Abstract Details

Title Neural Network-assisted Prediction of Venous Thromboembolism after Spine and Neurological Operations - NeuroVeT Study

Topic Neuro Trauma and Critical Care

Presentation(s) P3 - Poster Session 3 (5:00 PM-6:00 PM)

Poster/Presentation
Number 19-008

Objective To develop and evaluate the predictive performance of a Deep Neural Network (DNN) model for identifying postoperative venous thromboembolism (VTE) risk following spine surgery using routinely collected clinical and operative data.

Background Venous thromboembolism (VTE) is a rare but serious complication after spine surgery, contributing to morbidity and mortality. Traditional risk assessment tools rely on static clinical parameters and may not capture complex, nonlinear relationships among patient variables. Advances in artificial intelligence, particularly deep learning, offer the potential to enhance predictive accuracy and enable personalized risk stratification. This study investigates whether a Deep Neural Network (DNN) can effectively predict postoperative VTE occurrence based on multidimensional clinical datasets

Design/Methods

A retrospective analysis of 4,972 spine surgery patients was conducted. Data were divided into training (n = 1,332) and testing (n = 3,640) cohorts. Structured preoperative, intraoperative, and postoperative variables were input into a multilayer DNN classifier. Model performance was evaluated using metrics including Area Under the Curve (AUC), accuracy, F1 score, precision, and recall. The primary outcome was the model’s ability to distinguish VTE versus non-VTE cases in the test dataset.

Results The DNN demonstrated strong discriminative ability with an AUC of 0.92, accuracy of 0.96, F1 score of 0.98, and recall (sensitivity) of 0.98, indicating minimal missed VTE cases. While precision was 0.78, the combination of high recall and high overall accuracy underscores the robustness of the model in clinical prediction settings

Conclusions The DNN model achieved excellent predictive performance for postoperative VTE, outperforming traditional statistical approaches. Its ability to identify high-risk patients with high sensitivity supports its clinical utility as a decision-support tool for individualized thromboprophylaxis planning following spine surgery

Authors/Disclosures
Suchita Mylavarapu, MBBS PRESENTER	Dr. Mylavarapu has nothing to disclose.
Sanjana Avajigari, MBBS	Miss Avajigari has nothing to disclose.
Shradha P. Kakde, MBBS	Dr. Kakde has nothing to disclose.
Chirag L Sagar, MBBS	Dr. L Sagar has nothing to disclose.
Harshawardhan D. Ramteke, Sr., MBBS	Dr. Ramteke has nothing to disclose.
Dr Pratiksha S. Baliga, MBBS	Dr. Baliga has nothing to disclose.
Ramya Manojna Hota, MBBS	Dr. Hota has nothing to disclose.
Syed Faiz Ahmed	Mr. Faiz Ahmed has nothing to disclose.
Ahmed R. Harb	Mr. Harb has nothing to disclose.
Sharath Chandra Anne, MBBS	Mr. Anne has nothing to disclose.
Anas Mansour, MD	Dr. Mansour has nothing to disclose.
Rakhshanda Khan, MBBS	Dr. Khan has nothing to disclose.
Meghana Chennupati, MBBS	Miss Chennupati has nothing to disclose.

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