Clinical Data Acquisition Research Articles

Electronic data capture in resource-limited settings using the lightweight clinical data acquisition and recording system

Our prototype system designed for clinical data acquisition and recording of studies is a novel electronic data capture (EDC) software for simple and lightweight data capture in clinical research. Existing software tools are either costly or suffer from very limited features. To overcome these shortcomings, we designed an EDC software together with a mobile client. We aimed at making it easy to set-up, modifiable, scalable and thereby facilitating research. We wrote the software in R using a modular approach and implemented existing data standards along with a meta data driven interface and database structure. The prototype is an adaptable open-source software, which can be installed locally or in the cloud without advanced IT-knowledge. A mobile web interface and progressive web app for mobile use and desktop computers is added. We show the software’s capability, by demonstrating four clinical studies with over 1600 participants and 679 variables per participant. We delineate a simple deployment approach for a server-installation and indicate further use-cases. The software is available under the MIT open-source license. Conclusively the software is versatile, easily deployable, highly modifiable, and extremely scalable for clinical studies. As an open-source R-software it is accessible, open to community-driven development and improvement in the future.

Preoperative and Intraoperative Identification of Sentinel Lymph Nodes in Melanoma Surgery.

According to the American Joint Commission on Cancer (AJCC) 8th edition guidelines, SLN biopsy is recommended for primary melanomas with a Breslow thickness of at least 1 mm. Additionally, the National Comprehensive Cancer Network (NCCN) recommends that a SLN biopsy may be considered for melanoma patients with T1b lesions, which are 0.8-1 mm thick or less than 0.8 mm thick with ulceration. It can also be considered for T1a lesions that are less than 0.8 mm thick but have other adverse features, such as a high mitotic rate, lymphovascular invasion, or a positive deep margin. To reduce the false negative rate of melanoma SLN biopsy, we have introduced the intraoperative use of Sentinella, a gamma camera, to enhance the identification rate of SLNs beyond that of the traditional gamma hand-held probe. At the Center for Melanoma Research and Treatment at the California Pacific Medical Center, a multidisciplinary approach has been established to treat melanoma patients when the diagnosis of primary melanoma is made with a referral to our melanoma center. This comprehensive approach at the melanoma tumor board, including the efforts of pathologists, radiologists, dermatologists, surgical, medical and radiation oncologists, results in a consensus to deliver personalized and high-quality care for our melanoma patients. This multidisciplinary program for the management of melanoma can be duplicated for other types of cancer. This article consists of current knowledge to document the published methods of identification of sentinel lymph nodes. In addition, we have included new data as developed in our melanoma center as newly published materials in this article to demonstrate the utility of these methods in melanoma sentinel lymph node surgery. Informed consent has been waived by our IRB regarding the acquisition of clinical data as presented in this study.

Electronic oral health surveillance system for Egyptian preschoolers using District Health Information System (DHIS2): design description and time motion study

BackgroundEarly childhood caries (ECC) is a major global health issue affecting millions of children. Mitigating this problem requires up-to-date information from reliable surveillance systems. This enables evidence-based decision-making to devise oral health policies. The World Health Organization (WHO) advocates the adoption of mobile technologies in oral disease surveillance because of their efficiency and ease of application. The study describes developing an electronic, oral health surveillance system (EOHSS) for preschoolers in Egypt, using the District Health Information System (DHIS2) open-source platform along with its Android App, and assesses its feasibility in data acquisition.MethodsThe DHIS2 Server was configured for the DHIS2 Tracker Android Capture App to allow individual-level data entry. The EOHSS indicators were selected in line with the WHO Action Plan 2030. Two modalities for the EOHSS were developed based on clinical data capture: face-to-face and tele/asynchronous. Eight dentists in the pilot team collected 214 events using modality-specific electronic devices. The pilot’s team's feedback was obtained regarding the EOHSS's feasibility in collecting data, and a time-motion study was conducted to assess workflow over two weeks. Independent t-test and Statistical Process Control techniques were used for data analysis.ResultsThe pilot team reported positive feedback on the structure of the EOHSS. Workflow adaptations were made to prioritize surveillance tasks by collecting data from caregivers before acquiring clinical data from children to improve work efficiency. A shorter data capture time was required during face-to-face modality (4.2 ± 0.7 min) compared to telemodality (5.1 ± 0.9 min), p < 0.001). The acquisition of clinical data accounted for 16.9% and 21.1% of the time needed for both modalities, respectively. The time required by the face-to-face modality showed random variation, and the tele-modality tasks showed a reduced time trend to perform tasks.ConclusionsThe DHIS2 provides a feasible solution for developing electronic, oral health surveillance systems. The one-minute difference in data capture time in telemodality compared to face-to-face indicates that despite being slightly more time-consuming, telemodality still shows promise for remote oral health assessments that is particularly valuable in areas with limited access to dental professionals, potentially expanding the reach of oral health screening programs.

WONOEP 2022: Neurotechnology for the diagnosis of epilepsy.

Epilepsy's myriad causes and clinical presentations ensure that accurate diagnoses and targeted treatments remain a challenge. Advanced neurotechnologies are needed to better characterize individual patients across multiple modalities and analytical techniques. At the XVIth Workshop on Neurobiology of Epilepsy: Early Onset Epilepsies: Neurobiology and Novel Therapeutic Strategies (WONOEP 2022), the session on "advanced tools" highlighted a range of approaches, from molecular phenotyping of genetic epilepsy models and resected tissue samples to imaging-guided localization of epileptogenic tissue for surgical resection of focal malformations. These tools integrate cutting edge research, clinical data acquisition, and advanced computational methods to leverage the rich information contained within increasingly large datasets. A number of common challenges and opportunities emerged, including the need for multidisciplinary collaboration, multimodal integration, potential ethical challenges, and the multistage path to clinical translation. Despite these challenges, advanced epilepsy neurotechnologies offer the potential to improve our understanding of the underlying causes of epilepsy and our capacity to provide patient-specific treatment.

Improving Microstructural Estimation in Time-Dependent Diffusion MRI With a Bayesian Method.

Accurately fitting diffusion-time-dependent diffusion MRI (td-dMRI) models poses challenges due to complex and nonlinear formulas, signal noise, and limited clinical data acquisition. Introduce a Bayesian methodology to refine microstructural fitting within the IMPULSED (Imaging Microstructural Parameters Using Limited Spectrally Edited Diffusion) model and optimize the prior distribution within the Bayesian framework. Retrospective. Involving 69 pediatric patients (median age 6 years, interquartile range [IQR] 3-9 years, 61% male) with 41 low-grade and 28 high-grade gliomas, of which 76.8% were identified within the brainstem or cerebellum. 3 T, oscillating gradient spin-echo (OGSE) and pulsed gradient spin-echo (PGSE). The Bayesian method's performance in fitting cell diameter ( ), intracellular volume fraction ( ), and extracellular diffusion coefficient ( ) was compared against the NLLS method, considering simulated and experimental data. The tumor region-of-interest (ROI) were manually delineated on the b0 images. The diagnostic performance in distinguishing high- and low-grade gliomas was assessed, and fitting accuracy was validated against H&E-stained pathology. T-test, receiver operating curve (ROC), area under the curve (AUC) and DeLong's test were conducted. Significance considered at P < 0.05. Bayesian methodology manifested increased accuracy with robust estimates in simulation (RMSE decreased by 29.6%, 40.9%, 13.6%, and STD decreased by 29.2%, 43.5%, and 24.0%, respectively for , , and compared to NLLS), indicating fewer outliers and reduced error. Diagnostic performance for tumor grade was similar in both methods, however, Bayesian method generated smoother microstructural maps (outliers ratio decreased by 45.3% ± 19.4%) and a marginal enhancement in correlation with H&E staining result (r = 0.721 for compared to r = 0.698 using NLLS, P = 0.5764). The proposed Bayesian method substantially enhances the accuracy and robustness of IMPULSED model estimation, suggesting its potential clinical utility in characterizing cellular microstructure. 3 TECHNICAL EFFICACY: Stage 1.

Improving Drug Safety in Pediatric and Young Adult Patients with Hemato-Oncological Diseases: A Prospective Study of Active Pharmacovigilance.

The acquisition of relevant pediatric clinical safety data is essential to ensure tolerable drug therapies. Comparing the real number of Adverse Drug Reaction (ADR) reports in clinical practice with the literature, the idea of ADR underreporting emerges. An active pharmacovigilance observational prospective study was conducted to assess the safety of oncology pharmacological prescriptions in patients aged 0-24 years at Institute for Maternal and Child Health IRCCS Burlo Garofolo in Trieste and IRCCS CRO National Cancer Institute in Aviano (Italy) between January 2021 and October 2023. Prescriptions and ADRs were evaluated by a multidisciplinary team. A total of 1218 prescriptions for 38 patients were analyzed, and 190 ADRs of grade 3-5 were collected. As compared to historical data, we registered a significant increase (p < 0.001) in the number of ADRs. The risk of ADR was 3.4 times higher in the case of off-label prescriptions compared to on-label ones (OR 3.4; [1.47; 7.89]; p-value = 0.004). The risks of error and near-miss were reported for 6.3% and 18.2% of total prescriptions, respectively. Of the total of 133 interactions, 47 (35.3%) resulted in ADRs. This study shows the importance of pro-active pharmacovigilance to efficiently highlight ADRs, and the fundamental role of multidisciplinary teams (oncologist, pharmacist, pharmacologist, pediatrician, nurse) in improving patients' safety during therapy.

Identification and verification of PTPN3 as a novel biomarker in predicting cancer prognosis, immunity, and immunotherapeutic efficacy

BackgroundThe importance of protein tyrosine phosphatase non-receptor type 3 (PTPN3) in controlling multifaceted tumor cell behaviors throughout cancer development has received widespread attention. Nevertheless, little is known about the biological roles of PTPN3 in drug sensitivity, immunotherapeutic effectiveness, tumor immune microenvironment, and cancer prognosis.MethodsThe Cancer Genome Atlas (TCGA) database's RNAseq data were used to examine the expression of PTPN3 in 33 different cancer types. In addition, immunohistochemistry (IHC) was performed to validate the expression of PTPN3 across various cancer types within our clinical cohorts. The features of PTPN3 alterations were demonstrated throughout the cBioPortal database. This study focused on examining the prognostic and clinicopathological importance of PTPN3 through the acquisition of clinical data from the TCGA database. The investigation of PTPN3's probable role in the tumor immune microenvironment was demonstrated by the application of CIBERSORT, ESTIMATE algorithms, and the TISIDB database. Using Spearman's rank correlation coefficient, the relationships between PTPN3 expression and tumor mutation burden (TMB) and microsatellite instability (MSI) were evaluated. To further investigate the putative biological activities and downstream pathways of PTPN3 in various cancers in humans, Gene Set Enrichment Analysis (GSEA) was carried out. In addition, an examination was conducted to explore the associations between PTPN3 and the effectiveness of PD-1/PD-L1 inhibitors, utilizing data extracted from the GEO database.ResultsPTPN3 was abnormally expressed in multiple cancer types and was also strictly associated with the prognosis of cancer patients. IHC was used to investigate and confirm the various expression levels of PTPN3 in various malignancies, including breast cancer, lung cancer, sarcoma, and kidney renal clear cell carcinoma in our clinical cohorts. There is a high correlation between the levels of PTPN3 expression in different cancers and infiltrating immune cells, including mast cells, B cells, regulatory T cells, CD8 + T cells, macrophages, and dendritic cells. Infiltrating immune cells, such as regulatory T cells, CD8 + T cells, macrophages, B cells, dendritic cells, and mast cells, are strongly correlated with PTPN3 expression levels in various tumors. The expression of PTPN3 exhibited a substantial correlation with many immune-related biomolecules and the expression of TMB and MSI in multiple types of cancer. In addition, PTPN3 has demonstrated promise in predicting the therapeutic benefits of PD-1/PD-L1 inhibitors and the susceptibility to anti-cancer medications in the treatment of clinical cancer.ConclusionsOur findings highlight the importance of PTPN3 as a prognostic biomarker and predictor of immunotherapy success in various forms of cancer. Furthermore, PTPN3 appears to have an important role in modifying the tumor immune microenvironment, highlighting its potential as a promising biomarker for prognosis prediction, immunotherapeutic efficacy evaluation, and identification of immune-related characteristics in diverse cancer types.

The predictive efficacy of programmed cell death in immunotherapy of melanoma: A comprehensive analysis of gene expression data for programmed cell death biomarker and therapeutic target discovery.

In this study, genes linked to prognosis in skin cutaneous melanoma (SKCM) involved in programmed cell death (PCD) were identified and confirmed and prognostic models based on these genes were constructed. Acquisition and analysis of clinical data and RNA sequencing information from The Cancer Genome Atlas-SKCM (TCGA-SKCM) and Sangerbox databases, gene expression data for 477 tumor samples and 2 normal samples were successfully gathered. The patients were separated into two clusters based on consensus clustering of PCD-related genes, with Cluster A having greater tumor purity, ESTIMATE score, immune score, and matrix score, and Cluster B having a significantly distinct pattern of immune cell infiltration. The use of gene set enrichment analysis and weighted correlation network analysis showed significant associations between certain genes and factors such as tumor mutation burden, age, stage, grade, and tumor subtype. Finally, based on the 12 genes selected by Least Absolute Shrinkage and Selection Operator regression analysis (STAT3, IRF2, SLC7A11, ZEB1, LIPT1, PML, GCH1, GYS1, ABCC1, XBP1, TFAP2C, NOX4), a prognostic model of PGD-related genes was constructed. The effectiveness of the model's prognostic value was confirmed through survival analysis, time-dependent receiver operating characteristic curve, single-factor Cox regression analysis, and nomogram. We also verified the relationship between the GCH1 and MKI67 expression by wet experiment. This model has high prediction accuracy in SKCM patients and can provide a reference for clinical treatment.

Denoising magnetic resonance spectroscopy (MRS) data using stacked autoencoder for improving signal-to-noise ratio and speed of MRS.

While magnetic resonance imaging (MRI) provides high resolution anatomical images with sharp soft tissue contrast, magnetic resonance spectroscopy (MRS) enables non-invasive detection and measurement of biochemicals and metabolites. However, MRS has low signal-to-noise ratio (SNR) when concentrations of metabolites are in the range of millimolar. Standard approach of using a high number of signal averaging (NSA) to achieve sufficient SNR comes at the cost of a long acquisition time. We propose to use deep-learning approaches to denoise MRS data without increasing NSA. This method has potential to reduce the acquisition time as well as improve SNR and quality of spectra, which could enhance the diagnostic value and broaden the clinical applications of MRS. The study was conducted using data collected from the brain spectroscopy phantom and human subjects. We utilized a stack auto-encoder (SAE) network to train deep learning models for denoising low NSA data (NSA=1, 2, 4, 8, and 16) randomly truncated from high SNR data collected with high NSA (NSA=192), which were also used to obtain the ground truth. We applied both self-supervised and fully-supervised training approaches and compared their performance of denoising low NSA data based on improvement in SNR. To prevent overfitting, the SAE network was trained in a patch-based manner. We then tested the denoising methods on noise-containing data collected from the phantom and human subjects, including data from brain tumor patients. We evaluated their performance by comparing the SNR levels and mean squared errors (MSEs) calculated for the whole spectra against high SNR "ground truth", as well as the value of chemical shift of N-acetyl-aspartate (NAA) before and after denoising. With the SAE model, the SNR of low NSA data (NSA=1) obtained from the phantom increased by 28.5% and the MSE decreased by 42.9%. For low NSA data of the human parietal and temporal lobes, the SNR increased by 32.9% and the MSE decreased by 63.1%. In all cases, the chemical shift of NAA in the denoised spectra closely matched with the high SNR spectra without significant distortion to the spectra after denoising. Furthermore, the denoising performance of the SAE model was more effective in denoising spectra with higher noise levels. The reported SAE denoising method is a model-free approach to enhance the SNR of MRS data collected with low NSA. With the denoising capability, it is possible to acquire MRS data with a few NSA, shortening the scan time while maintaining adequate spectroscopic information for detecting and quantifying the metabolites of interest. This approach has the potential to improve the efficiency and effectiveness of clinical MRS data acquisition by reducing the scan time and increasing the quality of spectroscopic data.

Trueness and precision of mandibular complete-arch implant scans when different data acquisition methods are used

ObjectivesTo evaluate the effect of different data acquisition methods on the trueness and precision of mandibular complete-arch implant scans. MethodsAn edentulous polyurethane master mandibular model with 6 implants was digitized by using an industrial-grade blue light scanner (ATOS Core 80 5MP) to obtain a master standard tessellation language (MSTL) file. The master model was also digitized by using either direct digital workflow with a stereoscopic camera (iCam 4D (IM)) or intraoral scanners (CEREC Primescan (PS) and Trios 4 (T4)) or indirect digital workflow with laboratory scanners (inEos X5 (X5) and CARES 7 (S7)) to obtain test-scan STLs (n = 10). All STL files were imported into a metrology-grade analysis software (Geomagic Control X 2020.1) and test-scan STLs were superimposed over MSTL. The root mean square method was used to calculate surface deviations, while angular deviations were also calculated. Kruskal–Wallis and Dunn's tests were used to evaluate measured deviations (surface and angular) for trueness and precision (α = 0.05). ResultsX5 and S7 had the lowest, and IM had the highest surface deviations (P ≤ .036). The angular deviations of PS were lower than those of X5, S7, and IM (P ≤ .008). When surface deviations were considered, T4 had the lowest precision among tested scanners (P ≤ .002), and the scans of IM had higher precision than those of PS (P = .003). Scanner type did not affect the precision of the scans when angular deviations were considered (P = .084). ConclusionsThe data acquisition method affected the trueness (surface and angular deviations) and precision (surface deviations) of mandibular complete-arch implant scans. Clinical significanceTested data acquisition methods may be feasible to digitize mandibular complete-arch implants considering the deviations of the scans, which were in the range of previously reported thresholds, and the high precision of scans. However, the frameworks fabricated with the direct digital workflow that involves the scans of the stereoscopic camera might require more adjustments than those fabricated by using the scans of other tested scanners.

Advances in artificial intelligence, robotics, augmented and virtual reality in neurosurgery.

Neurosurgical practitioners undergo extensive and prolonged training to acquire diverse technical proficiencies, while neurosurgical procedures necessitate a substantial amount of pre-, post-, and intraoperative clinical data acquisition, making decisions, attention, and convalescence. The past decade witnessed an appreciable escalation in the significance of artificial intelligence (AI) in neurosurgery. AI holds significant potential in neurosurgery as it supplements the abilities of neurosurgeons to offer optimal interventional and non-interventional care to patients by improving prognostic and diagnostic outcomes in clinical therapy and assisting neurosurgeons in making decisions while surgical interventions to enhance patient outcomes. Other technologies including augmented reality, robotics, and virtual reality can assist and promote neurosurgical methods as well. Moreover, they play a significant role in generating, processing, as well as storing experimental and clinical data. Also, the usage of these technologies in neurosurgery is able to curtail the number of costs linked with surgical care and extend high-quality health care to a wider populace. This narrative review aims to integrate the results of articles that elucidate the role of the aforementioned technologies in neurosurgery.

The Story Behind the HL7 FHIR to CDISC Mapping Implementation Guide

BackgroundHealth data has become an abundant and rich source of information that can be leveraged for clinical trials. Health Level Seven (HL7) provides standards for health care data, while the Clinical Data Interchange Standards Consortium (CDISC) provides standards for research data. Bridging these worlds and leveraging health data has the potential to enhance knowledge and data quality, while reducing delays and cost in clinical trials.Objectives The aim was to provide an overview of the HL7 Fast Healthcare Interoperability Resources (FHIR®) to CDISC Joint Mapping Implementation Guide (IG) development. The ultimate aim is to increase awareness of this rich resource.MethodsA gap analysis was performed to assess the possibility and feasibility of mapping from FHIR to the CDISC’s Study Data Tabulation Model (SDTM) and Clinical Data Acquisition Standards Harmonization (CDASH) standards. The mapping of CDISC domains commenced using FHIRPath, a path-based navigation and extraction language. The domains mapped were Medical History (MH), Concomitant Medications (CM), Procedures (PR), Vital Signs (VS), Laboratory (LB), Adverse Events (AE), and Demographics (DM). The proposed mappings were evaluated through a formal review process.ResultsThe review process consisted of a CDISC Internal Review and a simultaneous CDISC Public Review and HL7 Ballot Review.&amp;nbsp; A total of 363 comments were obtained from the following reviews: 1) CDISC Internal Review (268), and 2) simultaneous Public Review (95) - CDISC (37), HL7 (58). The predominant gathered in the review included mapping updates, terminology, and requests for greater detail.ConclusionThe HL7 FHIR®&amp;nbsp;to CDISC Joint Mapping Implementation Guide (IG) provides a rich resource for bridging health care and research standards that can be used with CDISC LB to LOINC mapping guide for leveraging real-world data for clinical trials. The guide assists stakeholders in utilizing health data through the HL7 FHIR® resources to CDISC, CDASH, and SDTM variables. As the community uses this resource, it will continue to mature and potentially expand into new domains.

Twin anemia polycythemia sequence in discordant dichorionic twins.

A 23-year-old primiparous woman with spontaneous dichorionic pregnancy was diagnosed by early ultrasonography. She developed hypertensive disorder of pregnancy at 27 weeks of gestation. At 29 weeks and 3 days of gestation, she was transferred to our perinatal center due to increasing discrepancy between the fetal weights (fetus A 1203 g, fetus B 989 g). Notably, polyhydramnios-oligohydramnios sequence was not observed. An emergency cesarean section was performed at 31 weeks 1 day of gestation because of elevated liver enzyme levels. Two male neonates were delivered with birth weights of 1413 and 1098 g, respectively. At birth, twin A was peripherally cyanotic with hemoglobin of 26.4 g/dL and a reticulocyte count of 4.1%. He was treated using partial exchange transfusion for polycythemia. Twin B was pale with hemoglobin of 7.8 g/dL and a reticulocyte count of 20.5%; thus, an emergent red blood cell transfusion was performed for anemia. Based on their hematological values, the twins were diagnosed with twin anemia polycythemia sequence (TAPS). A marked difference in the coloration of the maternal surface was observed in the two placentas on a macroscopic examination (Fig. 1); however, no vascular anastomosis was detected during dye studies. Microscopic histopathology revealed two amniotic layers and two chorionic layers, which confirmed dichorionicity. Recent literature reviews reported that twin-to-twin transfusion syndrome (TTTS) or TAPS occurs in dichorionic twin placentas with evidence of vascular anastomoses, although it is extremely rare.1Kanagaretnam D. Nayyar R. Zen M. Twin anemia polycythemia sequence in dichorionic diamniotic twins: a case report and review of the literature.Clin Case Rep. 2021; 9e04184Crossref Scopus (1) Google Scholar, 2Lanna M. Faiola S. Casati D. Rustico M.A. Twin-twin transfusion syndrome in dichorionic twin pregnancy: rare but not impossible.Ultrasound Obstet Gynecol. 2019; 54: 412-418Crossref Scopus (2) Google Scholar, 3Zilliox M. Koch A. Farve R. Sananes N. Unusual twin anemia-polycythemia sequence in a dichorionic diamniotic pregnancy.J Gynecol Obstet Hum Reprod. 2019; 48: 359-361Crossref Scopus (4) Google Scholar Miniscule anastomoses undetectable by a dye study may have been present in our dichorionic twins. Prenatal diagnosis, close monitoring, appropriate early delivery, and postnatal management are necessary to improve the outcomes of TTTS and TAPS. Thus, this report highlights the importance of prenatal recognition and postnatal confirmation of TTTS or TAPS, regardless of chorionicity. S.H. and S.I. drafted the initial manuscript. S.H., S.I. and S.T. treated the patient, and contributed to the acquisition of clinical data and imaging. M.Y. performed pathological evaluation. S. Y. supervised the manuscript. All authors approved the final manuscript. The authors have no conflicts of interest to declare. We thank the patient's parents for their kind cooperation. We also thank Dr. Chizen Tarui, Department of Neonatology, and Dr. Tomoko Kibara and Megumi Kaneko, Department of Obstetrics, in Hyogo Prefectural Kobe Children's Hospital for their support.

The Incidence of IgG4-Related and Inflammatory Abdominal Aortic Aneurysm Is Rare in a 101 Patient Cohort.

Abdominal aortic aneurysms (AAA) are the most frequent aortic dilation, with considerable morbidity and mortality. Inflammatory (infl) and IgG4-positive AAAs represent specific subtypes of unclear incidence and clinical significance. Here, histologic and serologic analyses with retrospective clinical data acquisition are investigated via detailed histology, including morphologic (HE, EvG: inflammatory subtype, angiogenesis, and fibrosis) and immunhistochemic analyses (IgG and IgG4). In addition, complement factors C3/C4 and immunoglobulins IgG, IgG2, IgG4 and IgE were measured in serum samples and clinical data uses patients' metrics, as well as through semi-automated morphometric analysis (diameter, volume, angulation and vessel tortuosity). A total of 101 eligible patients showed five (5%) IgG4 positive (all scored 1) and seven (7%) inflammatory AAAs. An increased degree of inflammation was seen in IgG4 positive and inflAAA, respectively. However, serologic analysis revealed no increased levels of IgG or IgG4. The operative procedure time was not different for those cases and the short-term clinical outcomes were equal for the entire AAA cohort. Overall, the incidence of inflammatory and IgG4-positive AAA samples seems very low based on histologic and serum analyses. Both entities must be considered distinct disease phenotypes. Short-term operative outcomes were not different for both sub-cohorts.

Dissemination of CDASH eCRFs via the CDISC Electronic Case Report Form (eCRF) Portal and the OpenClinica Electronic Data Capture system

Electronic Data Capture (EDC) study designers can benefit from the adoption of high-quality content and study design standards. However, in practice there is often a significant lack of standardization and re-use of Electronic Case Report Form (eCRF) content, including data element naming conventions; question text and order; controlled terminologies; and overall design across organizations or across studies within an organization. We describe the implementation of validated case report form (CRF) content using the Clinical Data Acquisition Standards Harmonization (CDASH) obtained from the Clinical Data Interchange Standards Consortium (CDISC) eCRF Portal (https://www.cdisc.org/kb/ecrf) within an eCRF content library in the OpenClinica EDC system.The implementation of validated CDASH form content obtained from the CDISC eCRF Portal within an eCRF content library in the EDC system is intended to enhance the efficiency of study designers and to promote greater adoption of high-quality standards. With core content design standardized, more focus can be place on study-specific forms. Potential benefits discovered:Greater alignment between data points that are captured in eCRFs and those expected by statisticians and scientists. This can mean fewer delays in design and study implementation and fewer protocol amendments.Faster implementation times and lower study start-up costs, including reduced testing and validation times.Greater re-usability of data because of widely adopted naming conventions, data structures, and controlled terminologies.Out-of-the-box eCRF content that represents expert consensus yet is still easy to customize to the needs of each research program/trial.

From clinical data management to clinical data science: Time for a new educational model.

The purpose of this article is to propose and provide a blueprint for a graduate-level curriculum in clinical data science, devoted to the measurement, acquisition, care, treatment, and inferencing of clinical research data. The curriculum presented here contains a series of five required core courses, five required research courses, and a list of potential electives. The coursework draws from but does not duplicate content from the foundational areas of biostatistics, clinical medicine, biomedical informatics, and regulatory affairs, and may be reproduced by any institution interested in and capable of offering such a program. This new curriculum in "clinical" data science will prepare students for work in academic, industry, and government research settings as well as offer a unifying knowledge base for the profession.

Counterpoint: The value of including patients in prerounding.

Counterpoint: The value of including patients in prerounding.

Evaluation of the area subscore of the Palmoplantar Pustulosis Area and Severity Index using an attention U-net deep learning algorithm.

Palmoplantar pustulosis (PPP) is a variant of pustular psoriasis involving the palms and soles. The severity of PPP is usually evaluated using the Palmoplantar Pustulosis Area and Severity Index (PPPASI). Among the components of the PPPASI, the area of the involved lesion is evaluated differently by raters, who generally make a rough estimate using the eye and not through a specific calculation. To overcome inconsistent evaluation of the area subscore of PPPASI by human raters, we developed and validated deep-learning-based algorithms to enable automated and reliable assessment of the area involved in PPP to provide clinical advantages. In this study, we developed a dataset of 611 images of the palms and soles of 153 patients with PPP. We evaluated the area of the lesion by dividing the number of pixels in the area involved in PPP by the number of pixels in the area of the palms or soles. Using attention U-net, we developedtwo convolutional neural network (CNN) models that can evaluate the percentage of the affected area (%) and subsequently assign a score ranging from 0 to 6. The area subscore of PPPASI evaluated by the deep-learning algorithmwassameor differed by 1-point from the subscore of ground truth in 98.8% of the images. The intraclass correlation coefficient between the CNN and ground truth was 0.879, indicating good agreement. The accuracy and mean absolute error of the model were 66.7% and 0.344, respectively. In a Bland-Altman plot, most of the differences in the percentage of the affected area lay between the 95% confidence interval with a mean difference of 0 and a standard deviation of 0.2. The deep-learning algorithm can provide several clinical advantages by objectively evaluating the components of the PPPASI without concern for disagreement between clinicians. The algorithms further enable cumulative clinical data acquisition related to PPP severity.

Methylphenidate Enhances Spontaneous Fluctuations in Reward and Cognitive Control Networks in Children With Attention-Deficit/Hyperactivity Disorder

BackgroundMethylphenidate, a first-line treatment for attention-deficit/hyperactivity disorder (ADHD), is thought to influence dopaminergic neurotransmission in the nucleus accumbens (NAc) and its associated brain circuitry, but this hypothesis has yet to be systematically tested. MethodsWe conducted a randomized, placebo-controlled, double-blind crossover trial including 27 children with ADHD. Children with ADHD were scanned twice with resting-state functional magnetic resonance imaging under methylphenidate and placebo conditions, along with assessment of sustained attention. We examined spontaneous neural activity in the NAc and the salience, frontoparietal, and default mode networks and their links to behavioral changes. Replicability of methylphenidate effects on spontaneous neural activity was examined in a second independent cohort. ResultsMethylphenidate increased spontaneous neural activity in the NAc and the salience and default mode networks. Methylphenidate-induced changes in spontaneous activity patterns in the default mode network were associated with improvements in intraindividual response variability during a sustained attention task. Critically, despite differences in clinical trial protocols and data acquisition parameters, the NAc and the salience and default mode networks showed replicable patterns of methylphenidate-induced changes in spontaneous activity across two independent cohorts. ConclusionsWe provide reproducible evidence demonstrating that methylphenidate enhances spontaneous neural activity in NAc and cognitive control networks in children with ADHD, resulting in more stable sustained attention. Our findings identified a novel neural mechanism underlying methylphenidate treatment in ADHD to inform the development of clinically useful biomarkers for evaluating treatment outcomes.

Perspectives before incremental trans-disciplinary cross-validation of clinical self-evaluation tools and functional MRI in psychiatry: 10 years later.

Translational validity (or trans-disciplinary validity) is defined as one possible approach to achieving incremental validity by combining simultaneous clinical state-dependent measures and functional MRI data acquisition. It is designed under the assumption that the simultaneous administration of the two methods may produce a dataset with enhanced synchronization and concordance. Translational validation aims at “bridging” the explanatory gap by implementing validated psychometric tools clinically in the experimental settings of fMRI and then translating them back to clinical utility. Our studies may have identified common diagnostic task-specific denominators in terms of activations and network modulation. However, those common denominators need further investigation to determine whether they signify disease or syndrome-specific features (signatures), which, at the end of the day, raises one more question about the poverty of current conventional psychiatric classification criteria. We propose herewith a novel algorithm for translational validation based on our explorative findings. The algorithm itself includes pre-selection of a test based on its psychometric characteristics, adaptation to the functional MRI paradigm, exploration of the underpinning whole brain neural correlates in healthy controls as compared to a patient population with certain diagnoses, and finally, investigation of the differences between two or more diagnostic classes.