IMPORTANCE This is the first multisite, randomized clinical trial of stellate ganglion block (SGB) outcomes on posttraumatic stress disorder (PTSD) symptoms.OBJECTIVE To determine whether paired SGB treatments at 0 and 2 weeks would result in improvement in mean Clinician-Administered PTSD Scale for DSM-5 (CAPS-5) total symptom severity scores from baseline to 8 weeks. DESIGN, SETTING, AND PARTICIPANTSThis multisite, blinded, sham-procedure, randomized clinical trial used a 2:1 SGB:sham ratio and was conducted from May 2016 through March 2018 in 3 US Army Interdisciplinary Pain Management Centers. Only physicians performing the procedures and the procedure nurses were aware of the intervention (but not the participants or assessors); their interactions with the participants were scripted and limited to the 2 interventions. Active-duty service members on stable psychotropic medication dosages who had a PTSD Checklist-Civilian Version (PCL-C) score of 32 or more at screening were included. Key exclusion criteria included a prior SGB treatment, selected psychiatric disorders or substance use disorders, moderate or severe traumatic brain injury, or suicidal ideation in the prior 2 months.INTERVENTIONS Paired right-sided SGB or sham procedures at weeks 0 and 2.MAIN OUTCOMES AND MEASURES Improvement of 10 or more points on mean CAPS-5 total symptom severity scores from baseline to 8 weeks, adjusted for site and baseline total symptom severity scores (planned a priori). RESULTSOf 190 screened individuals, 113 (59.5%; 100 male and 13 female participants; mean [SD] age, 37.3 [6.7] years) were eligible and randomized (74 to SGB and 39 to sham treatment), and 108 (95.6% of 113) completed the study. Baseline characteristics were similar in the SGB and sham treatment groups, with mean (SD) CAPS-5 scores of 37.6 (11.2) and 39.8 (14.4), respectively (on a scale of 0-80); 91 (80.0%) met CAPS-5 PTSD criteria. In an intent-to-treat analysis, adjusted mean total symptom severity score change was −12.6 points (95% CI, −15.5 to −9.7 points) for the group receiving SGB treatments, compared with −6.1 points (95% CI, −9.8 to −2.3 points) for those receiving sham treatment (P = .01). CONCLUSIONS AND RELEVANCEIn this trial of active-duty service members with PTSD symptoms (at a clinical threshold and subthreshold), 2 SGB treatments 2 weeks apart were effective in reducing CAPS-5 total symptom severity scores over 8 weeks. The mild-moderate baseline level of PTSD symptom severity and short follow-up time limit the generalizability of these findings, but the study suggests that SGB merits further trials as a PTSD treatment adjunct.TRIAL REGISTRATION ClinicalTrials.gov identifier: NCT03077919
Evaluating risk of developing type 1 diabetes (T1D) depends on determining an individual’s HLA type, especially of the HLA DRB1 and DQB1 alleles. Individuals positive for HLA-DRB1*03 (DR3) or HLA-DRB1*04 (DR4) with DQB1*03:02 (DQ8) have the highest risk of developing T1D. Currently, HLA typing methods are relatively expensive and time consuming. We sought to determine the minimum number of single nucleotide polymorphisms (SNPs) that could rapidly define the HLA-DR types relevant to T1D, namely, DR3/4, DR3/3, DR4/4, DR3/X, DR4/X, and DRX/X (where X is neither DR3 nor DR4), and could distinguish the highest-risk DR4 type (DR4-DQ8) as well as the non-T1D–associated DR4-DQB1*03:01 type. We analyzed 19,035 SNPs of 10,579 subjects (7,405 from a discovery set and 3,174 from a validation set) from the Type 1 Diabetes Genetics Consortium and developed a novel machine learning method to select as few as three SNPs that could define the HLA-DR and HLA-DQ types accurately. The overall accuracy was 99.3%, area under curve was 0.997, true-positive rates were >0.99, and false-positive rates were <0.001. We confirmed the reliability of these SNPs by 10-fold cross-validation. Our approach predicts HLA-DR/DQ types relevant to T1D more accurately than existing methods and is rapid and cost-effective.
Objective To determine whether fluoroscopic guidance improves outcomes of injections for greater trochanteric pain syndrome.Design Multicentre double blind randomised controlled study.Setting Three academic and military treatment facilities in the United States and Germany.Participants 65 patients with a clinical diagnosis of greater trochanteric pain syndrome.Interventions Injections of corticosteroid and local anaesthetic into the trochanteric bursa, using fluoroscopy (n=32) or landmarks (that is, “blind” injections; n=33) for guidance.Main outcome measures Primary outcome measures: 0-10 numerical rating scale pain scores at rest and with activity at one month (positive categorical outcome predefined as ≥50% pain reduction either at rest or with activity, coupled with positive global perceived effect). Secondary outcome measures included Oswestry disability scores, SF-36 scores, reduction in drug use, and patients’ satisfaction.Results No differences in outcomes occurred favouring either the fluoroscopy or blind treatment groups. One month after injection the average pain scores were 2.7 at rest and 5.0 with activity in the fluoroscopy group compared with 2.2 and 4.0 in the blind injection group. Three months after the injection, 15 (47%) patients in the blind group and 13 (41%) in the fluoroscopy group continued to have a positive outcome.Conclusion Although using fluoroscopic guidance dramatically increases treatment costs for greater trochanteric pain syndrome, it does not necessarily improve outcomes.Trial registration Clinical trials NCT00480675
Most of the existing chest X-ray datasets include labels from a list of findings without specifying their locations on the radiographs. This limits the development of machine learning algorithms for the detection and localization of chest abnormalities. In this work, we describe a dataset of more than 100,000 chest X-ray scans that were retrospectively collected from two major hospitals in Vietnam. Out of this raw data, we release 18,000 images that were manually annotated by a total of 17 experienced radiologists with 22 local labels of rectangles surrounding abnormalities and 6 global labels of suspected diseases. The released dataset is divided into a training set of 15,000 and a test set of 3,000. Each scan in the training set was independently labeled by 3 radiologists, while each scan in the test set was labeled by the consensus of 5 radiologists. We designed and built a labeling platform for DICOM images to facilitate these annotation procedures. All images are made publicly available in DICOM format along with the labels of both the training set and the test set.
Most of the existing chest X-ray datasets include labels from a list of findings without specifying their locations on the radiographs. This limits the development of machine learning algorithms for the detection and localization of chest abnormalities. In this work, we describe a dataset of more than 100,000 chest X-ray scans that were retrospectively collected from two major hospitals in Vietnam. Out of this raw data, we release 18,000 images that were manually annotated by a total of 17 experienced radiologists with 22 local labels of rectangles surrounding abnormalities and 6 global labels of suspected diseases. The released dataset is divided into a training set of 15,000 and a test set of 3,000. Each scan in the training set was independently labeled by 3 radiologists, while each scan in the test set was labeled by the consensus of 5 radiologists. We designed and built a labeling platform for DICOM images to facilitate these annotation procedures. All images are made publicly available in DICOM format in company with the labels of the training set. The labels of the test set are hidden at the time of writing this paper as they will be used for benchmarking machine learning algorithms on an open platform.
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