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Placenta accreta index and morbidly adherent placenta: Methodological issues on reliability, accuracy and prediction

I read the paper by Agarwal S et al. published in the May 2021 issue of Ultrasound. The authors aimed to estimate the level of interobserver agreement in the calculation of placenta accreta index (PAI) as well as to evaluate the accuracy of PAI in prediction of morbidly adherent placenta. They included 45 pregnant women with at least one previous Caesarean section. The accuracy of the PAI and the level of interrater agreement was analyzed applying intraclass correlation efficient (ICC) and Cohen’s kappa. Based on their results, the PAI showed nearly 90% sensitivity, specificity and the predictive values. Interrater agreement in calculation of PAI by the two radiologists was perfect with an intraclass correlation efficient of 0.95. An easy-to-use morbid adherent placenta score was also predicted to simplify the results of PAI, which showed moderate agreement (j1⁄4 0.74). First, Cohen’s kappa to assess reliability has two important limitations as follows: the value of kappa extremely depends on the prevalence in each category, which means it can be possible to have different k values having the same percentage for both concordant and discordant cells. Table 1 shows that in both (a) and (b) situations, the prevalence of concordant cells are 90% and of discordant cells, 10%; however, we get different kappa values (0.4 as moderate and 0.8 as very good, respectively). Kappa value also depends on the number of categories. Second, sensitivity and specificity are appropriate indices in public health, and both positive and negative predictive values (PPV and NPV) are influenced by prevalence of the outcome. Therefore, I suggest the authors calculate the positive and negative likelihood ratios (LR), since they are not influenced by prevalence of the disease. Third, the aim of clinical prediction positively impacts clinical decision making and subsequent patient outcomes. For prediction of an outcome, we must assess the interactions between predictors. Without assessing interaction, any result is imprecise and consequently, diminishes the clinical value of prediction models. Finally, assessing internal and external validity is recommended. Using internal