Neuropathology-based risk scoring for dementia diagnosis in the elderly

Abstract

Current neuropathologic consensus criteria for diagnosis of dementia yield a classification of processes that likely contributed to dementia in that individual. While dementia diagnosis currently relies on clinical criteria, practicing neuropathologists and researchers might benefit from a simple, accurate risk scoring protocol for the neuropathologic diagnosis of dementia. Using 232 consecutive autopsies from the population-based Adult Changes in Thought study, we developed two logistic regression-based risk scoring systems; one solely using neuropathologic measures and a second additionally including demographic information. Inverse-probability weighting was used to adjust for inherent selection bias in autopsy-based studies of dementing illnesses. Both systems displayed high levels of predictive accuracy; bias-adjusted area-under-the-curve statistics were 0.78 (95% CI 0.71, 0.85) and 0.87 (95% CI 0.83, 0.92), indicating improved performance with the inclusion of demographic characteristics, specifically age and birth cohort information. Application of the combined neuropathology/demographic model yielded bias-adjusted sensitivity and specificity of 81% each. In contrast, application of NIA-Reagan criteria yielded sensitivity and specificity of 53% and 84%. Our proposed scoring systems provide neuropathologists with tools to make a diagnosis, and interpret their diagnosis in the light of known sensitivity and specificity estimates. Evaluation in independent samples will be important to verify our findings.

Figure 1

Receiver operating characteristic curves for the bootstrap-adjusted NP only and NPD risk scoring models. Areas under the curve (AUC) statistics are 0.78 (95% CI 0.71, 0.85) and 0.87 (95% CI 0.83, 0.92) for the NP only and NPD models, respectively.