ArticleAnnals of medicine and surgery (2012)2021
Predicting hyperbaric oxygen therapy success using the decision tree approach.
Article in Annals of medicine and surgery (2012), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
4 citing papers in PubMed, 7 citations in OpenAlex.
- Hyperbaric Oxygen Therapy in Surgical Wound Healing and Tissue Salvage: A Structured Narrative Review.Cureus · 2026Review
- HBOT as a Potential Adjunctive Therapy for Wound Healing in Dental Surgery-A Narrative Review.Journal of clinical medicine · 2026Review
- Review
- Bactericidal effect of hyperbaric oxygen therapy in burn injuries.Annals of medicine and surgery (2012) · 2022Article
Corrections and comments
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Authors and funding
10 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionHyperbaric 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.
methodsThis 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.
resultsBased 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.
conclusionHBOT cannot be separated from specific variables that contribute to and can predict its success.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.