A review of literature on risk prediction tools for hospital readmissions in older adults

Purpose The purpose of this paper is to identify and analyse the readmission risk prediction tools reported in the literature and their benefits when it comes to healthcare organisations and management.Design/methodology/approach Readmission risk prediction is a growing topic of interest with the aim of identifying patients in particular those suffering from chronic diseases such as congestive heart failure, chronic obstructive pulmonary disease and diabetes, who are at risk of readmission. Several models have been developed with different levels of predictive ability. A structured and extensive literature search of several databases was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-analysis strategy, and this yielded a total of 48,984 records.Findings Forty-three articles were selected for full-text and extensive review after following the screening process and according to the eligibility criteria. About 34 unique readmission risk prediction models were identified, in which their predictive ability ranged from poor to good (c statistic 0.5–0.86). Readmission rates ranged between 3.1 and 74.1% depending on the risk category. This review shows that readmission risk prediction is a complex process and is still relatively new as a concept and poorly understood. It confirms that readmission prediction models hold significant accuracy at identifying patients at higher risk for such an event within specific context.Research limitations/implications Since most prediction models were developed for specific populations, conditions or hospital settings, the generalisability and transferability of the predictions across wider or other contexts may be difficult to achieve. Therefore, the value of prediction models remains limited to hospital management. Future research is indicated in this regard.Originality/value This review is the first to cover readmission risk prediction tools that have been published in the literature since 2011, thereby providing an assessment of the relevance of this crucial KPI to health organisations and managers.

Control in the Hospital by Extensive Clinical rules for Unplanned hospitalizations in older Patients (CHECkUP); study design of a multicentre randomized study

Background Due to ageing of the population the incidence of multimorbidity and polypharmacy is rising. Polypharmacy is a risk factor for medication-related (re)admission and therefore places a significant burden on the healthcare system. The reported incidence of medication-related (re)admissions varies widely due to the lack of a clear definition. Some medications are known to increase the risk for medication-related admission and are therefore published in the triggerlist of the Dutch guideline for Polypharmacy in older patients. Different interventions to support medication optimization have been studied to reduce medication-related (re)admissions. However, the optimal template of medication optimization is still unknown, which contributes to the large heterogeneity of their effect on hospital readmissions. Therefore, we implemented a clinical decision support system (CDSS) to optimize medication lists and investigate whether continuous use of a CDSS reduces the number of hospital readmissions in older patients, who previously have had an unplanned probably medication-related hospitalization. Methods The CHECkUP study is a multicentre randomized study in older (≥60 years) patients with an unplanned hospitalization, polypharmacy (≥5 medications) and using at least two medications from the triggerlist, from Zuyderland Medical Centre and Maastricht University Medical Centre+ in the Netherlands. Patients will be randomized. The intervention consists of continuous (weekly) use of a CDSS, which generates a Medication Optimization Profile, which will be sent to the patient’s general practitioner and pharmacist. The control group will receive standard care. The primary outcome is hospital readmission within 1 year after study inclusion. Secondary outcomes are one-year mortality, number of emergency department visits, nursing home admissions, time to hospital readmissions and we will evaluate the quality of life and socio-economic status. Discussion This study is expected to add evidence on the knowledge of medication optimization and whether use of a continuous CDSS ameliorates the risk of adverse outcomes in older patients, already at an increased risk of medication-related (re)admission. To our knowledge, this is the first large study, providing one-year follow-up data and reporting not only on quality of care indicators, but also on quality-of-life. Trial registration The trial was registered in the Netherlands Trial Register on October 14, 2018, identifier: NL7449 (NTR7691). https://www.trialregister.nl/trial/7449.

Towards Predicting 30-Day Readmission among Oncology Patients: Identifying Timely and Actionable Risk Factors

Purpose: Predicting 30-day readmission risk is paramount to improving the quality of patient care. Previous studies have examined clinical risk factors associated with hospital readmissions. In this study, we compare sets of patient, provider, and community-level variables that are available at two different points of a patient's inpatient encounter (first 48 hours and the full encounter) to train readmission prediction models in order to identify and target appropriate actionable interventions that can potentially reduce avoidable readmissions. Methods: Using EHR data from a retrospective cohort of 2460 oncology patients, two sets of binary classification models predicting 30-day readmission were developed; one trained on variables that are available within the first 48 hours of admission and another trained on data from the entire hospital encounter. A comprehensive machine learning analysis pipeline was leveraged including preprocessing and feature transformation, feature importance and selection, machine learning modeling, and post-analysis. Results: Leveraging all features, the LGB (light gradient boosted machines) model produced higher, but comparable performance: (AUC: 0.711 and APS: 0.225) compared to Epic (AUC: 0.697 and APS: 0.221). Given features in the first 48-hours, the random forest model produces higher AUC (0.684), but lower PRC (0.18) and APS (0.184) than the Epic model (AUC: 0.676). In terms of the characteristics of patients flagged by these models, both the full LGB and 48-hour (random forest) feature models were highly sensitive in flagging more patients than the Epic models. Both models flagged patients with a similar distribution of race and sex; however, our LGB and random forest models more inclusive flagging more patients among younger age groups. The Epic models were more sensitive to identifying patients with an average lower zip income. Our 48-hour models were powered by novel features at various levels: patient (weight changeover 365 days, depression symptoms, laboratory values, cancer type), provider (winter discharge, hospital admission type), community (zip income, marital status of partner). Conclusion: We demonstrated that we could develop and validate models comparable to existing Epic 30-day readmission models, but provide several actionable insights that could create service interventions deployed by the case management or discharge planning teams that may decrease readmission rates over time.