Using Continuous Glucose Monitoring Values for Bolus Size Calculation in Smart Multiple Daily Injection Systems: No Negative Impact on Post-bolus Glycemic Outcomes Found in Real-World Data.

BACKGROUND: Recent evidence shows that it may be safe to estimate bolus sizes based on continuous glucose monitoring (CGM) rather than blood glucose (BG) values using glycemic trend-adjusted bolus calculators. Users may already be doing this in the real world, though it is unclear whether this is safe or effective for calculators not employing trend adjustment. METHODS: We assessed real-world data from a smart multiple daily injections (MDIs) device users with a CGM system, hypothesizing that four-hour post-bolus outcomes using CGM values are not inferior to those using BG values. Our data set included 184 users and spanned 18 months with 79 000 bolus observations. We tested differences using logistic regression predicting CGM or BG value usage based on outcomes and confirmed initial results using a mixed model regression accounting for within-subject correlations. RESULTS: Comparing four-hour outcomes for bolus events using CGM and BG values revealed no differences using our initial approach (P > .183). This finding was confirmed by our mixed model regression approach in all cases (P > .199), except for times below range outcomes. Higher times below range were predictive of lower odds of CGM-based bolus calculations (OR = 0.987, P < .0001 and OR = 0.987, P = .0276, for time below 70 and 54 mg/dL, respectively). CONCLUSIONS: We found no differences in four-hour post-bolus glycemic outcomes when using CGM or BG except for time below range, which showed evidence of being lower for CGM. Though preliminary, our results confirm prior findings showing non-inferiority of using CGM values for bolus calculation compared with BG usage in the real world.

Quantifying Brain [18F]FDG Uptake Noninvasively by Combining Medical Health Records and Dynamic PET Imaging Data.

Full quantification of regional cerebral metabolic rate of glucose (rCMRglu) with [18F]fluorodeoxy-glucose ([18F]FDG) positron emission tomography (PET) imaging requires measurement of an arterial input function (AIF) curve, which is obtained with an invasive arterial blood sampling procedure during the scan. We previously proposed a non-invasive simultaneous estimation (nSIME) method that quantifies binding of a PET radioligand by combining individual electronic health records information and a pharmacokinetic AIF (PK-AIF) model. Initially applied only to [11C]DASB data, in this study we validate nSIME for a different radioligand, [18F]FDG, adapting the algorithm to the specific distribution and metabolism of this radioligand. We evaluate the impact of the PK-AIF model, the number of [18F]FDG-specific soft constraints, and the type of predictive strategy. The accuracy of nSIME is then compared to a population-based approach. All analyses are conducted on 67 [18F]FDG PET scans with arterial blood data available for comparison. nSIME performance is optimal for [18F]FDG when using the PK-AIF model, two soft constraints, and an aggregate model to predict the soft constraint values. Higher correlation and lower Bland-Altman spread against gold standard rCMRglu values based on arterial blood measurements are observed for nSIME (r = 0.83, spread = 1.55) compared to the population-based approach (r = 0.77, spread = 2.12). nSIME provides a data-driven estimation of both amplitude and shape of the AIF curve at the individual level and potentially enables non-invasive quantification of PET data across radioligands, avoiding the need for arterial blood sampling.

Toward noninvasive quantification of brain radioligand binding by combining electronic health records and dynamic PET imaging data.

Quantitative analysis of positron emission tomography (PET) brain imaging data requires a metabolite-corrected arterial input function (AIF) for estimation of distribution volume and related outcome measures. Collecting arterial blood samples adds risk, cost, measurement error, and patient discomfort to PET studies. Minimally invasive AIF estimation is possible with simultaneous estimation (SIME), but at least one arterial blood sample is necessary. In this study, we describe a noninvasive SIME (nSIME) approach that utilizes a pharmacokinetic input function model and constraints derived from machine learning applied to an electronic health record database consisting of "long tail" data (digital records, paper charts, and handwritten notes) that were collected ancillary to the PET studies. We evaluated the performance of nSIME on 95 [(11)C]DASB PET scans that had measured AIFs. The results indicate that nSIME is a promising alternative to invasive AIF measurement. The general framework presented here may be expanded to other metabolized radioligands, potentially enabling quantitative analysis of PET studies without blood sampling. A glossary of technical abbreviations is provided at the end of this paper.

Non-invasive quantification of brain [¹8F]-FDG uptake by combining medical health records and dynamic PET imaging data.

Quantification of regional cerebral metabolic rate of glucose (rCMRglu) via positron emission tomography (PET) imaging requires measuring the arterial input function (AIF) via invasive arterial blood sampling. In this study we describe a non-invasive approach, the non-invasive simultaneous estimation (nSIME), for the estimation of rCMRglu that considers a pharmacokinetic input function model and constraints derived from machine learning applied to a fusion of individual medical health records and dynamic [(18)F]-FDG-PET brain images data. The results obtained with our data indicate potential for future clinical application of nSIME, with correlation measures of 0.87 for rCMRglu compared to quantification with full arterial blood sampling.

LOCALLY WEIGHTED TOTAL VARIATION DENOISING FOR RINGING ARTIFACT SUPPRESSION IN PET RECONSTRUCTION USING PSF MODELING.

Iterative reconstruction with point spread function (PSF) modeling improves contrast recovery in positron emission tomography (PET) images, but also introduces ringing artifacts and over enhancement that is contrast and object size dependent. Mitigation of these artifacts is crucial for clinical and research purposes. In this work we introduce a new iterative regularized reconstruction method that incorporates locally-weighted total variation denoising designed to suppress artifacts induced by PSF modeling. The reconstruction method is evaluated on a simulated cylindrical phantom and preliminary results show that ringing artifacts are suppressed while contrast recovery is maintained.

MULTIMODAL CLASSIFICATION OF DEMENTIA USING FUNCTIONAL DATA, ANATOMICAL FEATURES AND 3D INVARIANT SHAPE DESCRIPTORS.

Multimodality classification of Alzheimer's disease (AD) and its prodromal stage, Mild Cognitive Impairment (MCI), is of interest to the medical community. We improve on prior classification frameworks by incorporating multiple features from MRI and PET data obtained with multiple radioligands, fluorodeoxyglucose (FDG) and Pittsburg compound B (PIB). We also introduce a new MRI feature, invariant shape descriptors based on 3D Zernike moments applied to the hippocampus region. Classification performance is evaluated on data from 17 healthy controls (CTR), 22 MCI, and 17 AD subjects. Zernike significantly outperforms volume, accuracy (Zernike to volume): CTR/AD (90.7% to 71.6%), CTR/MCI (76.2% to 60.0%), MCI/AD (84.3% to 65.5%). Zernike also provides comparable and complementary performance to PET. Optimal accuracy is achieved when Zernike and PET features are combined (accuracy, specificity, sensitivity), CTR/AD (98.8%, 99.5%, 98.1%), CTR/MCI (84.3%, 82.9%, 85.9%) and MCI/AD (93.3%, 93.6%, 93.3%).

Prediction of extubation failure for neonates with respiratory distress syndrome using the MIMIC-II clinical database.

Extubation failure (EF) is an ongoing problem in the neonatal intensive care unit (NICU). Nearly 25% of neonates fail their first extubation attempt, requiring re-intubations that are associated with risk factors and financial costs. We identified 179 mechanically ventilated neonatal patients that were intubated within 24 hours of birth in the MIMIC-II intensive care database. We analyzed data from the patients 2 hours prior to their first extubation attempt, and developed a prediction algorithm to distinguish patients whose extubation attempt was successful from those that had EF. From an initial list of 57 candidate features, our machine learning approach narrowed down to six features useful for building an EF prediction model: monocyte cell count, rapid shallow breathing index, fraction of inspired oxygen (FiO(2)), heart rate, PaO(2)/FiO(2) ratio where PaO(2) is the partial pressure of oxygen in arterial blood, and work of breathing index. Algorithm performance had an area under the receiver operating characteristic curve (AUC) of 0.871 and sensitivity of 70.1% at 90% specificity.

Brain tissue selection procedures for image derived input functions derived using independent components analysis.

Absolute quantification of positron emission tomography (PET) data requires invasive blood sampling in order to obtain the arterial input function (AIF). This procedure involves considerable costs and risks. A less invasive approach is to estimate the AIF directly from images, known as an image derived input function (IDIF). One promising method, EPICA, extracts IDIF by applying independent components analysis (ICA) on dynamic PET data from the entire brain. EPICA requires exclusion of non-brain voxels from the PET images, which is achieved by using a brain mask prior to ICA. Including the entire brain in the mask may degrade the performance of ICA due to noise, artifacts and confounding information. We applied EPICA to 3 [(18)F]FDG and 3 [(11)C]WAY data sets and investigated if altering the brain mask by including or excluding tissue structures improves EPICA performance. EPICA applied to whole brain data yields poor performance but with the appropriate brain mask IDIF curves approximate the AIF well. Different tissue structures are important for different radiotracers suggesting that the kinetics of the radiotracer and its diffusion characteristics in the brain influence IDIF estimation with ICA.

Hydrocortisone responsiveness in Gulf War veterans with PTSD: effects on ACTH, declarative memory hippocampal [(18)F]FDG uptake on PET.

Neuroendocrine, cognitive and hippocampal alterations have been described in Gulf War (GW) veterans, but their inter-relationships and significance for posttraumatic stress disorder (PTSD) have not been described. Hydrocortisone (Hcort) was administered to GW veterans with (PTSD+ n=12) and without (PTSD- n=8) chronic PTSD in a randomized, placebo-controlled, double-blind challenge. Changes in plasma ACTH, memory, and hippocampal [(18)F]FDG uptake on positron emission tomography were assessed. The low-dose dexamethasone suppression test was also administered. The PTSD+ group showed greater cortisol and ACTH suppression, reflecting greater peripheral glucocorticoid receptor (GR) responsiveness, and did not show an Hcort-induced decrement in delayed recall or retention. The groups had comparable relative regional hippocampal [(18)F]FDG uptake at baseline, but only the PTSD- group had an Hcort-associated decrease in hippocampal [(18)F]FDG uptake. Asymmetry in hippocampal hemispheric volumes differed between PTSD+ and PTSD- groups. This asymmetry was associated with cortisol, ACTH, retention and functional hippocampal asymmetry before, but not after, Hcort administration. Differences in brain metabolic responses between GW veterans with and without PTSD may reflect differences in peripheral and central GR responsiveness.

Pittsburgh compound B (11C-PIB) and fluorodeoxyglucose (18 F-FDG) PET in patients with Alzheimer disease, mild cognitive impairment, and healthy controls.

Amyloid load in the brain using Pittsburgh compound B ((11)C-PIB) positron emission tomography (PET) and cerebral glucose metabolism using fluorodeoxyglucose ((18)F-FDG) PET were evaluated in patients with mild Alzheimer disease (AD, n = 18), mild cognitive impairment (MCI, n = 24), and controls (CTR, n = 18). ( 11)C-PIB binding potential (BP(ND)) was higher in prefrontal cortex, cingulate, parietal cortex, and precuneus in AD compared to CTR or MCI and in prefrontal cortex for MCI compared to CTR. For (18)F-FDG, regional cerebral metabolic rate for glucose (rCMRGlu) was decreased in precuneus and parietal cortex in AD compared to CTR and MCI, with no MCI-CTR differences. For the AD-CTR comparison, precuneus BP(ND) area under the receiver operating characteristic (ROC) curve (AUC) was 0.938 and parietal cortex rCMRGlu AUC was 0.915; for the combination, AUC was 0.989. ( 11)C-PIB PET BP(ND) clearly distinguished diagnostic groups and combined with (18)F-FDG PET rCMRGlu, this effect was stronger. These PET techniques provide complementary information in strongly distinguishing diagnostic groups in cross-sectional comparisons that need testing in longitudinal studies.

Higher serotonin 1A binding in a second major depression cohort: modeling and reference region considerations.

BACKGROUND: Serotonin 1A receptors (5-HT(1A)) are implicated in major depressive disorder (MDD). We previously reported higher 5-HT(1A) binding potential (BP(F)) in antidepressant naive MDD subjects compared with control subjects, while other studies report lower BP(ND). Discrepancies can be related to differences in study population or methodology. We sought to replicate our findings in a novel cohort and determine whether choice of reference region and outcome measure could explain discrepancies. METHODS: Nine new control subjects and 22 new not recently medicated (NRM) MDD subjects underwent positron emission tomography. BP(F) and BP(ND) were determined using a metabolite and free fraction corrected arterial input function. BP(ND) was also determined using cerebellar gray matter (CGM) and cerebellar white matter (CWM) reference regions as input functions. RESULTS: BP(F) was higher in the new NRM cohort (p = .037) compared with new control subjects, comparable to the previous cohort (p = .04). Cohorts were combined to examine the reference region and outcome measure. BP(F) was higher in the NRM compared with control subjects (p = .0001). Neither BP(ND) using CWM (p = .86) nor volume of distribution (V(T)) (p = .374) differed between groups. When CGM was used, the NRM group had lower 5-HT(1A) BP(ND) compared with control subjects (p = .03); CGM V(T) was higher in NRM compared with control subjects (p = .007). CONCLUSIONS: Choice of reference region and outcome measure can produce different 5-HT(1A) findings. Higher 5-HT(1A) BP(F) in MDD was found with the method with fewest assumptions about nonspecific binding and a reference region without receptors.

Voxel-based analysis of 11C-PIB scans for diagnosing Alzheimer's disease.

UNLABELLED: The positron emission tomography (PET) radioligand N-methyl-11C-2-(4-methylaminophenyl)-6-hydroxybenzothiazole (also known as 11C-6-OH-BTA-1 or 11C-PIB) binds to amyloid-beta (Abeta), which accumulates pathologically in Alzheimer's disease (AD). Although 11C-PIB accumulation is greater in patients with AD than in healthy controls at a group level, the optimal method for discriminating between these 2 groups has, to our knowledge, not been established. We assessed the use of data-determined standardized voxels of interest (VOIs) to improve the classification capability of 11C-PIB scans on patients with AD. METHODS: A total of 16 controls and 14 AD age-matched patients were recruited. All subjects underwent a 11C-PIB scan and structural MRI. Binding potential (a measure of amyloid burden) was calculated for each voxel using the Logan graphical method with cerebellar gray matter as the reference region. Voxel maps were then partial-volume corrected and spatially normalized by MRI onto a standardized template. The subjects were divided into 2 cohorts. The first cohort (control, 12; AD, 9) was used for statistical parametric mapping analysis and delineation of data-based VOIs. These VOIs were tested in the second cohort (control, 4; AD, 5) of subjects. RESULTS: Statistical parametric mapping analysis revealed significant differences between control and AD groups. The VOI map determined from the first cohort resulted in complete separation between the control and the AD subjects in the second cohort (P < 0.02). Binding potential values based on this VOI were in the same range as other reported individual and mean cortical VOI results. CONCLUSION: A standardized VOI template that is optimized for control or AD group discrimination provides excellent separation of control and AD subjects on the basis of 11C-PIB uptake. This VOI template can serve as a potential replacement for manual VOI delineation and can eventually be fully automated, facilitating potential use in a clinical setting. To facilitate independent analysis and validation with more and a broader variety of subjects, this VOI template and the software for processing will be made available through the Internet.