Clinical Repository Research Articles

Blockchain‐based IoT architecture to secure healthcare system using identity‐based encryption

AbstractNowadays, blockchain and Internet of Things (IoT) are two emerging areas of the Information Technology (IT) sector. These two emerging areas are used in various fields, such as supply chain, logistics and automotive industry. Due to the low processing power and storage space of IoT devices, users' medical information is usually saved in a centralized third party like a clinical repository or a cloud computing environment. Thus, in many cases, users lose control of their medical information, which can result in security disclosure and a single‐point impediment. So, an advanced solution is required to improve the data sharing process, while restricting it in terms of security. Blockchain technology with IoT can significantly affect the healthcare industry by improving its efficiency, security and transparency, as well as can provide more business opportunities. The efficient sharing of Electronic Health Record (EHR) can improve the treatment process, diagnosis accuracy, security and privacy. This article proposes a blockchain‐based IoT architecture to provide enhanced security of healthcare data by using Identity‐Based Encryption (IBE) algorithm. Here, the smart contract defines all the basic operations of the healthcare system, which can be beneficial to all stakeholders. Many experiments are executed to evaluate the efficiency of the proposed scheme. The results show that the proposed scheme is better than the existing renowned schemes.

INNV-04. A MULTI-INSTITUTIONAL CLINICAL AND MRI REPOSITORY OF NEUROFIBROMATOSIS TYPE 1-ASSOCIATED PERIPHERAL NERVE SHEATH TUMORS

Abstract BACKGROUND Individuals with neurofibromatosis type 1 (NF1) frequently have peripheral nerve sheath tumors (PNST), including plexiform neurofibromas (PNF), atypical neurofibromas (ANF), and malignant peripheral nerve sheath tumors (MPNST). These tumors reflect a histologic spectrum from benign to malignant. Various clinical and MRI-based features are proposed as risk factors for MPNST development based on small single-institution studies. A major barrier to study these risk factors is collation and annotation of multi-center serial MRIs. To address this, we created a standardized database of clinical data and longitudinal MRIs from NF1-associated PNST from nine international NF1 referral centers. METHODS Clinical data from NF1 patients are collected in Research Electronic Data Capture databases housed at Massachusetts General Hospital and Washington University, including demographic information, genotype, disease course, treatment history, and survival. ANF and MPNST require histologic confirmation whereas a diagnosis of PNF can also be made based on clinical/radiographic stability. Longitudinal MRIs predating the histologic diagnosis are uploaded to a HIPAA-compliant cloud-based system. RESULTS Data from 200 patients (87 females, 113 males) with 217 tumors (75 PNF, 40 ANF, 102 MPNST) have been collected. 280 regional and 108 whole-body MRIs have been identified. Median age at the time of histologic diagnosis is 30 years (range 5-64). All tumors are histologically confirmed except for 6 PNF which remained stable over time. Median follow-up time is 32 months. Of 147 patients with available survival data, 32 (21.7%) have died from MPNST progression; estimated median overall survival is 20 months. CONCLUSIONS In this ongoing work, we have assembled one of the largest systematically annotated clinical and MRI repositories of NF1-associated PNST from pediatric and adult NF1 patients. The data will be accessible to outside researchers which will promote interdisciplinary and multi-center collaborations. Active efforts include the identification of radiomic MRI features to differentiate between PNF and MPNST.

Clinical and laboratory characteristics of Brazilian versus non-Brazilian primary antiphospholipid syndrome patients in AntiPhospholipid Syndrome Alliance for Clinical Trials and InternatiOnal Networking (APS ACTION) clinical database and repository

BackgroundAntiphospholipid syndrome (APS) is characterized by episodes of thrombosis, obstetric morbidity or both, associated with persistently positive antiphospholipid antibodies (aPL). Studying the profile of a rare disease in an admixed population is important as it can provide new insights for understanding an autoimmune disease. In this sense of miscegenation, Brazil is characterized by one of the most heterogeneous populations in the world, which is the result of five centuries of interethnic crosses of people from three continents. The objective of this study was to compare the clinical and laboratory characteristics of Brazilian vs. non-Brazilian primary antiphospholipid syndrome (PAPS) patients.MethodsWe classified PAPS patients into 2 groups: Brazilian PAPS patients (BPAPS) and PAPS patients from other countries (non-BPAPS). They were compared regarding demographic characteristics, criteria and non-criteria APS manifestations, antiphospholipid antibody (aPL) profile, and the adjusted Global Antiphospholipid Syndrome Score (aGAPSS).ResultsWe included 415 PAPS patients (88 [21%] BPAPS and 327 [79%] non-BPAPS). Brazilian patients were significantly younger, more frequently female, sedentary, obese, non-white, and had a higher frequency of livedo (25% vs. 10%, p < 0.001), cognitive dysfunction (21% vs. 8%, p = 0.001) and seizures (16% vs. 7%, p = 0.007), and a lower frequency of thrombocytopenia (9% vs. 18%, p = 0.037). Additionally, they were more frequently positive for lupus anticoagulant (87.5% vs. 74.6%, p = 0.01), and less frequently positive to anticardiolipin (46.6% vs. 73.7%, p < 0.001) and anti-ß2-glycoprotein-I (13.6% vs. 62.7%, p < 0.001) antibodies. Triple aPL positivity was also less frequent (8% vs. 41.6%, p < 0.001) in Brazilian patients. Median aGAPSS was lower in the Brazilian group (8 vs. 10, p < 0.0001). In the multivariate analysis, BPAPS patients still presented more frequently with livedo, cognitive dysfunction and sedentary lifestyle, and less frequently with thrombocytopenia and triple positivity to aPL. They were also less often white.ConclusionsOur study suggests a specific profile of PAPS in Brazil with higher frequency of selected non-criteria manifestations and lupus anticoagulant positivity. Lupus anticoagulant (not triple positivity) was the major aPL predictor of a classification criteria event.

Machine learning‐enabled multitrust audit of stroke comorbidities using natural language processing

With the increasing adoption of electronic records in the health system, machine learning-enabled techniques offer the opportunity for greater computer-assisted curation of these data for audit and research purposes. In this project, we evaluate the consistency of traditional curation methods used in routine clinical practice against a new machine learning-enabled tool, MedCAT, for the extraction of the stroke comorbidities recorded within the UK's Sentinel Stroke National Audit Programme (SSNAP) initiative. A total of 2327stroke admission episodes from three different National Health Service (NHS) hospitals, between January 2019 and April 2020, were included in this evaluation. In addition, current clinical curation methods (SSNAP) and the machine learning-enabled method (MedCAT) were compared against a subsample of 200 admission episodes manually reviewed by our study team. Performance metrics of sensitivity, specificity, precision, negative predictive value, and F1 scores are reported. The reporting of stroke comorbidities with current clinical curation methods is good for atrial fibrillation, hypertension, and diabetes mellitus, but poor for congestive cardiac failure. The machine learning-enabled method, MedCAT, achieved better performances across all four assessed comorbidities compared with current clinical methods, predominantly driven by higher sensitivity and F1 scores. We have shown machine learning-enabled data collection can support existing clinical and service initiatives, with the potential to improve the quality and speed of data extraction from existing clinical repositories. The scalability and flexibility of these new machine-learning tools, therefore, present an opportunity to revolutionize audit and research methods.

Big Data in Nephrology.

A huge array of data in nephrology is collected through patient registries, large epidemiological studies, electronic health records, administrative claims, clinical trial repositories, mobile health devices and molecular databases. Application of these big data, particularly using machine-learning algorithms, provides a unique opportunity to obtain novel insights into kidney diseases, facilitate personalized medicine and improve patient care. Efforts to make large volumes of data freely accessible to the scientific community, increased awareness of the importance of data sharing and the availability of advanced computing algorithms will facilitate the use of big data in nephrology. However, challenges exist in accessing, harmonizing and integrating datasets in different formats from disparate sources, improving data quality and ensuring that data are secure and the rights and privacy of patients and research participants are protected. In addition, the optimism for data-driven breakthroughs in medicine is tempered by scepticism about the accuracy of calibration and prediction from in silico techniques. Machine-learning algorithms designed to study kidney health and diseases must be able to handle the nuances of this specialty, must adapt as medical practice continually evolves, and must have global and prospective applicability for external and future datasets.

Challenges in the remote monitoring of cardiac implantable electronic devices in 2021.

Patients with cardiac implantable electronic devices have usually been scheduled for routine in‑hospital visits. In addition, they are now monitored remotely. The remote monitoring of cardiac implantable electronic devices is a valuable tool to screen and triage patients at very high risk of deterioration. The continuous expansion of remote monitoring in real‑world settings brought a substantial increase of published evidence on the topic. Therefore, this review aims to summarize challenges and knowledge gaps in the field. Challenges that were identified as issues to be solved comprise warranty of data security and accessibility, integration with clinical repositories, patient selection and persistence, and resource availability. Future improvements of telemedicine will need to face these significant residual challenges.

Use of a Clinical Electrocardiographic Database to Enhance Atrial Fibrillation/Atrial Flutter Identification Algorithms Based on Administrative Data.

BackgroundAdministrative data have limited sensitivity for case finding of atrial fibrillation/atrial flutter (AF/AFL). Linkage with clinical repositories of interpreted ECGs may enhance diagnostic yield of AF/AFL.Methods and ResultsWe retrieved 369 ECGs from the institutional Marquette Universal System for Electrocardiography (MUSE) repository as validation samples, with rhythm coded as AF (n=49), AFL (n=50), or other competing rhythm diagnoses (n=270). With blinded, duplicate review of ECGs as the reference comparison, we compared multiple MUSE coding definitions for identifying AF/AFL. We tested the agreement between MUSE diagnosis and reference comparison, and calculated the sensitivity and specificity. Using a data set linking clinical registries, administrative data, and the MUSE repository (n=11 662), we assessed the incremental diagnostic yield of AF/AFL by incorporating ECG data to administrative data‐based algorithms. The agreement between MUSE diagnosis and reference comparison depended on the coding definitions applied, with the Cohen κ ranging from 0.57 to 0.75. Sensitivity ranged from 60.6% to 79.1%, and specificity ranged from 93.2% to 98.0%. A coding definition with AF/AFL appearing in the first 3 ECG statements had the highest sensitivity (79.1%), with little loss of specificity (94.5%). Compared with the algorithms with only administrative data, incorporating ECG data increased the diagnostic yield of preexisting AF/AFL by 14.5% and incident AF/AFL by 7.5% to 16.1%.ConclusionsRoutine ECG interpretation using MUSE coding is highly specific and moderately sensitive for AF/AFL detection. Inclusion of MUSE ECG data in AF/AFL case identification algorithms can identify cases missed using administrative data‐based algorithms alone.

Second-hand smoke and NFE2L2 genotype interaction increases paediatric asthma risk and severity.

Second-hand smoke (SHS) exposure is associated with paediatric asthma, and oxidative stress is believed to play a role in mediating this association. The nuclear factor (erythroid-derived 2)-like 2 (NFE2L2) is important for the defence against oxidative stress. To explore interactions between NFE2L2 genotype and SHS exposure in paediatric asthma risk. We used a genotyped subset of patients of European ancestry (N=669, median age at enrolment=6.8years) enrolled in the clinical cohort Greater Cincinnati Pediatric Clinic Repository as the study population, and a population-based paediatric cohort (N=791) to replicate our findings. History of asthma diagnosis was obtained from medical records, and SHS exposure was obtained from questionnaires. Four NFE2L2 tagging SNPs were included in the analysis, and interactions between SHS and NFE2L2 genotype were evaluated using logistic regression. Three of the analysed SNPs, rs10183914, rs1806649 and rs2886161, interacted significantly with SHS exposure to increase asthma risk (p≤.02). The interaction was replicated in an independent cohort for rs10183914 (p=.04). Interactions between SHS exposure and NFE2L2 genotype were also associated with an increased risk of hospitalization (p=.016). In stratified analyses, NFE2L2 genotype was associated with daily asthma symptoms in children with SHS exposure (OR=3.1; p=.048). No association was found in children without SHS exposure. Examination of publicly available chromatin immunoprecipitation followed by sequencing (ChIP-seq) data sets confirmed the presence of active histone marks and binding sites for particular transcription factors overlapping the coordinates for the significantly associated SNPs. Our study provides evidence that NFE2L2 genotype interacts with SHS exposure to affect both asthma risk and severity in children and identifies a population of children at increased risk of asthma development.

1696. The Black Box of Using Health Claims-Based Analyses to Estimate UTI Prevalence in Community-Dwelling Women

BackgroundUrinary tract infections (UTIs) are common in women but most epidemiology studies occurred in specialized settings (university health clinics) or used outdated methods (random digit dialing). Currently, women receive UTI care in systems with electronic health records (EHR), thus documenting care of a wider female demographic in real-world settings. We estimated the prevalence of acute, uncomplicated UTIs in community-dwelling women in a health claims database using various operational definitions of UTI.MethodsWe conducted a retrospective analysis of claims data from the OptumLabs® Data Warehouse (OLDW), a de-identified claims and clinical information repository for privately insured and Medicare Advantage enrollees in a large, private US health plan. Non-pregnant female patients ≥ 15 years of age with two years of continuous enrollment between 2007-2015 and a visit encounter in an outpatient office, urgent care, or emergency department were included. Women with lower urinary tract disease/abnormalities, neurological disease, urological treatment, procedures or urinary catheter use, cancer or HIV treatment were excluded. Decision rules for identifying UTIs were derived using one or more combinations of: relevant ICD-9 codes, UTI symptom diagnosis codes, positive urine test results, and/or antibiotic prescription recorded in the EHR and claims. Prevalence rates were calculated for each decision rule.ResultsOf the 7,337,700 females in the claims database, 947,041 (12.97%) had an index UTI diagnosis or symptoms and met eligibility criteria. The table below illustrates prevalence rates according to each decision rule. As shown, applying decision rules based on common UTI definitions resulted in large differences in prevalence rates.Table ConclusionUsing common definitions for UTI to analyze claims data from an insurer of large proportions of the US, we obtained significantly different prevalence rates. This study highlights major limitations in using EHR and claims data for UTI quality initiatives such as tracking of practices associated with antimicrobial stewardship and lends credibility to proposals to track these infections as a reportable disease.Disclosures All Authors: No reported disclosures

Machine Learning Algorithms Predict Clinically Significant Improvements in Satisfaction After Hip Arthroscopy

To develop machine learning algorithms to predict failure to achieve clinically significant satisfaction after hip arthroscopy. We queried a clinical repository for consecutive primary hip arthroscopy patients treated between January 2012 and January 2017. Five supervised machine learning algorithms were developed in a training set of patients and internally validated in an independent testing set of patients by discrimination, Brier score, calibration, and decision-curve analysis. The minimal clinically important difference (MCID) for the visual analog scale (VAS) score for satisfaction was derived by an anchor-based method and used as the primary outcome. A total of 935 patients were included, of whom 148 (15.8%) did not achieve the MCID for the VAS satisfaction score at a minimum of 2 years postoperatively. The best-performing algorithm was the neural network model (C statistic, 0.94; calibration intercept, -0.43; calibration slope, 0.94; and Brier score, 0.050). The 5 most important features to predict failure to achieve the MCID for the VAS satisfaction score were history of anxiety or depression, lateral center-edge angle, preoperative symptom duration exceeding 2 years, presence of 1 or more drug allergies, and Workers' Compensation. Supervised machine learning algorithms conferred excellent discrimination and performance for predicting clinically significant satisfaction after hip arthroscopy, although this analysis was performed in a single population of patients. External validation is required to confirm the performance of these algorithms. Level III, therapeutic case-control study.

Efficacy and Safety of Sofosbuvir/Daclatasvir in the Treatment of COVID-19: A Randomized, Controlled Study

BACKGROUND: Data from molecular docking, in-vitro experiments and 2 published small clinical studies suggested a potential therapeutic benefit for the anti-hepatitis C drugs, sofosbuvir (SOF) and daclatasvir (DCV), to repurpose for the treatment of COVID-19. We planned this study to evaluate efficacy and safety of dual SOF/DCV as add-on treatment to the standard of care (SOC) in patients with COVID-19, initially hospitalized to a non-intensive care setting.METHODS: Eighty nine consecutive eligible patients presenting to a single center in Cairo were included in the study and randomly assigned to two treatment groups. The experimental group was treated with the SOC therapy (as per the Egyptian ministry of health protocol) in addition to one 400 mg tablet SOF and one 60 mg DCV daily for 10 days; while the control group was treated with the SOC therapy alone. Baseline clinical, laboratory and imaging data were measured and followed up for 21 days. Data were compared between the two treatment groups.FINDINGS: The proportion of cumulative clinical recovery in the experimental group at day 21 was numerically greater than the control group (40/44 (91%; CI: 78.8-96.4%) versus 35/45 (77.8%; 63.7-87.5%)). The Hazard Ratio (HR) for time to clinical recovery adjusted for baseline severity by a Cox-regression model was statistically significant: HR: 1.59 (CI: 1.001-2.5), signifying nearly 1.6 times higher probability of clinical recovery in the experimental group than the control at any time point during the study. Concordantly, the experimental group also showed trends to greater numerical improvement in other efficacy endpoints including the mean 8 points ordinal scale score, the mean severity of lung lesions score and the case fatality rate (4.5% versus 11.1%) than the control group. All these effects, though did not reach statistical significance at the study sample size, but being all concordant with the HR, they support the study concept. No serious or severe adverse events were reported in both groups and the treatment was well tolerated.INTERPRETATION: This study provides support to the potential benefits and safety of sofosbuvir combined with daclatasvir in the treatment of COVID-19. It is hoped to encourage further large sized multinational studies to confirm these encouraging results.Trial Registration: The study protocol was registered in the German clinical trial database repository (DRKS00022203) before the study initiation.Funding Statement: This study was funded by Pharco Corporate.Declaration of Interests: SH and OE are employees of Pharco, SH holds stock in Pharco. MY, AH conducted clinical studies and provided consultations for Pharco. Others have nothing to declare. Ethics Approval Statement: The study protocol was reviewed and approved by the Research Ethics Committee of Faculty of Medicine, Alexandria University (IRB00007555) and the Central Egyptian Ministry of Health and People Research Ethics Committee according to the Declaration of Helsinki. All subjects gave written informed consent before any treatment interventions were performed.

Development of Machine Learning Algorithms to Predict Being Lost to Follow-up After Hip Arthroscopy for Femoroacetabular Impingement Syndrome

PurposeTo determine factors predictive of patients who are at risk for being lost to follow-up after hip arthroscopy for femoroacetabular impingement syndrome (FAIS).MethodsA prospective clinical repository was queried between January 2012 and October 2017 and all patients who underwent hip arthroscopy for primary or revision FAIS with minimum 2-year follow-up were included. A total of 27 potential risk factors for loss to follow-up were available and tested for predictive value. An 80:20 random sample split of all patients was performed to create training and testing sets. Cross-validation, minimum Bayes information criteria, and adaptive machine-learning algorithms were used to develop the predictive model. The model with the best predictive performance was selected based off of the lowest postestimation deviance between the training and testing samples. The c-statistic is a measure of discrimination. It ranges from 0.5 to 1.0, with 1.0 being perfect discrimination and 0.5 indicating the model is no better than chance. A log-likelihood χ2 test was used to evaluate the goodness-of-fit of the logistic regression model.ResultsA total of 2113 patients were included. Inference of minimum Bayes information criteria model indicated that male sex (odds ratio [OR] 1.82, P = .028), non-white race (African American OR 2.41, P = .013; other non-white OR 1.42, P = .042), smoking (OR 1.07, P = .021), and failure to provide a phone number (OR 1.78, P = .032) increased the risk for being lost to follow-up. Furthermore, greater preoperative International Hip Outcome Tool 12-item component questionnaire (OR 1.03, P = .004), and modified Harris Hip Score (OR 1.05, P = .014) scores increased the risk of being lost to follow-up. The c-statistic was 0.76 (95% confidence interval 0.701-0.848). The log-likelihood indicated that the regression model as a whole was statistically significant (P = .002).ConclusionsPatients who are male, non-white, smokers, fail to provide a telephone number, and have greater preoperative modified Harris Hip Score and International Hip Outcome Tool 12-item component questionnaire scores are at an increased risk for being lost to follow-up 2 years after hip arthroscopy for FAIS.Level of EvidenceLevel III, case control study

Pharmacotherapy virtual attachment during COVID-19 pandemic: Use of online experiential assessment in a Malaysian pharmacy school

&#x0D; &#x0D; &#x0D; &#x0D; This case study aims to report the efforts made by International Islamic University Malaysia to pursue the experiential pharmacotherapy attachment for the final-year of pharmacy students via online mediums in the light of the COVID-19 restrictions on face-to-face clinical experiential learning. A four-week virtual pharmacotherapy experiential attachments were redesigned and conducted via an online platform (Google Meet). Students were required to have a two-hour virtual discussion and consultation session with their preceptors twice a week throughout the attachment period. In each week, students were provided with real-life clinical cases that were chosen by their preceptors from the Department of Pharmacy Practice’s clinical case repository. Students were assessed via clinical case reports, virtual seminar presentations, weekly virtual discussions with preceptors, and virtual objective structured clinical examinations. All the virtual sessions were conducted on a synchronous basis to assure students’ active participation. Alongside this, a revised online final assessment was also designed.&#x0D; &#x0D; &#x0D; &#x0D;

Antiphospholipid Antibody Profile Stability Over Time: Prospective Results From the APS ACTION Clinical Database and Repository.

The APS ACTION Registry studies long-term outcomes in persistently antiphospholipid antibody (aPL)-positive patients. Our primary objective was to determine whether clinically meaningful aPL profiles at baseline remain stable over time. Our secondary objectives were to determine (1) whether baseline characteristics differ between patients with stable and unstable aPL profiles, and (2) predictors of unstable aPL profiles over time. A clinically meaningful aPL profile was defined as positive lupus anticoagulant (LAC) test and/or anticardiolipin (aCL)/anti-β2 glycoprotein-I (anti-β2-GPI) IgG/M ≥ 40 U. Stable aPL profile was defined as a clinically meaningful aPL profile in at least two-thirds of follow-up measurements. Generalized linear mixed models with logit link were used for primary objective analysis. Of 472 patients with clinically meaningful aPL profile at baseline (median follow-up 5.1 yrs), 366/472 (78%) patients had stable aPL profiles over time, 54 (11%) unstable, and 52 (11%) inconclusive. Time did not significantly affect odds of maintaining a clinically meaningful aPL profile at follow-up in univariate (P = 0.906) and multivariable analysis (P = 0.790). Baseline triple aPL positivity decreased (OR 0.25, 95% CI 0.10-0.64, P = 0.004) and isolated LAC test positivity increased (OR 3.3, 95% CI 1.53-7.13, P = 0.002) the odds of an unstable aPL profile over time. Approximately 80% of our international cohort patients with clinically meaningful aPL profiles at baseline remain stable at a median follow-up of 5 years; triple aPL-positivity increase the odds of a stable aPL profile. These results will guide future validation studies of stored blood samples through APS ACTION Core Laboratories.

Bone marrow lesion on magneticresonance imaging indicates the last chance for hip osteonecrosis treated with vascularized fibular grafting before collapse.

Timing of surgery is the most critical prognostic factor for hip osteonecrosis treated with free vascularized fibular grafting (FVFG). Bone marrow lesion (BML) on MRI usually occurs immediately before femoral head collapse. We conducted a retrospective cohort study to evaluate whether the noncollapsed hips with BML can benefit from FVFG. Consecutive patients undergoing modified FVFG were identified from our clinical repository between January 2014 and December 2014. Based on whether BML was pre-operatively detected, the four year radiographic and clinical outcomes were compared. In the BML cohort, 22 of 53 hips (42%) showed radiographic signs of osteonecrosis progression, which was significantly higher than that in the control cohort (8 of 49, 16%; P = 0.005). The BML hips showed a significantly lower pre-operative Harris Hip Score (HHS) than those without BML (77.8 vs. 85.5, P = 0.046), whereas no such difference was observed in the final HHS or its post-operative improvement (HHS 90 vs. 94, P = 0.397; HHS improvement 12 vs. 8, P = 0.067). In the subgroup of patients with a pre-operative HHS lower than 80, BML hips were associated with a slightly lower final HHS than hips without BML. Four of 5 (80%) failed hips with BML had a poor pre-operative hip function. BML indicates the last chance for a reproducible improvement in the treatment of hip osteonecrosis with FVFG before collapse. However, the concomitant lower pre-operative HHS (< 80) is a poor prognostic factor for BML-positive hips.

Genetic Architecture of Parkinson's Disease in the Indian Population: Harnessing Genetic Diversity to Address Critical Gaps in Parkinson's Disease Research.

Over the past two decades, our understanding of Parkinson's disease (PD) has been gleaned from the discoveries made in familial and/or sporadic forms of PD in the Caucasian population. The transferability and the clinical utility of genetic discoveries to other ethnically diverse populations are unknown. The Indian population has been under-represented in PD research. The Genetic Architecture of PD in India (GAP-India) project aims to develop one of the largest clinical/genomic bio-bank for PD in India. Specifically, GAP-India project aims to: (1) develop a pan-Indian deeply phenotyped clinical repository of Indian PD patients; (2) perform whole-genome sequencing in 500 PD samples to catalog Indian genetic variability and to develop an Indian PD map for the scientific community; (3) perform a genome-wide association study to identify novel loci for PD and (4) develop a user-friendly web-portal to disseminate results for the scientific community. Our “hub-spoke” model follows an integrative approach to develop a pan-Indian outreach to develop a comprehensive cohort for PD research in India. The alignment of standard operating procedures for recruiting patients and collecting biospecimens with international standards ensures harmonization of data/bio-specimen collection at the beginning and also ensures stringent quality control parameters for sample processing. Data sharing and protection policies follow the guidelines established by local and national authorities.We are currently in the recruitment phase targeting recruitment of 10,200 PD patients and 10,200 healthy volunteers by the end of 2020. GAP-India project after its completion will fill a critical gap that exists in PD research and will contribute a comprehensive genetic catalog of the Indian PD population to identify novel targets for PD.

COVID-19 coronavirus research has overall low methodological quality thus far: case in point for chloroquine/hydroxychloroquine

Objectives/Background and ObjectivesPrior epidemics of high-mortality human coronaviruses, such as the acute respiratory syndrome coronavirus (SARS-CoV or SARS-1) in 2003, have driven the characterization of compounds that could be possibly active against the currently emerging novel coronavirus SARS-CoV-2 (COVID-19). Presently, no approved treatment or prophylaxis is available for COVID-19. We comment on the existing COVID-19 research methodologies in general and the published reporting. Given the media attention and claims of effectiveness, we chose chloroquine and hydroxychloroquine, in combination with azithromycin, as an area of COVID-19 research to examine. Methods/Study Design and SettingMEDLINE and EMBASE electronic databases were searched from 2019 to present (April 3rd, 2020) using a mix of keywords such as COVID-19 and chloroquine and hydroxychloroquine. We also searched the largest clinical medicine preprint repository, medRxiv.org. ResultsWe found 6 studies, 3 randomized control trials and 3 observational studies, focusing on chloroquine and hydroxychloroquine (with azithromycin). We critically appraised the evidence. ConclusionWe found that the COVID-19 research methodology is very poor in the area of chloroquine/hydroxychloroquine research. In screening the literature, we observed the same across COVID-19 research in relation to potential treatments. The reporting is very poor and sparse, and patient-important outcomes needed to discern decision-making priorities are not reported. We do understand the barriers to perform rigorous research in health care settings overwhelmed by a novel deadly disease. However, this emergency pandemic situation does not transform flawed methods and data into credible results. The adequately powered, comparative, and robust clinical research that is needed for optimal evidence-informed decision-making remains absent in COVID-19.

Deep convolutional neural networks with transfer learning for automated brain image classification

MR brain image categorization has been an active research domain from the last decade. Several techniques have been devised in the past for MR image categorization, starting from classical to the deep learning methods like convolutional neural networks (CNNs). Classical machine learning methods need handcrafted features to perform classification. The CNNs, on the other hand, perform classification by extracting image features directly from raw images via tuning the parameters of the convolutional and pooling layer. The features extracted by CNN strongly depend on the size of the training dataset. If the training dataset is small, CNN tends to overfit after several epochs. So, deep CNNs (DCNNs) with transfer learning have evolved. The prime objective of the present work is to explore the capability of different pre-trained DCNN models with transfer learning for pathological brain image classification. Various pre-trained DCNNs, namely Alexnet, Resnet50, GoogLeNet, VGG-16, Resnet101, VGG-19, Inceptionv3, and InceptionResNetV2, were used in the present study. The last few layers of these models were replaced to accommodate new image categories for our application. These models were extensively evaluated on data from Harvard, clinical, and benchmark Figshare repository. The dataset was then partitioned in the ratio 60:40 for training and testing. The validation on the test set reveals that the pre-trained Alexnet with transfer learning exhibited the best performance in less time compared to other proposed models. The proposed method is more generic as it does not need any handcrafted features and can achieve an accuracy value of 100%, 94%, and 95.92% for three datasets. Other performance measures used in the study include sensitivity, specificity, precision, false positive rate, error, F-score, Mathew correlation coefficient, and area under the curve. The results are compared with both the traditional machine learning methods and those using CNN.

First-line combination therapy versus first-line monotherapy for primary hypertension.

This is the first update of a review originally published in 2017. Starting with one drug and starting with a combination of two drugs are strategies suggested in clinical guidelines as initial treatment of hypertension. The recommendations are not based on evidence about clinically relevant outcomes. Some antihypertensive combinations have been shown to be harmful. The actual harm-to-benefit balance of each strategy is unknown. To determine if there are differences in clinical outcomes between monotherapy and combination therapy as initial treatment for primary hypertension. The Cochrane Hypertension Information Specialist searched the following databases for randomised controlled trials up to April 2019: the Cochrane Hypertension Specialised Register, the Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE (from 2005), Embase (from 1974), the World Health Organization International Clinical Trials Registry Platform, and ClinicalTrials.gov. We used no language restrictions. We also searched clinical studies repositories of pharmaceutical companies, reviews of combination drugs on the US Food and Drug Administration and European Medicines Agency websites, and lists of references in reviews and clinical practice guidelines. We included randomised, double-blind trials with at least 12 months' follow-up in adults with primary hypertension (systolic blood pressure/diastolic blood pressure 140/90 mmHg or higher, or 130/80 mmHg or higher if participants had diabetes), which compared combination of two first-line antihypertensive drugs with monotherapy as initial treatment. Trials had to include at least 50 participants per group and report mortality, cardiovascular mortality, cardiovascular events, or serious adverse events. Two review authors independently selected trials for inclusion, evaluated the risk of bias, and performed data entry. The primary outcomes were mortality, serious adverse events, cardiovascular events, and cardiovascular mortality. Secondary outcomes were withdrawals due to drug-related adverse effects, reaching blood pressure control (as defined in each trial), and blood pressure change from baseline. Analyses were based on the intention-to-treat principle. We summarised data on dichotomous outcomes as risk ratios (RR) with 95% confidence intervals (CI). This update included one new study in which a subgroup of participants met our inclusion criteria. As none of the four included studies focused solely on people initiating antihypertensive treatment, we asked investigators for data for this subgroup. One study (PREVER-treatment 2016) used a combination of thiazide-type diuretic/potassium-sparing diuretic; as the former is not indicated in monotherapy, we analysed this study separately. The three original trials in the main comparison (monotherapy: 335 participants; combination therapy: 233 participants) included outpatients, mostly European and white people. Two trials only included people with type 2 diabetes; the remaining trial excluded people treated with diabetes, hypocholesterolaemia, or cardiovascular drugs. The follow-up was 12 months in two trials and 36 months in one trial. It is very uncertain whether combination therapy versus monotherapy reduces total mortality (RR 1.35, 95% CI 0.08 to 21.72), cardiovascular mortality (zero events reported), cardiovascular events (RR 0.98, 95% CI 0.22 to 4.41), serious adverse events (RR 0.77, 95% CI 0.31 to 1.92), or withdrawals due to adverse effects (RR 0.85, 95% CI 0.53 to 1.35); all outcomes had 568 participants, and the evidence was rated as of very low certainty due to serious imprecision and for using a subgroup that was not defined in advance. The confidence intervals were extremely wide for all important outcomes and included both appreciable harm and benefit. The PREVER-treatment 2016 trial, which used a combination therapy with potassium-sparing diuretic (monotherapy: 84 participants; combination therapy: 116 participants), included outpatients. This trial was conducted in Brazil and had a follow-up of 18 months. The number of events was very low and confidence intervals very wide, with zero events reported for cardiovascular mortality and withdrawals due to adverse events. It is very uncertain if there are differences in clinical outcomes between monotherapy and combination therapy in this trial. The numbers of included participants, and hence the number of events, were too small to draw any conclusion about the relative efficacy of monotherapy versus combination therapy as initial treatment for primary hypertension. There is a need for large clinical trials that address the review question and report clinically relevant endpoints.

Sources of Variability in Pediatric Head Computed Tomography Use Among Emergency Departments in New Jersey.

Published guidelines have been developed to limit ionizing radiation exposure in children related to diagnostic computed tomography (CT). This study examines the sources of variability in head CT use in children in emergency departments (EDs) in New Jersey despite the presence of such consensus recommendations. The New Jersey Hospital Association (NJHA) Clinical Repository was queried for study data on all patients younger than 18 years discharged from the ED over a 1-year period. Patient information collected included: treating hospital, patient age, discharge diagnosis, use of head CT, children's hospital (CH) certification, presence of licensed in-patient pediatric beds, association with a hospital system and hospital annual pediatric ED volume. A potential diagnosis requiring a head CT (PDRCT) was defined as one of the following discharge diagnoses: Head Injury, Seizure, Syncope or Headache. Analysis of CT use per 10,000 ED visits was performed through ANOVA, analysis of means for variances, and χ. A total of 735,866 ED visits were examined with 16,942 (2.3%) undergoing head CTs. Mean Pediatric Head CT use per 10,000 ED visits for the state was 275 (±16; range, 27-640). During the study period 47,169 (6.4%) ED visits met the PDRCT criteria, 11,495 (27%) of which underwent head CTs. Mean Pediatric Head CT use in this group per 10,000 PDRCT visits was 2948 (±152; range, 728-5806). Characteristics associated with lowest use of head CTs in the PDRCT group included: ED census greater than 10,000 visits per year, CH designation, and younger patient. The presence of in-patient pediatric beds and association with a hospital system with or without an in system CH were not associated with lower head CT use. Despite existing recommendation regarding head CTs in children, there exists a large degree of variability in use of this diagnostic study in EDs in New Jersey.