![]() For example, many cognitive tasks in the MoCA (e.g., executive, memory, abstraction) were inadequate for stroke patients with higher education. A closer examination of the findings supports the importance of education stratification in ROC analysis. On the other hand, the MoCA generally showed adequate sensitivity and specificity for stroke patients. Further evidence is warranted in Asia to determine whether the addition of a visual processing speed task can improve the detection accuracy of the MMSE and MoCA in stroke settings. This aligns with recent studies suggesting visual processing speed as an underlying cognitive function that affects performance in other cognitive domains in neurocognitive disorders. , sensitivity improved by approximately 20% after a visuomotor processing speed test was added in the ROC analysis. The extent to which these factors are statistically significant remained a question because of the limited number of studies ( N = 4) nevertheless, high dropout rate can erroneously estimate PSCI, e.g., in aging studies, dropouts were prevalent among individuals with worse white matter integrity. These studies reported large sample sizes ( N = 229–400), over 50% dropout rate at follow-up, and younger patients. In other words, despite their equivalence, some studies found both screening tests to be inadequate for stroke patients. Studies that directly compared the MMSE and MoCA found them to be equivalent in detecting PSCI, but at varying accuracy levels. Methods for education adjustment across studies. Another study showed fair SE (70%) but good SP (82%). Two of the three studies that examined the NINDS-CNS 5 demonstrated adequate sensitivity (82–92%) and specificity (67–68%). Only one study showed poor specificity, attributable to the ceiling effect among patients with higher education levels. Out of the nine studies that examined the MoCA, six reported adequate sensitivity (78–97%) and specificity (64–90%). Studies that found equivalent detection accuracy for the MMSE and MoCA also recruited older patients (see Table 1). reported poor sensitivity for both tests (MMSE = 68%, MoCA = 64%). Detection accuracy improved after a processing speed test was added in the ROC analysis (sensitivity = 97–98%, specificity = 76–78%). , there was a substantial difference in sensitivity between the optimal (MMSE = 61%, MoCA = 69%) and recommended (MMSE = 71%, MoCA = 78%) cutoff scores. However, only two studies met the detection accuracy standard of 80% sensitivity and 60% specificity, as suggested by Stolwyk et al. In brief, increasing evidence reveals that sociocultural considerations are indispensable in interpreting the results of the MMSE and MoCA.įour studies that compared the MMSE and MoCA showed equivalent sensitivity and specificity to identify PSCI. Furthermore, it is uncertain which cutoffs should be used in societies with greater educational disparities. This has a significant clinical impact, as many Asian studies report inadequate detection accuracy using the one additional point recommendation for the MoCA for patients with <12 years of education. For example, education stratification in receiver operating characteristics (ROCs) was rarely applied. However, only a few studies maintained methodological rigor in examining the optimal clinical cutoff for stroke patients. Preliminary evidence in Asia suggests that the MoCA is more sensitive than the MMSE in predicting cognitive deficits after stroke. Rather, optimal values were shown to range from 19 to 27, conditional on whether screening was conducted in the acute or chronic phase of stroke. Many studies have found the cutoff of 26 in the MoCA to be inadequate in addressing cognitive impairments in stroke settings. In addition to the inherent limitations of the MMSE and MoCA, it is also critical to select a valid cutoff score for PSCI due to its influence on detection accuracy. ![]()
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