Siwar Albashir

Endoscopic Ultrasound, Secretin Endoscopic Pancreatic Function Test, and Histology: Correlation in Chronic Pancreatitis

OBJECTIVES:Endoscopic ultrasound (EUS) and hormone-stimulated pancreatic function tests are considered useful, and possibly complementary, in the diagnosis of early chronic pancreatitis (CP). Few past studies have compared either methods with a histological gold standard. The aims were to assess correlations of EUS score and endoscopic pancreatic function test (ePFT) results with the degree of histological fibrosis, as well as the sensitivity of each method for detecting fibrosis.METHODS:This was a retrospective study of patients who underwent EUS, ePFT, or both within 12 months of pancreatic resection or wedge biopsy. EUS scoring was performed using 9 standard criteria, with ≥4 considered abnormal. An ePFT peak bicarbonate concentration <80 mM was considered abnormal. Surgical specimens were reviewed in a blinded manner by an expert pancreatic pathologist and assigned a fibrosis score from 0 to 12. Correlations of the EUS score and ePFT peak bicarbonate with the fibrosis score are reported using the Spearman correlation coefficient. Sensitivity and specificity was calculated for each method against the histological gold standard (fibrosis score ≥2).RESULTS:Twenty-five patients were included. The fibrosis score significantly correlated with the EUS score (r=0.72; 95% confidence interval (CI)=0.43, 0.87; P<0.001) and the ePFT peak bicarbonate (r=−0.57; 95% CI=−0.81, −0.10; P=0.016). EUS had a sensitivity of 84% (95% CI=69, 100) and specificity of 100% (95% CI=40, 100) compared with histology. The ePFT had a sensitivity of 86% (95% CI=67, 100) and specificity of 67% (95% CI=13, 100). When both modalities were combined, the sensitivity increased to 100% (95% CI=63, 100).CONCLUSIONS:Both EUS and ePFT are useful tests in the diagnosis of CP. Combining EUS with ePFT may improve the sensitivity for detection of early fibrosis.

Endoscopic ultrasonography to evaluate pancreatitis

Endoscopic ultrasonography (EUS) has become a well-accepted test in the workup of acute and chronic pancreatitis. However, further studies are needed to define its diagnostic role in patients with recurrent acute pancreatitis and minimal-change chronic pancreatitis. This test has become well accepted in the workup of acute and chronic pancreatitis. Further study is needed in recurrent acute pancreatitis and minimal-change chronic pancreatitis.

Progressive muscle weakness: More there than meets the eye.

A 56-year-old woman presents with proximal weakness in all four limbs. What are the possible causes?

Criteria for use of composite end points for competing risks—a systematic survey of the literature with recommendations

BACKGROUND Composite end points are frequently used in reports of clinical trials. One rationale for the use of composite end points is to account for competing risks. In the presence of competing risks, the event rate of a specific event depends on the rates of other competing events. One proposed solution is to include all important competing events in one composite end point. Clinical trialists require guidance regarding when this approach is appropriate. OBJECTIVES To identify publications describing criteria for use of composite end points for competing risk and to offer guidance regarding when a composite end point is appropriate on the basis of competing risks. METHODS, DATA SOURCES, STUDY SELECTION AND DATA EXTRACTION We searched MEDLINE, CINAHL, EMBASE, The Cochranes Central & Systematic Review databases including the Health Technology Assessment database, and the Cochranes Methodology register from inception to April 2015, and candidate textbooks, to identify all articles providing guidance on this issue. Eligible publications explicitly addressed the issue of a composite outcome to address competing risks. Two reviewers independently screened the titles and abstracts for full-text review; independently reviewed full-text publications; and abstracted specific criteria authors offered for use of composite end points to address competing risks. RESULTS Of 63,645 titles and abstracts, 166 proved potentially relevant of which 43 publications were included in the final review. Most publications note competing risks as a reason for using composite end points without further elaboration. None of the articles or textbook chapters provide specific criteria for use of composite end points for competing risk. Some advocate using composite end points to avoid bias due to competing risks and others suggest that composite end points seldom or never be used for this purpose. We recommend using composite end points for competing risks only if the competing risk is plausible and if it occurs with sufficiently high frequency to influence the interpretation of the effect of intervention on the end point of interest. These criteria will seldom be met. Review of heart failure trials published in the New England Journal of Medicine revealed that many of them use the composite end point of death or hospitalization; none of the trials, however, satisfied our criteria. CONCLUSION The existing literature fails to provide clear guidance regarding use of composite end point for competing risks. We recommend using composite end points for competing risks only if the competing risk is plausible and if it occurs sufficiently often.

Deep Learning in Gastrointestinal Endoscopy

Gastrointestinal (GI) endoscopy is used to inspect the lumen or interior of the GI tract for several purposes, including, (1) making a clinical diagnosis, in real time, based on the visual appearances; (2) taking targeted tissue samples for subsequent histopathological examination; and (3) in some cases, performing therapeutic interventions targeted at specific lesions. GI endoscopy is therefore predicated on the assumption that the operator-the endoscopist-is able to identify and characterize abnormalities or lesions accurately and reproducibly. However, as in other areas of clinical medicine, such as histopathology and radiology, many studies have documented marked interobserver and intraobserver variability in lesion recognition. Thus, there is a clear need and opportunity for techniques or methodologies that will enhance the quality of lesion recognition and diagnosis and improve the outcomes of GI endoscopy. Deep learning models provide a basis to make better clinical decisions in medical image analysis. Biomedical image segmentation, classification, and registration can be improved with deep learning. Recent evidence suggests that the application of deep learning methods to medical image analysis can contribute significantly to computer-aided diagnosis. Deep learning models are usually considered to be more flexible and provide reliable solutions for image analysis problems compared to conventional computer vision models. The use of fast computers offers the possibility of real-time support that is important for endoscopic diagnosis, which has to be made in real time. Advanced graphics processing units and cloud computing have also favored the use of machine learning, and more particularly, deep learning for patient care. This paper reviews the rapidly evolving literature on the feasibility of applying deep learning algorithms to endoscopic imaging.