Abusive head trauma (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 patient data analysis we derived the Predicting Abusive Head Trauma (PredAHT) tool, using multilevel logistic regression to predict likelihood of AHT. This study aims to externally validate the PredAHT tool.


Consecutive children aged <36 months admitted with an intracranial injury, confirmed as abusive or nonabusive, to 2 sites used in the original model were ascertained. Details of 6 influential features were recorded (retinal hemorrhage, rib and long -bone fractures, apnea, seizures, and head or neck bruising). 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).


Data included 133 non-AHT cases and 65 AHT cases, 97% of children were <24 months old. Consistent with original predictions, when ≥3 features were present in a child <36 months old with intracranial injury, 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).


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 child protection process.

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