Predicting hyperbaric oxygen therapy success using the decision tree approach

Abstract

INTRODUCTION: Hyperbaric oxygen therapy (HBOT), a procedure that involves the patient inhaling 100% oxygen gas under pressure, is currently used as an adjunctive treatment option for certain inflammatory conditions. HBOT can improve wound healing by increasing the rate of angiogenesis in injured tissue by increasing levels of vascular endothelial growth factor (VEGF). VEGF causes re-epithelialization, the migration of endothelial cells, and the formation of granulation tissue, which are involved in the wound healing process. METHODS: This study contains secondary data analyses of information previously collected in two separate studies, each concerning the effects of HBOT on diabetic foot ulcers and crush injury fractures at Prof. Dr. R. D. Kandou Hospital Manado and Siloam Hospital Manado from 2019 to early 2020. RESULTS: Based on the classification tree analysis, the predictors of HBOT success were leukocytes level (34%), platelet count (32%), and age (26%). The conditional inference tree analysis also indicated significant leukocyte levels, age, and platelet counts (p < 0.001), with which the interpretation of these results was the same as the classification tree analysis method. The results obtained from the random forest analysis revealed that the mean value of Gini reduction for leukocytes (207.3), platelets (110.2), age (97.9), and hemoglobin (57.9) can be used as indicators of successful HBOT. These three methods support that age, leukocytes, and platelets are determinants of HBOT success, while hemoglobin levels were only significant in one analysis method. Therefore, a new, proposed algorithm containing these factors was assembled from the results of this study. CONCLUSION: HBOT cannot be separated from specific variables that contribute to and can predict its success.

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