Abstract
Objective: We aim to develop a sex-specific risk scoring system for predicting cognitive normal (CN) to mild cognitive impairment (MCI), abbreviated SRSS-CNMCI, to provide a reliable tool for the prevention of MCI.Methods: Participants aged 61-90 years old with a baseline diagnosis of CN and an endpoint diagnosis of MCI were screened from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database with at least one follow-up. Multivariable Cox proportional hazards models were used to identify risk factors associated with conversion from CN to MCI and to build risk scoring systems for male and female groups. Receiver operating characteristic (ROC) curve analysis was applied to determine the risk probability cutoff point corresponding to the optimal prediction effect. We ran an external validation of the discrimination and calibration based on the Harvard Aging Brain Study (HABS) database.Results: A total of 471 participants, including 240 women (51%) and 231 men (49%), aged 61 to 90 years, were included in the study cohort for subsequent primary analysis. The final multivariable models and the risk scoring systems for females and males included age, APOE ε4, Mini-Mental State Examination (MMSE) and Clinical Dementia Rating (CDR). The scoring systems for females and males revealed C statistics of 0.902 (95% CI 0.840-0.963) and 0.911 (95% CI 0.863-0.959), respectively, as measures of discrimination. The cutoff point of high and low risk was 33% in females, and more than 33% was considered high risk, while more than 9% was considered high risk for males. The external validation effect of the scoring systems was good: C statistic 0.950 for the females and C statistic 0.965 for the males. Conclusions: Our parsimonious model accurately predicts conversion from CN to MCI with four risk factors and can be used as a predictive tool for the prevention of MCI.
Development and Validation of a Risk Scoring System for Severe Acute Lower Gastrointestinal Bleeding