Chapter 5 126 In addition to the accuracy calculations of our clinical example addressing the three cognitive entities as well as possible to their appropriate policy, as debated above, there are more arguments to be found in literature to use a double threshold for the MoCA. First, a substantial number of MCI patients scoring below <21 on the MoCA are at very high risk of converting to MD, while those above this cut-off are considerably less at risk (Smith et al., 2007; Julayanont et al., 2014; Dautzenberg et al., 2021). Second, a double threshold for the MoCA also reduces the ‘uncertain test scores’ due to ‘random classification errors’. These outcomes result from the distribution of the different diagnostic groups in the middle range MoCA scores (Landsheer, 2020; Thomann et al., 2020). By applying an uncertainty interval, as these MoCA-scores are the most error prone, the PPV and NPV improve in the studied prevalence and become less dependent on the setting. Even if their study objective was not to identify the subthreshold state, the implementation is similar; applying an (uncertainty) interval improves the accuracy of the MoCA. Third, as mentioned in the introduction, several variables are found to be of importance in different clinical populations and these can lead to an inflated rate of FPs particularly older age and lower education (Carson et al., 2018; Thomann et al., 2020). Lifestyle and physical activity are found to significantly influence MoCA scores even more than age and education (Ihara et al., 2013; Innocenti et al., 2017). Although education ( Wong et al., 2015; Borland et al., 2017; Pinto et al., 2018), ethnicity ( Rossetti et al., 2011; Tan et al., 2014; Wong et al., 2015), race (Goldstein et al., 2014; Tan et al., 2014; O’Driscoll and Shaikh, 2017) and (rural) habitat (Goldstein et al., 2014; Hilgeman, Boozer and Davis, 2018) are known factors, others debate that these factors are better represented by ‘literacy in late life’ (Sisco et al., 2015). More important for our setting are the negative influence of substance abuse (Rojo-Mota et al., 2013; Pugh et al., 2018) or psychiatric diseases (Musso et al., 2014; Gierus and Mosiolek, 2015; Blair et al., 2016; Srisurapanont et al., 2016; Wu et al., 2017; Korsnes, 2020). The above enumeration shows that there are many reasons for heterogeneity affecting the MoCA score. It shows that a single cut-off rarely fits a pluriform clinical practice where many covariates influence the individual MoCA-score. A single cut-off is associated with substantially high rates of misclassification. Stratification was suggested for age and education as a solution (Oren et al., 2014; Wong et al., 2015; Borland et al., 2017). However, stratification of patients is impracticable if one needs to take all the possible confounders into account.
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