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P2‐192: Leukoencephalopathy and microhemorrhages
Author(s) -
Costa Sonia,
Conceição Carla,
Valverde Ana
Publication year - 2011
Publication title -
alzheimer's and dementia
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.713
H-Index - 118
eISSN - 1552-5279
pISSN - 1552-5260
DOI - 10.1016/j.jalz.2011.05.1077
Subject(s) - medicine , leukoencephalopathy , cerebral amyloid angiopathy , encephalopathy , lumbar puncture , pathology , papilledema , dementia , radiology , disease , cerebrospinal fluid
Background: While the Alzheimer’s disease Assessment Scale-Cognitive Subscale (ADAS-Cog) is commonly used to measure cognitive decline in probable Alzheimer’s disease (pAD) and mild cognitive impairment (MCI) patients, rates of decline vary. We previously introduced a single index, Hypometabolic Convergence Index (HCI), to characterize the extent to which the pattern and magnitude of cerebral hypometabolism in a person’s fluorodeoxyglucose-positron emission tomography (FDGPET) image corresponds to that in pAD patients. Here, we used data from ADNI to examine the extent to which 1) a person’s baseline HCI predicts cognitive decline using the ADAS-Cog, Mini-Mental State Exam (MMSE), Clinical Dementia Rating-sum of boxes (CDR-SB) and Auditory-Verbal Learning Test (AVLT-Total), and 2) the HCI could be used to enrich clinical trials for clinical decline and reduce the number of patients needed to detect a treatment’s clinical effects. Methods: Baseline HCIs were computed for 120 MCI and 54 mild AD patients who had up to 24-month data. We first characterized the extent to which HCIs correlated with 12-month and 24-month clinical declines.We then estimated the sample sizes needed to detect an AD-slowing treatment’s effects on ADAS-Cog before/after enrichment for those patients with HCIs greater than the predetermined threshold of 13.82 for pAD and 8.19 for MCI (Chen et al., 2011, Neuroimage) with 80% power, 0.05 type-I error and a 25% treatment effect. Results: In pAD, HCIs correlated (p < 1⁄4 0.05) with subsequent ADAS-Cog, MMSE, CDR-SB and AVLT-Total 12month(24-month) decline (r 1⁄4 0.42(0.47), -0.35(-0.55), 0.28(0.29), -0.40(-0.43), respectively). InMCI, HCI also correlated with subsequent decline at 12-month(24-month) (r 1⁄4 0.33(0.32), -0.43(-0.50), 0.29(0.37), -0.17(-0.32), respectively). HCI enrichment is estimated to reduce the number of pAD patients needed per treatment arm to detect an AD-slowing treatment’s effect on ADAS-Cog from 406 to 243 in a 12-month trial and from 168 to 137 in a 24-month trial. Similarly, it is estimated to reduce the number ofMCI patients from 1,718 to 749 in a 12-month trial and from 926 to 374 in a 24-month trial. Conclusions: Baseline HCIs could be used to predict subsequent clinical declines in pAD andMCI patients and to reduce the number of patients needed to detect an AD-slowing treatment’s effects in randomized clinical trials.