Protocol for validating an algorithm to identify neurocognitive disorders in Canadian Longitudinal Study on Aging...

Area of Science:

• Gerontology
• Neuroscience
• Epidemiology

Background:

• Neurocognitive disorders (NCDs) pose a significant public health challenge.
• Accurate disease ascertainment is crucial for epidemiological research on NCDs.
• Population-based studies often require cost-effective methods for diagnosis.

Purpose of the Study:

• To validate a neurocognitive disorder (NCD) ascertainment algorithm for the Canadian Longitudinal Study on Aging (CLSA).
• To optimize the use of routinely acquired CLSA data for identifying participants with NCDs.
• To establish a reliable and cost-effective method for NCD diagnosis in a large cohort.

Main Methods:

• Recruitment of up to 600 CLSA participants stratified by NCD likelihood.
• Clinical assessments and informant interviews to establish a preliminary NCD diagnosis.
• Central Review Panel final categorization (no NCD, mild NCD, major NCD) serving as the gold standard.
• Validation of an NCD ascertainment algorithm using Weighted Kappa, sensitivity, specificity, C-statistic, and phi coefficient.

Main Results:

• The study aims to determine the accuracy of the NCD ascertainment algorithm against a gold standard diagnosis.
• Primary measure of agreement will be Weighted Kappa statistics.
• Sensitivity, specificity, C-statistic, and phi coefficient will also be estimated to assess algorithm performance.

Conclusions:

• Validated algorithms can accurately identify neurocognitive disorders (NCDs) in population-based studies.
• This approach enhances the efficiency and cost-effectiveness of NCD research.
• Findings will improve the understanding of NCD epidemiology and burden within the CLSA cohort.