Association of Neutralizing Antispike Monoclonal Antibody Treatment With Coronavirus Disease 2019 Hospitalization and Assessment of the Monoclonal Antibody Screening Score.

Objective: To test the hypothesis that the Monoclonal Antibody Screening Score performs consistently better in identifying the need for monoclonal antibody infusion throughout each "wave" of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variant predominance during the coronavirus disease 2019 (COVID-19) pandemic and that the infusion of contemporary monoclonal antibody treatments is associated with a lower risk of hospitalization. Patients and Methods: In this retrospective cohort study, we evaluated the efficacy of monoclonal antibody treatment compared with that of no monoclonal antibody treatment in symptomatic adults who tested positive for SARS-CoV-2 regardless of their risk factors for disease progression or vaccination status during different periods of SARS-CoV-2 variant predominance. The primary outcome was hospitalization within 28 days after COVID-19 diagnosis. The study was conducted on patients with a diagnosis of COVID-19 from November 19, 2020, through May 12, 2022. Results: Of the included 118,936 eligible patients, hospitalization within 28 days of COVID-19 diagnosis occurred in 2.52% (456/18,090) of patients who received monoclonal antibody treatment and 6.98% (7,037/100,846) of patients who did not. Treatment with monoclonal antibody therapies was associated with a lower risk of hospitalization when using stratified data analytics, propensity scoring, and regression and machine learning models with and without adjustments for putative confounding variables, such as advanced age and coexisting medical conditions (eg, relative risk, 0.15; 95% CI, 0.14-0.17). Conclusion: Among patients with mild to moderate COVID-19, including those who have been vaccinated, monoclonal antibody treatment was associated with a lower risk of hospital admission during each wave of the COVID-19 pandemic.

Assessment of surgeons' attitude towards awake spine surgery under spinal anesthesia.

BACKGROUND: General anesthesia (GA) and spinal anesthesia (SA) have been adopted for lumbar spine surgery (LSS), but GA is used far more widely. We conducted a survey of spine surgeons to explore their attitudes and preferences regarding awake spine surgery under SA. METHODS: A survey was emailed to 150 spine surgeons. Exposure and attitudes towards spine surgery under SA were elicited. A five-point Likert scale of agreement examined perceptions of SA, while attitudes towards SA were recorded by categorizing free text into themes. RESULTS: Seventy-five surgeons completed the survey, 50 % response rate. Only 27 % said they perform LSS under SA. Most surgeons, 83 %, would recommend GA to a healthy patient undergoing lumbar laminectomy. Only 41 % believes SA to be as safe as GA, and only 30 % believes SA is associated with better postoperative pain control. The most common reasons why SA is not favored was lack of proven benefits over GA (65 %). When asked if a randomized trial finds SA to lead to less postoperative fatigue, 50 % said they would be more likely to offer SA, a significant increase from the baseline response of 27 % (p = 0.002). CONCLUSIONS: Our survey indicates that the low adoption of SA for LSS is due to lack of surgeons' belief in the benefits of SA over GA, and that a randomized patient-centered trial has the potential of changing surgeons' perspective and increasing adoption of SA for LSS.

Assessment of Patients' Willingness to Participate in a Randomized Trial of Spinal versus General Anesthesia for Lumbar Spine Surgery.

BACKGROUND: Prospective nonrandomized studies have found less postoperative fatigue and improved quality of life in patients undergoing awake spine surgery under spinal anesthesia compared with general anesthesia. Randomized trials are needed to validate these findings. OBJECTIVE: To prospectively investigate patients' willingness to enroll in randomized trials of lumbar spine surgery under spinal versus general anesthesia and identify any potential barriers. METHODS: We recruited patients undergoing lumbar spine surgery for degenerative disease. We described a randomized trial of spine surgery under spinal versus general anesthesia and assessed patients' willingness to participate in such trial. We elicited preferences for treatment along with demographics. The association between these factors and willingness to participate in the trial was examined. RESULTS: Fifty patients completed interviews; 58% were female, mean age of 60.9 ± 12.5 years. A total of 52% patients stated that they were definitely willing to participate in the hypothetical randomized trial, and 8% probably willing. Only 16% of patients were aware of spinal anesthesia as an option for low back surgery, and 60% indicated no strong preference for the anesthesia techniques. Patients without strong preferences stated a greater willingness to participate than those with strong preferences (80% vs. 10% definitely willing, P < 0.0001). Age, sex, education, work status, and race were not significantly associated with willingness to participate. CONCLUSION: Sixty percent of patients stated that they were either definitely or probably willing to participate in the randomized trial. Subjects lacking strong preferences for the anesthesia technique stated a greater willingness to enroll than those with strong preference.

Using Facial Recognition Tools for Health Assessment.

The number of applications for facial recognition technology is increasing due to the improvement in image quality, artificial intelligence, and computer processing power that has occurred during the last decades. Algorithms can be used to convert facial anthropometric landmarks into a computer representation, which can be used to help identify nonverbal information about an individual's health status. This article discusses the potential ways a facial recognition tool can perform a health assessment. Because facial attributes may be considered biometric data, clinicians should be informed about the clinical, ethical, and legal issues associated with its use.

NIH funding trends for neurosurgeon-scientists from 1993-2017: Biomedical workforce implications for neurooncology.

INTRODUCTION: Neurosurgeons represent 0.5% of all physicians and currently face a high burden of disease. Physician-scientists are essential to advance the mission of National Academies of Science (NAS) and National Institutes of Health (NIH) through discovery and bench to bedside translation. We investigated trends in NIH neurosurgeon-scientist funding over time as an indicator of physician-scientist workforce training. METHODS: We used NIH Research Portfolio Online Reporting Tools (RePORTER) to extract grants to neurosurgery departments and neurosurgeons from 1993 to 2017. Manual extraction of each individual grant awardee was conducted. RESULTS: After adjusting for U.S. inflation (base year: 1993), NIH funding to neurosurgery departments increased yearly (P < 0.00001). However, neurosurgeon-scientists received significantly less NIH funding compared to scientists (including basic scientists and research only neurosurgeons) (P = 0.09). The ratio of neurosurgeon-scientists to scientists receiving grants was significantly reduced (P = 0.002). Interestingly, the percentage of oncology-related neurosurgery grants significantly increased throughout the study period (P = 0.002). The average number of grants per neurosurgeon-scientists showed an upward trend (P < 0.001); however, the average number of grants for early-career neurosurgeon-scientists, showed a significant downward trend (P = 0.05). CONCLUSION: Over the past 23 years, despite the overall increasing trends in the number of NIH grants awarded to neurosurgery departments overall, the proportion of neurosurgeon-scientists that were awarded NIH grants compared to scientists demonstrates a declining trend. This observed shift is disproportionate in the number of NIH grants awarded to senior level compared to early-career neurosurgeon-scientists, with more funding allocated towards neurosurgical-oncology-related grants.

Cost Effectiveness of an Electrocardiographic Deep Learning Algorithm to Detect Asymptomatic Left Ventricular Dysfunction.

OBJECTIVE: To evaluate the cost-effectiveness of an artificial intelligence electrocardiogram (AI-ECG) algorithm under various clinical and cost scenarios when used for universal screening at age 65. PATIENTS AND METHODS: We used decision analytic modeling to perform a cost-effectiveness analysis of the use of AI-ECG to screen for asymptomatic left ventricular dysfunction (ALVD) once at age 65 compared with no screening. This screening consisted of an initial screening decision tree and subsequent construction of a Markov model. One-way sensitivity analysis on various disease and cost parameters to evaluate cost-effectiveness at both $50,000 per quality-adjusted life year (QALY) and $100,000 per QALY willingness-to-pay threshold. RESULTS: We found that for universal screening at age 65, the novel AI-ECG algorithm would cost $43,351 per QALY gained, test performance, disease characteristics, and testing cost parameters significantly affect cost-effectiveness, and screening at ages 55 and 75 would cost $48,649 and $52,072 per QALY gained, respectively. Overall, under most of the clinical scenarios modeled, coupled with its robust test performance in both testing and validation cohorts, screening with the novel AI-ECG algorithm appears to be cost-effective at a willingness-to-pay threshold of $50,000. CONCLUSION: Universal screening for ALVD with the novel AI-ECG appears to be cost-effective under most clinical scenarios with a cost of <$50,000 per QALY. Cost-effectiveness is particularly sensitive to both the probability of disease progression and the cost of screening and downstream testing. To improve cost-effectiveness modeling, further study of the natural progression and treatment of ALVD and external validation of AI-ECG should be undertaken.

Wearable electronic devices for chronic pain intensity assessment: A systematic review.

Wearable electronic devices are a convenient solution to pain intensity assessment as they can provide continuous monitoring for more precise medication adjustments. However, there is little evidence regarding the use of wearable electronic devices for chronic pain intensity assessment. Our primary objective was to examine the physiologic parameters used by wearable electronic devices for chronic pain intensity assessment. We initially inquired PubMed, CINAHL, and Embase for studies evaluating the use of wearable electronic devices for chronic pain intensity assessment. We updated our inquiry by searching on PubMed, Embase, Scopus, and Google Scholar. English peer-reviewed studies were included, with no exclusions based on time frame or publication status. Of 348 articles that were identified on the first inquiry, 8 fulfilled the eligibility criteria. Of 179 articles that were identified on the last inquiry, only 1 fulfilled the eligibility criteria. We found articles evaluating wristbands, smartwatches, and belts. Parameters evaluated were psychomotor and sleep patterns, space and time mobility, heart rate variability, and skeletal muscle electrical activity. Most of the studies found significant positive associations between physiological parameters measured by wearable electronic devices and self-reporting pain scales. Wearable electronic devices reliably reflect physiologic or biometric parameters, providing a physiological correlation for pain. Early stage investigation suggests that the degree of pain intensity can be discerned, which ideally will reduce the bias inherent to existing numeric/verbal scales. Further research on the use of these devices is vital.

Using Facial Recognition Tools for Health Assessment.

The number of applications for facial recognition technology is increasing due to the improvement in image quality, artificial intelligence, and computer processing power that has occurred during the last decades. Algorithms can be used to convert facial anthropometric landmarks into a computer representation, which can be used to help identify nonverbal information about an individual's health status. This article discusses the potential ways a facial recognition tool can perform a health assessment. Because facial attributes may be considered biometric data, clinicians should be informed about the clinical, ethical, and legal issues associated with its use.

Convalescent Plasma Use in the United States was inversely correlated with COVID-19 Mortality: Did Plasma Hesitancy cost lives?

BACKGROUND: The US Food and Drug Administration authorized Convalescent Plasma (CCP) therapy for hospitalized COVID-19 patients via the Expanded Access Program (EAP) and the Emergency Use Authorization (EUA), leading to use in about 500,000 patients during the first year of the pandemic for the US. METHODS: We tracked the number of CCP units dispensed to hospitals by blood banking organizations and correlated that usage with hospital admission and mortality data. RESULTS: CCP usage per admission peaked in Fall 2020, with more than 40% of inpatients estimated to have received CCP between late September and early November 2020. However, after randomized controlled trials failed to show a reduction in mortality, CCP usage per admission declined steadily to a nadir of less than 10% in March 2021. We found a strong inverse correlation (r = -0.52, P = 0.002) between CCP usage per hospital admission and deaths occurring two weeks after admission, and this finding was robust to examination of deaths taking place one, two or three weeks after admission. Changes in the number of hospital admissions, SARS-CoV-2 variants, and age of patients could not explain these findings. The retreat from CCP usage might have resulted in as many as 29,000 excess deaths from mid-November 2020 to February 2021. CONCLUSIONS: A strong inverse correlation between CCP use and mortality per admission in the USA provides population level evidence consistent with the notion that CCP reduces mortality in COVID-19 and suggests that the recent decline in usage could have resulted in excess deaths.

Artificial Intelligence-Enabled Assessment of the Heart Rate Corrected QT Interval Using a Mobile Electrocardiogram Device.

BACKGROUND: Heart rate-corrected QT interval (QTc) prolongation, whether secondary to drugs, genetics including congenital long QT syndrome, and/or systemic diseases including SARS-CoV-2-mediated coronavirus disease 2019 (COVID-19), can predispose to ventricular arrhythmias and sudden cardiac death. Currently, QTc assessment and monitoring relies largely on 12-lead electrocardiography. As such, we sought to train and validate an artificial intelligence (AI)-enabled 12-lead ECG algorithm to determine the QTc, and then prospectively test this algorithm on tracings acquired from a mobile ECG (mECG) device in a population enriched for repolarization abnormalities. METHODS: Using >1.6 million 12-lead ECGs from 538 200 patients, a deep neural network (DNN) was derived (patients for training, n = 250 767; patients for testing, n = 107 920) and validated (n = 179 513 patients) to predict the QTc using cardiologist-overread QTc values as the "gold standard". The ability of this DNN to detect clinically-relevant QTc prolongation (eg, QTc ≥500 ms) was then tested prospectively on 686 patients with genetic heart disease (50% with long QT syndrome) with QTc values obtained from both a 12-lead ECG and a prototype mECG device equivalent to the commercially-available AliveCor KardiaMobile 6L. RESULTS: In the validation sample, strong agreement was observed between human over-read and DNN-predicted QTc values (-1.76±23.14 ms). Similarly, within the prospective, genetic heart disease-enriched dataset, the difference between DNN-predicted QTc values derived from mECG tracings and those annotated from 12-lead ECGs by a QT expert (-0.45±24.73 ms) and a commercial core ECG laboratory [10.52±25.64 ms] was nominal. When applied to mECG tracings, the DNN's ability to detect a QTc value ≥500 ms yielded an area under the curve, sensitivity, and specificity of 0.97, 80.0%, and 94.4%, respectively. CONCLUSIONS: Using smartphone-enabled electrodes, an AI DNN can predict accurately the QTc of a standard 12-lead ECG. QTc estimation from an AI-enabled mECG device may provide a cost-effective means of screening for both acquired and congenital long QT syndrome in a variety of clinical settings where standard 12-lead electrocardiography is not accessible or cost-effective.

Survival Disparities in Male Patients With Breast Cancer.

BACKGROUND/AIM: We evaluated factors associated with mortality among men with breast cancer. MATERIALS AND METHODS: We used the National Cancer Database to identify men with breast cancer and evaluated factors associated with mortality, using a Cox regression model. RESULTS: Black patients experienced an increased risk of death from any cause compared to white patients [hazard ratio (HR)=1.19, 95%CI=1.05-1.37]. Patients with government insurance had a greater risk of death compared to privately insured patients (HR=1.57, 95%CI=1.41-1.75). When compared to patients with an income of >$46,000, those with an income <$30,000 presented an increased risk of death (HR=1.35, 95%CI=1.14-1.60). Finally, patients treated at a comprehensive community cancer program (HR=1.129, 95%CI=1.021-1.248), community cancer program (HR=1.164, 95%CI=1.010-1.343), or integrated network cancer program (HR=1.216; 95%CI=1.056-1.401) experienced elevated risk of death compared to those treated at academic/research-programs. CONCLUSION: Race, insurance, income, education, and facility type are associated with the risk of mortality in male patients with breast cancer.

Quantitative assessment of lung stiffness in patients with interstitial lung disease using MR elastography.

PURPOSE: To investigate the use of magnetic resonance elastography (MRE) in the quantitative assessment of pulmonary fibrosis by comparing quantitative shear stiffness measurements of lung parenchyma in patients diagnosed with fibrotic interstitial lung disease (ILD) and healthy controls. MATERIALS AND METHODS: A 1.5T spin-echo, echo planar imaging MRE (SE-EPI MRE) pulse sequence was utilized to assess absolute lung shear stiffness in 15 patients with diagnosed ILD and in 11 healthy controls. Data were collected at residual volume (RV) and total lung capacity (TLC). Spirometry data were obtained immediately prior to scanning. To test for statistical significance between RV and TLC shear stiffness estimates a two-sample t-test was performed. To assess variability within individual subject shear stiffness estimates, the intraclass correlation coefficient (ICC) and Krippendorff's alpha were calculated. RESULTS: Patients with ILD exhibited an average (±1 standard deviation) shear stiffness of 2.74 (±0.896) kPa at TLC and 1.32 (±0.300) kPa at RV. The corresponding values for healthy individuals were 1.33 (±0.195) kPa and 0.849 (±0.250) kPa, respectively. The difference in shear stiffness between RV and TLC was statistically significant (P < 0.001). At TLC, the ICC and alpha values were 0.909 and 0.887, respectively. At RV, the ICC and alpha values were 0.852 and 0.862, respectively. CONCLUSION: In subjects with known fibrotic interstitial lung disease, parenchymal shear stiffness is increased when compared to normal controls at both RV and TLC, with TLC demonstrating the most significant difference. MRE-derived parenchymal shear stiffness is a promising new noninvasive imaging-based biomarker of interstitial lung disease. LEVEL OF EVIDENCE: 1 Technical Efficacy: Stage 2 J. MAGN. RESON. IMAGING 2017;46:365-374.

Assessment of Low-Contrast Resolution for the American College of Radiology Computed Tomographic Accreditation Program: What Is the Impact of Iterative Reconstruction?

OBJECTIVE: To compare contrast-to-noise ratio (CNR) thresholds with visual assessment of low-contrast resolution (LCR) in filtered back projection (FBP) and iteratively reconstructed (IR) computed tomographic (CT) images. METHODS: American College of Radiology (ACR) CT accreditation phantom LCR images were acquired at CTDIvol levels of 8, 12, and 16 mGy using 2 scanner models and reconstructed using one FBP and 2 IR kernels. Acquisitions were repeated 100 times. Three board-certified medical physicists blindly reviewed the LCR section images. Pass-percentage rates (PPRs) using previous and current ACR CT accreditation criteria were compared. RESULTS: Observer PPRs for FBP images were less than 32%. For IR images, 5 of 18 settings/dose/model configurations had PPRs greater than 32% (maximum 76.3%). For CNR evaluation of FBP images, PPRs for 15 configurations were greater than 70%. For IR images, all PPRs were at least 96%. CONCLUSIONS: The CNR threshold used by the ACR CT accreditation program yields higher PPRs than visual assessment of LCR, potentially resulting in lower-quality images passing the ACR CNR criteria.

Effects of experimental sleep restriction on caloric intake and activity energy expenditure.

BACKGROUND: Epidemiologic studies link short sleep duration to obesity and weight gain. Insufficient sleep appears to alter circulating levels of the hormones leptin and ghrelin, which may promote appetite, although the effects of sleep restriction on caloric intake and energy expenditure are unclear. We sought to determine the effect of 8 days/8 nights of sleep restriction on caloric intake, activity energy expenditure, and circulating levels of leptin and ghrelin. METHODS: We conducted a randomized study of usual sleep vs a sleep restriction of two-thirds of normal sleep time for 8 days/8 nights in a hospital-based clinical research unit. The main outcomes were caloric intake, activity energy expenditure, and circulating levels of leptin and ghrelin. RESULTS: Caloric intake in the sleep-restricted group increased by +559 kcal/d (SD, 706 kcal/d, P=.006) and decreased in the control group by -118 kcal/d (SD, 386 kcal/d, P=.51) for a net change of +677 kcal/d (95% CI, 148-1,206 kcal/d; P=.014). Sleep restriction was not associated with changes in activity energy expenditure (P=.62). No change was seen in levels of leptin (P=.27) or ghrelin (P=.21). CONCLUSIONS: Sleep restriction was associated with an increase in caloric consumption with no change in activity energy expenditure or leptin and ghrelin concentrations. Increased caloric intake without any accompanying increase in energy expenditure may contribute to obesity in people who are exposed to long-term sleep restriction. TRIAL REGISTRATION: ClinicalTrials.gov; No.: NCT01334788; URL: www.clinicaltrials.gov.

Combining body mass index with measures of central obesity in the assessment of mortality in subjects with coronary disease: role of "normal weight central obesity".

OBJECTIVES: This study sought to assess the mortality risk of patients with coronary artery disease (CAD) based ona combination of body mass index (BMI) with measures of central obesity. BACKGROUND: In CAD patients, mortality has been reported to vary inversely with BMI ("obesity paradox"). In contrast,central obesity is directly associated with mortality. Because of this bidirectionality, we hypothesized that CAD patients with normal BMI but central obesity would have worse survival compared to individuals with other combinations of BMI and central adiposity. METHODS: We included 15,547 participants with CAD who were part of 5 studies from 3 continents. Multivariate stratifiedCox-proportional hazard models adjusted for potential confounders were used to assess mortality risk according to different patterns of adiposity that combined BMI with measures of central obesity. RESULTS: Mean age was 66 years, 60% were men. There were 5,507 deaths over a median follow-up of 2.4 years (IQR: 0.5 to 7.4 years). Individuals with normal weight central obesity had the worst long-term survival: a person with BMI of 22 kg/m2 and waist circumference (WC) of 101 cm had higher mortality than a person with similar BMI but WC of 85 cm (HR: 1.10[95% CI: 1.05 to 1.17]), than a person with BMI of 26 kg/m2 and WC of 85 cm (HR: 1.20 [95% CI: 1.09 to 1.31]), than a person with BMI of 30 kg/m2 and WC of 85 cm (HR: 1.61 [95% CI: 1.39 to 1.86]) and than a person with BMI of 30kg/m2 and WC of 101 cm (HR: 1.27 [95% CI: 1.18 to 1.39), p < 0.0001 for all). CONCLUSIONS: In patients with CAD, normal weight with central obesity is associated with the highest risk of mortality [corrected].

Lowering kilovoltage to reduce radiation dose in contrast-enhanced abdominal CT: initial assessment of a prototype automated kilovoltage selection tool.

OBJECTIVE: The purpose of this study was to determine whether the use of an automated CT kilovoltage (kV) selection tool (Auto kV) can result in lower radiation dose without sacrificing image quality in contrast-enhanced abdominopelvic CT. MATERIALS AND METHODS: Tube potential, radiation dose, and iodine contrast-to-noise ratio (CNR) were retrospectively evaluated in 36 patients who underwent abdominopelvic CT with Auto kV, and compared with results from size-matched control patients using identical protocols. Two radiologists evaluated image quality (sharpness, noise, and diagnostic confidence) blinded to kV. Volume CT dose index (CTDI(vol)) was also compared with what each patient would have received from scanning at 120 kV. RESULTS: Mean (SD) CTDI(vol) was 16.0 (4.4) mGy after Auto kV versus 19.5 (4.0) mGy using standard 120-kV prescription and was 19.3 (6.0) mGy in control subjects (yielding dose reductions of 18.0% and 17.2%, respectively; p < 0.001 for both). Thirty of 36 patients were scanned at 100 kV (median dose reduction, 25%). Auto kV images were rated as very sharp in 33 (92%) and 36 (100%) cases versus 36 (100%) and 35 (97%) of the control cases, with all cases scored as having optimal noise. Readers had full diagnostic confidence in 34 (94%) and 36 (100%) of Auto kV cases; one reader scored "probably confident" in two cases (6%). Iodine CNRs for the aorta, liver, and portal vein were similar between Auto kV cases and control cases (p > 0.50, all comparisons). CONCLUSION: The use of an automated kV selection tool results in significant dose savings while maintaining diagnostic image quality and iodine CNR.

Visual assessment versus quantitative three-dimensional stereotactic surface projection fluorodeoxyglucose positron emission tomography for detection of mild cognitive impairment and Alzheimer disease.

INTRODUCTION: We examined the clinical impact of commercially available quantitation software using 3-dimensional stereotactic surface projection (3D-SSP) on the diagnostic accuracy of 18F fluorodeoxyglucose positron emission tomography (18F FDG PET) in mild cognitive impairment (MCI) and Alzheimer disease (AD). METHODS: Enrollees underwent clinical evaluation to determine cognitive status and subsequent 18F FDG PET neuroimaging. Four blinded readers (2 novices and 2 experts) rated the images for degree of abnormality and interpretive confidence without and with 3D-SSP. Diagnostic accuracy was determined with area under the curve (area under the curve) of a receiver operating characteristic (receiver operating characteristic) curve analysis and change in confidence with model-based means (LSMeans). RESULTS: Twenty-three normal controls and 31 patients with cognitive impairment (18 MCI and 13 AD) were enrolled (28 female and 26 male; mean age 74 years). During follow-up (mean 3.6 years), all normal participants remained normal, 12 of 18 participants with MCI progressed to dementia, and all participants with baseline dementia progressed. The area under the curve with 3D-SSP (0.88; 95% CI: 0.76-0.95) was significantly higher than without it (0.72; 95% CI: 0.55-0.83). The specificity increased from 26% to 63% for novices and from 56% to 87% for experts with addition of 3D-SSP, whereas the sensitivity was essentially unchanged at 86% and 86% for the beginners and 81% and 79% for the experts. The interpretive confidence increased significantly from 3.3 to 4.0 (maximum value = 5, P = 0.048). CONCLUSION: The use of commercially available 3D-SSP quantitation improved diagnostic accuracy for evaluation of MCI and AD with 18F FDG PET.

Magnetic resonance assessment of parenchymal elasticity in normal and edematous, ventilator-injured lung.

Magnetic resonance elastography (MRE) is a MR imaging method capable of spatially resolving the intrinsic mechanical properties of normal lung parenchyma. We tested the hypothesis that the mechanical properties of edematous lung exhibit local properties similar to those of a fluid-filled lung at transpulmonary pressures (P(tp)) up to 25 cm H(2)O. Pulmonary edema was induced in anesthetized female adult Sprague-Dawley rats by mechanical ventilation to a pressure of 40 cm H(2)O for ≈ 30 min. Prior to imaging the wet weight of each ex vivo lung set was measured. MRE, high-resolution T(1)-weighted spin echo and T(2)* gradient echo data were acquired at each P(tp) for both normal and injured ex vivo lungs. At P(tp)s of 6 cm H(2)O and greater, the shear stiffness of normal lungs was greater than injured lungs (P ≤ 0.0003). For P(tp)s up to 12 cm H(2)O, shear stiffness was equal to 1.00, 1.07, 1.16, and 1.26 kPa for the injured and 1.31, 1.89, 2.41, and 2.93 kPa for normal lungs at 3, 6, 9, and 12 cm H(2)O, respectively. For injured lungs MRE magnitude signal and shear stiffness within regions of differing degrees of alveolar flooding were calculated as a function of P(tp). Differences in shear stiffness were statistically significant between groups (P < 0.001) with regions of lower magnitude signal being stiffer than those of higher signal. These data demonstrate that when the alveolar space filling material is fluid, MRE-derived parenchymal shear stiffness of the lung decreases, and the lung becomes inherently softer compared with normal lung.

Population-based study of severe hypoglycemia requiring emergency medical service assistance reveals unique findings.

OBJECTIVE: The objective is to report a contemporary population-based estimate of hypoglycemia requiring emergency medical services (EMS), its burden on medical resources, and its associated mortality in patients with or without diabetes mellitus (DM, non-DM), which will enable development of prospective strategies that will capture hypoglycemia promptly and provide an integrated approach for prevention of such episodes. METHODS: We retrieved all ambulance calls activated for hypoglycemia in Olmsted County, Minnesota, between January 1, 2003 and December 31, 2009. RESULTS: A total of 1473 calls were made by 914 people (DM 8%, non-DM 16%, unknown DM status 3%). Mean age was 60 ± 16 years with 49% being female. A higher percentage of calls were made by DM patients (87%) with proportionally fewer calls coming from non-DM patients (11%) (chi-square test, p < .001), and the remaining 2% calls by people with unknown DM status. Emergency room transportation and hospitalization were significantly higher in non-DM patients compared to DM patients (p < .001) and type 2 diabetes mellitus compared to type 1 diabetes mellitus (p < .001). Sulphonylureas alone or in combination with insulin varied during the study period (p = .01). The change in incidence of EMS for hypoglycemia was tracked during this period. However, causality has not been established. Death occurred in 240 people, 1.2 (interquartile range 0.2-2.7) years after their first event. After adjusting for age, mortality was higher in non-DM patients compared with DM patients (p < .001) but was not different between the two types of DM. CONCLUSIONS: The population burden of EMS requiring hypoglycemia is high in both DM and non-DM patients, and imposes significant burden on medical resources. It is associated with long-term mortality.