Optimizing Surgical Approaches for Patients with Inherited Factor VII Deficiency

Abstract

Background

Inherited factor VII deficiency (FVIID) presents a highly variable bleeding phenotype. The weak correlation between plasma FVII levels (FVII:C) and bleeding severity results in diverse management strategies and complicates surgical decision-making.

Objectives

To describe surgical management and bleeding outcomes in patients with FVIID, and to identify key decision-making variables and predictors of surgical bleeding.

Patients/Methods

We conducted a multicenter, retrospective study of 380 surgeries performed in 215 patients with FVIID. Patients were classified by FVII:C levels as mild, moderate, or severe deficiency. Bleeding score (BS) was defined according to ISTH-BAT. Surgeries were categorized as low-moderate risk (LR) or high risk (HR) for bleeding. A decision-tree simulation was performed.

Results

Most patients had mild FVIID (76%), and 68% of surgeries were classified as LR. Prophylactic treatment with tranexamic acid (TA) and/or rFVIIa was administered in 42.8% of LR and 62.8% of HR surgeries. Prophylaxis was given to 73.9% of moderate/severe and 41% of mild FVIID patients, especially for HR procedures. FVII:C levels and surgical bleeding risk were key factors that influenced the selection of treatment. The overall bleeding rate was 3.1% (HR: 9%; LR: 0.4%). Most bleeding events occurred in mild FVIID patients with BS ≥3. Our algorithm recommends hemostatic treatment for all moderate/severe, and for mild patients HR surgeries and LR procedures when BS is ≥3.

Conclusion

FVII:C levels and surgery type influence prophylactic hemostatic treatment strategies. Patients with mild FVIID, higher BS, and no hemostatic treatment had a greater risk of bleeding. Bleeding score and procedural risk were identified as predictors of surgical bleeding.

Keywords

factor VII - surgery - inherited - rare bleeding disorders

Declaration of GenAI Use

During the preparation of this work, the authors used “DeepL Write and Grammarly (1.113.1.0)” to enhance English-language proficiency. Generative intelligence based on the “Python” programming tool was used to create the graphics. After using this tool, the authors reviewed and edited the content as needed and take full responsibility for the content of the publication.

^‡ These authors share senior authorship.

Publication History

Received: 21 July 2025

Accepted after revision: 05 November 2025

Accepted Manuscript online:
24 December 2025

Article published online:
06 January 2026

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