Identification of patients at higher risk for myocardial injury following elective coronary artery intervention.

OBJECTIVES: To evaluate myocardial injury and infarction (MI) following elective percutaneous coronary intervention (PCI). BACKGROUND: The substantially higher analytical power of high-sensitivity troponin (hsTn) assays allows detection of minor cardiac troponin (cTn) levels, which may be useful in monitoring myocardial injury and guiding therapies. METHODS: Serial hsTnT measurements were conducted in patients undergoing elective PCI and were related to the extent of coronary artery disease (CAD) as reflected by the SYNTAX score risk categories and American College of Cardiology/American Heart Association classification of coronary lesions. Myocardial injury and MI were diagnosed according to the second and third versions of universal MI definition. RESULTS: The study population consisted of 530 patients, who were grouped into low (41.3%), intermediate (35.4%), and high (23.3%) SYNTAX risk categories. The treated coronary lesions were classified into A 7.8%, B1 24.1%, B2 21.1%, C1 24.6%, and C2 22.4%. Postprocedural hsTnT increases correlated significantly with the complexity of treated coronary lesions (p < .05) and CAD magnitude (p < .05). Rates of MI type 4a according to the second and third MI definition criteria were 98 (27.5%) and 15 (4.2%) cases in patients with normal baseline hsTnT values (N = 357, 67.4%), as well as 137 (79.2%) and 27 (15.6%) cases in those with elevated baseline hsTnT values (N = 173, 32.6%), respectively. CONCLUSIONS: After elective PCI, cTn releases correlate significantly with lesion complexity and CAD extent. Use of hsTnT assay enables precise monitoring of PCI-related myocardial injury and may identify patients at higher risk for ischemic events, who may benefit from potent platelet inhibition, which needs to be investigated in randomized trials.

Crowdtruth validation: a new paradigm for validating algorithms that rely on image correspondences.

PURPOSE: Feature tracking and 3D surface reconstruction are key enabling techniques to computer-assisted minimally invasive surgery. One of the major bottlenecks related to training and validation of new algorithms is the lack of large amounts of annotated images that fully capture the wide range of anatomical/scene variance in clinical practice. To address this issue, we propose a novel approach to obtaining large numbers of high-quality reference image annotations at low cost in an extremely short period of time. METHODS: The concept is based on outsourcing the correspondence search to a crowd of anonymous users from an online community (crowdsourcing) and comprises four stages: (1) feature detection, (2) correspondence search via crowdsourcing, (3) merging multiple annotations per feature by fitting Gaussian finite mixture models, (4) outlier removal using the result of the clustering as input for a second annotation task. RESULTS: On average, 10,000 annotations were obtained within 24 h at a cost of $100. The annotation of the crowd after clustering and before outlier removal was of expert quality with a median distance of about 1 pixel to a publically available reference annotation. The threshold for the outlier removal task directly determines the maximum annotation error, but also the number of points removed. CONCLUSIONS: Our concept is a novel and effective method for fast, low-cost and highly accurate correspondence generation that could be adapted to various other applications related to large-scale data annotation in medical image computing and computer-assisted interventions.

Surgical versus medical treatment of type 2 diabetes mellitus in nonseverely obese patients: a systematic review and meta-analysis.

OBJECTIVE: To compare surgical versus medical treatment of type 2 diabetes mellitus (T2DM) remission and comorbidities in patients with a body mass index (BMI) less than 35 kg/m2. BACKGROUND: Obesity surgery can achieve remission of T2DM and its comorbidities. Metabolic surgery has been proposed as a treatment option for diabetic patients with BMI less than 35 kg/m2 but the efficacy of metabolic surgery has not been conclusively determined. METHODS: A systematic literature search identified randomized (RCT) and nonrandomized comparative observational clinical studies (OCS) evaluating surgical versus medical T2DM treatment in patients with BMI less than 35 kg/m2. The primary outcome was T2DM remission. Additional analyses comprised glycemic control, BMI, HbA1c level, remission of comorbidities, and safety. Random effects meta-analyses were calculated and presented as weighted odds ratio (OR) or mean difference (MD) with 95% confidence intervals (95% CI). RESULTS: Five RCTs and 6 OCSs (706 total T2DM patients) were included. Follow-up ranged from 12 to 36 months. Metabolic surgery was associated with a higher T2DM remission rate (OR: 14.1, 95% CI: 6.7-29.9, P < 0.001), higher rate of glycemic control (OR: 8.0, 95% CI: 4.2-15.2, P < 0.001) and lower HbA1c level (MD: -1.4%, 95% CI -1.9% to -0.9%, P < 0.001) than medical treatment. BMI (MD: -5.5 kg/m2, 95% CI: -6.7 to -4.3 kg/m2, P < 0.001), rate of arterial hypertension (OR: 0.25, 95% CI: 0.12-0.50, P < 0.001) and dyslipidemia (OR: 0.21, 95% CI: 0.10-0.44, P < 0.001) were lower after surgery. CONCLUSION: Metabolic surgery is superior to medical treatment for short-term remission of T2DM and comorbidities. Further RCTs should address the long-term effects on T2DM complications and mortality.

Crowdsourcing for reference correspondence generation in endoscopic images.

Computer-assisted minimally-invasive surgery (MIS) is often based on algorithms that require establishing correspondences between endoscopic images. However, reference annotations frequently required to train or validate a method are extremely difficult to obtain because they are typically made by a medical expert with very limited resources, and publicly available data sets are still far too small to capture the wide range of anatomical/scene variance. Crowdsourcing is a new trend that is based on outsourcing cognitive tasks to many anonymous untrained individuals from an online community. To our knowledge, this paper is the first to investigate the concept of crowdsourcing in the context of endoscopic video image annotation for computer-assisted MIS. According to our study on publicly available in vivo data with manual reference annotations, anonymous non-experts obtain a median annotation error of 2 px (n = 10,000). By applying cluster analysis to multiple annotations per correspondence, this error can be reduced to about 1 px, which is comparable to that obtained by medical experts (n = 500). We conclude that crowdsourcing is a viable method for generating high quality reference correspondences in endoscopic video images.