Evaluation of the Protégé^(TM) stent in the treatment of carotid artery stenosis with adjunctive use of a filter Embolic Protection Device (PROCAR)-one-month follow-up data on 77 patients

Objective The trial was designed to evaluate the safety and performance of the ev3 Protégé TM stent in the treatment of de novo or re-stenotic common and/or internal carotid artery stenoses with adjunctive use of a CE-marked filter embolic protection device.Methods This study was a prospective multi-center, single-arm trial. Between June and October 2003, 77 patients were enrolled in 8 investigational centers throughout Europe. The primary endpoint was the incidence of Major Neurological Events (MANE) through one month. Other endpoints were the ability to properly place the stent, and primary patency and MANE after six months. Eligible for the study were patients with a de novo or restenotic target lesion located in the common and/or internal carotid artery (>70% stenosis for asymptomatic and >50% stenosis for symptomatic patients). The ev3 Spider (Embolic Protection Filter was used in 75 of 77 cases. Results In 76 out of the 77 patients (99%), the stent could be successfully implanted with a residual stenosis ≤30% as criterion. Of the 74 patients that had a carotid ultrasound at one month follow-up, none had a re-stenosis of the target lesion. There were three MANEs during or immediately after the procedure (3.9%), two were major and one was a minor stroke. There were eight severe complications (9.1%); six of these happened during or immediately after the procedure and were related to the procedure, none was related to the device. They are resolved without sequelae. No deaths have occurred.Conclusions The Protégé stent is safe and performs well in the treatment of carotid artery stenosis. The technical success rate for placement of the Protégé stent as assessed by the residual stenosis post implant was very high and all stents were successfully deployed. The incidence of MANE was comparable with that in other recent carotid stent studies and still lower than standard CEA.

Lutetium in prostate cancer: Reconstruction of patient-level data from published trials and generation of a multi-trial Kaplan-Meier curve

BACKGROUND Lutetium has been shown to be an important potential innovation in pre-treated metastatic castration-resistant prostate cancer.Two clinical trials have evaluated lutetium thus far(therap and vision with 99 and 385 patients,respectively),but their results are discordant.AIM To synthetize the available evidence on the effectiveness of lutetium in pre-treated metastatic castration-resistant prostate cancer;and to test the application of a new artificial intelligence technique that synthetizes effectiveness based on reconstructed patient-level data.METHODS We employed a new artificial intelligence method(shiny method)to pool the survival data of these two trials and evaluate to what extent the lutetium cohorts differed from one another.The shiny technique employs an original reconstruction of individual patient data from the Kaplan-Meier curves.The progression-free survival graphs of the two lutetium cohorts were analyzed and compared.RESULTS The hazard ratio estimated was in favor of the vision trial;the difference was statistically significant(P<0.001).These results indicate that further studies on lutetium are needed because the survival data of the two trials published thus far are conflicting.CONCLUSION Our study confirms the feasibility of reconstructing patient-level data from survival graphs in order to generate a survival statistics.

Investigations of CT Dose with Contrast Agent and Its Effects on the CTDI

Purpose: Computed tomography is a leading imaging technique for head & neck and brain and most of these imaging protocols iodine-based contrast media are utilised. The chief aim of this research is to utilize the effects of the contrast media “CM” used in computed tomography “CT” which is used to enhance subject contrast on the delivered CT via its inclusion into the CT dose index “CTDI”, and to introduce a simple method to determine this effect via the available CT numbers at the imaged targets. Method: The CT dose increase is estimated theoretically and measured experimentally and then related to the average CT number in the volume of CM uptake. A factor dependent on CM concentration and beam energy is added to the CTDI equation to represent the increased dose burden. A simple holed Perspex phantom was built to measure the variation of imaged CT number. CT Gafchromic type film was alternately imaged in a reservoir of CM and water. The relative difference in the dose burden as obtained by scanning the two films represents the dose difference and hence the CM dependent increase. Results: Measured dose effects due to the inclusion of the CM varied depending on the concentration. The increase in dose is estimated to be about 17% for 20% contrast media in the target while that for 10% by volume is around 6.6%. These are estimated from the CT numbers. Patients’ data also shows influence of the CM on the CTDI values. Conclusion: The dosimetric effects of the contrast media are included into the CTDI and can be estimated by using the CT numbers obtained.

Hyperledger Fabric Blockchain: Secure and Efficient Solution for Electronic Health Records

Background:Electronic Health Record(EHR)systems are used as an efficient and effective technique for sharing patient’s health records among different hospitals and various other key stakeholders of the healthcare industry to achieve better diagnosis and treatment of patients globally.However,the existing EHR systems mostly lack in providing appropriate security,entrusted access control and handling privacy and secrecy issues and challenges in current hospital infrastructures.Objective:To solve this delicate problem,we propose a Blockchain-enabled Hyperledger Fabric Architecture for different EHR systems.Methodology:In our EHR blockchain system,Peer nodes from various organizations(stakeholders)create a ledger network,where channels are created to enable secure and private communication between different stakeholders on the ledger network.Individual patients and other stakeholders are identified and registered on the network by unique digital certificates issued by membership service provider(MSP)component of the fabric architecture.Results:We created and implemented different Chaincodes to handle the business logic for executing separate EHR transactions on the network.The proposed fabric architecture provides a secure,transparent and immutable mechanism to store,share and exchange EHRs in a peer-to-peer network of different healthcare stakeholders.It ensures interoperability,scalability and availability in adapting the existing EHRs for strengthening and providing an effective and secure method to integrate and manage patient records among medical institutions in the healthcare ecosystem.

The Development of the Japanese Psychiatric Nursing Assessment Classification System (PsyNACS©)

The purpose of this study was to develop the Japanese Psychiatric Nursing Assessment Classification System (PsyNACS)©. This study used the on-line survey method from February to April 2015. There were 644 respondents comprised of professional nurses with minimum qualification of 3 years’ experience being in a psychiatric unit of a stand-alone 200-bed or more Psychiatric Hospital in Japan. Assessment items were derived from 211 specific items selected from nursing textbooks, excluding the North-American-Nursing-Diagnosis-Association (NANDA). The questionnaire contained Cover Letter, Demographic Data Sheet, and the PsyNACS© questionnaire. There were 644 questionnaires accessed but only 435 were completed and valid. Item levels of importance were evaluated: 1) Unnecessary, 2) Not very important, 3) Important and 4) Very important. Data analysis used descriptive and multivariate statistics. Factor analysis (principal factor analysis, varimax rotation), the Kaiser-Meyer-Olkin (KMO) and the Bartlett’s test of Sphericity assessed the adaptive validity of the factor analysis. Factor loadings were set at 0.4 or more for the configuration of items. Internal consistency and reliability were established using Cronbach’s alpha coefficient. Mean factor points were calculated, including standard deviation, range, and confidence interval at 95%. Statistical significance was at 0.05 level. Nine Patient Assessment Data (PAD) with 2 to 5 Cluster Assessment Data (CAD) each were categorized. Thirty one CADs comprised the Patient Assessment Data: (PAD1) Psychological symptom and stress, (PAD2) Information about treatment, (PAD3) Function of eating and balance of water, (PAD4) Life and value, (PAD5) Vital signs and health assessment, (PAD6) Self-care, (PAD7) Social support, (PAD8) Activity, sleeping and mobility capability, and (PAD9) Sexual function and sexual behavior. The PsyNACS© is a classification of items assessing health care needs within the Japanese psychiatric nursing care environment, and also can be used in various psychiatric patient care situations in all psychiatric units.

Individual Patient Data Meta-Analysis Is Needed in Chinese Medical Research

Publication biases and collection limitations are the main disadvantages of a traditional meta-analysis based on aggregate patient data（APD）from published articles.Individual patient data（IPD）meta-analysis,as the gold standard of systematic review,is a possible alternative in this context.However,the publications relative to IPD meta-analyses are still rare compared with the traditional ones,especially in the research oriented to Chinese medicine（CM）.In this article,the strengths and detailed functioning of IPD meta-analysis are described.Furthermore,the need for IPD meta-analysis to assess the treatments based on CM was also discussed.Compared with the traditional APD meta-analysis,the IPD meta-analysis might give a more accurate and unbiased assessment and is worth to be recommended to CM researchers.

Robust multifocus deep neural network for progression prediction on patient trajectory data

Purpose-Patient treatment trajectory data are used to predict the outcome of the treatment to particular disease that has been carried out in the research.In order to determine the evolving disease on the patient and changes in the health due to treatment has not considered existing methodologies.Hence deep learning models to trajectory data mining can be employed to identify disease prediction with high accuracy and less computation cost.Design/methodology/approach-Multifocus deep neural network classifiers has been utilized to detect the novel disease class and comorbidity class to the changes in the genome pattern of the patient trajectory data can be identified on the layers of the architecture.Classifier is employed to learn extracted feature set with activation and weight function and then merged on many aspects to classify the undetermined sequence of diseases as a new variant.The performance of disease progression learning progress utilizes the precision of the constituent classifiers,which usually has larger generalization benefits than those optimized classifiers.Findings-Deep learning architecture uses weight function,bias function on input layers and max pooling.Outcome of the input layer has applied to hidden layer to generate the multifocus characteristics of the disease,and multifocus characterized disease is processed in activation function using ReLu function along hyper parameter tuning which produces the effective outcome in the output layer of a fully connected network.Experimental results have proved using cross validation that proposed model outperforms methodologies in terms of computation time and accuracy.Originality/value-Proposed evolving classifier represented as a robust architecture on using objective function to map the data sequence into a class distribution of the evolving disease class to the patient trajectory.Then,the generative output layer of the proposed model produces the progression outcome of the disease of the particular patient trajectory.The model tries to produce the accurate prognosis outcomes by employing data conditional probability function.The originality of the work defines 70%and comparisons of the previous methods the method of values are accurate and increased analysis of the predictions.