Causal Impact of the Hospital Readmissions Reduction Program on Hospital Readmissions and Mortality

Estimating causal effects of the Hospital Readmissions Reduction Program (HRRP), part of the Affordable Care Act, has been very controversial. Associational studies have demonstrated decreases in hospital readmissions, consistent with the intent of the program, although analyses with different data sources and methods have differed in estimating effects on patient mortality. To address these issues, we define the estimands of interest in the context of potential outcomes, we formalize a Bayesian structural time-series model for causal inference, and discuss the necessary assumptions for estimation of effects using observed data. The method is used to estimate the effect of the passage of HRRP on both the 30-day readmissions and 30-day mortality. We show that for acute myocardial infarction and congestive heart failure, HRRP caused reduction in readmissions while it had no statistically significant effect on mortality. However, for pneumonia, HRRP had no statistically significant effect on readmissions but caused an increase in mortality.Comment: 10 pages, 1 figure, 2 table

Prediction models for hospital readmissions in patients with heart disease: a systematic review and meta-analysis

Objective: To describe the discrimination and calibration of clinical prediction models, identify characteristics that contribute to better predictions and investigate predictors that are associated with unplanned hospital readmissions. Design: Systematic review and meta-analysis. Data source: Medline, EMBASE, ICTPR (for study protocols) and Web of Science (for conference proceedings) were searched up to 25 August 2020. Eligibility criteria for selecting studies: Studies were eligible if they reported on (1) hospitalised adult patients with acute heart disease; (2) a clinical presentation of prediction models with c-statistic; (3) unplanned hospital readmission within 6 months. Primary and secondary outcome measures: Model discrimination for unplanned hospital readmission within 6 months measured using concordance (c) statistics and model calibration. Meta-regression and subgroup analyses were performed to investigate predefined sources of heterogeneity. Outcome measures from models reported in multiple independent cohorts and similarly defined risk predictors were pooled. Results: Sixty studies describing 81 models were included: 43 models were newly developed, and 38 were externally validated. Included populations were mainly patients with heart failure (HF) (n=29). The average age ranged between 56.5 and 84 years. The incidence of readmission ranged from 3% to 43%. Risk of bias (RoB) was high in almost all studies. The c-statistic was 0.8 in 5 models. The study population, data source and number of predictors were significant moderators for the discrimination. Calibration was reported for 27 models. Only the GRACE (Global Registration of Acute Coronary Events) score had adequate discrimination in independent cohorts (0.78, 95% CI 0.63 to 0.86). Eighteen predictors were pooled. Conclusion: Some promising models require updating and validation before use in clinical practice. The lack of independent validation studies, high RoB and low consistency in measured predictors limit their applicability. Prospero registration number: CRD42020159839. Keywords: adverse events; cardiology; risk management

Young Women With Acute Myocardial Infarction: Risk Prediction Model for 1-Year Hospital Readmission

Background Although young women ( aged ≤ 55 years) are at higher risk than similarly aged men for hospital readmission within 1 year after an acute myocardial infarction (AMI), no risk prediction models have been developed for them. The present study developed and internally validated a risk prediction model of 1-year post-AMI hospital readmission among young women that considered demographic, clinical, and gender-related variables. Methods We used data from the US Variation in Recovery: Role of Gender on Outcomes of Young AMI Patients (VIRGO) study (n = 2007 women), a prospective observational study of young patients hospitalized with AMI. Bayesian model averaging was used for model selection and bootstrapping for internal validation. Model calibration and discrimination were respectively assessed with calibration plots and area under the curve. Results Within 1-year post-AMI, 684 women (34.1%) were readmitted to the hospital at least once. The final model predictors included: any in-hospital complication, baseline perceived physical health, obstructive coronary artery disease, diabetes, history of congestive heart failure, low income ( < $30,000 US), depressive symptoms, length of hospital stay, and race (White vs Black). Of the 9 retained predictors, 3 were gender-related. The model was well calibrated and exhibited modest discrimination (area under the curve = 0.66). Conclusions Our female-specific risk model was developed and internally validated in a cohort of young female patients hospitalized with AMI and can be used to predict risk of readmission. Whereas clinical factors were the strongest predictors, the model included several gender-related variables (ie, perceived physical health, depression, income level). However, discrimination was modest, indicating that other unmeasured factors contribute to variability in hospital readmission risk among younger women

Bringing the pieces together:Integrating cardiac and geriatric care in older patients with heart disease

Due to the increasing aging population, the number of older cardiac patients is also expected to rise in the next decades. The treatment of older cardiac patients is complex due to the simultaneously presence of comorbidities and polypharmacy, and geriatric conditions such as functional impairment, fall risk and malnutrition. However, the assessment of geriatric conditions is not part of the medical routine in cardiology and therefore these conditions are frequently unrecognized although they have a significant impact on treatment and on outcomes. In addition, treatments are mostly based on single-disease oriented guidelines and inadequately take other conditions into account. This may lead to conflicting recommendations and treatments that do not address important outcomes for older patients such as daily functioning, symptom relief and quality of life. Thus, the care of older cardiac patients is currently suboptimal which increases the risk of functional loss, readmission and mortality. The overall aim of the work described in this thesis is to explore the integration of cardiac and geriatric care for older patients with heart disease. First, by examining how hospitalized older cardiac patients at high risk for adverse events could be identified. Second, by investigating lifestyle-related secondary prevention of cardiovascular complications in older cardiac patients. And third, by developing a transitional care intervention for older cardiac patients and evaluating the effect on unplanned hospital readmission and mortality

Diagnosis-Specific Readmission Risk Prediction Using Electronic Health Data: a Retrospective Cohort Study

Background: Readmissions after hospital discharge are a common occurrence and are costly for both hospitals and patients. Previous attempts to create universal risk prediction models for readmission have not met with success. In this study we leveraged a comprehensive electronic health record to create readmission-risk models that were institution- and patient- specific in an attempt to improve our ability to predict readmission. Methods: This is a retrospective cohort study performed at a large midwestern tertiary care medical center. All patients with a primary discharge diagnosis of congestive heart failure, acute myocardial infarction or pneumonia over a two-year time period were included in the analysis. The main outcome was 30-day readmission. Demographic, comorbidity, laboratory, and medication data were collected on all patients from a comprehensive information warehouse. Using multivariable analysis with stepwise removal we created three risk disease-specific risk prediction models and a combined model. These models were then validated on separate cohorts. Results: 3572 patients were included in the derivation cohort. Overall there was a 16.2% readmission rate. The acute myocardial infarction and pneumonia readmission-risk models performed well on a random sample validation cohort (AUC range 0.73 to 0.76) but less well on a historical validation cohort (AUC 0.66 for both). The congestive heart failure model performed poorly on both validation cohorts (AUC 0.63 and 0.64). Conclusions: The readmission-risk models for acute myocardial infarction and pneumonia validated well on a contemporary cohort, but not as well on a historical cohort, suggesting that models such as these need to be continuously trained and adjusted to respond to local trends. The poor performance of the congestive heart failure model may suggest that for chronic disease conditions social and behavioral variables are of greater importance and improved documentation of these variables within the electronic health record should be encouraged

Medication Regimen Complexity and Readmissions after Hospitalization for Heart Failure, Acute Myocardial Infarction, Pneumonia, and Chronic Obstructive Pulmonary Disease

Objectives: Readmission rate is increasingly being viewed as a key indicator of health system performance. Medication regimen complexity index scores may be predictive of readmissions; however, few studies have examined this potential association. The primary objective of this study was to determine whether medication regimen complexity index is associated with all-cause 30-day readmission after admission for heart failure, acute myocardial infarction, pneumonia, or chronic obstructive pulmonary disease. Methods: This study was an institutional review board–approved, multi-center, case–control study. Patients admitted with a primary diagnosis of heart failure, acute myocardial infarction, pneumonia, or chronic obstructive pulmonary disease were randomly selected for inclusion. Patients were excluded if they discharged against medical advice or expired during their index visit. Block randomization was utilized for equal representation of index diagnosis and site. Discharge medication regimen complexity index scores were compared between subjects with readmission versus those without. Medication regimen complexity index score was then used as a predictor in logistic regression modeling for readmission. Results: Seven hundred and fifty-six patients were randomly selected for inclusion, and 101 (13.4%) readmitted within 30days. The readmission group had higher medication regimen complexity index scores than the no-readmission group (p\u3c0.01). However, after controlling for demographics, disease state, length of stay, site, and medication count, medication regimen complexity index was no longer a significant predictor of readmission (odds ratio 0.99, 95% confidence interval 0.97–1.01) or revisit (odds ratio 0.99, 95% confidence interval 0.98–1.02). Conclusion: There is little evidence to support the use of medication regimen complexity index in readmission prediction when other measures are available. Medication regimen complexity index may lack sufficient sensitivity to capture an effect of medication regimen complexity on all-cause readmission

Higher Readmissions at Safety-Net Hospitals and Potential Policy Solutions

The Hospital Readmissions Reduction Program (HRRP), established by the Affordable Care Act, ties a hospital's payments to its readmission rates -- with penalties for hospitals that exceed a national benchmark -- to encourage hospitals to reduce avoidable readmissions. This new Commonwealth Fund analysis uses publicly reported 30-day hospital readmission rate data to examine whether safety-net hospitals are more likely to have higher readmission rates, compared with other hospitals. Results of this analysis find that safety-net hospitals are 30 percent more likely to have 30-day hospital readmission rates above the national average, compared with non-safety-net hospitals, and will therefore be disproportionately impacted by the HRRP. Policy solutions to help safety-net hospitals reduce their readmission rates include targeting quality improvement initiatives for safety-net hospitals; ensuring that broader delivery system improvements include safety-net hospitals and care delivery systems; and enhancing bundled payment rates to account for socioeconomic risk factors

Risk factors for rehospitalization for acute coronary syndromes and unplanned revascularization following acute myocardial infarction

Background Rehospitalizations for acute coronary syndromes (ACS) and coronary revascularization after an acute myocardial infarction (AMI) are not only common and costly but can also impact patients’ quality of life. In contrast to mortality and all‐cause readmissions, little insight is available into risk factors associated with ACS and revascularization after AMI. Methods and Results In a multicenter AMI registry, we examined the rates and predictors of rehospitalizations for ACS and revascularization within the year after AMI among 3283 patients. Staged revascularization procedures were excluded. Kaplan–Meier estimated rates of rehospitalization due to ACS and revascularization were 6.8% and 4.1%, respectively. In hierarchical, multivariable models, the strongest predictors of rehospitalization for ACS were coronary artery bypass graft prior to AMI hospitalization (hazard ratio [HR] 2.12, 95% CI 1.45 to 3.10), female sex (HR 1.67, 95% CI 1.23 to 2.25), and in‐hospital PCI (HR 1.85, 95% CI 1.28 to 2.69). The strongest predictors of subsequent revascularization were multivessel disease (HR 2.89, 95% CI 1.90 to 4.39) and in‐hospital percutaneous coronary intervention with a bare metal stent (HR 2.08, 95% CI 1.19 to 3.63). The Global Registry of Acute Coronary Events mortality risk score was not associated with the risk of rehospitalization for ACS or revascularization. Conclusions Unique characteristics are associated with admissions for ACS and revascularization, as compared with survival. These multivariable risk predictors may help identify patients at high risk for ACS and revascularization, in whom intensification of secondary prevention therapies or closer post‐AMI follow‐up may be warranted

Predicting one-year mortality among elderly survivors of hospitalization for an acute myocardial infarction: results from the Cooperative Cardiovascular Project

AbstractOBJECTIVESWe sought to develop a model based on information available from the medical record that would accurately stratify elderly patients who survive hospitalization with an acute myocardial infarction (AMI) according to their risk of one-year mortality.BACKGROUNDPrediction of the risk of mortality among older survivors of an AMI has many uses, yet few studies have determined the prognostic importance of demographic, clinical and functional data that are available on discharge in a population-based sample.METHODSIn a cohort of patients aged ≥65 years who survived hospitalization for a confirmed AMI from 1994 to 1995 at acute care, nongovernmental hospitals in the U.S., we developed a parsimonious model to stratify patients by their risk of one-year mortality.RESULTSThe study sample of 103,164 patients, with a mean age of 76.8 years, had a one-year mortality of 22%. The factors with the strongest association with mortality were older age, urinary incontinence, assisted mobility, presence of heart failure or cardiomegaly any time before discharge, presence of peripheral vascular disease, body mass index <20 kg/m2, renal dysfunction (defined as creatinine >2.5 mg/dl or blood urea nitrogen >40 mg/dl) and left ventricular dysfunction (left ventricular ejection fraction <40%). On the basis of the coefficients in the model, patients were stratified into risk groups ranging from 7% to 49%.CONCLUSIONSWe demonstrate that a simple risk model can stratify older patients well by their risk of death one year after discharge for AMI