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  • A Novel Neurosurgical DVT Prediction Model

    Final Number:

    David L Dornbos III MD; Varun Shah; Ammar Shaikhouni MD, PhD; Blake Priddy; Victoria Schunemann MD; Shahid Mehdi Nimjee MD, PhD; Ciaran J. Powers MD, PhD

    Study Design:
    Clinical Trial

    Subject Category:

    Meeting: Congress of Neurological Surgeons 2018 Annual Meeting

    Introduction: Venous thromboembolic events (VTE), including deep venous thrombosis (DVT) and pulmonary embolism (PE), are a common source of morbidity and mortality. Developing a predictive model from a neurosurgical population to accurately stratify peri-operative DVT/PE risk would allow providers to more accurately tailor DVT prophylaxis.

    Methods: 2879 patients between July 2014 and June 2015 were retrospectively evaluated. DVT/PE risk factors, patient demographics, surgical procedure, VTE prophylaxis, and DVT/PE confirmation were collected. In half of the cohort, the impact of surgical categorization and risk factors on peri-operative VTE development were assessed using logistic regression analysis. Odds ratios of significant risk factors were used to generate a DVT prediction model, and area under the receiver operating curve (AUROC) was used to validate the risk assessment model in the second cohort.

    Results: On initial univariate analysis, surgical procedure, history of DVT/PE, malignancy, sepsis, pneumonia, length of surgery, recent stroke, prolonged bed rest, and spinal cord injury significantly (p<0.01) correlated with VTE development. Based on this analysis, a neurosurgical DVT prediction model was developed. Surgical procedures were awarded the following points: functional (0), endovascular (1), shunt (2), spine (2), and cranial (3). Malignancy and length of surgery >3 hours received 1 point. History of DVT/PE, sepsis, stroke and spinal cord injury were awarded 2 points. Prolonged bed rest and pneumonia were awarded 3 points. This scoring system generated an AUROC of 0.830 (95% CI: 0.791-0.869).

    Conclusions: This novel neurosurgical-tailored DVT prediction model appropriately stratifies DVT/PE risk with very good accuracy. Utilizing the ROC analysis, patients at low risk (score of <3, sensitivity 97.6%), moderate risk (4-6), high risk (7-10), and very high risk (=11, specificity 95.9%) carry a 0.5%, 2.7%, 12.8%, and 23.3% risk of VTE, respectively. This model can be used to improve decision-making regarding DVT prophylactic strategies in neurosurgical patients.

    Patient Care: DVT/PE remains a significant source of morbidity following neurosurgical procedures. Developing a risk assessment model that accurately predicts and stratifies patient risk for DVT/PE development following neurosurgical procedures will allow providers to more appropriately tailor DVT prophylactic regimens, minimizing both DVT/PE development and complications of prophylactic strategies.

    Learning Objectives: 1. Describe the importance of DVT/PE prevention in neurosurgical patients. 2. Discuss the importance of risk assessment modeling in neurosurgical patients. 3. Identify effective way to implement this risk assessment model to decrease DVT/PE incidence and complications of prophylaxis.


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