Dataset Information

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

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.

SUBMITTER: Siriussawakul A

PROVIDER: S-EPMC6117055 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

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

Siriussawakul Arunotai A Maboonyanon Patcharee P Kueprakone Subongkot S Samankatiwat Suthasinee S Komoltri Chulaluk C Thanakiattiwibun Chayanan C

PloS one 20180830 8

<h4>Background</h4>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.<h4>Methods</h4>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 intu ...[more]

PMID: 30161197

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Project description:BACKGROUND:Unplanned postoperative intubation is an important quality indicator, and is associated with significantly increased mortality in children. Infant patients are more likely than older pediatric patients to experience unplanned postoperative intubation, yet the literature provides few characterizations of this outcome in our youngest patients. The objective of this study was to identify risk factors for unplanned postoperative intubation and to develop a scoring system to predict this complication in infants undergoing major surgical procedures. METHODS:In this retrospective cohort study, The National Surgical Quality Improvement Program-Pediatric database was surveyed for all infants who underwent noncardiac surgery between January 1, 2012 and December 31, 2015 (derivation cohort, n = 56,962) and between January 1 and December 31, 2016 (validation cohort, n = 20,559). Demographic and perioperative clinical characteristics were examined in association with our primary outcome of unplanned postoperative intubation within 30 days of surgery. Risk factors were analyzed in the derivation cohort (2012-2015 data) using multivariable logistic regression with stepwise selection. Parameters from the final model were used to create a scoring system for predicting unplanned postoperative intubation. Data from the validation cohort were utilized to assess the performance of the scoring system using the area under the receiver operating characteristic curve. RESULTS:In the derivation cohort, 2.2% of the infants experienced unplanned postoperative intubation within 30 days of surgery. Of the 14 risk factors identified in multivariable analysis, 10 (age, prematurity, American Society of Anesthesiologists physical status, inpatient status, operative time >120 minutes, cardiac disease, malignancy, hematologic disorder, oxygen supplementation, and nutritional support) were included in the final multivariable logistic regression model to create the risk score. The area under the receiver operating characteristic curve of the final model was 0.86 (95% CI, 0.85-0.87) for the derivation cohort and 0.83 (95% CI, 0.82-0.85) for the validation cohort. CONCLUSIONS:About 1 in 50 infants undergoing major surgical procedures experiences unplanned postoperative intubation. Our scoring system based on routinely collected perioperative assessment data can predict risk in infants with good accuracy. Further investigation should assess the clinical utility of the scoring system for risk stratification and improvement in perioperative care quality and patient outcomes.

Project description:IntroductionThe significance of difficult or failed tracheal intubation following induction is a well-recognized cause of morbidity and mortality in anesthetic practice. Nevertheless, the need to predict potentially difficult tracheal intubation has received a little attention. During routine anesthesia, the incidence of difficult tracheal intubation has been estimated at 1.5%-8% of general anesthetics. Difficulties in intubation have been associated with serious complications, such as brain damage or death, particularly when failed intubation has occurred. Occasionally, in a patient with a difficult airway, the anesthetist is faced with the situation where mask ventilation proves difficult or impossible. This is one of the most critical emergencies that may be faced in the practice of anesthesia. If the anesthetist can predict which patients are likely to prove difficult to intubate, he/she may reduce the risks of anesthesia considerably. In Ethiopia, there are no data on the magnitude of difficult laryngoscopic tracheal intubation and no standard guidelines for preoperative tests. The main concern of this study was to provide information on the magnitude of difficult laryngoscopic intubation and to determine valuable preoperative tests to predict difficult laryngoscopy and intubation in patients with apparently normal airways which can help anesthetists to improve preoperative airway assessment and contribute to decrease anesthesia-related morbidity and mortality.ObjectiveThe main objective of this study was to assess the magnitude and predictive values of preoperative tests for difficult laryngoscopy and intubation, among surgical patients who underwent elective surgery under general anesthesia with endotracheal intubation in Tikur Anbessa Hospital from February 1 to March 30, 2016.Study designA facility-based cross-sectional study design was used.ResultIn this study, we found the magnitude of difficult laryngoscopy and intubation as 13.6% and 5%, respectively. 33.3% of patients with difficult laryngoscopy were found to be difficult for intubation. Mallampati test, interincisor distance, and thyromental distance were identified to be good preoperative tests to predict difficult laryngoscopic intubation when used in combination.RecommendationWe recommend anesthesia professionals to use combination of MMC/TMD/IID for their routine preoperative airway assessment.

Project description:PurposeTo investigate the characteristics of difficult intubation and identify novel efficient predictors in patients with acromegaly.MethodsPatients with either untreated acromegaly or non-functional pituitary adenomas were enrolled. Patients with acromegaly underwent hormone assays, upper airway computed tomography and magnetic resonance imaging examinations and preoperative overnight polysomnography. The modified Mallampati classification, mouth opening, neck circumference, and neck extension were assessed, and the Cormack-Lehane grades and the time of tracheal intubation were recorded.ResultsPatients with acromegaly had a higher incidence of difficult intubation (62.5%). The time of tracheal intubation was prolonged, the neck circumference was enlarged, and the neck extension was confined. In patients with acromegaly and difficult intubation, the insulin-like growth factor 1 levels and apnea/hypoxia index were significantly higher compared to patients without difficult intubation (1115.40?±?253.73 vs. 791.67?±?206.62 ng/ml, P?=?0.020; 22.17?±?23.25 vs. 2.47?±?2.84, P?=?0.026, respectively). The bilateral regression analysis revealed that high levels of insulin-like growth factor 1 were an independent risk factor for developing difficult intubation (p?=?0.042, Exp B?=?1.006). The modified Mallampati classification was positively correlated with apnea/hypoxia index and could be calculated using the following logarithmic equation: MMC?=?0.2982 * ln (AHI)?+?2.1836.ConclusionsIn patients with acromegaly, neck movement is confined, the time of tracheal intubation is prolonged, and the neck circumference is enlarged, and these patients suffer from an increased incidence of difficult intubation (62.5%) during anesthesia induction. The apnea/hypoxia index and insulin-like growth factor 1 levels are both increased in acromegalic patients with difficult intubation, and elevated insulin-like growth factor 1 levels are an independent risk factor of difficult intubation in acromegalic patients.

Project description:BackgroundReduction of readmissions after discharge represents an important challenge for many hospitals and has attracted the interest of many researchers in the past few years. Most of the studies in this field focus on building cross-sectional predictive models that aim to predict the occurrence of readmission within 30-days based on information from the current hospitalization. The aim of this study is demonstration of predictive performance gain obtained by inclusion of information from historical hospitalization records among morbidly obese patients.MethodsThe California Statewide inpatient database was used to build regularized logistic regression models for prediction of readmission in morbidly obese patients (n = 18,881). Temporal features were extracted from historical patient hospitalization records in a one-year timeframe. Five different datasets of patients were prepared based on the number of available hospitalizations per patient. Sample size of the five datasets ranged from 4,787 patients with more than five hospitalizations to 20,521 patients with at least two hospitalization records in one year. A 10-fold cross validation was repeted 100 times to assess the variability of the results. Additionally, random forest and extreme gradient boosting were used to confirm the results.ResultsArea under the ROC curve increased significantly when including information from up to three historical records on all datasets. The inclusion of more than three historical records was not efficient. Similar results can be observed for Brier score and PPV value. The number of selected predictors corresponded to the complexity of the dataset ranging from an average of 29.50 selected features on the smallest dataset to 184.96 on the largest dataset based on 100 repetitions of 10-fold cross-validation.DiscussionThe results show positive influence of adding information from historical hospitalization records on predictive performance using all predictive modeling techniques used in this study. We can conclude that it is advantageous to build separate readmission prediction models in subgroups of patients with more hospital admissions by aggregating information from up to three previous hospitalizations.

Project description:Unexpected difficult airway management can cause significant morbidity and mortality in patients admitted for elective procedures. Ultrasonography is a promising tool for perioperative airway assessment, nevertheless it is still unclear which sonographic parameters are useful predictors of difficult laryngoscopy and tracheal intubation. To determine the ultrasonographic predictors of a difficult airway that could be applied for routine practice, a systematic review and meta-analysis was conducted. Literature search was performed on PubMED, Web of Science and Embase using the selected keywords. Human primary studies, published in English with the use of ultrasonography to prediction of difficult laryngoscopy or tracheal intubation were included. A total of 19 articles (4,570 patients) were analyzed for the systematic review and 12 articles (1,141 patients) for the meta-analysis. Standardized mean differences between easy and difficult laryngoscopy groups were calculated and the parameter effect size quantified. A PRISMA methodology was used and the critical appraisal tool from Joanna Briggs Institute was applied. Twenty-six sonographic parameters were studied. The overall effect of the distance from skin to hyoid bone (p = 0.02); skin to epiglottis (p = 0.02); skin to the anterior commissure of vocal cords (p = 0.02), pre-epiglottis space to distance between epiglottis and midpoint between vocal cords (p = 0.01), hyomental distance in neutral (p < 0.0001), and extended (p = 0.0002) positions and ratio of hyomental distance in neutral to extended (p = 0.001) was significant. This study shows that hyomental distance in the neutral position is the most reliable parameter for pre-operative airway ultrasound assessment. The main limitations of the study are the small sample size, heterogeneity of studies, and absence of a standardized ultrasonographic evaluation method [Registered at International prospective register of systematic reviews (PROSPERO): number 167931].

Project description:BACKGROUND:The Optiscope™ can be used for intubation with minimal neck motion. We retrospectively investigated radiographic predictors of difficult intubation using the Optiscope™ by analyzing preoperative radiographic images. METHODS:One hundred eighty-four patients who were intubated with the Optiscope™ under manual in-line cervical stabilization for cervical spine surgery were enrolled. Radiographic indices were measured on preoperative cervical spine lateral X-ray and magnetic resonance imaging images. Difficult intubation was defined as failure or time consumption more than 90?s on the first attempt. To identify significant predictors of difficult intubation using the Optiscope™ and evaluate their diagnostic value, multivariable logistic regression and receiver operating characteristic analyses were used. RESULTS:Fourty-seven patients showed difficult intubation. There was no significant difference in radiographic indices between the difficult and easy intubation groups, but higher body mass index (BMI) (26.5 [3.0] vs. 24.6 [3.5] kg/m2, P?=?0.001), shorter sternomental distance (SMD) (122.0 [104.0 to 150.0] vs. 150.0 [130.0 to 170.0] mm, P?=?0.001), shorter interincisor gap (40.0 [35.0 to 45.0] vs. 43.0 [40.0 to 50.0] mm, P?=?0.006), and higher incidence of excessive oral secretions (10.6% vs. 2.9%, P?=?0.049) were observed in patients with difficult intubation. In multivariable analysis, BMI (odds ratio [95% confidence interval]; 1.15 [1.03 to 1.28], P?=?0.011) and SMD (odds ratio [95% confidence interval]; 0.98 [0.97 to 1.00], P?=?0.008) were associated with difficult intubation with the Optiscope™. In receiver operating characterstic analysis, the area under the curve for body mass index was 0.68 (95% confidence interval; 0.60 to 0.77, P?<?0.001) and that for sternomental distance was 0.66 (95% confience interval; 0.57 to 0.75, P?=?0.001). CONCLUSIONS:The incidence of difficult intubation using the Optiscope™ under manual in-line cervical stabilization was 25.5% in cervical spine surgery patients. No significant predictor of difficult intubation with the Optiscope™ was identified among the measured radiographic indices. Although high BMI and short SMD were predictive of difficult intubation with the Optiscope™, their discrimination power was weak.

Dataset Information

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

Background

Methods

Results

Conclusions

Publications

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

Similar Datasets

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