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

Similar Datasets

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:The COVID-19 pandemic is impressively challenging the healthcare system. Several prognostic models have been validated but few of them are implemented in daily practice. The objective of the study was to validate a machine-learning risk prediction model using easy-to-obtain parameters to help to identify patients with COVID-19 who are at higher risk of death. The training cohort included all patients admitted to Fondazione Policlinico Gemelli with COVID-19 from March 5, 2020, to November 5, 2020. Afterward, the model was tested on all patients admitted to the same hospital with COVID-19 from November 6, 2020, to February 5, 2021. The primary outcome was in-hospital case-fatality risk. The out-of-sample performance of the model was estimated from the training set in terms of Area under the Receiving Operator Curve (AUROC) and classification matrix statistics by averaging the results of fivefold cross validation repeated 3-times and comparing the results with those obtained on the test set. An explanation analysis of the model, based on the SHapley Additive exPlanations (SHAP), is also presented. To assess the subsequent time evolution, the change in paO2/FiO2 (P/F) at 48 h after the baseline measurement was plotted against its baseline value. Among the 921 patients included in the training cohort, 120 died (13%). Variables selected for the model were age, platelet count, SpO2, blood urea nitrogen (BUN), hemoglobin, C-reactive protein, neutrophil count, and sodium. The results of the fivefold cross-validation repeated 3-times gave AUROC of 0.87, and statistics of the classification matrix to the Youden index as follows: sensitivity 0.840, specificity 0.774, negative predictive value 0.971. Then, the model was tested on a new population (n = 1463) in which the case-fatality rate was 22.6%. The test model showed AUROC 0.818, sensitivity 0.813, specificity 0.650, negative predictive value 0.922. Considering the first quartile of the predicted risk score (low-risk score group), the case-fatality rate was 1.6%, 17.8% in the second and third quartile (high-risk score group) and 53.5% in the fourth quartile (very high-risk score group). The three risk score groups showed good discrimination for the P/F value at admission, and a positive correlation was found for the low-risk class to P/F at 48 h after admission (adjusted R-squared = 0.48). We developed a predictive model of death for people with SARS-CoV-2 infection by including only easy-to-obtain variables (abnormal blood count, BUN, C-reactive protein, sodium and lower SpO2). It demonstrated good accuracy and high power of discrimination. The simplicity of the model makes the risk prediction applicable for patients in the Emergency Department, or during hospitalization. Although it is reasonable to assume that the model is also applicable in not-hospitalized persons, only appropriate studies can assess the accuracy of the model also for persons at home.

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.

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

OmicsDI is part of the ELIXIR infrastructure

Tweets