Prognostic nomogram for predicting lower extremity deep venous thrombosis in ruptured intracranial aneurysm patients who underwent endovascular treatment

Chengwei Zhang; Jiaqian Zhu; Minghong Zhang; Ziru Yuan; Xiaoxiong Wang; Chengxing Ye; Haojie Jiang; Xiong Ye

doi:10.3389/fneur.2023.1202076

Prognostic nomogram for predicting lower extremity deep venous thrombosis in ruptured intracranial aneurysm patients who underwent endovascular treatment

Front Neurol. 2023 Aug 7:14:1202076. doi: 10.3389/fneur.2023.1202076. eCollection 2023.

Authors

Chengwei Zhang^#^{1

2}, Jiaqian Zhu^#^{1

2}, Minghong Zhang^{1

2}, Ziru Yuan^{1

2}, Xiaoxiong Wang^{1

2}, Chengxing Ye^{1

2}, Haojie Jiang^{1

2}, Xiong Ye^{1

2}

Affiliations

¹ Department of Neurosurgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
² Wenzhou Medical University, Wenzhou, China.

^# Contributed equally.

Abstract

Background: Lower extremity deep vein thrombosis (DVT) is one of the major postoperative complications in patients with ruptured intracranial aneurysms (RIA) who underwent endovascular treatment (EVT). However, patient-specific predictive models are still lacking. This study aimed to construct and validate a nomogram model for estimating the risk of lower extremity DVT for RIA patients who underwent EVT.

Methods: This cohort study enrolled 471 RIA patients who received EVT in our institution between 1 January 2020 to 4 February 2022. Perioperative information on participants is collected to develop and validate a nomogram for predicting lower extremity DVT in RIA patients after EVT. Predictive accuracy, discriminatory capability, and clinical effectiveness were evaluated by concordance index (C-index), calibration curves, and decision curve analysis.

Result: Multivariate logistic regression analysis showed that age, albumin, D-dimer, GCS score, middle cerebral artery aneurysm, and delayed cerebral ischemia were independent predictors for lower extremity DVT. The nomogram for assessing individual risk of lower extremity DVT indicated good predictive accuracy in the primary cohort (c-index, 0.92) and the validation cohort (c-index, 0.85), with a wide threshold probability range (4-82%) and superior net benefit.

Conclusion: The present study provided a reliable and convenient nomogram model developed with six optimal predictors to assess postoperative lower extremity DVT in RIA patients, which may benefit to strengthen the awareness of lower extremity DVT control and supply appropriate resources to forecast patients at high risk of RIA-related lower extremity DVT.

Keywords: aneurysmal subarachnoid hemorrhage; deep venous thrombosis; endovascular treatment; nomogram; prediction.

Grants and funding

This work was supported by the Wenzhou Science and Technology Bureau (no. Y20220120) and Major Science and Technology Special Project of Wenzhou Science and Technology Bureau (no. ZY2021006).