Iodofiltic Acid I 123 (BMIPP) Fatty Acid Imaging Improves Initial Diagnosis in Emergency Department Patients With Suspected Acute Coronary Syndromes A Multicenter Trial

ObjectivesThe aim of this study was to assess the performance of β-methyl-p-[123I]-iodophenyl-pentadecanoic acid (BMIPP) single-photon emission computed tomography (SPECT) to detect acute coronary syndromes (ACS) in emergency department patients with chest pain.BackgroundEmergency department diagnosis of chest pain is problematic, often requiring prolonged observation and stress testing. BMIPP SPECT detects abnormalities in fatty acid metabolism resulting from myocardial ischemia, even many hours after symptom cessation.MethodsEmergency department patients with suspected ACS were enrolled at 50 centers. Patients received 5 mCi BMIPP within 30 h of symptom cessation. BMIPP SPECT images were interpreted semiquantitatively by 3 blinded readers. Initial clinical diagnosis was based on symptoms, initial electrocardiograms, and troponin, whereas the final diagnosis was based on all available data (including angiography and stress SPECT) but not BMIPP SPECT. Final diagnoses were adjudicated by a blinded committee as ACS, intermediate likelihood of ACS, or negative for ACS.ResultsA total of 507 patients were studied and efficacy was evaluated in 448 patients with sufficient data. The sensitivity of BMIPP by 3 blinded readers for a final diagnosis of ACS and intermediate likelihood of ACS was 71% (95% confidence interval [CI]: 64% to 79%), 74% (95% CI: 68% to 81%), and 69% (95% CI: 62% to 77%); the corresponding specificity of BMIPP was 67% (95% CI: 61% to 73%), 54% (95% CI: 48% to 60%), and 70% (95% CI: 64% to 76%). Compared with the initial diagnosis alone, BMIPP + initial diagnosis increased sensitivity from 43% to 81% (p < 0.001), negative predictive value from 62% to 83% (p < 0.001), and positive predictive value from 41% to 58% (p < 0.001), whereas specificity was unchanged (61% to 62%, p = NS).ConclusionsThe addition of BMIPP data to the initially available clinical information adds incremental value toward the early diagnosis of an ACS, potentially allowing determination of the presence or absence of ACS to be made earlier in the evaluation process. (Safety and Efficacy Iodofiltic Acid I 123 in the Treatment of Acute Coronary Syndrome [Zeus-ACS]; NCT00514501

Fine Mapping of Genetic Variants in BIN1, CLU, CR1 and PICALM for Association with Cerebrospinal Fluid Biomarkers for Alzheimer's Disease

Recent genome-wide association studies of Alzheimer's disease (AD) have identified variants in BIN1, CLU, CR1 and PICALM that show replicable association with risk for disease. We have thoroughly sampled common variation in these genes, genotyping 355 variants in over 600 individuals for whom measurements of two AD biomarkers, cerebrospinal fluid (CSF) 42 amino acid amyloid beta fragments (Aβ42) and tau phosphorylated at threonine 181 (ptau181), have been obtained. Association analyses were performed to determine whether variants in BIN1, CLU, CR1 or PICALM are associated with changes in the CSF levels of these biomarkers. Despite adequate power to detect effects as small as a 1.05 fold difference, we have failed to detect evidence for association between SNPs in these genes and CSF Aβ42 or ptau181 levels in our sample. Our results suggest that these variants do not affect risk via a mechanism that results in a strong additive effect on CSF levels of Aβ42 or ptau181

Detecting global and local hippocampal shape changes in Alzheimer's disease using statistical shape models

The hippocampus is affected at an early stage in the development of Alzheimer's disease (AD). With the use of structural magnetic resonance (MR) imaging, we can investigate the effect of AD on the morphology of the hippocampus. The hippocampal shape variations among a population can be usually described using statistical shape models (SSMs). Conventional SSMs model the modes of variations among the population via principal component analysis (PCA). Although these modes are representative of variations within the training data, they are not necessarily discriminative on labeled data or relevant to the differences between the sub-populations. We use the shape descriptors from SSM as features to classify AD from normal control (NC) cases. In this study, a Hotelling's T-2 test is performed to select a subset of landmarks which are used in PCA. The resulting variation modes are used as predictors of AD from NC. The discrimination ability of these predictors is evaluated in terms of their classification performances with bagged support vector machines (SVMs). Restricting the model to landmarks with better separation between AD and NC increases the discrimination power of SSM. The predictors extracted on the subregions also showed stronger correlation with the memory-related measurements such as Logical Memory, Auditory Verbal Learning Test (AVLT) and the memory subscores of Alzheimer Disease Assessment Scale (ADAS). Crown Copyright (C) 2011 Published by Elsevier Inc. All rights reserved