Reliability of clinical guidelines in the detection of patients at risk following mild head injury: results of a prospective study

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Object. The aims of this study were to analyze the relevance of risk factors in mild head injury (MHI) by studying the possibility of establishing prediction models based on these factors and to evaluate the reliability of the clinical guidelines proposed for the management of MHI.

Methods. A series of 1101 patients with MHI were prospectively enrolled in this study. In all cases clinical data were collected and a computerized tomography (CT) scan was obtained. The relationship between clinical findings and the presence of intracranial lesions was studied to establish prediction models based on logistic regression and recursive partitioning analysis. Recently proposed guidelines and recommendations for the treatment of MHI were selected, calculating their diagnostic efficiency when applying each of them to our series.

The incidence of acute intracranial lesions was 7.5% (83 patients). A Glasgow Coma Scale score of 14, loss of consciousness, vomiting, headache, signs of basilar skull fracture, neurological deficit, coagulopathies, hydrocephalus treated with shunt insertion, associated extracranial lesions, and patient age greater than 65 years were identified as independent risk factors. Prediction models built on clinical variables were able to indicate patients with clinically important lesions, but failed to achieve 100% sensitivity in the detection of all patients with CT scans positive for intracranial lesions within reasonable specificity limits.

Conclusions. Clinical variables are insufficient to predict all cases of intracranial lesions following MHI, although they can be used to detect patients with relevant injuries. Avoiding systematic CT scan indication implies a rate of misdiagnosis that should be known and assumed when planning treatment in these patients by using guidelines based on clinical parameters.

Article Information

Address reprint requests to: Javier Ibañez, M.D., Department of Neurosurgery, Vall d'Hebron University Hospital, Ps. Vall d' Hebron, 119–129, 08035 Barcelona, Spain. email: 30066jid@telefonica.net.

© AANS, except where prohibited by US copyright law.

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Figures

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    Graph demonstrating an ROC curve, summarizing the predictive performance of the logistic regression model based on clinical data for the detection of patients with relevant positive CT scanning results. The area under the ROC curve is 0.853 (95% CI 0.803–0.901). Sensitivity of 100% is achieved at a cutoff value of 0.0042, lowering specificity to a level of 3.2%. Predicted risk = 1/(1 + exp [−logit]); logit = −5.175 + 1.357 × GCS Score 14 + 1.931 × LOC + 1.984 × severe headache + 0.77 × mild or moderate headache + 1.122 × vomiting − 0.683 × dizziness + 2.08 × signs of skull base fracture + 2.325 × neurological deficit + 0.861 × extracranial injuries + 2.78 × hydrocephalus treated with shunt placement + 0.822 × age more than 65 years + 1.222 × coagulation disorder.

  • View in gallery

    Graph demonstrating an ROC curve, summarizing the predictive performance of the logistic regression model based on clinical data for the detection of patients with clinically important lesions. The area under the ROC curve is 0.919 (95% CI 0.89–0.949). Sensitivity of 100% is achieved at a cutoff value of 0.0063 and a specificity of 42.5%. Predicted risk = 1/(1 + exp [−logit]); logit = −5.983 + 1.461 × GCS Score 14 + 2.508 × LOC + 1.983 × severe headache + 0.731 × mild or moderate headache − 1.785 × nausea + 3.234 × vomiting − 0.825 × dizziness + 2.475 × signs of skull base fracture + 3.03 × neurological deficit + 0.918 × extracranial injuries + 3.29 × hydrocephalus treated with shunt insertion + 1.02 × age greater than 65 years + 1.277 × coagulation disorder.

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    Diagram of classification tree based on recursive partitioning analysis for the detection of patients with relevant positive CT scanning results. This tree was obtained by progressively increasing the cost of misclassification of patients with relevant lesions. The sensitivity of this model is 94%, with a specificity of 53.3%. Five patients with relevant CT scanning results were lost. The next most sensitive model provides a single-node tree, recommending systematic CT scan indication in all patients (QUEST, version 1.8.19; split point selection with exhaustive search method by using Pearson chi-square analysis).

  • View in gallery

    Diagram of classification tree based on recursive partitioning analysis for the detection of patients with clinically important lesions. This tree was obtained by progressively increasing the cost of misclassification of patients with clinically important lesions. The sensitivity of this model is 100% with a specificity of 52.6% (QUEST, version 1.8.19; split point selection with exhaustive search method by using Pearson chi-square analysis).

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