Prediction models for the prediction of unplanned hospital admissions in community-dwelling older adults: A systematic review

Abstract

Background Identification of community-dwelling older adults at risk of unplanned hospitalizations is of importance to facilitate preventive interventions. Our objective was to review and appraise the methodological quality and predictive performance of prediction models for predicting unplanned hospitalizations in community-dwelling older adults Methods and findings We searched MEDLINE, EMBASE and CINAHL from August 2013 to January 2021. Additionally, we checked references of the identified articles for the inclusion of relevant publications and added studies from two previous reviews that fulfilled the eligibility criteria. We included prospective and retrospective studies with any follow-up period that recruited adults aged 65 and over and developed a prediction model predicting unplanned hospitalizations. We included models with at least one (internal or external) validation cohort. The models had to be intended to be used in a primary care setting. Two authors independently assessed studies for inclusion and undertook data extraction following recommendations of the CHARMS checklist, while quality assessment was performed using the PROBAST tool. A total of 19 studies met the inclusion criteria. Prediction horizon ranged from 4.5 months to 4 years. Most frequently included variables were specific medical diagnoses (n = 11), previous hospital admission (n = 11), age (n = 11), and sex or gender (n = 8). Predictive performance in terms of area under the curve ranged from 0.61 to 0.78. Models developed to predict potentially preventable hospitalizations tended to have better predictive performance than models predicting hospitalizations in general. Overall, risk of bias was high, predominantly in the analysis domain. Conclusions Models developed to predict preventable hospitalizations tended to have better predictive performance than models to predict all-cause hospitalizations. There is however substantial room for improvement on the reporting and analysis of studies. We recommend better adherence to the TRIPOD guidelines.

Predicting unplanned admissions to hospital in older adults using routinely recorded general practice data: development and validation of a prediction model

“…We identified 19 existing prediction models to predict hospitalisations in older adults (12). Our model showed similar performance and overlap in the most commonly included variables.…”

“…For the data provided by Statistics Netherlands, income data had missing values for 116 individuals (<0.01%). This justified conducting a complete case analysis considering the negligible proportion of missing data and the minimal potential impact on our results (12,13,22,23).…”

“…Updating existing prediction models was not feasible, due to the incomparability of the predictors in our dataset with the predictors in existing models as well as the low methodological quality of these studies (12). To (partially) incorporate information from existing models, we selected variables commonly included in existing models as candidate predictors for our model, e.g.…”

“…To (partially) incorporate information from existing models, we selected variables commonly included in existing models as candidate predictors for our model, e.g. prior hospitalisations and several chronic conditions (12). In addition, we selected variables based on the insights from a focus group study we organised among primary healthcare professionals (to be published) and based on the clinical expertise of the authors.…”

“…The utilisation of these readily available data for the development of a prediction model facilitates ease of use and reduces time burden on GPs. Previous research has shown that administrative data can be useful in accurately predicting unplanned hospitalisations (12,13). However, the methodological quality of these studies was limited and many models required additional data collection, making clinical use difficult.…”