In this study, nomograms were developed to predict the risk of DL and DTI. These nomograms were constructed by combination of traditional DA assessment methods (such as ULBT, MMT, IID) and ultrasound airway assessment methods (such as TMJ, TT), which have achieved good performance with high AUC values. To our knowledge, this study is the first report that combines traditional DA assessment and ultrasonography-guided airway assessment by nomograms to predict DA.

In our nomogram models, the DL prediction nomogram incorporated 9 variables (ULBT, MMT, sex, TMJ, age, BMI, TMD, IID and TT), and the DTI prediction nomogram included 5 variables (ULBT, TMJ, age, IID and TT). Among these variables, ULBT, MMT, sex, age, BMI, TMD, IID were traditional DA assessment methods while TMJ and TT were ultrasonography-assisted airway assessment methods. In both DL and DTI nomograms, the performance of ultrasonography-assisted airway assessments was good. For example, TT has high nomogram scores in both DL (> 67 mm, 48 points) and DTI (> 62 mm, 89 points) prediction. In particular, TMJ has the highest scores in both DL and DTI nomograms. Our previous study [19, 20] found that TMJ and TT measured by ultrasound can be used to predict DA. TMJ measured by ultrasound performs better predication for DL, compared to traditional DA assessment methods such as IID, ULBT, TMD and MMT. However, due to small sample size (484 patients with 41 DL cases), the performance of TMJ to predict DTI was not assessed [19]. In current study, we showed that TMJ has the highest nomogram scores in both DL (< 12 mm, 100 points) and DTI (< 11 mm, 100 points) prediction, confirming the predictive performance for DA. In a previous study, we have shown that TT is another useful DA predictor [20]. Taken together, our findings indicated that the ultrasonography-assisted DA prediction methods are effective. Hence, we incorporated the TMJ and TT indicators measurement by ultrasound into the nomograms for DL and DTI prediction.

Many studies have attempted to combine indicators to improve predictive capability of DA. Most of these models were created by simple addition of indicators, such as the “3–3-2” rule [21], LEMON criteria [22], and “Wilson” scores [23]. In consistence with these studies, previously we found that the 3–3 rule (IID less than three fingers, a hyoidmental distance less than three fingers) is useful for DA prediction, with AUC 0.709 for DL and 0.822 for DTI [31]. In addition, by using the ratio of TT to TMD, the AUCs for DL and DTI could be significantly improved to 0.75 (95%CI, 0.73–0.76) and 0.86 (95%CI, 0.84–0.87), which are better than that of TT, TMD and MMT alone [20]. In current study, when nomograms were used, the AUCs were increased to 0.933 [95% CI, 0.912–0.954] and 0.974 (95% CI, 0.954–0.995) for DL and DTI, respectively. These data indicated that the nomograms are more powerful tools to predict DA.

The nomograms in this study are easy to implement in routinely clinical practice. As long as nine predictors (ULBT, MMT, Sex, TMJ, Age, BMI, TMD, IID, and TT) were collected, the incidence of DTI and DL in patients could be assessed by the nomograms. Among the nine predictive indicators, ULBT, MMT, Sex, Age, BMI, TMD and IID are all classic indicators commonly used in clinical practice, and the data are very easy to obtain. Furthermore, TT and TMJ can be well measured by conventional clinical ultrasound equipment, and previous studies have shown that the reliability of measurements between different sonographers is comparable [19]. In the actual use of the nomogram, we only need to convert the corresponding predictor value into the corresponding nomogram score value, and then add the score values to obtain the total score. Then the risk incidence corresponding to the total score is obtained, as described in the results section. The operation of nomogram is simple and intuitive, does not require complicated calculation, is less time-consuming, is very convenient to use, and can be quickly popularized.

This study has some limitations. Firstly, data were from a single center and the sample size of DTI and DL patients was relatively small. Secondly, as the patients in this work were all Asian, potential bias and influencing factors must be considered when the models are used for patients in Western countries. Therefore, multicenter, multiracial studies, especially international multicenter research are needed. Thirdly, the ultrasound measurement method adopted in this study may have certain obstacles in clinical application, and not every hospital has the conditions for ultrasound assessment of DA. Finally, more streamlined and efficient methods, and more advanced algorithms are needed to improve the models further.

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