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Intra- and Early Postoperative Evaluation of Malperfused Areas in an Irradiated Random Pattern Skin Flap Model Using Indocyanine Green Angiography and Near-Infrared Reflectance-Based Imaging and Infrared Thermography

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Background: Assessment of tissue perfusion after irradiation of random pattern flaps still remains a challenge. Methods: Twenty-five rats received harvesting of bilateral random pattern fasciocutaneous flaps. Group 1 served as… Click to show full abstract

Background: Assessment of tissue perfusion after irradiation of random pattern flaps still remains a challenge. Methods: Twenty-five rats received harvesting of bilateral random pattern fasciocutaneous flaps. Group 1 served as nonirradiated control group. The right flaps of the groups 2–5 were irradiated with 20 Gy postoperatively (group 2), 3 × 12 Gy postoperatively (group 3), 20 Gy preoperatively (group 4) and 3 × 12 Gy preoperatively (group 5). Imaging with infrared thermography, indocyanine green angiography and near-infrared reflectance-based imaging were performed to detect necrotic areas of the flaps. Results: Analysis of the percentage of the necrotic area of the irradiated flaps showed a statistically significant increase from day 1 to 14 only in group 5 (p < 0.05). Indocyanine green angiography showed no differences (p > 0.05) of the percentage of the nonperfused area between all days in group 1 and 3, but a decrease in group 2 in both the left and the right flaps. Infrared thermography and near-infrared reflectance-based imaging did not show evaluable differences. Conclusion: Indocyanine green angiography is more precise in prediction of necrotic areas in random pattern skin flaps when compared to hyperspectral imaging, thermography or clinical impression. Preoperative fractional irradiation with a lower individual dose but a higher total dose has a more negative impact on flap perfusion compared to higher single stage irradiation.

Keywords: indocyanine green; group; random pattern; green angiography; thermography

Journal Title: Journal of Personalized Medicine
Year Published: 2022

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