title: Prediction of difficult tracheal intubation: time for a paradigm change.
abstract: BACKGROUND
  It has been suggested that predicting difficult tracheal intubation is useless because of the poor predictive capacity of individual signs and scores. The authors tested the hypothesis that an accurate prediction of difficult tracheal intubation using simple clinical signs is possible using a computer-assist model.
  
  
  METHODS
  In a cohort of 1,655 patients, the authors analyzed the predictive properties of each of the main signs (Mallampati score, mouth opening, thyromental distance, and body mass index) to predict difficult tracheal intubation. They built the best score possible using a simple logistic model (SCOREClinic) and compared it with the more recently described score in the literature (SCORENaguib). Then they used a boosted tree analysis to build the best score possible using computer-assisted calculation (SCOREComputer).
  
  
  RESULTS
  Difficult tracheal intubation occurred in 101 patients (6.1%). The predictive properties of each sign remain low (maximum area under the receiver operating characteristic curve 0.70). Using receiver operating characteristic curve, the global prediction of the SCOREClinic (0.74, 95% CI: 0.72-0.76) was greater than that of the SCORENaguib (0.66, 95% CI: 0.60-0.72, P<0.001) but significantly lower than that of the SCOREComputer (0.86, 95% CI: 0.84-0.91, P<0.001). The proportion of patients in the inconclusive zone was 71% using SCORENaguib, 56% using SCOREClinic, and only 32 % using SCOREComputer (all P<0.001).
  
  
  CONCLUSIONS
  Computer-assisted models using complex interaction between variables enable an accurate prediction of difficult tracheal intubation with a low proportion of patients in the inconclusive zone. An external validation of the model is now required.
authors.affiliation: Department of Anesthesiology and Critical Care, Université Pierre et Marie Curie-Paris, CHU Pitié-Salpêtrière, Paris, France. olivier.langeron@psl.aphp.fr
authors.forename: Olivier
authors.initials: O
authors.lastname: Langeron
authors.forename: Philippe
authors.initials: P
authors.lastname: Cuvillon
authors.forename: Cristina
authors.initials: C
authors.lastname: Ibanez-Esteve
authors.forename: François
authors.initials: F
authors.lastname: Lenfant
authors.forename: Bruno
authors.initials: B
authors.lastname: Riou
authors.forename: Yannick
authors.initials: Y
authors.lastname: Le Manach
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D015992:Body Mass Index; D003658:Decision Making, Computer-Assisted; D005260:Female; D006801:Humans; D007442:Intubation, Intratracheal / Q000009:adverse effects* / Q000379:methods / Q000639:trends*; D008297:Male; D008875:Middle Aged; D011237:Predictive Value of Tests; D013997:Time Factors; D055815:Young Adult