Impact of a Pharmacist-Led Intervention on 30-Day Readmission and Assessment of Factors Predictive of Readmission in African American Men With Heart Failure

Multidimensional Interventions on Supporting Disease Management for Hospitalized Patients with Heart Failure: The Role of Clinical and Community Pharmacists

BACKGROUND

existing trials on the role of clinical pharmacists in managing chronic disease patients have focused on variety of interventions, including preparing patients for the transition from hospital to home. However, little quantitative evidence is available regarding the effect of multidimensional interventions on supporting disease management for hospitalized patients with heart failure (HF). The present paper reviews the effects of inpatient, discharge and/or after-discharge interventions performed on hospitalized HF patients by multidisciplinary teams, including pharmacists.

METHODS

articles were identified through search engines in three electronic databases following the PRISMA Protocol. Randomized controlled trials (RCTs) or non-randomized intervention studies conducted in the period 1992-2022 were included. In all studies, baseline characteristics of patients as well as study end-points were described in relation to a control group i.e., usual care and a group of subjects that received care from a clinical and/or community pharmacist, as well as other health professionals (Intervention). Study outcomes included all-cause hospital 30-day re-admission or emergency room (ER) visits, all-cause hospitalization within >30 days after discharge, specific-cause hospitalization rates, medication adherence and mortality. The secondary outcomes included adverse events and quality of life. Quality assessment was carried out using RoB 2 Risk of Bias Tool. Publication bias across studies was determined using the funnel plot and Egger's regression test.

RESULTS

a total of 34 protocols were included in the review, while the data from 33 trials were included in further quantitative analyses. The heterogeneity between studies was high. Pharmacist-led interventions, usually performed within interprofessional care teams, reduced the rates of 30-day all-cause hospital re-admission (odds ratio, OR = 0.78; 95% CI 0.62-0.98; p = 0.03) and all-cause hospitalization >30 days after discharge (OR = 0.73; 95% CI 0.63-0.86; p = 0.0001). Subjects hospitalized primarily due to heart failure demonstrated reduced risk of hospital admission within longer periods, i.e., from 60 to 365 days after discharge (OR = 0.64; 95% CI 0.51-0.81; p = 0.0002). The rate of all-cause hospitalization was reduced by multidimensional interventions taken by pharmacists: reviews of medicine lists and/or their reconciliation at discharge (OR = 0.63; 95% CI 0.43-0.91; p = 0.014), as well as interventions that were based mainly on patient education and counseling (OR = 0.65; 95% CI 0.49-0.88; p = 0.0047). In conclusion, given that HF patients often have complex treatment regimens and multiple comorbid conditions, our findings highlight the need for greater involvement from skilled clinical and community pharmacists in disease management.

Survival prediction of heart failure patients using motion-based analysis method

BACKGROUND AND OBJECTIVE

Survival prediction of heart failure patients is critical to improve the prognostic management of the cardiovascular disease. The existing survival prediction methods focus on the clinical information while lacking the cardiac motion information. we propose a motion-based analysis method to predict the survival risk of heart failure patients for aiding clinical diagnosis and treatment.

METHODS

We propose a motion-based analysis method for survival prediction of heart failure patients. First, our method proposes the hierarchical spatial-temporal structure to capture the myocardial border. It promotes the model discrimination on border features. Second, our method explores the dense optical flow structure to capture motion fields. It improves the tracking capability on cardiac images. The cardiac motion information is obtained by fusing boundary information and motion fields of cardiac images. Finally, our method proposes the multi-modality deep-cox structure to predict the survival risk of heart failure patients. It improves the survival probability of heart failure patients.

RESULTS

The motion-based analysis method is confirmed to be able to improve the survival prediction of heart failure patients. The precision, recall, F1-score, and C-index are 0.8519, 0.8333, 0.8425, and 0.8478, respectively, which is superior to other state-of-the-art methods.

CONCLUSIONS

The experimental results show that the proposed model can effectively predict survival risk of heart failure patients. It facilitates the application of robust clinical treatment strategies.

Evaluating Pharmacist-Led Heart Failure Transitions of Care Clinic: Impact of Analytic Approach on Readmission Rate Endpoints

Studies evaluating pharmacist-led transitions of care (TOC) services for heart failure patients reported profound decreases in hospital readmissions. Most studies restricted their analysis to clinic attendees (as-treated analysis), which can introduce selection and immortal time bias. In this study, we evaluated the impact of including only clinic attendees vs all clinic referrals in assessing the effectiveness of a pharmacist-led heart failure transitions of care (PharmD HF TOC) clinic program on 30-day readmissions. This is a retrospective, observational study of patients discharged from a heart failure hospitalization at a large urban academic medical center from August 2016 to December 2018. Primary exposure was the provision of a PharmD HF TOC clinic appointment in the intent-to-treat analysis and the attendance of the clinic in the as-treated analysis. Primary outcome was all-cause readmissions within 30 days of discharge. There were 766 and 1015 patients included in the as-treated and intent-to-treat analyses, respectively. In the as-treated analysis, 30-day all-cause readmissions were significantly lower in the intervention group compared to the control group (12.4% vs 19.6%, P = 0.018). In contrast, the intent-to-treat analysis did not reveal a significant difference in 30-day all-cause readmissions between the intervention group and the control group (18.2% vs 19.6%, P = 0.643). Pharmacist-led heart failure TOC program is associated with a reduction in 30-day all-cause readmissions only when restricting the analysis to clinic attendees. Future studies evaluating the effectiveness of post-discharge TOC services need to carefully consider the biases inherent in the evaluation methods employed.

Influence of Social Determinants of Health on Heart Failure Outcomes: A Systematic Review

Background Prior research suggests an association between clinical outcomes in heart failure (HF) and social determinants of health (SDoH). Because providers should identify and address SDoH in care delivery, we evaluated how SDoH have been defined, measured, and evaluated in studies that examine HF outcomes. Methods and Results Following Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines, databases were searched for observational or interventional studies published between 2009 and 2021 that assessed the influence of SDoH on outcomes. Selected articles were assessed for quality using a validated rating scheme. We identified 1373 unique articles for screening; 104 were selected for full-text review, and 59 met the inclusion criteria, including retrospective and prospective cohort, cross-sectional, and intervention studies. The majority examined readmissions and hospitalizations (k=33), mortality or survival (k=29), and success of medical devices and transplantation (k=8). SDoH examined most commonly included race, ethnicity, age, sex, socioeconomic status, and education or health literacy. Studies used a range of 1 to 9 SDoH as primary independent variables and 0 to 7 SDoH as controls. Multiple data sources were employed and frequently were electronic medical records linked with national surveys and disease registries. The effects of SDoH on HF outcomes were inconsistent because of the heterogeneity of data sources and SDoH constructs. Conclusions Our systematic review reveals shortcomings in measurement and deployment of SDoH variables in HF care. Validated measures need to be prospectively and intentionally collected to facilitate appropriate analysis, reporting, and replication of data across studies and inform the design of appropriate, evidence-based interventions that can ameliorate significant HF morbidity and societal costs.

Current state of artificial intelligence-based algorithms for hospital admission prediction in patients with heart failure: a scoping review. EUROPEAN HEART JOURNAL. DIGITAL HEALTH 2022;3:415-425. [PMID: 36712159 PMCID: PMC9707890 DOI: 10.1093/ehjdh/ztac035]

AIMS

Patients with congestive heart failure (HF) are prone to clinical deterioration leading to hospital admissions, burdening both patients and the healthcare system. Predicting hospital admission in this patient group could enable timely intervention, with subsequent reduction of these admissions. To date, hospital admission prediction remains challenging. Increasing amounts of acquired data and development of artificial intelligence (AI) technology allow for the creation of reliable hospital prediction algorithms for HF patients. This scoping review describes the current literature on strategies and performance of AI-based algorithms for prediction of hospital admission in patients with HF.

METHODS AND RESULTS

PubMed, EMBASE, and the Web of Science were used to search for articles using machine learning (ML) and deep learning methods to predict hospitalization in patients with HF. After eligibility screening, 23 articles were included. Sixteen articles predicted 30-day hospital (re-)admission resulting in an area under the curve (AUC) ranging from 0.61 to 0.79. Six studies predicted hospital admission over longer time periods ranging from 6 months to 3 years, with AUC's ranging from 0.65 to 0.78. One study prospectively evaluated performance of a disposable sensory patch at home after hospitalization which resulted in an AUC of 0.89 for unplanned hospital admission prediction.

CONCLUSION

AI has the potential to enable prediction of hospital admission in HF patients. Improvement of data management, adding new data sources such as telemonitoring data and ML models and prospective and external validation of current models must be performed before clinical applicability is possible.

Impact of Initial Therapy on Readmissions in Minority Versus White Populations With Heart Failure

Background

Minority populations are often underrepresented in landmark trials for the management of heart failure with reduced ejection fraction (HFrEF). Major trials shaping the guidelines sometimes include as few as 5% black patients.

Objective

The purpose of this pilot study was to evaluate the initiation of guideline-directed medical therapy (GDMT) for HFrEF on hospital discharge for minority vs white populations and its impact on all-cause 30-day readmission rates to identify areas for larger future research studies and opportunities for pharmacist intervention.

Methods

A retrospective analysis was conducted on patients with HFrEF patients discharged over a 3-month period. The primary objective was to compare all-cause 30-day readmissions in minority vs white patients with HFrEF who were discharged on initial GDMT.

Results

300 patients were included in this study, with 188 patients in the minority group and 112 patients in the white group. The minority group was predominantly African American (92%). The primary endpoint demonstrated significantly higher 30-day all-cause readmissions in minority patients compared to white patients who received initial GDMT (20.5 vs 7.7%, P = .0144), despite similar rates of GDMT therapy between groups.

Conclusion

Initial GDMT in minority patients may not reduce readmissions to the same extent seen in white patients. Special emphasis should be placed on evaluating minority patients with HFrEF for additional therapeutic interventions.

Application of Artificial Intelligence in Cardiovascular Medicine

The advent of advances in machine learning (ML)-based techniques has popularized wide applications of artificial intelligence (AI) in various fields ranging from robotics to medicine. In recent years, there has been a surge in the application of AI to research in cardiovascular medicine, which is largely driven by the availability of large-scale clinical and multi-omics datasets. Such applications are providing a new perspective for a better understanding of cardiovascular disease (CVD), which could be used to develop novel diagnostic and therapeutic strategies. For example, studies have shown that ML has a substantial potential for early diagnosis of different types of CVD, prediction of adverse disease outcomes such as heart failure, and development of newer and personalized treatments. In this article, we provide an overview and discuss the current status of a wide range of AI applications, including machine learning, reinforcement learning, and deep learning, in cardiovascular medicine. © 2021 American Physiological Society. Compr Physiol 11:1-12, 2021.

Application of machine learning in predicting hospital readmissions: a scoping review of the literature

Background

Advances in machine learning (ML) provide great opportunities in the prediction of hospital readmission. This review synthesizes the literature on ML methods and their performance for predicting hospital readmission in the US.

Methods

This review was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Extension for Scoping Reviews (PRISMA-ScR) Statement. The extraction of items was also guided by the Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS). Electronic databases PUBMED, MEDLINE, and EMBASE were systematically searched from January 1, 2015, through December 10, 2019. The articles were imported into COVIDENCE online software for title/abstract screening and full-text eligibility. Observational studies using ML techniques for hospital readmissions among US patients were eligible for inclusion. Articles without a full text available in the English language were excluded. A qualitative synthesis included study characteristics, ML algorithms utilized, and model validation, and quantitative analysis assessed model performance. Model performances in terms of Area Under the Curve (AUC) were analyzed using R software. Quality in Prognosis Studies (QUIPS) tool was used to assess the quality of the reviewed studies.

Results

Of 522 citations reviewed, 43 studies met the inclusion criteria. A majority of the studies used electronic health records (24, 56%), followed by population-based data sources (15, 35%) and administrative claims data (4, 9%). The most common algorithms were tree-based methods (23, 53%), neural network (NN) (14, 33%), regularized logistic regression (12, 28%), and support vector machine (SVM) (10, 23%). Most of these studies (37, 85%) were of high quality. A majority of these studies (28, 65%) reported ML algorithms with an AUC above 0.70. There was a range of variability within AUC reported by these studies with a median of 0.68 (IQR: 0.64–0.76; range: 0.50–0.90).

Conclusions

The ML algorithms involving tree-based methods, NN, regularized logistic regression, and SVM are commonly used to predict hospital readmission in the US. Further research is needed to compare the performance of ML algorithms for hospital readmission prediction.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12874-021-01284-z.

Predicting mortality and hospitalization in heart failure using machine learning: A systematic literature review

Objective

The partnership between humans and machines can enhance clinical decisions accuracy, leading to improved patient outcomes. Despite this, the application of machine learning techniques in the healthcare sector, particularly in guiding heart failure patient management, remains unpopular. This systematic review aims to identify factors restricting the integration of machine learning derived risk scores into clinical practice when treating adults with acute and chronic heart failure.

Methods

Four academic research databases and Google Scholar were searched to identify original research studies where heart failure patient data was used to build models predicting all-cause mortality, cardiac death, all-cause and heart failure-related hospitalization.

Results

Thirty studies met the inclusion criteria. The selected studies' sample size ranged between 71 and 716 790 patients, and the median age was 72.1 (interquartile range: 61.1–76.8) years. The minimum and maximum area under the receiver operating characteristic curve (AUC) for models predicting mortality were 0.48 and 0.92, respectively. Models predicting hospitalization had an AUC of 0.47 to 0.84. Nineteen studies (63%) used logistic regression, 53% random forests, and 37% of studies used decision trees to build predictive models. None of the models were built or externally validated using data originating from Africa or the Middle-East.

Conclusions

The variation in the aetiologies of heart failure, limited access to structured health data, distrust in machine learning techniques among clinicians and the modest accuracy of existing predictive models are some of the factors precluding the widespread use of machine learning derived risk calculators.

Machine learning vs. conventional statistical models for predicting heart failure readmission and mortality

Aims

This study aimed to review the performance of machine learning (ML) methods compared with conventional statistical models (CSMs) for predicting readmission and mortality in patients with heart failure (HF) and to present an approach to formally evaluate the quality of studies using ML algorithms for prediction modelling.

Methods and results

Following Preferred Reporting Items for Systematic Reviews and Meta‐Analyses guidelines, we performed a systematic literature search using MEDLINE, EPUB, Cochrane CENTRAL, EMBASE, INSPEC, ACM Library, and Web of Science. Eligible studies included primary research articles published between January 2000 and July 2020 comparing ML and CSMs in mortality and readmission prognosis of initially hospitalized HF patients. Data were extracted and analysed by two independent reviewers. A modified CHARMS checklist was developed in consultation with ML and biostatistics experts for quality assessment and was utilized to evaluate studies for risk of bias. Of 4322 articles identified and screened by two independent reviewers, 172 were deemed eligible for a full‐text review. The final set comprised 20 articles and 686 842 patients. ML methods included random forests (n = 11), decision trees (n = 5), regression trees (n = 3), support vector machines (n = 9), neural networks (n = 12), and Bayesian techniques (n = 3). CSMs included logistic regression (n = 16), Cox regression (n = 3), or Poisson regression (n = 3). In 15 studies, readmission was examined at multiple time points ranging from 30 to 180 day readmission, with the majority of studies (n = 12) presenting prediction models for 30 day readmission outcomes. Of a total of 21 time‐point comparisons, ML‐derived c‐indices were higher than CSM‐derived c‐indices in 16 of the 21 comparisons. In seven studies, mortality was examined at 9 time points ranging from in‐hospital mortality to 1 year survival; of these nine, seven reported higher c‐indices using ML. Two of these seven studies reported survival analyses utilizing random survival forests in their ML prediction models. Both reported higher c‐indices when using ML compared with CSMs. A limitation of studies using ML techniques was that the majority were not externally validated, and calibration was rarely assessed. In the only study that was externally validated in a separate dataset, ML was superior to CSMs (c‐indices 0.913 vs. 0.835).

Conclusions

ML algorithms had better discrimination than CSMs in most studies aiming to predict risk of readmission and mortality in HF patients. Based on our review, there is a need for external validation of ML‐based studies of prediction modelling. We suggest that ML‐based studies should also be evaluated using clinical quality standards for prognosis research. Registration: PROSPERO CRD42020134867