The nürnberg approach: a decision model for local vs. microvascular reconstruction in oral cancer surgery.

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.