Introduction In a confirmatory diagnostic accuracy study, sensitivity and specificity are considered as co-primary endpoints. For the sample size calculation, the prevalence of the target population must be taken into account to obtain a representative sample. In this context, a general problem arises. With a low or high prevalence, the study may be overpowered in one subpopulation. One further issue is the correct pre-specification of the true prevalence. With an incorrect assumption about the prevalence, an over- or underestimated sample size will result. Methods To obtain the desired power independent of the prevalence, a method for an optimal sample size calculation for the comparison of a diagnostic experimental test with a prespecified minimum sensitivity and specificity is proposed. To face the problem of an incorrectly pre-specified prevalence, a blinded one-time re-estimation design of the sample size based on the prevalence and a blinded repeated re-estimation design of the sample size based on the prevalence are evaluated by a simulation study. Both designs are compared to a fixed design and additionally among each other. Results The type I error rates of both blinded re-estimation designs are not inflated. Their empirical overall power equals the desired theoretical power and both designs offer unbiased estimates of the prevalence. The repeated re-estimation design reveals no advantages concerning the mean squared error of the re-estimated prevalence or sample size compared to the one-time re-estimation design. The appropriate size of the internal pilot study in the one-time re-estimation design is 50% of the initially calculated sample size. Conclusions A one-time re-estimation design of the prevalence based on the optimal sample size calculation is recommended in single-arm diagnostic accuracy studies.

A model to classify the difficulty of videolaryngoscopic tracheal intubation has yet to be established. The videolaryngoscopic intubation and difficult airway classification (VIDIAC) study aimed to develop one based on variables associated with difficult videolaryngoscopic tracheal intubation. We studied 374 videolaryngoscopic tracheal intubations in 320 adults scheduled for ear, nose and throat or oral and maxillofacial surgery, for whom airway management was expected to be difficult. The primary outcome was whether an anaesthetist issued a `difficult airway alert´after videolaryngoscopy. An alert was issued after 183 (49%) intubations. Random forest and lasso regression analysis selected six intubation-related variables associated with issuing an alert: impaired epiglottic movement; increased lifting force; direct epiglottic lifting; vocal cords clearly visible; vocal cords not visible; and enlarged arytenoids. Internal validation was performed by a 10-fold cross-validation, repeated 20 times. The mean (SD or 95%CI) area under the receiver operating characteristic curve was 0.92 (0.05) for the cross validated coefficient model and 0.92 (0.89-0.95) for a simplified unitary score (VIDIAC score with component values of À1 or 1 only). The calibration belt for the coefficient model was consistent with observed alert probabilities, from 0% to 100%, while the unitary VIDIAC score overestimated probabilities < 20% and underestimated probabilities > 70%. Discrimination of the VIDIAC score for patients more or less likely to be issued an alert was better than discrimination by the Cormack-Lehane classification, with mean (95%CI) areas under the receiver operating characteristic curve of 0.92 (0.89-0.95) vs. 0.75 (0.70-0.80), respectively, p < 0.001. Our model and score can be used to calculate the probabilities of difficult airway alerts after videolaryngoscopy.

Background Mean kurtosis (MK), one of the parameters derived from diffusion kurtosis imaging (DKI), has shown increased sensitivity to tissue microstructure damage in several neurological disorders. Methods Thirty-seven patients with relapsing-remitting MS and eleven healthy controls (HC) received brain imaging on a 3T MR scanner, including a fast DKI sequence. MK and mean diffusivity (MD) were measured in the white matter of HC, normal-appearing white matter (NAWM) of MS patients, contrast-enhancing lesions (CE-L), FLAIR lesions (FLAIR-L) and black holes (BH). Results Overall 1529 lesions were analyzed, including 30 CE-L, 832 FLAIR-L and 667 BH. Highest MK values were obtained in the white matter of HC (0.814 ± 0.129), followed by NAWM (0.724 ± 0.137), CE-L (0.619 ± 0.096), FLAIR-L (0.565 ± 0.123) and BH (0.549 ± 0.12). Lowest MD values were obtained in the white matter of HC (0.747 ± 0.068 10−3mm2/sec), followed by NAWM (0.808 ± 0.163 10−3mm2/sec), CE-L (0.853 ± 0.211 10−3mm2/sec), BH (0.957 ± 0.304 10−3mm2/sec) and FLAIR-L (0.976 ± 0.35 10−3mm2/sec). While MK differed significantly between CE-L and non-enhancing lesions, MD did not. Conclusion MK adds predictive value to differentiate between MS lesions and might provide further information about diffuse white matter injury and lesion microstructure.

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