Predicting Post-Dive Inert Gas Bubble Grades in Non-Decompression Scuba Diving with Air: Simplified Model for Enhanced Diver Safety.

IF 4.1 2区 医学 Q1 SPORT SCIENCES
Andreas Fichtner, Björn Hannesen, Felix Stein, Benedikt Schrofner-Brunner, Thomas Pohl, Thomas Grab, Thea Koch, Tobias Fieback
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引用次数: 0

Abstract

Background: Even well-planned no-decompression dives can still produce inert gas bubbles that increase decompression sickness risk. A previously proposed formula for predicting post-dive bubble grades integrates individual factors (age, breathing gas consumption) with dive parameters (maximum depth, surface interval). This study aimed to confirm the formula's validity in an independent dataset and to find out whether detailed dive profile data are of further relevance in predicting echocardiography-derived post-dive bubble grades. Additionally, we explored whether machine learning models leveraging detailed dive profile data could enhance predictive accuracy.

Results: A total of 59 divers performed 359 no-decompression open-circuit air dives in freshwater and saltwater. Post-dive transthoracic echocardiography detected bubbles (Eftedal-Brubakk grade ≥ 1) in 29.8% of dives. Maximum depth, total dive time, air consumption, and age correlated significantly with observed bubble grades (rs=0.37, rs=0.16, rs=0.27, rs=0.13, respectively). The original prediction formula remained valid (rs=0.39) and adequately captured higher-grade dives. Spending additional time in shallow water after deep segments reduced bubble formation. Machine learning approaches based on typical dive computer data (e.g. dive profile) provided stronger predictions (rs=0.49).

Conclusions: This study shows that maximum depth, age, surface interval and total breathing gas consumption are sufficient predictors of post-dive bubble load in no-decompression air dives. This allows divers to individually adopt bubble-reducing measures-such as resting, hydrating, and extending surface intervals-once alerted to a higher-risk class. Integrating the formula into dive computers may offer real-time, individualised risk guidance and help prevent decompression sickness despite following computer-derived profiles in recreational diving.

背景:即使是计划周密的无减压潜水,也会产生惰性气体气泡,从而增加减压病的风险。之前提出的预测潜水后气泡等级的公式综合了个人因素(年龄、呼吸气体消耗量)和潜水参数(最大深度、水面间隔)。本研究旨在通过一个独立的数据集确认该公式的有效性,并了解详细的潜水剖面数据在预测超声心动图得出的潜水后气泡等级方面是否具有进一步的相关性。此外,我们还探讨了利用详细潜水剖面数据的机器学习模型能否提高预测准确性:共有 59 名潜水员在淡水和海水中进行了 359 次无减压开环空气潜水。在 29.8% 的潜水中,潜水后经胸超声心动图检查发现了气泡(Eftedal-Brubakk 等级≥ 1)。最大深度、总潜水时间、耗气量和年龄与观察到的气泡等级显著相关(分别为 rs=0.37、rs=0.16、rs=0.27 和 rs=0.13)。最初的预测公式仍然有效(rs=0.39),并能充分捕捉到较高等级的潜水。深潜后在浅水中多待一段时间可减少气泡的形成。基于典型潜水计算机数据(如潜水轮廓)的机器学习方法提供了更强的预测能力(rs=0.49):这项研究表明,最大深度、年龄、水面间隔和呼吸气体总消耗量足以预测不减压空气潜水中潜水后气泡的负荷。这使得潜水员在发现高风险等级时,可以单独采取减少气泡的措施,如休息、补充水分和延长水面间隔时间。将该公式集成到潜水计算机中,可提供实时、个性化的风险指导,并有助于预防减压病,尽管在休闲潜水中遵循计算机得出的曲线。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Sports Medicine - Open
Sports Medicine - Open SPORT SCIENCES-
CiteScore
7.00
自引率
4.30%
发文量
142
审稿时长
13 weeks
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