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BackgroundNonalcoholic fatty liver disease (NAFLD) affects 25% of the global population. The standard of diagnosis, biopsy, is invasive and affected by sampling error and inter‐reader variability. We hypothesized that widely available rapid MRI techniques could be used to predict nonalcoholic steatohepatitis (NASH) noninvasively by measuring liver stiffness, with magnetic resonance elastography (MRE), and liver fat, with chemical shift‐encoded (CSE) MRI. Besides, we validate an automated image analysis technique to maximize the utility of these methods.PurposeTo implement and test an automated system for analyzing CSE‐MRI and MRE data coupled with model‐based prediction of NASH.Study TypeProspective.SubjectsEighty‐three patients with suspected NAFLD.Field Strength/SequenceA 1.5 T using a flow‐compensated motion‐encoded gradient echo MRE sequence and a multiecho CSE‐MRI sequence.AssessmentsThe MRE and CSE‐MRI data were analyzed by two readers (5+ and 1 years of experience) and an automated algorithm. A logistic regression model to predict pathology‐diagnosed NASH was trained based on stiffness and proton density fat fraction, and the area under the receiver operating characteristic curve (AUROC) was calculated using 10‐fold cross validation for models based on both automated and manual measurements. A separate model was trained to predict the NASH severity score (NAS).Statistical TestsPearson's correlation, Bland–Altman, AUROC, C‐statistic.ResultsThe agreement between automated measurements and the more experienced reader (R2 = 0.87 for stiffness and R2 = 0.99 for proton density fat fraction [PDFF]) was slightly better than the agreement between readers (R2 = 0.85 and 0.98). The model for predicting biopsy‐diagnosed NASH had an AUROC of 0.87. The NAS‐prediction model had a C‐statistic of 0.85.Data ConclusionWe demonstrated a workflow that used a limited MRI acquisition protocol and fully automated analysis to predict NASH with high accuracy. These methods show promise to provide a reliable noninvasive alternative to biopsy for NASH‐screening in populations with NAFLD.Level of Evidence2Technical Efficacy Stage2

Because up to 12% of obstetric patients meet criteria for the diagnosis of thrombocytopenia in pregnancy, it is not infrequent that the anesthesiologist must decide whether to proceed with a neuraxial procedure in an affected patient. Given the potential morbidity associated with general anesthesia for cesarean delivery, thoughtful consideration of which patients with thrombocytopenia are likely to have an increased risk of spinal epidural hematoma with neuraxial procedures, and when these risks outweigh the relative benefits is important to consider and to inform shared decision making with patients. Because there are substantial risks associated with withholding a neuraxial analgesic/anesthetic procedure in obstetric patients, every effort should be made to perform a bleeding history assessment and determine the thrombocytopenia etiology before admission for delivery. Whereas multiple other professional societies (obstetric, interventional pain, and hematologic) have published guidelines addressing platelet thresholds for safe neuraxial procedures, the US anesthesia professional societies have been silent on this topic. Despite a paucity of high-quality data, there are now meta-analyses that provide better estimations of risks. An interdisciplinary taskforce was convened to unite the relevant professional societies, synthesize the data, and provide a practical decision algorithm to help inform risk-benefit discussions and shared decision making with patients. Through a systematic review and modified Delphi process, the taskforce concluded that the best available evidence indicates the risk of spinal epidural hematoma associated with a platelet count ≥70,000 × 10 6 /L is likely to be very low in obstetric patients with thrombocytopenia secondary to gestational thrombocytopenia, immune thrombocytopenia (ITP), and hypertensive disorders of pregnancy in the absence of other risk factors. Ultimately, the decision of whether to proceed with a neuraxial procedure in an obstetric patient with thrombocytopenia occurs within a clinical context. Potentially relevant factors include, but are not limited to, patient comorbidities, obstetric risk factors, airway examination, available airway equipment, risk of general anesthesia, and patient preference.

Abstract Objective We aimed to develop a model for accurate prediction of general care inpatient deterioration. Materials and Methods Training and internal validation datasets were built using 2-year data from a quaternary hospital in the Midwest. Model training used gradient boosting and feature engineering (clinically relevant interactions, time-series information) to predict general care inpatient deterioration (resuscitation call, intensive care unit transfer, or rapid response team call) in 24 hours. Data from a tertiary care hospital in the Southwest were used for external validation. C-statistic, sensitivity, positive predictive value, and alert rate were calculated for different cutoffs and compared with the National Early Warning Score. Sensitivity analysis evaluated prediction of intensive care unit transfer or resuscitation call. Results Training, internal validation, and external validation datasets included 24 500, 25 784 and 53 956 hospitalizations, respectively. The Mayo Clinic Early Warning Score (MC-EWS) demonstrated excellent discrimination in both the internal and external validation datasets (C-statistic = 0.913, 0.937, respectively), and results were consistent in the sensitivity analysis (C-statistic = 0.932 in external validation). At a sensitivity of 73%, MC-EWS would generate 0.7 alerts per day per 10 patients, 45% less than the National Early Warning Score. Discussion Low alert rates are important for implementation of an alert system. Other early warning scores developed for the general care ward have achieved lower discrimination overall compared with MC-EWS, likely because MC-EWS includes both nursing assessments and extensive feature engineering. Conclusions MC-EWS achieved superior prediction of general care inpatient deterioration using sophisticated feature engineering and a machine learning approach, reducing alert rate.

AbstractBackgroundData shared for research use must be de‐identified to protect participant confidentiality. A recent study has shown that face imagery from brain imaging studies could be used to re‐identify anonymous participants via automatic face recognition, linking them with all study‐collected protected health information. Here we measure how well existing software for removing MRI face imagery protects against this privacy threat.MethodWe recruited 153 participants with existing 3D FLAIR MRI and photographed their face from 5 varying angles. We used surf_ice to reconstruct each participant’s face from their MRI and tested whether free, publicly‐available face recognition software (Microsoft Azure) could match their photos to their correct MRI. We tested two existing programs for removing face voxels in MRI: mri_deface (FreeSurfer) and pydeface (python package), and one in‐house implementation. We observed that existing methods often left participants’ eyes intact while removing most of their nose/mouth/chin. This prevented Azure from detecting the reconstructions as faces, therefore preventing its attempt of face recognition. We hypothesized that the retained eye imagery alone could be sufficient for face recognition, if a face were detected. Therefore, we also reconstructed each de‐faced MRI after applying novel pre‐processing that replaced any removed/missing face voxels with those from our population‐average template, e.g. replacing missing noses/mouths/chins with population‐average noses/mouths/chins, before testing face recognition.ResultsWithout any MRI de‐facing, Microsoft Azure correctly matched 78% of participants’ photos to their MRI. After mri_deface and pydeface, 8% and 6% were still identified, respectively. After preprocessing the de‐faced MRI to replace missing areas with those from a population‐average template prior to face reconstruction, the face imagery remaining in the de‐faced images was enough to allow re‐identification of 23% and 20% of participants, respectively. With an in‐house face removal implementation that removes the whole face from every image, 0% of faces were re‐identified from the standard reconstructions, and 5% were identified from the “missing‐parts‐replacement” reconstructions.ConclusionsEven after applying popular MRI de‐facing software, Microsoft Azure face recognition automatically re‐identified up to 23% of participants. Better de‐facing software is needed to remove more of the face more reliably, without affecting brain measurements from de‐faced images.

AbstractBackgroundThe Network Failure Quotient (NFQ) metric summarizes functional MRI network integrity. We wanted to investigate the stability of the NFQ results across time, hardware, software and protocol changes during the last decade in ADNI Resting State (RS‐) fMRI.MethodIn ADNI‐2 (2009‐2016) RS‐fMRI was acquired on Philips scanners. In ADNI‐3 (2017‐present) RS‐fMRI is acquired on GE, Philips, and Siemens systems with “Basic” and “Advanced” variants based on the availability of simultaneous multi‐slice (SMS, multi‐band) imaging. (Table 1.) Resting state images were preprocessed by performing slice timing correction, trimming volumes from the first 10 seconds of acquired data, despiking and within‐series alignment. Simultaneous bandpass and regression was performed using spatial registration parameters and their first temporal derivatives, mean WM signal, mean CSF signal and global signal. Dual regression via GIFT was applied creating regional time courses. The NFQ is calculated as NFQ=(pDMN_to_vDMN + pDMN_to_adDMN) / (pDMN +vDMN) where pDMN_to_vDMN and pDMN_to_adDMN are correlations between time courses in the posterior DMN, ventral DMN and anterior‐dorsal DMN. pDMN and vDMN are the within‐region correlations. NFQ values from 1447 scans of 739 individual study participants (Table 1) were analyzed using linear mixed effects models to account for age, clinical diagnosis, vendor, and time trends.ResultBox plots of NFQ values by diagnosis within ADNI‐phase and protocol are shown in Figure 2 and suggest important differences among clinical diagnoses and between ADNI‐2 and ADNI‐3. The mixed model analysis indicates that a 10‐year increase in age is associated with a 0.05±0.01 (mean±SD) increase in NFQ; individuals with MCI average roughly 0.05±0.01 higher than unimpaired and individuals with dementia average 0.14±0.02 higher. Differences by vendor for ADNI‐2 and ADNI‐3 are summarized in Figures 2 and 3 with effect sizes on par with these clinical factors. Mean NFQ for Phillips ADNI‐2 were highest (0.54±0.02); somewhat lower were Philips ADNI‐3 (0.51±0.02) and Siemens ADNI‐3 advanced (0.49±0.03); Siemens ADNI‐3 basic (0.40±0.02) and GE ADNI‐3 were the lowest (0.38±0.02).ConclusionNFQ values consistently increase with impairment. Vendor‐specific differences are observed and vendor changes over the life of a study introduce variability on the scale of disease‐related changes in NFQ.

AbstractBackgroundThe NIA‐AA Research Framework for Alzheimer’s disease (AD) includes consideration of two clinical schemes for staging AD: (1) traditional cognitively unimpaired (CU) to MCI to dementia and (2) a new numeric staging (1‐6) for individuals who are on the Alzheimer’s continuum.MethodThe Mayo Clinic Study of Aging is a population‐based longitudinal study of aging and cognitive impairment in Olmsted County, Minnesota. We analyzed CU persons who had completed three consecutive clinical visits and calculated measures for cross‐sectional categories: objective cognitive impairment and function and measures for change including subjective cognitive impairment (SCD), objective cognitive change (ΔOBJ), and new onset of neurobehavioral symptoms (NBS). We calculated the frequencies for Stage 2 by varying the cutoffs for each of the measures above and assessed stability of this stage over 15 months.ResultThe frequency of Stage 2 varied from 5‐48% depending upon the age of the participants and the cutoff scores used. Membership in Stage 2 required abnormal SCD, ΔOBJ, or ΔNBS and having only ΔOBJ abnormal was the primary reason for inclusion in Stage 2 (45‐62%) followed by 10‐21% having only SCD abnormal. The stability of Stage 2 over 15 months ranged from 31‐65% with 5‐13% progressing to Stage 3 or higher and 22‐43% percent reverting to Stage 1.ConclusionStage 2 was characterized as a transitional period between CU and MCI and could be operationalized with a variety of measures. The staging scheme performed well considering the variables evaluated and cutoff scores employed.

Abstract Glioblastoma multiforme (GBM) is the most aggressive CNS neoplasm with a mere 15 month progression-free survival following current standards-of-care. Furthermore, conventional therapies have been effective, in part, by inducing a senescent-like phenotype in at least a proportion of glioma, as well as within the tumor-adjacent, healthy brain cells. Through an N-glyco-capture large-scale screening method, we were able to identify 25 genes exclusively overexpressed on glioma cell surfaces. Of those 25 targets, CD38 was an attractive target due to pre-existing FDA approved therapeutics. Recent studies have shown, however, that senescent cells, such as those induced via chemo- and radiotherapies, secrete a pro-inflammatory array of cytokines, known collectively as senescence associated secretory phenotype (SASP), that are capable of increasing CD38 expression in monocytes. CD38 functions as an ectoenzyme to convert extracellular NAD+ to nicotinamide (required for glioma cell salvage pathway NAD+ synthesis) and ADPR/cADPR (a cellular proliferation signal). To examine the effects of radiation on human glioma tissue, we performed reverse cyclase assays (RCA) on paired primary and recurrent human glioma tissue samples and found an increase in CD38 activity in post-irradiated tissues (recurrent glioma) compared to pre-irradiated (primary glioma). Through proliferation assays, we also found an increase in glioma cell growth following treatment with cADPR compared to untreated. Our results demonstrate that CD38 activity is tumorgenic, and furthermore that conventional chemo- and radiotherapies increase this CD38 activity. This indicates that treating CD38 with previously FDA approved therapeutics may provide hope for increasing progression free survival in our GBM patient population, and moreover emphasize the importance of leveraging novel large scale screening methods for identifying additional opportunities for treatment.