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Construction of nomogram model for risk of venous thromboembolism after spine surgery based on thromboelastography and coagulation indices

  • 0Department of Orthopedics, The Fourth Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
Frontiers in Medicine +

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November 29, 2024

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November 29, 2024

View abstract on PubMed

Summary

This summary is machine-generated.

A new nomogram model accurately predicts venous thromboembolism risk after spinal surgery using thromboelastography and coagulation indices. This tool aids in identifying high-risk patients for targeted prevention strategies.

Area Of Science

  • Coagulation Science
  • Surgical Risk Assessment
  • Thromboembolism Research

Background

  • Venous thromboembolism (VTE) is a significant complication following spinal surgery.
  • Accurate risk stratification is crucial for effective VTE prevention.
  • Existing prediction models may not fully incorporate dynamic coagulation parameters.

Purpose Of The Study

  • To develop and validate a nomogram model for predicting VTE risk post-spinal surgery.
  • To utilize thromboelastography (TEG) and routine coagulation indices in the model.
  • To identify independent predictors of VTE in spinal surgery patients.

Main Methods

  • Retrospective analysis of 277 spinal fracture surgery patients.
  • Comparison of coagulation parameters and TEG data between VTE occurrence and non-occurrence groups.
  • Univariate and multivariate logistic regression to identify independent risk factors.
  • Construction and validation of a nomogram model using identified factors.

Main Results

  • Significant differences in age (≥51 years), alpha Angle, coagulation index (CI), maximum thrombus amplitude (MA), D-dimer (D-D), fibrinogen (FIB), fibrin degradation products (FDP), and thrombin-antithrombin complex (TAT) between groups.
  • Clot formation time (K) and coagulation reaction time (R) were lower in the VTE group.
  • Independent predictors identified: alpha Angle, K, D-D, FDP, and TAT.
  • The nomogram demonstrated good calibration (goodness of fit P=0.191) and discrimination (C-index 0.838).

Conclusions

  • A nomogram integrating alpha Angle, K, D-D, FDP, and TAT provides a reliable tool for VTE risk prediction after spinal surgery.
  • The model exhibits high fitting accuracy and predictive value.
  • This tool can aid clinicians in assessing and managing VTE risk in this patient population.

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