Development and validation of the Tool for Pharmacists to Predict 30-day hospital readmission in patients with Heart Failure (ToPP-HF)

Practice-enhancing publications about the medication-use process in 2021

PURPOSE

This article identifies, prioritizes, and summarizes published literature on the medication-use process (MUP) from calendar year 2021 that can impact health-system pharmacy daily practice. The MUP is the foundational system that provides the framework for safe medication utilization within the healthcare environment. The MUP is defined in this article as having the following components: prescribing/transcribing, dispensing, administration, and monitoring, and monitoring/medication reconciliation. Articles evaluating at least one step of the MUP were assessed for their usefulness toward practice improvement.

SUMMARY

A PubMed search was conducted in January 2022 for articles published in calendar year 2021 using targeted Medical Subject Headings (MeSH) keywords, and searches of the table of contents of selected pharmacy journals were conducted, providing a total of 7,178 articles. A thorough review identified 79 potentially practice-enhancing articles: 15 for prescribing/transcribing, 17 for dispensing, 4 for administration, 21 for monitoring, and 22 for monitoring/medication reconciliation. Ranking of the articles for importance by peers led to the selection of key articles from each category. The highest-ranked articles are briefly summarized, with a mention of their importance within health-system pharmacy. The other articles are listed for further review and evaluation.

CONCLUSION

It is important to routinely review the published literature and to incorporate significant findings into daily practice. This article assists in identifying and summarizing the most impactful publications. Health-system pharmacists have an active role in improving the MUP in their institution, and awareness of the significant published studies can assist in changing practice at the institutional level.

The Efficacy of Machine Learning Models for Predicting the Prognosis of Heart Failure: A Systematic Review and Meta-Analysis

INTRODUCTION

Heart failure (HF) is a major global public health concern. The application of machine learning (ML) to identify individuals at high risk and enable early intervention is a promising approach for improving HF prognosis. We aim to systematically evaluate the performance and value of ML models for predicting HF prognosis.

METHODS

PubMed, Web of Science, Scopus, and Embase online databases were searched up to April 30, 2023, to identify studies on the use of ML models to predict HF prognosis. HF prognosis primarily encompasses readmission and mortality. The meta-analysis was conducted by MedCalc software. Subgroup analyses include grouping based on types of ML models, time intervals, sample sizes, the number of predictive variables, validation methods, whether to conduct hyperparameter optimization and calibration, data set partitioning methods.

RESULTS

A total of 31 studies were included. The most common ML models were random forest, boosting, support vector machine, neural network. The area under the receiver operating characteristic curve (AUC) for predicting HF readmission was 0.675 (95% CI: 0.651-0.699, p < 0.001), and the AUC for predicting HF mortality was 0.790 (95% CI: 0.765-0.816, p < 0.001). Subgroup analyses revealed that models with the prediction time interval of 1 year, sample sizes ≥10,000, the number of predictive variables ≥100, external validation, hyperparameter tuning, calibration adjustment, and data set partitioning using 10-fold cross-validation exhibited favorable performance within their respective subgroups.

CONCLUSION

The performance of ML models in predicting HF readmission is relatively poor, while its performance in predicting HF mortality is moderate. The quality of the relevant studies is generally low, it is essential to enhance the predictive capabilities of ML models through targeted improvements in practical applications.

Prediction of Early and Long-Term Hospital Readmission in Patients with Severe Obesity: A Retrospective Cohort Study

Adults with obesity have a higher risk of hospitalization and high hospitalization-related healthcare costs. However, a predictive model for the risk of readmission in patients with severe obesity is lacking. We conducted a retrospective cohort study enrolling all patients admitted for severe obesity (BMI ≥ 40 kg/m2) between 2009 and 2018 to the Istituto Auxologico Italiano in Piancavallo. For each patient, all subsequent hospitalizations were identified from the regional database by a deterministic record-linkage procedure. A total of 1136 patients were enrolled and followed up for a median of 5.7 years (IQR: 3.1-8.2). The predictive factors associated with hospital readmission were age (HR = 1.02, 95%CI: 1.01-1.03, p < 0.001), BMI (HR = 1.02, 95%CI: 1.01-1.03, p = 0.001), smoking habit (HR = 1.17, 95%CI: 0.99-1.38, p = 0.060), serum creatinine (HR = 1.22, 95%CI: 1.04-1.44, p = 0.016), diabetes (HR = 1.17, 95%CI: 1.00-1.36, p = 0.045), and number of admissions in the previous two years (HR = 1.15, 95%CI: 1.07-1.23, p < 0.001). BMI lost its predictive role when restricting the analysis to readmissions within 90 days. BMI and diabetes lost their predictive roles when further restricting the analysis to readmissions within 30 days. In conclusion, in this study, we identified predictive variables associated with early and long-term hospital readmission in patients with severe obesity. Whether addressing modifiable risk factors could improve the outcome remains to be established.

Prediction tool Development and Implementation in pharmacy praCTice (PreDICT) proposed guidance

PURPOSE

Proposed guidance is presented for Prediction tool Development and Implementation in pharmacy praCTice (PreDICT). This guidance aims to assist pharmacists and their collaborators with planning, developing, and implementing custom risk prediction tools for use by pharmacists in their own health systems or practice settings. We aimed to describe general considerations that would be relevant to most prediction tools designed for use in health systems or other pharmacy practice settings.

SUMMARY

The PreDICT proposed guidance is organized into 3 sequential phases: (1) planning, (2) development and validation, and (3) testing and refining prediction tools for real-world use. Each phase is accompanied by a checklist of considerations designed to be used by pharmacists or their trainees (eg, residents) during the planning or conduct of a prediction tool project. Commentary and a worked example are also provided to highlight some of the most relevant and impactful considerations for each phase.

CONCLUSION

The proposed guidance for PreDICT is a pharmacist-focused set of checklists for planning, developing, and implementing prediction tools in pharmacy practice. The list of considerations and accompanying commentary can be used as a reference by pharmacists or their trainees before or during the completion of a prediction tool project.