The recommendations in this 2015 American Heart Association (AHA) Guidelines Update for Cardiopulmonary Resuscitation and Emergency Cardiovascular Care are based on an extensive evidence review process that was begun by the International Liaison Committee on Resuscitation (ILCOR) after the publication of the 2010 International Consensus on Cardiopulmonary Resuscitation and Emergency Cardiovascular Care Science With Treatment Recommendations 1,2 and was completed in February 2015.3,4

In this in-depth evidence review process, ILCOR examined topics and then generated a prioritized list of questions for systematic review. Questions were first formulated in PICO (population, intervention, comparator, outcome) format,5 and then search strategies and inclusion and exclusion criteria were defined and a search for relevant articles was performed. The evidence was evaluated by the ILCOR task forces by using the standardized methodological approach proposed by the Grading of Recommendations Assessment, Development and Evaluation (GRADE) Working Group.6

The quality of the evidence was categorized based on the study methodologies and the 5 core GRADE domains of risk of bias, inconsistency, indirectness, imprecision, and other considerations (including publication bias). Then, where possible, consensus-based treatment recommendations were created.

To create this 2015 Guidelines Update, the AHA formed 15 writing groups, with careful attention to manage conflicts of interest, to assess the ILCOR treatment recommendations and to write AHA treatment recommendations by using the AHA Class of Recommendation (COR) and Level of Evidence (LOE) system. The recommendations made in the Guidelines are informed by the ILCOR recommendations and GRADE classification, in the context of the delivery of medical care in North America. The AHA writing group made new recommendations only on topics specifically reviewed by ILCOR in 2015. This chapter delineates instances where the AHA writing group developed recommendations that are significantly stronger or weaker than the ILCOR statements. In the online …

Part 8: Post-cardiac arrest care: 2015 American Heart Association guidelines update for cardiopulmonary resuscitation and emergency cardiovascular care

The International Liaison Committee on Resuscitation (ILCOR) Advanced Life Support (ALS) Task Force performed detailed systematic reviews based on the recommendations of the Institute of Medicine of the National Academies1 and using the methodological approach proposed by the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) Working Group.2 Questions to be addressed (using the PICO [population, intervention, comparator, outcome] format)3 were prioritized by ALS Task Force members (by voting). Prioritization criteria included awareness of significant new data and new controversies or questions about practice. Questions about topics no longer relevant to contemporary practice or where little new research has occurred were given lower priority. The ALS Task Force prioritized 42 PICO questions for review. With the assistance of information specialists, a detailed search for relevant articles was performed in each of 3 online databases (PubMed, Embase, and the Cochrane Library).

By using detailed inclusion and exclusion criteria, articles were screened for further evaluation. The reviewers for each question created a reconciled risk of bias assessment for each of the included studies, using state-of-the-art tools: Cochrane for randomized controlled trials (RCTs),4 Quality Assessment of Diagnostic Accuracy Studies (QUADAS)-2 for studies of diagnostic accuracy,5 and GRADE for observational studies that inform both therapy and prognosis questions.6

GRADE evidence profile tables7 were then created to facilitate an evaluation of the evidence in support of each of the critical and important outcomes. The quality of the evidence (or confidence in the estimate of the effect) was categorized as high, moderate, low, or very low,8 based on the study methodologies and the 5 core GRADE domains of risk of bias, inconsistency, indirectness, imprecision, and other considerations (including publication bias).9

These evidence profile tables were then used to create a …

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Part 4: Advanced life support: 2015 International consensus on cardiopulmonary resuscitation and emergency cardiovascular care science with treatment recommendations

Objective: To systematically review outcomes in comatose survivors after cardiac arrest and cardiopulmonary resuscitation (CPR). Methods: The authors analyzed studies (1966 to 2006) that explored predictors of death or unconsciousness after 1 month or unconsciousness or severe disability after 6 months. Results: The authors identified four class I studies, three class II studies, and five class III studies on clinical findings and circumstances. The indicators of poor outcome after CPR are absent pupillary light response or corneal reflexes, and extensor or no motor response to pain after 3 days of observation (level A), and myoclonus status epilepticus (level B). Prognosis cannot be based on circumstances of CPR (level B) or elevated body temperature (level C). The authors identified one class I, one class II, and nine class III studies on electrophysiology. Bilateral absent cortical responses on somatosensory evoked potential studies recorded 3 days after CPR predicted poor outcome (level B). Burst suppression or generalized epileptiform discharges on EEG predicted poor outcomes but with insufficient prognostic accuracy (level C). The authors identified one class I, 11 class III, and three class IV studies on biochemical markers. Serum neuron-specific enolase higher than 33 μg/L predicted poor outcome (level B). Ten class IV studies on brain monitoring and neuroimaging did not provide data to support or refute usefulness in prognostication (level U). Conclusion: Pupillary light response, corneal reflexes, motor responses to pain, myoclonus status epilepticus, serum neuron-specific enolase, and somatosensory evoked potential studies can reliably assist in accurately predicting poor outcome in comatose patients after cardiopulmonary resuscitation for cardiac arrest.

https://n.neurology.org/content/neurology/67/2/203.full.pdf

Practice parameter: prediction of outcome in comatose survivors after cardiopulmonary resuscitation (an evidence-based review): report of the Quality Standards Subcommittee of the American Academy of Neurology.

Part 8 : Advanced life support : 2010 International Consensus on Cardiopulmonary Resuscitation and Emergency Cardiovascular Care Science With Treatment Recommendations

https://www.ahajournals.org/doi/pdf/10.1161/CIRCULATIONAHA.110.971051

Part 8: Advanced Life Support: 2010 International Consensus on Cardiopulmonary Resuscitation and Emergency Cardiovascular Care Science With Treatment Recommendations

There has been a great deal of material in the area of discrete-time transforms that has been published in recent years. This book does an excellent job of presenting important aspects of such material in a clear manner. The book has 11 chapters and a very useful appendix. Seven of these chapters are essentially devoted to the Fourier series/transform, discrete Fourier transform, fast Fourier transform (FFT), and applications of the FFT in the area of spectral estimation. Chapters 8 through 10 deal with many other discrete-time transforms and algorithms to compute them. Of these transforms, the KarhunenLoeve, the discrete cosine, and the Walsh-Hadamard transform are perhaps the most well-known. A lucid discussion of number theoretic transforms i5 presented in Chapter 11. This reviewer feels that the authors have done a fine job of compiling the pertinent material and presenting it in a concise and clear manner. There are a number of problems at the end of each chapter, an appreciable number of which are challenging. The authors have included a comprehensive set of references at the end of the book. In brief, this book is a valuable contribution to the general areas of signal processing and communications. It can be used for a graduate level course in perhaps two ways. One would be to cover the first seven chapters in great detail. The other would be to cover the whole book by focussing on different topics in a selective manner. This book  by  Elliott  and Rao  is extremely  useful to researchers/engineers who are working  in the areas of signal processing and communications. It i s also an excellent reference book, and hence a valuable addition to one’s library

http://ieeexplore.ieee.org/iel5/5/31347/01457444.pdf

Multirate digital signal processing

Recently, Deep Learning, especially through Convolutional Neural Networks (CNNs) has been widely used to enable the extraction of highly representative features This is done among the network layers by filtering, selecting, and using these features in the last fully connected layers for pattern classification However, CNN training for automated endoscopic image classification still provides a challenge due to the lack of large and publicly available annotated databases In this work we explore Deep Learning for the automated classification of colonic polyps using different configurations for training CNNs from scratch (or full training) and distinct architectures of pretrained CNNs tested on 8-HD-endoscopic image databases acquired using different modalities We compare our results with some commonly used features for colonic polyp classification and the good results suggest that features learned by CNNs trained from scratch and the "off-the-shelf" CNNs features can be highly relevant for automated classification of colonic polyps Moreover, we also show that the combination of classical features and "off-the-shelf" CNNs features can be a good approach to further improve the results

/pdf/exploring-deep-learning-and-transfer-learning-for-colonic-1tlrr32prw.pdf

Exploring Deep Learning and Transfer Learning for Colonic Polyp Classification

Background and aims: The role of antibiotic treatment in early stage gastric mucosa associated lymphoid tissue (MALT) lymphoma not associated with Helicobacter pylori infection has not been investigated. Patients and methods: Six patients with localised gastric MALT lymphoma underwent antibiotic treatment with clarithromycin, metronidazole, and pantoprazole. Staging, including endosonography plus gastroscopy, computed tomography of the thorax and abdomen, colonoscopy, magnetic resonance imaging of the salivary glands, and bone marrow biopsy were performed to rule out distant spread of the disease. In addition, MALT specific genetic changes, including reverse transcriptase-polymerase chain reaction for t(11;18)(q21;q21), were tested in all patients. H pylori infection was ruled out by histology, urease breath test, serology, and stool antigen testing. Results: All six patients had MALT lymphoma restricted to the stomach, and no evidence of infection with H pylori was found. Only one patient tested positive for t(11;18)(q21;q21) while the remaining five displayed no genetic aberrations. Following antibiotic treatment, endoscopic controls were performed every three months. Five patients responded with lymphoma regression between three and nine months following antibiotic treatment (one partial remission and four complete responses). One patient had stable disease for 12 months and was then referred for chemotherapy. Conclusions: Patients with early stage gastric MALT lymphoma negative for H pylori might still benefit from antibiotic treatment as the sole treatment modality.

/pdf/successful-antibiotic-treatment-of-helicobacter-pylori-2loc2jfej1.pdf

Successful antibiotic treatment of Helicobacter pylori negative gastric mucosa associated lymphoid tissue lymphomas

Texture patch classification is an important task in many different computer-aided medical systems. Convolutional Neural Networks (CNN's) have become state-of-the-art for many computer vision tasks in recent years. In this paper, we propose the use of CNN's for the automated classification of colonic mucosa for colon polyp staging in the context of colon cancer screening. This deep learning approach has the property of extracting features and classifying images in the same architecture by exploiting directly the input image pixels being successful in handling distortions such as different light conditions, presence of partial occlusions, etc. For this type of deep learning approach it is common to require that the database contains large amounts of data, which is quite rare in the medical field. The method proposed allows the use of small patches (subimages) to increase the size of the database as well to classify different regions in the same image. We show experimentally that this model is more efficient than some of the commonly used features for colonic polyp classification.

Colonic Polyp Classification with Convolutional Neural Networks

Objective: To assess the validity of early sensory evoked potential (SEP) recording for reliable outcome prediction in comatose cardiac arrest survivors within 48 h after restoration of spontaneous circulation (ROSC). Design and setting: Prospective cohort study in a medical intensive care unit of a university hospital. Patients: Twenty-five comatose, mechanically ventilated patients following cardiopulmonary resuscitation Measurements and results: Median nerve short- and long-latency SEP were recorded 4, 12, 24, and 48 h after ROSC. Cortical N20 peak latency and cervicomedullary conduction time decreased (improved) significantly between 4, 12, and 24 h after resuscitation in 22 of the enrolled patients. There was no further change in short-latency SEP at 48 h. The cortical N70 peak was initially detectable in seven patients. The number of patients with increased N70 peak increased to 11 at 12 h and 14 at 24 h; there was no further change at 48 h. Specificity of the N70 peak latency (critical cutoff 130 ms) increased from 0.43 at 4 h to 1.0 at 24 h after ROSC. Sensitivity decreased from 1.0 at 4 h to 0.83 at 24 h after ROSC. Conclusion: Within 24 h after ROSC there was a significant improvement in SEP. Therefore we recommend allowing a period of at least 24 h after cardiopulmonary resuscitation for obtaining a reliable prognosis based on SEP.

Time-dependency of sensory evoked potentials in comatose cardiac arrest survivors

Although diagnostic endoscopic retrograde cholangiopancreatography (ERCP) has been replaced in many fields by magnetic resonance cholangiopancreatography (MRCP), considerable amounts of research are still ongoing. Major fields of interest include ways of reducing the incidence of post-ERCP pancreatitis, new ways of improving the yield of tissue sampling, and the diagnosis of sphincter of Oddi dysfunction. In addition, there are new data comparing the diagnostic accuracy of ERCP with that of MRCP and endoscopic ultrasonography.

Michael Häfner

Papers

Exploring Deep Learning and Transfer Learning for Colonic Polyp Classification

Successful antibiotic treatment of Helicobacter pylori negative gastric mucosa associated lymphoid tissue lymphomas

Colonic Polyp Classification with Convolutional Neural Networks

Time-dependency of sensory evoked potentials in comatose cardiac arrest survivors

Diagnostic endoscopic retrograde cholangiopancreatography.