Development of a Sex-Specific Risk Scoring System for the Prediction of Cognitively Normal People to Patients With Mild Cognitive Impairment (SRSS-CNMCI)

ObjectiveWe aimed to develop a sex-specific risk scoring system, abbreviated as SRSS-CNMCI, for the prediction of the conversion of cognitively normal (CN) people into patients with Mild Cognitive Impairment (MCI) to provide a reliable tool for the prevention of MCI.MethodsCN at baseline participants 61-90 years of age were selected from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database with at least one follow-up. Multivariable Cox proportional hazards models were used to identify the major risk factors associated with the conversion from CN to MCI and to develop the SRSS-CNMCI. Receiver operating characteristic (ROC) curve analysis was used to determine risk cutoff points corresponding to an optimal prediction. The results were externally validated, including evaluation of the discrimination and calibration in the Harvard Aging Brain Study (HABS) database.ResultsA total of 471 participants, including 240 female (51%) and 231 male participants (49%) aged from 61 to 90 years, were included in the study cohort. The final multivariable models and the SRSS-CNMCI included age, APOE e4, mini mental state examination (MMSE) and clinical dementia rating (CDR). The C-statistics of the SRSS-CNMCI were 0.902 in the female subgroup and 0.911 in the male subgroup. The cutoff point of high and low risks was 33% in the female subgroup, indicating that more than 33% female participants were considered to have a high risk, and more than 9% participants were considered to have a high risk in the male subgroup. The SRSS-CNMCI performed well in the external cohort: the C-statistics were 0.950 in the female subgroup and 0.965 in the male subgroup.ConclusionThe SRSS-CNMCI performs well in various cohorts and provides an accurate prediction and a generalization.

Automated summary

This study developed a sex-specific risk scoring system, SRSS-CNMCI, to predict the conversion of cognitively normal individuals to mild cognitive impairment and provide a reliable tool for MCI prevention. The system was validated in two independent databases and demonstrated good predictive performance.