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The nürnberg approach: a decision model for local vs. microvascular reconstruction in oral cancer surgery
Johannes Raphael Kupka1, Jan Marten2, Frank Tavassol3
1Department of Oral and Maxillofacial Surgery, University Hospital of the Paracelsus Medical Private University, Nürnberg, Germany. johanneskupka@web.de.
Objective:
This study aims to develop a data-driven decision model to optimize reconstructive strategies for oral squamous cell carcinoma. We investigated the threshold between local/regional techniques and microvascular reconstruction by analyzing objective tumor-related and healthcare-economic parameters.
Materials And Methods:
A retrospective analysis was conducted on 88 patients with OSCC. The cohort was divided into Group I (local/regional reconstruction, n = 57) and Group II (microvascular reconstruction, n = 31). We evaluated predictors such as tumor location, diameter, and resection type. Statistical analysis included also classification tree analysis and healthcare-economic data (DRG-based reimbursement).
Results:
Tumor location, diameter, and resection type emerged as the decisive predictors for the reconstructive strategy. The classification tree analysis demonstrated high diagnostic performance, providing a robust cut-off to differentiate between local and microvascular indications. Local reconstructions were significantly superior regarding perioperative morbidity, showing lower rates of tracheotomies, PEG tube requirements, and shorter ICU/hospital stays. Economically, regional techniques proved substantially more cost-efficient, while microvascular procedures required significantly longer operating times. Flap survival was excellent at 96.7%.
Conclusion:
The study introduces a data-driven reconstruction algorithm representing the clinical workflow of a high-volume center. Replacing subjective "expert heuristics" with objective data-driven thresholds can standardize institutional treatment pathways. Local techniques remain a resource-efficient alternative for small-to-medium defects, showing favorable perioperative outcomes in selected cases.
Clinical Relevance:
By implementing this classification tree as a clinical decision support system, surgeons can reduce cognitive load and unintended practice variation, ensuring the most effective and least invasive treatment strategy for each patient.
