Table 2.
CN and MCI Classification Results for Iterative Models and the Stepwise Model
| Model | AUC + CI | ACC (%) | Sensitivity (%) | Specificity (%) |
|---|---|---|---|---|
| Covariates | .604 (.521-.688) | 60.1 | 68.1 | 53.0 |
| Covariates + Cognition | .832 (.771-.893) | 78.4 | 73.1 | 83.1 |
| Covariates + MRI | .819 (.758-.880) | 74.3 | 68.8 | 79.3 |
| Covariates + Cognition + MRI | .896 (.851-.941) | 83.3 | 76.7 | 89.1 |
| Covariates + Cognition + MRI + CSF | .901 (.857-.945) | 83.0 | 76.7 | 88.5 |
| Covariates + Cognition + MRI + CSF + ApoE | .908 (.867-.950) | 84.7 | 78.0 | 90.7 |
| Stepwise Model | .868 (.817-.918) | 76.6 | 67.6 | 84.7 |
ACC = accuracy; ApoE = Apolipoprotein ε4 status; AUC = area under the curve; CI = confidence interval (95%). Covariates included age, sex, and education. Iterative models used the p-tau181p CSF biomarker. The bold in the upper part of the table denotes the model that had the highest classification accuracy and a significant increase in the F-value relative to less complex models.