Predictive performance of a multivariable difficult intubation model for obese patients.

PLoS One

Integrated Perioperative Geriatric Excellent Research Center, Faculty of Medicine, Siriraj Hospital, Mahidol University, Bangkok, Thailand.

Published: February 2019

AI Article Synopsis

  • A predictive model for difficult intubation (DI) in obese patients was developed and assessed to enhance preoperative screening and reduce complications.
  • The study involved 1,015 obese adults (BMI ≥ 30) undergoing intubation, measuring various factors such as neck circumference and airway tests.
  • Results indicated only fair predictive accuracy for the model (AUC 0.71), suggesting limited effectiveness in identifying DI risks for obese patients during surgery.

Article Abstract

Background: A predictive model of scores of difficult intubation (DI) may help physicians screen for airway difficulty to reduce morbidity and mortality in obese patients. The present study aimed to set up and evaluate the predictive performance of a newly developed, practical, multivariate DI model for obese patients.

Methods: A prospective multi-center study was undertaken on adults with a body mass index (BMI) of 30 kg/m2 or more who were undergoing conventional endotracheal intubation. The BMI and 10 preoperative airway tests (namely, malformation of the teeth in the upper jaw, the modified Mallampati test [MMT], the upper lip bite test, neck mobility testing, the neck circumference [NC], the length of the neck, the interincisor gap, the hyomental distance, the thyromental distance [TM] and the sternomental distance) were examined. A DI was defined as one with an intubation difficulty scale (IDS) score ≥ 5.

Results: The 1,015 patients recruited for the study had a mean BMI of 34.2 (standard deviation: 4.3 kg/m2). The proportions for easy intubation, slight DI and DI were 81%, 15.8% and 3.2%, respectively. Drawing on the results of a multivariate analysis, clinically meaningful variables related to obesity (namely, BMI, MMT, and the ratio of NC to TM) were used to build a predictive model for DI. Nevertheless, the best model only had a fair predictive performance. The area under the receiver operating characteristic curve (AUC) was 0.71 (95% confidence interval 0.68-0.84).

Conclusions: The predictive performance of the selected model showed limited benefit for preoperative screening to predict DI among obese patients.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6117055PMC
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0203142PLOS

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