Predicting unplanned hospital visits in older home care recipients: a cross-country external validation study.

To access publisher's full text version of this article, please click on the hyperlink in Additional Links field or click on the hyperlink at the top of the page marked DownloadBackground: Accurate identification of older persons at risk of unplanned hospital visits can facilitate preventive interventions. Several risk scores have been developed to identify older adults at risk of unplanned hospital visits. It is unclear whether risk scores developed in one country, perform as well in another. This study validates seven risk scores to predict unplanned hospital admissions and emergency department (ED) visits in older home care recipients from six countries. Methods: We used the IBenC sample (n = 2446), a cohort of older home care recipients from six countries (Belgium, Finland, Germany, Iceland, Italy and The Netherlands) to validate four specific risk scores (DIVERT, CARS, EARLI and previous acute admissions) and three frailty indicators (CHESS, Fried Frailty Criteria and Frailty Index). Outcome measures were unplanned hospital admissions, ED visits or any unplanned hospital visits after 6 months. Missing data were handled by multiple imputation. Performance was determined by assessing calibration and discrimination (area under receiver operating characteristic curve (AUC)). Results: Risk score performance varied across countries. In Iceland, for any unplanned hospital visits DIVERT and CARS reached a fair predictive value (AUC 0.74 [0.68-0.80] and AUC 0.74 [0.67-0.80]), respectively). In Finland, DIVERT had fair performance predicting ED visits (AUC 0.72 [0.67-0.77]) and any unplanned hospital visits (AUC 0.73 [0.67-0.77]). In other countries, AUCs did not exceed 0.70. Conclusions: Geographical validation of risk scores predicting unplanned hospital visits in home care recipients showed substantial variations of poor to fair performance across countries. Unplanned hospital visits seem considerably dependent on healthcare context. Therefore, risk scores should be validated regionally before applied to practice. Future studies should focus on identification of more discriminative predictors in order to develop more accurate risk scores. Keywords: Emergency department visits; Geographical validation; Home care; Risk prediction models; Unplanned hospitalizations.European Commissio

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

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

Full search strategies.

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CHARMS and PROBAST forms.

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PRISMA checklist.

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Prediction models developed using regression methods.

Prediction models developed using regression methods.</p

Prediction model developed using machine learning techniques.

Prediction model developed using machine learning techniques.</p

PRISMA flow diagram of included risk prediction models.

PRISMA flow diagram of included risk prediction models.</p

Methodological quality assessment of included prediction models according the recommendations of the PROBAST.

Methodological quality assessment of included prediction models according the recommendations of the PROBAST.</p

Variables included in and excluded from the models.

Variables included in and excluded from the models.</p