Table 9.
Predictors of future decline
| Predictor | Measurement of decline | Statistical measurement | Patient group | Cross-validated? | Reference |
|---|---|---|---|---|---|
| Baseline temporal lobe measures | MMSE | P <.05 | MCI | [112] | |
| MCI to AD conversion | P < .05 | MCI | |||
| CDR-SB | P < .05 | CN, MCI, AD | |||
| Baseline temporal lobe measures | CDR-SB | AUC = 0.83, SEN = 87%, SPE = 66% | MCI | Yes | [163] |
| CSF biomarkers + FDG-PET ROIs | AUC = 0.70, SEN = 93%, SPE = 48% | ||||
| TL measures + CSF + FDG-PET ROIs | AUC = 0.83, SEN = 90%, SPE = 69% | ||||
| Baseline hippocampal, amygdala, temporal horn volume |
MMSE | β (P) = 0.14 (.04), 0.18 (.004), −0.2 (.003) | Pooled sample | [164] | |
| CDR-SB | β (P) = −0.19 (.005), −0.12 (.06), 0.2 (.005) | ||||
| Baseline hippocampal volume | MCI to AD conversion | Cohen d = 0.603 | MCI-nc vs MCI-c | [114] | |
| Baseline inferior temporal gyrus volume | Cohen d = 0.535 | ||||
| Baseline middle temporal gyrus volume | Cohen d = 0.529 | ||||
| Baseline entorhinal cortical volume | Cohen d = 0.493 | ||||
| Baseline ventricular expansion | MMSE, global CDR, CDR-SB | P <.05 | Pooled sample | [126] | |
| Baseline ventricular expansion | MMSE, global CDR, CDR-SB | P <.05 | Pooled sample | [127] | |
| Baseline right caudate volume | MMSE | P < .05 | Pooled sample | [130] | |
| MCI to AD conversion | P <.05 | ||||
| Baseline cortical thickness in ROIs | MCI to AD conversion | Accuracy = 76% | MCI | Yes | [165] |
| Baseline cortical thickness in ROIs | [147] | ||||
| Longitudinal cortical thickness | MCI to AD conversion | Accuracy = 81.7% | MCI | Yes | [157] |
| Baseline white matter hyperintensity volume | ADAS-cog | β (P) = 0.34 (.05) | Pooled sample | [166] | |
| MMSE | β (P) = −0.096 (<.001) | ||||
| Multiple ROI atrophy score | MMSE | r (P) = 0.39 (<.001) | MCI | [117] | |
| Structural phenotypic score | MCI to AD conversion | AUC = 0.77 | MCI | Yes | [118] |
| STAND score | CDR-SB | MCI, AD | [167] | ||
| MCI to AD conversion | Cox proportional hazards ratio = 2.6 | MCI | |||
| Log (t-tau/Aβ–42) | MCI to AD conversion | Cox proportional hazards ratio = 2.0 | |||
| SPARE-AD score | MCI to AD conversion | AUC = 0.734, SEN = 94.7%, SPE = 37.8% | MCI | Yes | [119] |
| MMSE | P <.05 | ||||
| FDG-PET hypermetabolic convergence index | MCI to AD conversion | Cox proportional hazards ratio = 7.38 | MCI | [85] | |
| FDG-PET HCI + hippocampal volume | Cox proportional hazards ratio = 36.72 | ||||
| FDG-PET sco | MCI to AD conversion | AUC = 0.75, sens – 57%, spe = 67% | MCI | [291] | |
| Aβ load | MCI to AD conversion | 75th vs 25th percentile Cox HR = 2.6 (P < .001) | MCI | [152] | |
| Baseline hippocampal volume | 25th vs 75th percentile Cox HR = 2.6 (P<.001) | ||||
| Baseline ADAS-cog (from meta-analysis) | ADAS-cog | Slope of disease progression = 5.49 points/yr, baseline five point increase in ADAS-cog effect on slope = 0.669/yr |
MCI, AD | [171] | |
| Baseline ADAS. Tree | MCI to AD conversion | P = 6.23E-10, AUC = 0.746 | MCI | Yes | [96] |
| Baseline MMSE | P = .0188, AUC = 0.589 | ||||
| Baseline hippocampal volume | CDR-SB, MMSE, LM delayed change |
r = −0.29, 0.29, 0.41 | MCI | [155] | |
| Baseline entorhinal volume | r = −0.17, 0.23, 0.34 | ||||
| Baseline retrosplenial volume | r = −0.43, 0.42, 0.35 | ||||
| Baseline entorhinal metabolism | r = −0.30, 0.38, 0.28 | ||||
| Baseline retrosplenial volume | r = −0.22, 0.47, 0.11 | ||||
| t-tau/Aβ−42 | r = 0.02, 0.08, −0.23 | ||||
| APOE ε4+ | Hippocampal volume change (P<.05). Multivariate model |
Coefficient of effect on annual change = −0.36 | MCI | [154] | |
| FDG-PET ROI-avg | Coefficient of effect on annual change = 9.3 | ||||
| CSF tau | Coefficient of effect on annual change = −8.7 | AD | |||
| FDG-PET ROIs | MCI to AD conversion | β (SE) = 1.00 (0.51), Cox HR = 2.72 | MCI | [173] | |
| AVLT | β (SE) = 1.46 (0.64), Cox HR = 4.30 | ||||
| FDG-PET ROIs | ADAS-cog | β (SE) = 1.26 (0.43) | |||
| p-tau181/Aβ–42 | β (SE) = 1.10 (0.53) | ||||
| Right entorhinal cortical volume | MCI to AD conversion | Prediction accuracy (95% CI) = 68.5% (59.5, 77.4) | MCI | Yes | [161] |
| TMT-B test | Prediction accuracy = 64.6% (55.5, 73.4) | ||||
| p-tau181/Aβ−42, hippocampal volume, TMT–B, age | Prediction accuracy = 76.3 (68.4, 84.2) | ||||
| AVLT-delayed, LM-delayed, left middle temporal lobe thickness |
MCI to AD conversion | AUC = 0.80 | MCI | Yes | [294] |
| Baseline multi-modal multi-task learning: MR, FDG- PET CSF |
MMSE | r = 0.511 | MCI | Yes | [239] |
| ADAS-cog | r = 0.531 | MCI | |||
| Multi-modality disease marker | MCI to AD conversion | MCI | Yes | [247] | |
| Biological (baseline) | AUC = 0.5292 | ||||
| Imaging (baseline) | AUC = 0.7378 | ||||
| Imaging (longitudinal) | AUC = 0.7911 | ||||
| Neuropsych (baseline) | AUC = 0.6693 | ||||
| Neuropsych (longitudinal) | AUC = 0.7385 | ||||
| Combined modalities | AUC = 0.7667 | ||||
| Disease state index | MCI to AD conversion | AUC = 0.752 | MCI | [252] | |
| Disease state index | MCI to AD conversion | Prediction accuracy = 68.6% | All MCI | [253] | |
| Prediction accuracy = 84.4% | MCI – strong evidence of AD pathology |
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| Prediction accuracy = 93.7% | MCI – very strong evidence of AD pathology |
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| Baseline and longitudinal multimodal data | MCI to AD conversion | AUC = 0.768, ACC = 78.4%, SEN = 79%, SPE = 78% |
MCI | Yes | [239] |