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). CONCLUSION: 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.
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). CONCLUSION: 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.