Diagnosing physical abuse using Bayes' Theorem: a preliminary study

Kemp, Alison; Kemp, Kenneth W.; Evans, R.; Murray, L; Guildea, Z. E. S.; Dunstan, Frank David John; Sibert, Jonathan Richard

doi:10.1002/(sici)1099-0852(199805/06)7:3<178::aid-car353>3.0.co;2-m

Cited by 14 publications

(18 citation statements)

References 9 publications

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“…Clinicians have tended to rely on a Bayesian approach to decisionmaking within this complex area, using existing information along with the pattern of injuries observed to obtain posterior probabilities of abuse. 21,22 There has also been an increasing emphasis on going beyond the simple triad of injuries that is often cited when arriving at a diagnosis of AHT, namely retinal hemorrhage (RH), subdural hemorrhage, and encephalopathy. 23 One major criticism of CDRs is how rarely they are developed appropriately, that is, derived by identifying appropriate elements from previous high-quality literature; applied to a relevant population with sufficient sample size to evaluate each element of the tool; validated on a novel data set in an appropriate multicenter population; and implemented in practice.…”

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confidence: 99%

Validation of a Prediction Tool for Abusive Head Trauma

et al. 2015

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BACKGROUND AND OBJECTIVES: 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.METHODS: 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).RESULTS: 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).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 child protection process. WHAT'S KNOWN ON THIS SUBJECT:A previous multivariable statistical model, using individual patient data, estimated the probability of abusive head trauma based on the presence or absence of 6 clinical features: rib fracture, long-bone fracture, apnea, seizures, retinal hemorrhage, and head or neck bruising. WHAT THIS STUDY ADDS:The model performed well in this validation, with a sensitivity of 72.3%, specificity of 85.7%, and area under the curve of 0.88. In children ,3 years old with intracranial injury plus $3 features, the estimated probability of abuse is .81.5%. Ms Cowley drafted the Methods section, produced the tables, revised the article, formatted the article, and contributed to overall editing; Dr Morris collected the data in data set 1 and entered all anonymized data into the database; Dr. Maguire had the initial study idea, drafted and revised the article, and contributed to the study design and overall editing; Dr Farewell designed and conducted all statistical analyses, drafted the statistical components of the article, produced the figures, and contributed to overall editing; Prof Kemp revised the article and contributed to overall study design and editing; and all authors approved the final manuscript as submitted. Clinicians who undertake child protection investigations of children presenting with head injury can meet resistance and face a barrage of critici...

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Validation of a Prediction Tool for Abusive Head Trauma

et al. 2015

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“…In Cardiff we have been studying this Bayesian approach with the diagnosis of abuse since 1998 (Kemp et al. , 1998; Dunstan et al.…”

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A Bayesian Approach to Complex Clinical Diagnoses: A Case-Study in Child Abuse

Best

Ashby

Dunstan

et al. 2012

Journal of the Royal Statistical Society Series A: Statistics in Society

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Clinical diagnosis is often a complex task of decision making in the face of uncertainty. Diagnosis of child abuse is a particular example where misdiagnosis in either direction is very serious. We have developed a formal Bayesian methodology to quantify the intrinsic uncertainty in complex clinical diagnostic problems. The motivating case-study was the diagnosis of abuse in an infant presenting with an acute life threatening event and oronasal haemorrhage (nosebleed). Since no direct evidence was available on the probability of abuse given an acute life threatening event and nosebleed, we used Bayes theorem to formulate the diagnosis in terms of prior and inverse conditional probabilities and adapted systematic review methodology and Bayesian evidence synthesis to estimate these and to propagate the associated uncertainty. The estimated probability of abuse was far more uncertain than might be supposed from either expert advice or an informal reading of the literature, and estimates depended crucially on assumptions that were made about the conditional independence of multiple signs of abuse. This highlights the importance of having a formal statistical methodology such as this to assist clinicians in reaching a diagnosis. The process that we worked through is likely to have wider application to other problems of complex clinical diagnosis.

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“…In addition, accidental bruises in pre-ambulatory infants are usually few 15. Certain areas are unlikely to be bruised in accidental trauma including the chest, abdomen, back, buttocks, soft tissue of the face, ears and protected areas such as the neck, the genital area and the inner thighs 2 3 16 17. Injuries to the lower legs are uncommon in children <18 months of age, and <1% of children <3 years of age have lumbar bruises 18…”

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confidence: 99%