Diagnostic accuracy, reliability, and construct validity of the German quick mild cognitive impairment screen - Patrick Manser; Eling D. de Bruin (BMC Geriatrics, 2024)
Abstract Background Early detection of cognitive impairment is among the top research priorities aimed at reducing the global burden of dementia. Currently used screening tools have high sensitivity but lack specificity at their original cut-off, while decreasing the cut-off was repeatedly shown to improve specificity, but at the cost of lower sensitivity. In 2012, a new screening tool was introduced that aims to overcome these limitations – the Quick mild cognitive impairment screen (Qmci). The original English Qmci has been rigorously validated and demonstrated high diagnostic accuracy with both good sensitivity and specificity. We aimed to determine the optimal cut-off value for the German Qmci, and evaluate its diagnostic accuracy, reliability (internal consistency) and construct validity. Methods We retrospectively analyzed data from healthy older adults (HOA; n = 43) and individuals who have a clinical diagnosis of ‘mild neurocognitive disorder’ (mNCD; n = 37) with a biomarker supported characterization of the etiology of mNCD of three studies of the ‘Brain-IT’ project. Using Youden’s Index, we calculated the optimal cut-off score to distinguish between HOA and mNCD. Receiver operating characteristic (ROC) curve analysis was performed to evaluate diagnostic accuracy based on the area under the curve (AUC). Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated. Reliability (internal consistency) was analyzed by calculating Cronbach’s α. Construct validity was assessed by analyzing convergent validity between Qmci-G subdomain scores and reference assessments measuring the same neurocognitive domain. Results The optimal cut-off score for the Qmci-G was ≤ 67 (AUC = 0.96). This provided a sensitivity of 91.9% and a specificity of 90.7%. The PPV and NPV were 89.5% and 92.9%, respectively. Cronbach’s α of the Qmci-G was 0.71 (CI95% [0.65 to 0.78]). The Qmci-G demonstrated good construct validity for subtests measuring learning and memory. Subtests that measure executive functioning and/or visuo-spatial skills showed mixed findings and/or did not correlate as strongly as expected with reference assessments. Conclusion Our findings corroborate the existing evidence of the Qmci’s good diagnostic accuracy, reliability, and construct validity. Additionally, the Qmci shows potential in resolving the limitations of commonly used screening tools, such as the Montreal Cognitive Assessment. To verify these findings for the Qmci-G, testing in clinical environments and/or primary health care and direct comparisons with standard screening tools utilized in these settings are warranted.