Pediatric trauma venous thromboembolism prediction algorithm outperforms current anticoagulation prophylaxis guidelines: a pilot study

Abstract

Purpose: Venous thromboembolism (VTE) in injured children is rare, but sequelae can be morbid and life-threatening. Recent trauma society guidelines suggesting that all children over 15 years old should receive thromboprophylaxis may result in overtreatment. We sought to evaluate the efficacy of a previously published VTE prediction algorithm and compare it to current recommendations.

Methods: Two institutional trauma registries were queried for all pediatric (age < 18 years) patients admitted from 2007 to 2018. Clinical data were applied to the algorithm and the area under the receiver operating characteristic (AUROC) curve was calculated to test algorithm efficacy.

Results: A retrospective review identified 8271 patients with 30 episodes of VTE (0.36%). The VTE prediction algorithm classified 51 (0.6%) as high risk (> 5% risk), 322 (3.9%) as moderate risk (1-5% risk) and 7898 (95.5%) as low risk (< 1% risk). AUROC was 0.93 (95% CI 0.89-0.97). In our population, prophylaxis of the 'moderate-' and 'high-risk' cohorts would outperform the sensitivity (60% vs. 53%) and specificity (96% vs. 77%) of current guidelines while anticoagulating substantially fewer patients (373 vs. 1935, p < 0.001).

Conclusion: A VTE prediction algorithm using clinical variables can identify injured children at risk for venous thromboembolic disease with more discrimination than current guidelines. Prospective studies are needed to investigate the validity of this model.

Level of evidence: III-Clinical decision rule evaluated in a single population.

Keywords: Guidelines; Pediatric trauma; Thromboprophylaxis; Venous thromboembolism.

Fig. 1

Venous thromboembolism (VTE) prediction tool in pediatric trauma patients. A scoring system to predict VTE in pediatric trauma patients was previously developed from the National Trauma Data Bank and reported in Connelly et al. [13], recreated with permission above. a VTE prediction model with assigned point value to each clinical characteristic. The cumulative VTE risk score is tabulated and applied to the prediction curve. b VTE risk scores of 0–523 corresponds with low risk (< 1%), scores of 524–688 correspond with moderate risk (1–5%), and scores of 689–797 correspond to high risk (> 5%) of VTE. Cutoff values for the above risk categories are identified by dashed lines. GCS Glasgow Coma Score, Y year, ICU intensive care unit

Fig. 2

Receiver operating characteristic curve for the VTE prediction algorithm. Retrospective application of the VTE prediction algorithm was performed in a population of 8271 children collected from institutional trauma registries. The VTE risk score was calculated and plotted against the outcome of VTE, the area under the receiver operating characteristic (AUROC) curve was calculated and reported with a 95% confidence interval. An AUROC of 0.931 demonstrates excellent fidelity of the model to predict VTE. VTE: venous thromboembolism. AUROC area under the receiver operating characteristic curve