Metoclopramide for post-pyloric placement of naso-enteral feeding tubes

Background Enteral nutrition by feeding tube is a common and efficient method of providing nutritional support to prevent malnutrition in hospitalised patients who have adequate gastrointestinal function but who are unable to eat. Gastric feeding may be associated with higher rates of food aspiration and pneumonia than post-pyloric naso-enteral tubes. Thus, enteral feeding tubes are placed directly into the small intestine rather than the stomach, and the use of metoclopramide, a prokinetic agent, has been recommended to achieve post-pyloric placement, but its efficacy is controversial. Moreover, metoclopramide may include adverse reactions, which with high doses or prolonged use may be serious and irreversible. Objectives To determine the effect of intravenous metoclopramide on post-pyloric placement of the naso-enteral tube in adults. Search methods Trials were identified by searching the Cochrane Central Register of Controlled Trials (CENTRAL; 2014, Issue 10) which includes the CUGPD group's specialised register of trials, MEDLINE (1996 to 21 October 2014), EMBASE (1988 to 21 October 2014), LILACS (2005 to 21 October 2014) We did not confine our search to English language publications. Searches in all databases were updated originally in January 2005, then in November 2008 and again in October 2014. No new studies were found in 2008 or in 2014. Selection criteria We selected randomised controlled trials of adults needing enteral nutrition, who received intravenous or intramuscular metoclopramide to aid placement of transpyloric naso-enteral feeding tubes, compared to placebo or no intervention. Data collection and analysis We used standard methodological procedures expected by The Cochrane Collaboration. All analyses were performed according to the intention-to-treat method. We present risk ratios (RR) with 95% confidence intervals (CI). Main results Four studies, with a total of 204 participants were included and analysed. The trials compared metoclopramide with placebo (two trials) or with no intervention (two trials). Metoclopramide was investigated at doses of 10 mg (two trials) and 20 mg (two trials). There was no statistically significant difference between metoclopramide versus placebo or no intervention administered to promote tube placement (RR 0.82, 95% CI 0.61 to 1.10). Metoclopramide at doses of 10 mg (RR 0.82, 95% CI 0.60 to 1.11) and 20 mg (RR 0.62, 95% CI 0.15 to 2.62) were equally ineffective in facilitating post-pyloric intubation when compared with placebo or no intervention. Authors' conclusions In this review, we found only four studies that fitted our inclusion criteria. These were small, underpowered studies, in which metoclopramide was given at doses of 10 mg and 20 mg. Our analysis showed that metoclopramide did not assist post-pyloric placement of naso-enteral feeding tubes. Ideally randomised clinical trials should be performed that have a significant sample size, administering metoclopramide against control, however, given the lack of efficacy revealed by this review it is unlikely that further studies will be performe

Randomized trial evaluating serial protein C levels in severe sepsis patients treated with variable doses of drotrecogin alfa (activated)

International audienceINTRODUCTION: Serial alterations in protein C levels appear to correlate with disease severity in patients with severe sepsis, and it may be possible to tailor severe sepsis therapy with the use of this biomarker. The purpose of this study was to evaluate the dose and duration of drotrecogin alfa (activated) treatment using serial measurements of protein C compared to standard therapy in patients with severe sepsis. METHODS: This was a phase 2 multicenter, randomized, double-blind, controlled study. Adult patients with two or more sepsis-induced organ dysfunctions were enrolled. Protein C deficient patients were randomized to standard therapy (24 μg/kg/hr infusion for 96 hours) or alternative therapy (higher dose and/or variable duration; 24/30/36 μg/kg/hr for 48 to 168 hours). The primary outcome was a change in protein C level in the alternative therapy group, between study Day 1 and Day 7, compared to standard therapy. RESULTS: Of 557 patients enrolled, 433 patients received randomized therapy; 206 alternative, and 227 standard. Baseline characteristics of the groups were largely similar. The difference in absolute change in protein C from Day 1 to Day 7 between the two therapy groups was 7% (P = 0.011). Higher doses and longer infusions were associated with a more pronounced increase in protein C level, with no serious bleeding events. The same doses and longer infusions were associated with a larger increase in protein C level; higher rates of serious bleeding when groups received the same treatment; but no clear increased risk of bleeding during the longer infusion. This group also experienced a higher mortality rate; however, there was no clear link to infusion duration. CONCLUSIONS: The study met its primary objective of increased protein C levels in patients receiving alternative therapy demonstrating that variable doses and/or duration of drotrecogin alfa (activated) can improve protein C levels, and also provides valuable information for incorporation into potential future studies. TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT00386425

Comparing Regularized Kelvinlet Functions and the Finite Element Method for Registration of Medical Images to Sparse Organ Data

Image-guided surgery collocates patient-specific data with the physical environment to facilitate surgical decision making in real-time. Unfortunately, these guidance systems commonly become compromised by intraoperative soft-tissue deformations. Nonrigid image-to-physical registration methods have been proposed to compensate for these deformations, but intraoperative clinical utility requires compatibility of these techniques with data sparsity and temporal constraints in the operating room. While linear elastic finite element models are effective in sparse data scenarios, the computation time for finite element simulation remains a limitation to widespread deployment. This paper proposes a registration algorithm that uses regularized Kelvinlets, which are analytical solutions to linear elasticity in an infinite domain, to overcome these barriers. This algorithm is demonstrated and compared to finite element-based registration on two datasets: a phantom dataset representing liver deformations and an in vivo dataset representing breast deformations. The regularized Kelvinlets algorithm resulted in a significant reduction in computation time compared to the finite element method. Accuracy as evaluated by target registration error was comparable between both methods. Average target registration errors were 4.6 +/- 1.0 and 3.2 +/- 0.8 mm on the liver dataset and 5.4 +/- 1.4 and 6.4 +/- 1.5 mm on the breast dataset for the regularized Kelvinlets and finite element method models, respectively. This work demonstrates the generalizability of using a regularized Kelvinlets registration algorithm on multiple soft tissue elastic organs. This method may improve and accelerate registration for image-guided surgery applications, and it shows the potential of using regularized Kelvinlets solutions on medical imaging data.Comment: 17 pages, 9 figure

LIBR+: Improving Intraoperative Liver Registration by Learning the Residual of Biomechanics-Based Deformable Registration

The surgical environment imposes unique challenges to the intraoperative registration of organ shapes to their preoperatively-imaged geometry. Biomechanical model-based registration remains popular, while deep learning solutions remain limited due to the sparsity and variability of intraoperative measurements and the limited ground-truth deformation of an organ that can be obtained during the surgery. In this paper, we propose a novel \textit{hybrid} registration approach that leverage a linearized iterative boundary reconstruction (LIBR) method based on linear elastic biomechanics, and use deep neural networks to learn its residual to the ground-truth deformation (LIBR+). We further formulate a dual-branch spline-residual graph convolutional neural network (SR-GCN) to assimilate information from sparse and variable intraoperative measurements and effectively propagate it through the geometry of the 3D organ. Experiments on a large intraoperative liver registration dataset demonstrated the consistent improvements achieved by LIBR+ in comparison to existing rigid, biomechnical model-based non-rigid, and deep-learning based non-rigid approaches to intraoperative liver registration.Comment: 12 pages, Medical Image Computing and Computer Assisted Intervention 202

Bayesian Nonparametric Dimensionality Reduction of Categorical Data for Predicting Severity of COVID-19 in Pregnant Women

The coronavirus disease (COVID-19) has rapidly spread throughout the world and while pregnant women present the same adverse outcome rates, they are underrepresented in clinical research. We collected clinical data of 155 test-positive COVID-19 pregnant women at Stony Brook University Hospital. Many of these collected data are of multivariate categorical type, where the number of possible outcomes grows exponentially as the dimension of data increases. We modeled the data within the unsupervised Bayesian framework and mapped them into a lower-dimensional space using latent Gaussian processes. The latent features in the lower dimensional space were further used for predicting if a pregnant woman would be admitted to a hospital due to COVID-19 or would remain with mild symptoms. We compared the prediction accuracy with the dummy/one-hot encoding of categorical data and found that the latent Gaussian process had better accuracy

Rehospitalization following percutaneous coronary intervention for commercially insured patients with acute coronary syndrome: a retrospective analysis

BACKGROUND: While prior research has provided important information about readmission rates following percutaneous coronary intervention, reports regarding charges and length of stay for readmission beyond 30 days post-discharge for patients in a large cohort are limited. The objective of this study was to characterize the rehospitalization of patients with acute coronary syndrome receiving percutaneous coronary intervention in a U.S. health benefit plan. METHODS: This study retrospectively analyzed administrative claims data from a large US managed care plan at index hospitalization, 30-days, and 31-days to 15-months rehospitalization. A valid Diagnosis Related Group code (version 24) associated with a PCI claim (codes 00.66, 36.0X, 929.73, 929.75, 929.78–929.82, 929.84, 929.95/6, and G0290/1) was required to be included in the study. Patients were also required to have an ACS diagnosis on the day of admission or within 30 days prior to the index PCI. ACS diagnoses were classified by the International Statistical Classification of Disease 9 (ICD-9-CM) codes 410.xx or 411.11. Patients with a history of transient ischemic attack or stroke were excluded from the study because of the focus only on ACS-PCI patients. A clopidogrel prescription claim was required within 60 days after hospitalization. RESULTS: Of the 6,687 ACS-PCI patients included in the study, 5,174 (77.4%) were male, 5,587 (83.6%) were <65 years old, 4,821 (72.1%) had hypertension, 5,176 (77.4%) had hyperlipidemia, and 1,777 (26.6%) had diabetes. At index hospitalization drug-eluting stents were the most frequently used: 5,534 (82.8%). Of the 4,384 patients who completed the 15-month follow-up, a total of 1,367 (31.2%) patients were rehospitalized for cardiovascular (CV)-related events, of which 811 (59.3%) were revascularization procedures: 13 (1.0%) for coronary artery bypass graft and 798 (58.4%) for PCI. In general, rehospitalizations associated with revascularization procedures cost more than other CV-related rehospitalizations. Patients rehospitalized for revascularization procedures had the shortest median time from post-index PCI to rehospitalization when compared to the patients who were rehospitalized for other CV-related events. CONCLUSIONS: For ACS patients who underwent PCI, revascularization procedures represented a large portion of rehospitalizations. Revascularization procedures appear to be the most frequent, most costly, and earliest cause for rehospitalization after ACS-PCI

Intraoperative Liver Surface Completion with Graph Convolutional VAE

In this work we propose a method based on geometric deep learning to predict the complete surface of the liver, given a partial point cloud of the organ obtained during the surgical laparoscopic procedure. We introduce a new data augmentation technique that randomly perturbs shapes in their frequency domain to compensate the limited size of our dataset. The core of our method is a variational autoencoder (VAE) that is trained to learn a latent space for complete shapes of the liver. At inference time, the generative part of the model is embedded in an optimisation procedure where the latent representation is iteratively updated to generate a model that matches the intraoperative partial point cloud. The effect of this optimisation is a progressive non-rigid deformation of the initially generated shape. Our method is qualitatively evaluated on real data and quantitatively evaluated on synthetic data. We compared with a state-of-the-art rigid registration algorithm, that our method outperformed in visible areas

Publications

This component includes all publications associated with the Sparse Data Challenge (see wiki

Contributions

This component contains summary benchmark outputs for each registration method contributed to the challenge