基于超声波和体格检查的气道评估机器学习模型。

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引用次数: 0

摘要

目的:证明机器学习模型在利用临床和超声参数预测困难气道方面的实用性:这是对接受择期手术的患者进行的前瞻性非连续性队列研究。我们收集了年龄、性别、体重指数、OSA、Mallampatti、甲状腺距离、咬合试验、宫颈周径、宫颈超声测量值和喉镜检查后的 Cormack-Lehanne 分级作为预测变量。我们对预测变量与 Cormack-Lehanne 分级的关系进行了单变量分析,并对每个预测变量分别和组合应用随机森林技术设计了机器学习模型。我们发现了每种设计的AUC-ROC、灵敏度、特异性以及阳性和阴性预测值:我们招募了 400 名患者。Cormack-Lehanne≥Ⅲ级患者的年龄、体重指数、宫颈周径、Mallampati分级≥Ⅲ级和咬合试验≥Ⅱ级均较高,其超声测量值也明显较高。基于体格检查的机器学习模型获得的 AUC-ROC 值优于超声测量值,但未达到统计学意义。我们称之为 "经典模型 "的物理变量组合在所有模型中获得了最高的 AUC-ROC 值[0.75(0.67-0.83)],与其他超声模型相比,这一差异具有统计学意义:结论:使用机器学习模型诊断 VAD 确实是一种可能,尽管它仍处于非常初步的发展阶段:临床注册:ClinicalTrials.gov:临床注册:ClinicalTrials.gov:NCT04816435。
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Machine learning models based on ultrasound and physical examination for airway assessment

Purpose

To demonstrate the utility of machine learning models for predicting difficult airways using clinical and ultrasound parameters.

Methods

This is a prospective non-consecutive cohort of patients undergoing elective surgery. We collected as predictor variables age, sex, BMI, OSA, Mallampatti, thyromental distance, bite test, cervical circumference, cervical ultrasound measurements, and Cormack-Lehanne class after laryngoscopy. We univariate analyzed the relationship of the predictor variables with the Cormack-Lehanne class to design machine learning models by applying the random forest technique with each predictor variable separately and in combination. We found each design's AUC-ROC, sensitivity, specificity, and positive and negative predictive values.

Results

We recruited 400 patients. Cormack-Lehanne patients  III had higher age, BMI, cervical circumference, Mallampati class membership  III, and bite test  II and their ultrasound measurements were significantly higher. Machine learning models based on physical examination obtained better AUC-ROC values than ultrasound measurements but without reaching statistical significance. The combination of physical variables that we call the “Classic Model” achieved the highest AUC-ROC value among all the models [0.75 (0.67−0.83)], this difference being statistically significant compared to the rest of the ultrasound models.

Conclusions

The use of machine learning models for diagnosing VAD is a real possibility, although it is still in a very preliminary stage of development.

Clinical registry

ClinicalTrials.gov: NCT04816435.
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