fold cross-validation with values of 0 848, 0.936, 0.859, 0.889, 0.927, and 0.666, respectively. The areas under the ROC on the external validation set were 0.854, 0.949, 0.647, 0.933, 0.855, and 0.74, respectively. K-nearest neighbor model got the best prediction performance across all models. Out of 6 machine learning models, the K-nearest neighbor algorithm model performed the best. The prediction model offers early, individualized diagnosis and treatment plans and assists in identifying the risk of heart failure occurrence in middle-aged and elderly patients with periodontitis.