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Record W2238092883 · doi:10.1097/eja.0000000000000398

Upper airway trauma during general anaesthesia for major surgery

2016· letter· en· W2238092883 on OpenAlexaboutno aff
Ahmad Elsharydah, Amber C. Benhardt, Abu Minhajuddin, Babatunde Ogunnaike, Girish P. Joshi

Bibliographic record

VenueEuropean Journal of Anaesthesiology · 2016
Typeletter
Languageen
FieldMedicine
TopicAirway Management and Intubation Techniques
Canadian institutionsnot available
FundersPacira Pharmaceuticals
KeywordsMedicinePerioperativeOdds ratioIncidence (geometry)IntubationGeneral anaesthesiaAirwayUnivariate analysisConfidence intervalLogistic regressionAirway managementAnesthesiaSurgeryEmergency medicineMultivariate analysisInternal medicine

Abstract

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Editor, Upper airway traumatic injury (UATI) is an uncommon perioperative anaesthetic complication that can have significant consequences. According to a closed claims analysis, 6% of claims against anaesthesia providers were for UATI, causing disability in 5% and death in 8%.1 Intubation-related injury increased hospital costs by 20%, prolonged hospital admission by 1 day and resulted in more re-admissions.2 We used the American College of Surgeons–National Surgical Quality Improvement Program (ACS-NSQIP) database to examine the incidence and trends of UATI. The latter were defined as any injury to the lips, teeth, tongue, pharynx and larynx sustained during tracheal intubation. As the ACS-NSQIP database (www.facs.org) is publically available, multicentre and outcome orientated, the study was considered to be exempt from review by the University of Texas Southwestern Medical Centre, Dallas, Texas, Institutional Review Board (IRB, number 8843), as confirmed by a letter from IRB, Chair, Scott Roberts, MD, on 8 June 2015. Data were prospectively collected from 2005 to 2009 from participating hospitals (n = 237 in 2009). UATI data collection was stopped after 2009. All major surgical procedures requiring general anaesthesia were included. Exclusions were patients aged below 18 years, transplant or trauma surgery and cases with missing UATI data. Univariate analyses were used to calculate the UATI incidence. Trends were assessed using the Cochran–Armitage χ2 trend test for proportions and slopes with 95% confidence intervals (CIs). Multivariable logistic regression and adjusted odds ratios (ORs) were used to evaluate the risk factors for UATI, including age, sex, Mallampati classification, BMI and the urgency of the procedure. Significance was defined as a two-tailed P value of less than 0.05. SAS 9.3 software (Cary, North Carolina, USA) was used. A total of 854 099 patients met the inclusion criteria. Reasons for exclusions were general anaesthesia not used (n = 117 356) and missing airway trauma data (n = 3). The overall incidence of UATI was 0.22% (95% CI, 0.20 to 0.23%), divided into lip laceration or haematoma (63%), dental injury (25%), tongue laceration or haematoma (6%), pharyngeal laceration (4.5%) and laryngeal laceration (1.5%). Over the 5 years, there were significant trends (Table 1) with an increase in the incidence of UATI from 0.10 to 0.23% (P < 0.001). Multivariable logistic regression modelling showed an increased risk of UATI in older patients (highest at age >80 years; OR relative to patients at the age 18 to 30 years 1.79; 95% CI, 1.33 to 2.41; P = 0.004) and Mallampati class IV (OR relative to classes I to II 2.38; 95% CI, 1.76 to 3.21 P < 0.001). There was a statistically significant increasing trend of UATI for age above 80 years (P < 0.001) but not for Mallampati class IV (P = 0.84) over the study period (Table 1).Table 1: Trends in upper airway traumatic injury (UATI) and risk factors from 2005 to 2009Our study examined UATI secondary to tracheal intubation during general anaesthesia from a large prospectively collected national database. The most common airway trauma was lip injury. One of the strengths of our study is that the large sample size enabled us to assess the incidences of some of the rarer events such as laryngeal injury (1 : 30 000). We found that there was an increase in the rate of UATI over the study period. Possible reasons for the increase in UATI rates may include increased reporting, increased numbers of patients at risk of UATI and changes in airway management techniques such as the use of videolaryngoscopy. Surveys of practicing anaesthesia providers in the United States and Canada have shown significant increases in the use of videolaryngoscopy since 2000.3,4 However, the use of videolaryngoscopy may increase the risk of oropharyngeal injury (related to the use of a rigid stylet), which may explain the increase in UATI.5 Also, videolaryngoscopy may play a role in detecting airway trauma from direct laryngoscopy. Another explanation for the increasing trend of UATI was the significant increase in patients older than 80 years in the healthcare system. Although we found that a high Mallampati score was significantly associated with an increase in UATI, there was minimal change in this risk factor over the study period. This study has several limitations. The data collection on UATI was discontinued in 2009, and therefore, no current airway trauma information exits. Also, there is no information regarding the use of airway device (e.g. videolaryngoscopy), limited information on the severity and sequelae of UATI, and other risk factors for UATI such as the experience of the anaesthesia provider. The overall incidence of UATI may be underestimated. The ACS-NSQIP database did not report airway trauma from other airway devices such as with supra-laryngeal devices, nor did it report other unrecognised injuries. It is also likely that minor injuries such as lip lacerations are under-reported. Nevertheless, Hua et al.6 reported similar incidences of UATI from a study with a markedly smaller sample size. There may be sampling bias as the data were collected only from hospitals participating in ACS-NSQIP. In conclusion, this large study revealed interesting trends in airway trauma and allowed us to determine some of the risk factors for UATI. Acknowledgements relating to this article Assistance with the study: none. Financial support and sponsorship: this work was supported by the Department of Anaesthesiology and Pain Management, University of Texas Southwestern Medical Centre, Dallas, Texas, USA. Conflicts of interest: GPJ is on the speaker bureau for Baxter Inc., Mallinkrodt Pharmaceuticals and Pacira Pharmaceuticals. For the remaining authors, there are none. None of the authors has any financial interest in any of the products and devices mentioned in this article.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.294
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.024
GPT teacher head0.254
Teacher spread0.230 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreCommentary

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Published2016
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