Validation of a Prediction Tool for Abusive XXXX .

@entity1 @entity410 @entity174 @entity542 @entity1504 @entity5861 @entity2981 @entity386
BACKGROUND AND OBJECTIVES: Abusive @entity542 (AHT) may be missed in the clinical setting. Clinical prediction tools are used to reduce variability in practice and inform decision-making. From a systematic review and individual @entity1 data analysis we derived the Predicting Abusive @entity542 (PredAHT) tool, using multilevel logistic regression to predict likelihood of AHT. This study aims to externally validate the PredAHT tool. METHODS: Consecutive @entity1 aged <36 months admitted with an @entity1504 , confirmed as abusive or nonabusive, to 2 sites used in the original model were ascertained. Details of 6 influential features were recorded ( @entity5861 , @entity174 , @entity386 , @entity410 , and head or neck @entity2981 ). We estimated the likelihood of an unrecorded feature being present with multiple imputation; analysis included sensitivity, specificity, and area under the curve, with 95% confidence intervals (CIs). RESULTS: Data included 133 non-AHT cases and 65 AHT cases, 97% of @entity1 were <24 months old. Consistent with original predictions, when >= 3 features were present in a @entity1 <36 months old with @entity1504 , the estimated probability of AHT was >81.5% (95% CI, 63.3-91.8). The sensitivity of the tool was 72.3% (95% CI, 60.4-81.7), the specificity was 85.7% (95% CI, 78.8-90.7), area under the curve 0.88 (95% CI, 0.823-0.926). CONCLUSIONS: When tested on novel data, the PredAHT tool performed well. This tool has the potential to contribute to decision-making in these challenging cases. An implementation study is needed to explore its performance and utility within the @entity1 protection process.
