Iterative refinement of a histologic algorithm for burn depth categorization based on 798 consecutive burn wound...

Area of Science:

• Dermatology
• Surgical Pathology
• Medical Imaging

Background:

• Accurate burn wound depth assessment is critical for treatment decisions.
• The previous Burn Biopsy Algorithm version 1 (BBA-V1) was developed for burn wound depth categorization.
• A simpler, updated version, BBA-V2, was developed to improve upon BBA-V1.

Purpose of the Study:

• To introduce and evaluate a newer, simplified Burn Biopsy Algorithm version 2 (BBA-V2).
• To compare the classification accuracy of BBA-V2 against BBA-V1.
• To assess the concordance of BBA-V2 with clinical visual assessment of burn wound depth.

Main Methods:

• A total of 798 burn wound biopsies were classified using both BBA-V1 and BBA-V2.
• For surgically treated burns, 4 mm biopsies were taken every 25 cm².
• For non-operative wounds, serial imaging at 72 hours and 21 days post-injury was performed to assess healing.
• Pixel analysis was used to quantify healing in non-operative wounds.

Main Results:

• BBA-V2 reclassified 21% of biopsies from a non-operative to an operative pathway compared to BBA-V1.
• BBA-V2 classified burns as 3% superficial partial-thickness (SPT), 67% deep partial-thickness (DPT), and 30% full-thickness (FT), significantly different from BBA-V1 (p < 0.0001).
• Non-operative wounds initially classified as SPT using BBA-V2 showed 89.6% healing accuracy at 21 days.

Conclusions:

• The Burn Biopsy Algorithm version 2 (BBA-V2) demonstrates significantly higher agreement with clinical visual assessment for deep partial and full-thickness burn wounds.
• BBA-V2 offers a more accurate and potentially simpler method for burn wound depth classification.
• The improved accuracy of BBA-V2 may lead to more appropriate treatment decisions for burn patients.