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

Abstract Background 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