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  • Author or Editor: Takayuki Motoshima x
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Keita Shibahashi, Hidenori Hoda, Takuto Ishida, Takayuki Motoshima, Kazuhiro Sugiyama, and Yuichi Hamabe

OBJECTIVE

The objective of this study was to derive and validate a new screening model using a nomogram that allows clinicians to quantify the risk of blunt cerebrovascular injury (BCVI).

METHODS

In this study, the authors examined 258,935 patients from a prospectively collected nationwide Japanese database (January 2009–December 2018) who experienced blunt injury. Patients were randomly divided into training (n = 129,468) and validation (n = 129,467) cohorts. First, the authors investigated the prevalence of BCVI, which was defined as blunt injury to any intracranial vessel, the extracranial vertebral artery, the extracranial carotid (common, internal) artery, or the internal jugular vein. Then, a new arterial BCVI screening model using a nomogram was derived, based on multivariate logistic regression analysis through quantifying the association of potential predictive factors with BCVI in the training cohort. The model’s discriminatory ability was validated using the area under the receiver operating characteristic curve (AUC) in the validation cohort.

RESULTS

Multivariate analysis in the training cohort showed that 13 factors were significantly associated with arterial BCVI and were included in our model. These factors were 1) male sex; 2) high-energy impact; 3) hypotension on hospital arrival; 4) Glasgow Coma Scale score < 9; 5) injury to the face; 6) injury to the neck; 7) injury to the spine; 8) skull base fracture; 9) cervical spine fracture or subluxation; and those with negative associations, i.e., 10) injury to the lower-extremity region; 11) supratentorial subdural hemorrhage; 12) lumbar spine fracture or subluxation; and 13) soft tissue injury of the face. In the validation cohort, the model had an AUC of 0.83 (95% CI 0.81–0.86). When the definition of BCVI was narrowed to include only carotid (common, internal) and vertebral artery injuries, the AUC of the model in predicting these injuries was 0.89 (95% CI 0.87–0.91).

CONCLUSIONS

A new screening model that incorporates an easy-to-use nomogram to quantify the risk of BCVI and assist clinicians in identifying patients who warrant additional evaluation was established.