Reliability of Predicting Early Hospital Readmission After Discharge for an Acute Coronary Syndrome Using Claims-Based Data

Systematic Review and Critical Appraisal of Prediction Models for Readmission in Coronary Artery Disease Patients: Assessing Current Efficacy and Future Directions

Purpose

Coronary artery disease (CAD) patients frequently face readmissions due to suboptimal disease management. Prediction models are pivotal for detecting early unplanned readmissions. This review offers a unified assessment, aiming to lay the groundwork for enhancing prediction models and informing prevention strategies.

Methods

A search through five databases (PubMed, Web of Science, EBSCOhost, Embase, China National Knowledge Infrastructure) up to September 2023 identified studies on prediction models for coronary artery disease patient readmissions for this review. Two independent reviewers used the CHARMS checklist for data extraction and the PROBAST tool for bias assessment.

Results

From 12,457 records, 15 studies were selected, contributing 30 models targeting various CAD patient groups (AMI, CABG, ACS) from primarily China, the USA, and Canada. Models utilized varied methods such as logistic regression and machine learning, with performance predominantly measured by the c-index. Key predictors included age, gender, and hospital stay duration. Readmission rates in the studies varied from 4.8% to 45.1%. Despite high bias risk across models, several showed notable accuracy and calibration.

Conclusion

The study highlights the need for thorough external validation and the use of the PROBAST tool to reduce bias in models predicting readmission for CAD patients.

Evaluation of Health Literacy Levels and Associated Factors Among Patients with Acute Coronary Syndrome and Heart Failure in Qatar

PURPOSE

To determine the prevalence of inadequate health literacy and its associated risk factors among patients with acute coronary syndrome (ACS) and/or heart failure (HF) in Qatar.

PATIENTS AND METHODS

This cross-sectional observational study was conducted among patients with ACS and/or HF attending the national Heart Hospital in Qatar. Health literacy was assessed using the abbreviated version of the Test of Functional Health Literacy in Adults (S-TOFHLA) and the Three-item Brief Health Literacy Screen (3-item BHLS).

RESULTS

Three hundred patients with ACS and/or HF, majority male (88%) and non-Qatari (94%), participated in the study. The median (IQR) age of the participants was 55 (11) years. The prevalence of inadequate to marginal health literacy ranged between 36% and 54%. There were statistically significant differences in health literacy level between patients based on their marital status (p=0.010), education (p≤0.001), ability to speak any of Arabic, English, Hindi, Urdu, Malayalam, or other languages (p-values ≤0.001 to 0.035), country of origin (p≤0.001), occupation (p≤0.001), and receiving information from a pharmacist (p=0.008), a physiotherapist (p≤0.001), or a nurse (p=0.004).

CONCLUSION

Inadequate health literacy is common among patients with ACS and/or HF. This study suggests a need for developing strategies to assist healthcare professionals in improving health literacy skills among patients with ACS and HF. A combination of interventions may be needed to improve patients' understanding of their disease and medications, and ultimately overall health outcomes.

Predictive Modeling of Hospital Readmission: Challenges and Solutions

Hospital readmission prediction is a study to learn models from historical medical data to predict probability of a patient returning to hospital in a certain period, e.g. 30 or 90 days, after the discharge. The motivation is to help health providers deliver better treatment and post-discharge strategies, lower the hospital readmission rate, and eventually reduce the medical costs. Due to inherent complexity of diseases and healthcare ecosystems, modeling hospital readmission is facing many challenges. By now, a variety of methods have been developed, but existing literature fails to deliver a complete picture to answer some fundamental questions, such as what are the main challenges and solutions in modeling hospital readmission; what are typical features/models used for readmission prediction; how to achieve meaningful and transparent predictions for decision making; and what are possible conflicts when deploying predictive approaches for real-world usages. In this paper, we systematically review computational models for hospital readmission prediction, and propose a taxonomy of challenges featuring four main categories: (1) data variety and complexity; (2) data imbalance, locality and privacy; (3) model interpretability; and (4) model implementation. The review summarizes methods in each category, and highlights technical solutions proposed to address the challenges. In addition, a review of datasets and resources available for hospital readmission modeling also provides firsthand materials to support researchers and practitioners to design new approaches for effective and efficient hospital readmission prediction.

Impact of health literacy in patients with cardiovascular diseases: A systematic review and meta-analysis

OBJECTIVE

To clarify the impacts of health literacy on mortality, readmission, and quality of life (QOL) in the secondary or tertiary prevention of cardiovascular diseases (CVD) through a meta-analysis.

METHODS

Six electronic databases were searched on June 11, 2020. Observational studies involving patients with CVD, health literacy as an exposure factor and mortality, readmission, or QOL as outcomes were included in this study. Two researchers screened the retrieved articles and extracted data independently. The meta-analysis calculated the pooled relative risk of mortality and readmission. We also assessed the body of evidence based on Grading of Recommendations Assessment, Development and Evaluation (GRADE).

RESULTS

Following screening of 1616 studies, 16 observational studies were included. The mean rate of low health literacy was 32.8%. All studies focusing on QOL showed significant impacts of health literacy. Pooled relative risk was 1.621 (95% confidence interval: 1.089-2.412) for mortality and 1.184 (95% confidence interval: 1.035-1.355) for readmission, indicating significant effects of health literacy. GRADE assessment showed "LOW" certainty for each outcome.

CONCLUSION

Low health literacy was significantly associated with increased mortality and hospital readmission and decreased QOL in patients with CVD.

PRACTICE IMPLICATIONS

Considering low health literacy in clinical practice is very important to improve prognosis of CVD patients.

A structural equation model linking health literacy, self efficacy and quality of life in adults with coronary heart disease

Background

Cardiovascular disease is the world major cause of death. There is sufficient evidence that patients with coronary heart disease (CHD) experience poor quality of life. Health literacy and self efficacy are modifiable psychosocial factors that could affect quality of life, and these factors should be considered as targets for intervention. As the relationships among health literacy, self efficacy, and quality of life in the CHD population have not been well understood. Thus, we constructed the structure equation model in these valuables.

Methods

A cross-sectional study of a convenience sample among 200 patients with CHD were participated from outpatient clinics in three tertiary general hospitals in Baoding City in mainland China, from December 2018 to June 2019. Data regarding demographic features, health literacy, self efficacy and quality of life were assessed. A structure equation model was used to construct and validate the pathways.

Results

The mean age of the study sampled patients was 65.37 years old. The average level of health literacy, self efficacy and quality of life were 9.6 ± 3.5, 28.8 ± 13.9 and 381.8 ± 130.1 respectively. Significant associations were observed from health literacy to quality of life, and self efficacy played a partial mediating role between health literacy and quality of life in the CHD population. Health literacy and self efficacy explained for 59.6% of the variance in quality of life.

Conclusions

Health literacy had a direct influence on quality of life, and an indirect influence on quality of life via self efficacy in the patients with CHD.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12872-022-02720-8.

Differences in Social Hardships in Women and Men with Acute Myocardial Infarction: Impact on 30-Day Readmission

Background:

Studies have shown that women with acute myocardial infarction (AMI) have a higher prevalence of unfavorable social variables then men and have a worse outcome. Less is known regarding the impact of these social variables on 30-day readmission after AMI.

Materials and Methods:

We analyzed adult patients with AMI enrolled in a Quality Improvement Program intended to improve the peri-discharge care of patients with an AMI, and decrease all-cause 30-day unplanned readmissions. We compared clinical and social variables by gender. Multivariate logistic regression, with separate adjustment for clinical and for social variable, was used to measure adjusted odds for readmission by gender.

Results:

Among 208 patients included in our project 68 (32.7%) were women. Only 30.9% of women were married or had domestic partner at the time of the interview and only 16.2% were employed. Nearly half of women (48.5%) needed help with medical care, and 39.7% of women did not speak English as their first language. These variables were significantly different by gender. Rates of 30-day readmissions were higher in women than men (22.1% vs. 7.8%, p = 0.024). After adjusting for clinical variables this difference by gender in 30-day readmissions remained significant (odds ratio [OR] 3.34 95% confidence interval [CI] 1.1–11.1, p = 0.049). However, when adjusting for social variables, this difference was no longer noted (OR 0.87 95% CI 0.27–2.78, p = 0.822).

Conclusion:

Women with AMI are more likely than men to have unfavorable social factors that can impact recovery from AMI and women have a higher 30-day readmission rate. The higher 30-day readmissions in women appears to be influenced by these social factors. Health care interventions aimed at reducing 30-day readmission after AMI should focus on eliciting a detailed social history and providing aid for those requiring additional social support at home.

Factors affecting hospital readmission rates following an acute coronary syndrome: A systematic review

Aim

To synthesise quantitative evidence on factors that impact hospital readmission rates following ACS with comorbidities.

Design

Systematic review and narrative synthesis.

Data sources

A search of eight electronic databases, including Embase, Medline, PsycINFO, Web of Science, CINAHL, Cochrane Library, Scopus and the Joanna Briggs Institute (JBI).

Review methods

The search strategy included keywords and MeSH terms to identify English language studies published between 2001 and 2020. The quality of included studies was assessed by two independent reviewers, using Joanna Briggs Institute (JBI) critical appraisal tools.

Results

Twenty‐four articles were included in the review. All cause 30‐day readmission rate was most frequently reported and ranged from 4.2% to 81%. Reported factors that were associated with readmission varied across studies from socio‐demographic, behavioural factors, comorbidity factors and cardiac factors. Findings from some of the studies were limited by data source, study designs and small sample size.

Conclusion

Strategies that integrate comprehensive discharge planning and individualised care planning to enhance behavioural support are related to a reduction in readmission rates. It is recommended that nurses are supported to influence discharge planning and lead the development of nurse‐led interventions to ensure discharge planning is both coordinated and person‐centred.

Non-specific pain and 30-day readmission in acute coronary syndromes: findings from the TRACE-CORE prospective cohort

Background

Patients with acute coronary syndromes often experience non-specific (generic) pain after hospital discharge. However, evidence about the association between post-discharge non-specific pain and rehospitalization remains limited.

Methods

We analyzed data from the Transitions, Risks, and Actions in Coronary Events Center for Outcomes Research and Education (TRACE-CORE) prospective cohort. TRACE-CORE followed patients with acute coronary syndromes for 24 months post-discharge from the index hospitalization, collected patient-reported generic pain (using SF-36) and chest pain (using the Seattle Angina Questionnaire) and rehospitalization events. We assessed the association between generic pain and 30-day rehospitalization using multivariable logistic regression (N = 787). We also examined the associations among patient-reported pain, pain documentation identified by natural language processing (NLP) from electronic health record (EHR) notes, and the outcome.

Results

Patients were 62 years old (SD = 11.4), with 5.1% Black or Hispanic individuals and 29.9% women. Within 30 days post-discharge, 87 (11.1%) patients were re-hospitalized. Patient-reported mild-to-moderate pain, without EHR documentation, was associated with 30-day rehospitalization (odds ratio [OR]: 2.03, 95% confidence interval [CI]: 1.14–3.62, reference: no pain) after adjusting for baseline characteristics; while patient-reported mild-to-moderate pain with EHR documentation (presumably addressed) was not (OR: 1.23, 95% CI: 0.52–2.90). Severe pain was also associated with 30-day rehospitalization (OR: 3.16, 95% CI: 1.32–7.54), even after further adjusting for chest pain (OR: 2.59, 95% CI: 1.06–6.35).

Conclusions

Patient-reported post-discharge generic pain was positively associated with 30-day rehospitalization. Future studies should further disentangle the impact of cardiac and non-cardiac pain on rehospitalization and develop strategies to support the timely management of post-discharge pain by healthcare providers.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12872-021-02195-z.

Capturing rich person-centred discharge information: exploring the challenges in developing a new model

Purpose

Capture, consumption and use of person-centred information presents challenges for hospitals when operating within the scope of limited resources and the push for organisational routines and efficiencies. This paper explores these challenges for patients with Acute Coronary Syndrome (ACS) and the examination of information that supports successful hospital discharge. It aims to determine how the likelihood of readmission may be prevented through the capturing of rich, person-specific information during in-patient care to improve the process for discharge to home.

Design/methodology/approach

The authors combine four research data collection and analysis techniques: one, an analysis of the patient record; two, semi-structured longitudinal interviews; three, an analysis of the patient's journey using process mining to provide analytics about the discharge process, and four, a focus group with nurses to validate and confirm our findings.

Findings

The authors’ contribution is to show that information systems which support discharge need to consider models focused on individual patient stressors. The authors find that current discharge information capture does not provide the required person-centred information to support a successful discharge. Data indicate that rich, detailed information about the person acquired through additional nursing assessments are required to complement data provided about the patient's journey in order to support the patients’ post-discharge recovery at home.

Originality/value

Prior research has focused on information collection constrained by pre-determined limitations and barriers of system design. This work has not considered the information provided by multiple sources during the whole patient journey as a mechanism to reshape the discharge process to become more person-centred. Using a novel combination of research techniques and theory, the authors have shown that patient information collected through multiple channels across the patient care journey may significantly extend the quality of patient care beyond hospital discharge. Although not assessed in this study, rich, person-centred discharge information may also decrease the likelihood of patient readmission.

Patient-Reported Outcomes Predict Future Emergency Department Visits and Hospital Admissions in Patients With Stroke

Background

Identification of stroke patients at increased risk of emergency department (ED) visits or hospital admissions allows implementation of mitigation strategies. We evaluated the ability of the Patient‐Reported Outcomes Information Measurement System (PROMIS) patient‐reported outcomes (PROs) collected as part of routine care to predict 1‐year emergency department (ED) visits and admissions when added to other readily available clinical variables.

Methods and Results

This was a cohort study of 1696 patients with ischemic stroke, intracerebral hemorrhage, subarachnoid hemorrhage, or transient ischemic attack seen in a cerebrovascular clinic from February 17, 2015, to June 11, 2018, who completed the following PROs at the visit: Patient Health Questionnaire‐9, Quality of Life in Neurological Disorders cognitive function, PROMIS Global Health, sleep disturbance, fatigue, anxiety, social role satisfaction, physical function, and pain interference. A series of logistic regression models was constructed to determine the ability of models that include PRO scores to predict 1‐year ED visits and all‐cause and unplanned admissions. In the 1 year following the PRO encounter date, 1046 ED visits occurred in 548 patients; 751 admissions occurred in 453 patients. All PROs were significantly associated with future ED visits and admissions except PROMIS sleep. Models predicting unplanned admissions had highest optimism‐corrected area under the curve (range, 0.684–0.724), followed by ED visits (range, 0.674–0.691) and then all‐cause admissions (range, 0.628–0.671). PROs measuring domains of mental health had stronger associations with ED visits; PROs measuring domains of physical health had stronger associations with admissions.

Conclusions

PROMIS scales improve the ability to predict ED visits and admissions in patients with stroke. The differences in model performance and the most influential PROs in the prediction models suggest differences in factors influencing future hospital admissions and ED visits.

Development of Electronic Health Record-Based Prediction Models for 30-Day Readmission Risk Among Patients Hospitalized for Acute Myocardial Infarction

IMPORTANCE

In the US, more than 600 000 adults will experience an acute myocardial infarction (AMI) each year, and up to 20% of the patients will be rehospitalized within 30 days. This study highlights the need for consideration of calibration in these risk models.

OBJECTIVE

To compare multiple machine learning risk prediction models using an electronic health record (EHR)-derived data set standardized to a common data model.

DESIGN, SETTING, AND PARTICIPANTS

This was a retrospective cohort study that developed risk prediction models for 30-day readmission among all inpatients discharged from Vanderbilt University Medical Center between January 1, 2007, and December 31, 2016, with a primary diagnosis of AMI who were not transferred from another facility. The model was externally validated at Dartmouth-Hitchcock Medical Center from April 2, 2011, to December 31, 2016. Data analysis occurred between January 4, 2019, and November 15, 2020.

EXPOSURES

Acute myocardial infarction that required hospital admission.

MAIN OUTCOMES AND MEASURES

The main outcome was thirty-day hospital readmission. A total of 141 candidate variables were considered from administrative codes, medication orders, and laboratory tests. Multiple risk prediction models were developed using parametric models (elastic net, least absolute shrinkage and selection operator, and ridge regression) and nonparametric models (random forest and gradient boosting). The models were assessed using holdout data with area under the receiver operating characteristic curve (AUROC), percentage of calibration, and calibration curve belts.

RESULTS

The final Vanderbilt University Medical Center cohort included 6163 unique patients, among whom the mean (SD) age was 67 (13) years, 4137 were male (67.1%), 1019 (16.5%) were Black or other race, and 933 (15.1%) were rehospitalized within 30 days. The final Dartmouth-Hitchcock Medical Center cohort included 4024 unique patients, with mean (SD) age of 68 (12) years; 2584 (64.2%) were male, 412 (10.2%) were rehospitalized within 30 days, and most of the cohort were non-Hispanic and White. The final test set AUROC performance was between 0.686 to 0.695 for the parametric models and 0.686 to 0.704 for the nonparametric models. In the validation cohort, AUROC performance was between 0.558 to 0.655 for parametric models and 0.606 to 0.608 for nonparametric models.

CONCLUSIONS AND RELEVANCE

In this study, 5 machine learning models were developed and externally validated to predict 30-day readmission AMI hospitalization. These models can be deployed within an EHR using routinely collected data.

Thirty-Day Readmission Risk Model for Older Adults Hospitalized With Acute Myocardial Infarction

BACKGROUND

Early readmissions among older adults hospitalized for acute myocardial infarction (AMI) are costly and difficult to predict. Aging-related functional impairments may inform risk prediction but are unavailable in most studies. Our objective was to, therefore, develop and validate an AMI readmission risk model for older patients who considered functional impairments and was suitable for use before hospital discharge.

METHODS AND RESULTS

SILVER-AMI (Comprehensive Evaluation of Risk in Older Adults with AMI) is a prospective cohort study of 3006 patients of age ≥75 years hospitalized with AMI at 94 US hospitals. Participants underwent in-hospital assessment of functional impairments including cognition, vision, hearing, and mobility. Other variables plausibly associated with readmissions were also collected. The outcome was all-cause readmission at 30 days. We used backward selection and Bayesian model averaging to derive (N=2004) a risk model that was subsequently validated (N=1002). Mean age was 81.5 years, 44.4% were women, and 10.5% were nonwhite. Within 30 days, 547 participants (18.2%) were readmitted. Readmitted participants were older, had more comorbidities, and had a higher prevalence of functional impairments, including activities of daily living disability (17.0% versus 13.0%; P=0.013) and impaired functional mobility (72.5% versus 53.6%; P<0.001). The final risk model included 8 variables: functional mobility, ejection fraction, chronic obstructive pulmonary disease, arrhythmia, acute kidney injury, first diastolic blood pressure, P2Y12 inhibitor use, and general health status. Functional mobility was the only functional impairment variable retained but was the strongest predictor. The model was well calibrated (Hosmer-Lemeshow P value >0.05) with moderate discrimination (C statistics: 0.65 derivation cohort and 0.63 validation cohort). Functional mobility significantly improved performance of the risk model (net reclassification improvement index =20%; P<0.001).

CONCLUSIONS

In our final risk model, functional mobility, previously not included in readmission risk models, was the strongest predictor of 30-day readmission among older adults after AMI. The modest discrimination indicates that much of the variability in readmission risk among this population remains unexplained by patient-level factors.

CLINICAL TRIAL REGISTRATION

URL: https://www.clinicaltrials.gov. Unique identifier: NCT01755052.

Heart Disease and Stroke Statistics-2020 Update: A Report From the American Heart Association

BACKGROUND

The American Heart Association, in conjunction with the National Institutes of Health, annually reports on the most up-to-date statistics related to heart disease, stroke, and cardiovascular risk factors, including core health behaviors (smoking, physical activity, diet, and weight) and health factors (cholesterol, blood pressure, and glucose control) that contribute to cardiovascular health. The Statistical Update presents the latest data on a range of major clinical heart and circulatory disease conditions (including stroke, congenital heart disease, rhythm disorders, subclinical atherosclerosis, coronary heart disease, heart failure, valvular disease, venous disease, and peripheral artery disease) and the associated outcomes (including quality of care, procedures, and economic costs).

METHODS

The American Heart Association, through its Statistics Committee, continuously monitors and evaluates sources of data on heart disease and stroke in the United States to provide the most current information available in the annual Statistical Update. The 2020 Statistical Update is the product of a full year's worth of effort by dedicated volunteer clinicians and scientists, committed government professionals, and American Heart Association staff members. This year's edition includes data on the monitoring and benefits of cardiovascular health in the population, metrics to assess and monitor healthy diets, an enhanced focus on social determinants of health, a focus on the global burden of cardiovascular disease, and further evidence-based approaches to changing behaviors, implementation strategies, and implications of the American Heart Association's 2020 Impact Goals.

RESULTS

Each of the 26 chapters in the Statistical Update focuses on a different topic related to heart disease and stroke statistics.

CONCLUSIONS

The Statistical Update represents a critical resource for the lay public, policy makers, media professionals, clinicians, healthcare administrators, researchers, health advocates, and others seeking the best available data on these factors and conditions.

Understanding Readmissions in Medicare Beneficiaries During the 90-Day Follow-Up Period of an Acute Myocardial Infarction Admission

Background

Medicare has a voluntary episodic payment model for Medicare beneficiaries that bundles payment for the index acute myocardial infarction (AMI) hospitalization and all post‐discharge services for a 90‐day follow‐up period. The purpose of this study is to report on the types and frequency of readmissions and identify demographic and clinical factors associated with readmission of Medicare beneficiaries that survived their AMI hospitalization.

Methods and Results

This retrospective study used the Inpatient Standard Analytical File for 2014. There were 143 286 Medicare beneficiaries with AMI who were discharged alive from 3619 hospitals. All readmissions occurring in any hospital within 90 days of the index AMI discharge date were identified. Of 143 286 Medicare beneficiaries discharged alive from their index AMI hospitalization, 28% (40 145) experienced at least 1 readmission within 90 days and 8% (11 477) had >1 readmission. Readmission rates were higher among Medicare beneficiaries who did not undergo a percutaneous coronary intervention in their index AMI admission (34%) compared with those that underwent a percutaneous coronary intervention (20.2%). Using all Medicare beneficiary's index AMI, 27 comorbid conditions were significantly associated with the likelihood of a Medicare beneficiary having a readmission during the follow‐up period. The strongest clinical characteristics associated with readmissions were dialysis dependence, type 1 diabetes mellitus, and heart failure.

Conclusions

This study provides benchmark information on the types of hospital readmissions Medicare beneficiaries experience during a 90‐day AMI bundle. This paper also suggests that interventions are needed to alleviate the need for readmissions in high‐risk populations, such as, those managed medically and those at risk of heart failure.

Acute Myocardial Infarction Readmission Risk Prediction Models: A Systematic Review of Model Performance

BACKGROUND

Hospitals are subject to federal financial penalties for excessive 30-day hospital readmissions for acute myocardial infarction (AMI). Prospectively identifying patients hospitalized with AMI at high risk for readmission could help prevent 30-day readmissions by enabling targeted interventions. However, the performance of AMI-specific readmission risk prediction models is unknown.

METHODS AND RESULTS

We systematically searched the published literature through March 2017 for studies of risk prediction models for 30-day hospital readmission among adults with AMI. We identified 11 studies of 18 unique risk prediction models across diverse settings primarily in the United States, of which 16 models were specific to AMI. The median overall observed all-cause 30-day readmission rate across studies was 16.3% (range, 10.6%-21.0%). Six models were based on administrative data; 4 on electronic health record data; 3 on clinical hospital data; and 5 on cardiac registry data. Models included 7 to 37 predictors, of which demographics, comorbidities, and utilization metrics were the most frequently included domains. Most models, including the Centers for Medicare and Medicaid Services AMI administrative model, had modest discrimination (median C statistic, 0.65; range, 0.53-0.79). Of the 16 reported AMI-specific models, only 8 models were assessed in a validation cohort, limiting generalizability. Observed risk-stratified readmission rates ranged from 3.0% among the lowest-risk individuals to 43.0% among the highest-risk individuals, suggesting good risk stratification across all models.

CONCLUSIONS

Current AMI-specific readmission risk prediction models have modest predictive ability and uncertain generalizability given methodological limitations. No existing models provide actionable information in real time to enable early identification and risk-stratification of patients with AMI before hospital discharge, a functionality needed to optimize the potential effectiveness of readmission reduction interventions.

Hospital readmission risk prediction based on claims data available at admission: a pilot study in Switzerland

OBJECTIVES

Evaluating whether future studies to develop prediction models for early readmissions based on health insurance claims data available at the time of a hospitalisation are worthwhile.

DESIGN

Retrospective cohort study of hospital admissions with discharge dates between 1 January 2014 and 31 December 2016.

SETTING

All-cause acute care hospital admissions in the general population of Switzerland, enrolled in the Helsana Group, a large provider of Swiss mandatory health insurance.

PARTICIPANTS

The mean age of 138 222 hospitalised adults included in the study was 60.5 years. Patients were included only with their first index hospitalisation. Patients who deceased during the follow-up period were excluded, as well as patients admitted from and/or discharged to nursing homes or rehabilitation clinics.

MEASURES

The primary outcome was 30-day readmission rate. Area under the receiver operating characteristic curve (AUC) was used to measure the discrimination of the developed logistic regression prediction model. Candidate variables were theory based and derived from a systematic literature search.

RESULTS

We observed a 30-day readmission rate of 7.5%. Fifty-five candidate variables were identified. The final model included pharmacy-based cost group (PCG) cancer, PCG cardiac disease, PCG pain, emergency index admission, number of emergency visits, costs specialists, costs hospital outpatient, costs laboratory, costs therapeutic devices, costs physiotherapy, number of outpatient visits, sex, age group and geographical region as predictors. The prediction model achieved an AUC of 0.60 (95% CI 0.60 to 0.61).

CONCLUSIONS

Based on the results of our study, it is not promising to invest resources in large-scale studies for the development of prediction tools for hospital readmissions based on health insurance claims data available at admission. The data proved appropriate to investigate the occurrence of hospitalisations and subsequent readmissions, but we did not find evidence for the potential of a clinically helpful prediction tool based on patient-sided variables alone.

Circulating extracellular RNAs, myocardial remodeling, and heart failure in patients with acute coronary syndrome

BACKGROUND

Given high on-treatment mortality in heart failure (HF), identifying molecular pathways that underlie adverse cardiac remodeling may offer novel biomarkers and therapeutic avenues. Circulating extracellular RNAs (ex-RNAs) regulate important biological processes and are emerging as biomarkers of disease, but less is known about their role in the acute setting, particularly in the setting of HF.

METHODS

We examined the ex-RNA profiles of 296 acute coronary syndrome (ACS) survivors enrolled in the Transitions, Risks, and Actions in Coronary Events Center for Outcomes Research and Education Cohort. We measured 374 ex-RNAs selected a priori, based on previous findings from a large population study. We employed a two-step, mechanism-driven approach to identify ex-RNAs associated with echocardiographic phenotypes (left ventricular [LV] ejection fraction, LV mass, LV end-diastolic volume, left atrial [LA] dimension, and LA volume index) then tested relations of these ex-RNAs with prevalent HF (N=31, 10.5%). We performed further bioinformatics analysis of microRNA (miRNAs) predicted targets' genes ontology categories and molecular pathways.

RESULTS

We identified 44 ex-RNAs associated with at least one echocardiographic phenotype associated with HF. Of these 44 exRNAs, miR-29-3p, miR-584-5p, and miR-1247-5p were also associated with prevalent HF. The three microRNAs were implicated in the regulation p53 and transforming growth factor-β signaling pathways and predicted to be involved in cardiac fibrosis and cell death; miRNA predicted targets were enriched in gene ontology categories including several involving the extracellular matrix and cellular differentiation.

CONCLUSIONS

Among ACS survivors, we observed that miR-29-3p, miR-584-5p, and miR-1247-5p were associated with both echocardiographic markers of cardiac remodeling and prevalent HF.

RELEVANCE FOR PATIENTS

miR-29c-3p, miR-584-5p, and miR-1247-5p were associated with echocardiographic phenotypes and prevalent HF and are potential biomarkers for adverse cardiac remodeling in HF.

Relationship between patient's health literacy and adherence to coronary heart disease secondary prevention measures

AIMS AND OBJECTIVES

To examine the relationship between health literacy (HL) and adherence to secondary coronary heart disease (CHD) prevention behaviours in patients in China.

BACKGROUND

Adherence of patients to secondary CHD prevention behaviours is important in order to slow or reverse disease progression. The relationship between HL and adherence is varied across populations and warrants further research in order to inform nurses caring for such patients how to direct their teaching.

DESIGN

A descriptive cross-sectional design.

METHODS

A total of 598 patients with CHD were recruited during hospitalisation for a myocardial revascularization procedure at either of two tertiary hospitals in China. Data were collected by self-report on demographics, HL and adherence to secondary CHD prevention behaviours (medication-taking and heart-healthy lifestyle) prior to this admission. HL was measured by the short version of European Health Literacy Survey Questionnaire (HLS-EU-Q16), while adherence was measured with the Medical Outcomes Study Specific Adherence Scale (MOS-SAS). Descriptive, chi-square test and regression analyses were conducted. The study was reported based on STROBE checklist.

RESULTS

Overall, 74.5% of the patients had limited HL. Adherence rates to medication-taking and heart-healthy lifestyle were 84.7% and 53.2%, respectively. HL was not significantly associated with medication adherence, but in regression models patients with limited HL demonstrated significantly increased odds for nonadherence to heart-healthy lifestyle (OR 1.69).

CONCLUSION

HL was not significantly associated with medication adherence but was related to heart-healthy lifestyle adherence.

RELEVANCE TO CLINICAL PRACTICE

Nurses should assess the health literacy of discharging patients with CHD and focus on patients with limited health literacy to improve heart-healthy lifestyle behaviours, and not just taking medications.

Impact of anxiety on the post-discharge outcomes of patients discharged from the hospital after an acute coronary syndrome

BACKGROUND

Symptoms of anxiety are highly prevalent among survivors of an acute coronary syndrome (ACS), but do not necessarily indicate an anxiety disorder. The extent to which symptoms of anxiety or a diagnosis of this condition impacts hospital readmission and post-discharge mortality among patients with an ACS remains unclear.

METHODS

We used data from 1909 patients discharged from six hospitals in Massachusetts and Georgia after an ACS. Moderate/severe symptoms of anxiety were defined based on responses to a Generalized Anxiety Disorder questionnaire during the patient's index hospitalization. The diagnosis of an anxiety disorder was based on review of hospital medical records. Multivariable adjusted Poisson regression and Cox proportional-hazards models were used to estimate the risk of 30-day hospital readmissions and 2-year total mortality.

RESULTS

The mean age of the study population was 61 years, two thirds were men, and 78% were non-Hispanic whites. In this population, 10.4% had a documented diagnosis of an anxiety disorder, 18.8% had moderate/severe symptoms of anxiety, and 70.8% had neither a diagnosis nor symptoms of anxiety. Neither a diagnosis of an anxiety disorder nor symptoms of anxiety were associated with 30-day all-cause or cardiovascular-related rehospitalizations. Patients with an anxiety disorder (multivariable adjusted HR = 1.95, 95%CI = 1.11-3.42) were at greatest risk for dying during the 2-year follow-up period.

CONCLUSIONS

We identified patients with an anxiety disorder as being at greater risk for dying after hospital discharge for an ACS. Interventions may be more appropriately targeted to those with a history of, rather than acute symptoms of, anxiety.

Measurement of health literacy in patients with cardiovascular diseases: A systematic review

BACKGROUND

While many instruments have been developed, validated, and used to assess health literacy skills, their use and appropriateness among patients with cardiovascular diseases (CVDs) are not widely studied.

OBJECTIVE

To identify, appraise, and synthesize available health literacy assessment instruments used in patients with CVDs.

METHODS

Electronic databases were searched for studies that used validated measures to assess health literacy in patients with CVDs. Included studies were assessed for risk of bias and the identified instruments were evaluated based on their psychometric properties. Data were synthesized using a narrative approach.

RESULTS

Forty-three studies were included in the review, of which 20 were cross-sectional studies and 12 were randomized controlled trials. Eleven health literacy assessment instruments were identified, of which only one was disease-specific. The Abbreviated version of the Test of Functional Health Literacy in Adults (S-TOFHLA) (n = 19) and the Rapid Estimate of Adult Literacy in Medicine (REALM) (n = 13) were found to be the most commonly used instruments to assess health literacy in CVDs.

CONCLUSIONS

The S-TOFHLA and the REALM are the most widely used instruments to evaluate health literacy in CVD population. More CVD-specific health literacy screening instruments are warranted. Assessment of health literacy should be a standard of care in patients with CVDs and effective interventions should be developed to improve the impact of limited health literacy on health outcomes in this population.

Predictive models for hospital readmission risk: A systematic review of methods

OBJECTIVES

Hospital readmission risk prediction facilitates the identification of patients potentially at high risk so that resources can be used more efficiently in terms of cost-benefit. In this context, several models for readmission risk prediction have been proposed in recent years. The goal of this review is to give an overview of prediction models for hospital readmission, describe the data analysis methods and algorithms used for building the models, and synthesize their results.

METHODS

Studies that reported the predictive performance of a model for hospital readmission risk were included. We defined the scope of the review and accordingly built a search query to select the candidate papers. This query string was used as input for the chosen search engines, namely PubMed and Google Scholar. For each study, we recorded the population, feature selection method, classification algorithm, sample size, readmission threshold, readmission rate and predictive performance of the model.

RESULTS

We identified 77 studies that met the inclusion criteria, out of 265 citations. In 68% of the studies (n = 52) logistic regression or other regression techniques were utilized as the main method. Ten (13%) studies used survival analysis for model construction, while 14 (18%) used machine learning techniques for classification, of which decision tree-based methods and SVM were the most utilized algorithms. Among these, only four studies reported the use of any class imbalance addressing technique, of which resampling is the most frequent (75%). The performance of the models varied significantly among studies, with Area Under the ROC Curve (AUC) values in the ranges between 0.54 and 0.92.

CONCLUSION

Logistic regression and survival analysis have been traditionally the most widely used techniques for model building. Nevertheless, machine learning techniques are becoming increasingly popular in recent years. Recent comparative studies suggest that machine learning techniques can improve prediction ability over traditional statistical approaches. Regardless, the lack of an appropriate benchmark dataset of hospital readmissions makes a comparison of models' performance across different studies difficult.

Health Literacy and Cardiovascular Disease: Fundamental Relevance to Primary and Secondary Prevention: A Scientific Statement From the American Heart Association

Health literacy is the degree to which individuals are able to access and process basic health information and services and thereby participate in health-related decisions. Limited health literacy is highly prevalent in the United States and is strongly associated with patient morbidity, mortality, healthcare use, and costs. The objectives of this American Heart Association scientific statement are (1) to summarize the relevance of health literacy to cardiovascular health; (2) to present the adverse associations of health literacy with cardiovascular risk factors, conditions, and treatments; (3) to suggest strategies that address barriers imposed by limited health literacy on the management and prevention of cardiovascular disease; (4) to demonstrate the contributions of health literacy to health disparities, given its association with social determinants of health; and (5) to propose future directions for how health literacy can be integrated into the American Heart Association's mandate to advance cardiovascular treatment and research, thereby improving patient care and public health. Inadequate health literacy is a barrier to the American Heart Association meeting its 2020 Impact Goals, and this statement articulates the rationale to anticipate and address the adverse cardiovascular effects associated with health literacy.

[Perception of Primary Care physicians on the integration with cardiology through continuity of healthcare programs in secondary prevention]

OBJECTIVE

To determine the perception of Primary Care (PC) physicians on the integration with cardiology (CA) through continuity of healthcare programs.

MATERIAL AND METHODS

A cross-sectional and multicentre study was conducted, in which a total of 200 PC physicians from all over Spain completed a qualitative survey that evaluated the level of integration with CA in secondary prevention. Physicians were grouped according to the level of PC-CA integration.

RESULTS

The integration between CA and PC was good, but it was better in those centres with a higher integration (74.0% vs. 60.0%; p=.02) and in general, physicians considered that integration had improved (92.0% vs. 73.0%; p<.001). Almost all PC physicians received the hospital discharge report. The majority of the hospital discharge reports included recommendations about the CA and PC follow-up, control of risk factors, as well as the duration of secondary prevention treatment, with not significant differences according to the level of integration. In 55.8%, 63.6%, and 51.3% of hospital discharge reports, indications were given on when to perform the follow-up blood analysis, as well as information about returning to working life and sexual activity, respectively. The most common communication method was the paper-based report (75 vs. 84%; p=NS). The communication between healthcare levels was greater in those Primary Care centres with a higher level of integration, as well as periodicity of the communication and the satisfaction of physicians (80.0% vs. 63.0%; p=.005).

CONCLUSIONS

The level of integration between PC and CA is, in general, satisfactory, but those centres with a higher level of integration benefit more from a greater communication and satisfaction.

Health literacy and coronary artery disease: A systematic review

OBJECTIVE

Identify health literacy (HL) screening instruments available to CAD patients; describe the prevalence of low HL; explore the predictors of low HL; and, identify the association between HL, health behaviors, and outcomes among these patients.

METHODS

A literature search of electronic databases was conducted for published articles from database inception to February 2017. Eligible articles included the assessment of HL in CAD patients. Health behaviors and outcomes included diet, exercise, smoking, medication use, hospital readmission, knowledge, health-related quality of life (HRQoL), and psychosocial indicators.

RESULTS

Overall, ten articles were included, of which two were RCTs, and seven were considered "good" quality. The most used screening instruments were REALM and TOFHLA. The average prevalence of low HL was 30.5%. Low HL participants were more likely to be older, male, from a non-white ethnic group, have many CVD comorbidities, lower educational level, disadvantaged socioeconomic position, and less likely to be employed. Low HL was consistently associated with hospital readmissions, low HRQoL, higher anxiety and lower social support.

CONCLUSION

The literature on HL in CAD patients is very limited.

PRACTICE IMPLICATIONS

Healthcare providers should start adopting strategies that can potentially mitigate the impact of low HL in the care of CAD patients.

Clinical Data Reuse or Secondary Use: Current Status and Potential Future Progress

Objective: To perform a review of recent research in clinical data reuse or secondary use, and envision future advances in this field. Methods: The review is based on a large literature search in MEDLINE (through PubMed), conference proceedings, and the ACM Digital Library, focusing only on research published between 2005 and early 2016. Each selected publication was reviewed by the authors, and a structured analysis and summarization of its content was developed. Results: The initial search produced 359 publications, reduced after a manual examination of abstracts and full publications. The following aspects of clinical data reuse are discussed: motivations and challenges, privacy and ethical concerns, data integration and interoperability, data models and terminologies, unstructured data reuse, structured data mining, clinical practice and research integration, and examples of clinical data reuse (quality measurement and learning healthcare systems). Conclusion: Reuse of clinical data is a fast-growing field recognized as essential to realize the potentials for high quality healthcare, improved healthcare management, reduced healthcare costs, population health management, and effective clinical research.

Race and place differences in patients hospitalized with an acute coronary syndrome: Is there double jeopardy? Findings from TRACE-CORE

The objectives of this longitudinal study were to examine differences between whites and blacks, and across two geographical regions, in the socio-demographic, clinical, and psychosocial characteristics, hospital treatment practices, and post-discharge mortality for hospital survivors of an acute coronary syndrome (ACS). In this prospective cohort study, we performed in-person interviews and medical record abstractions for patients discharged from the hospital after an ACS at participating sites in Central Massachusetts and Central Georgia during 2011–2013. Among the 1143 whites in Central Massachusetts, 514 whites in Central Georgia, and 277 blacks in Central Georgia, we observed a gradient of socioeconomic position with whites in Central Massachusetts being the most privileged, followed by whites and then blacks from Central Georgia; similar gradients pertained to psychosocial vulnerability (e.g., 10.7%, 25.1%, and 49.1% had cognitive impairment, respectively) and to the hospital receipt of all 4 evidence-based cardiac medications (35.5%, 18.1%, and 14.4%, respectively) used in the acute management of patients hospitalized with an ACS. Multivariable adjusted odds ratios (95% confidence intervals) for the receipt of a percutaneous coronary intervention for whites and blacks in Georgia vs. whites in Massachusetts were 0.57 (0.46–0.71) and 0.40(0.30–0.52), respectively. Thirty-day and one-year mortality risks exhibited a similar gradient.

The results of this contemporary clinical/epidemiologic study in a diverse patient cohort suggest that racial and geographic disparities continue to exist for patients hospitalized with an ACS.

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We observed a gradient of socio-economic position, treatment practices, and dying.

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Interplay of race and place with treatment practices and post discharge outcomes.

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Racial and geographic disparities continue to exist for patients after an ACS.