Search

Food allergy is a significant health problem affecting approximately 8% of children and 11% of adults in the United States. It exhibits all the characteristics of a "complex" genetic trait; therefore, it is necessary to look at very large numbers of patients, far more than exist at any single organization, to eliminate gaps in the current understanding of this complex chronic disorder. Advances may be achieved by bringing together food allergy data from large numbers of patients into a Data Commons, a secure and efficient platform for researchers, comprising standardized data, available in a common interface for download and/or analysis, in accordance with the FAIR (Findable, Accessible, Interoperable, and Reusable) principles. Prior data commons initiatives indicate that research community consensus and support, formal food allergy ontology, data standards, an accepted platform and data management tools, an agreed upon infrastructure, and trusted governance are the foundation of any successful data commons. In this article, we will present the justification for the creation of a food allergy data commons and describe the core principles that can make it successful and sustainable., (Published by Elsevier Inc.)

Background: Timely referral for specialist evaluation in patients with advanced heart failure (HF) is a Class 1 recommendation. However, the transition from stage C HF to advanced or stage D HF often goes undetected in routine care, resulting in delayed referral and higher mortality rates., Objectives: The authors sought to develop an augmented intelligence-enabled workflow using machine learning to identify patients with stage D HF and streamline referral., Methods: We extracted data on HF patients with encounters from January 1, 2007, to November 30, 2020, from a HF registry within a regional, integrated health system. We created an ensemble machine learning model to predict stage C or stage D HF and integrated the results within the electronic health record., Results: In a retrospective data set of 14,846 patients, the model had a good positive predictive value (60%) and low sensitivity (25%) for identifying stage D HF in a 100-person, physician-reviewed, holdout test set. During prospective implementation of the workflow from April 1, 2021, to February 15, 2022, 416 patients were reviewed by a clinical coordinator, with agreement between the model and the coordinator in 50.3% of stage D predictions. Twenty-four patients have been scheduled for evaluation in a HF clinic, 4 patients started an evaluation for advanced therapies, and 1 patient received a left ventricular assist device., Conclusions: An augmented intelligence-enabled workflow was integrated into clinical operations to identify patients with advanced HF. Endeavors such as this require a multidisciplinary team with experience in design thinking, informatics, quality improvement, operations, and health information technology, as well as dedicated resources to monitor and improve performance over time.

Inadequate pathology personnel and high cost of running a Pathology facility are factors affecting access to timely and quality pathology services in resource-constrained settings. Telepathology is a novel technology that allows Pathologists to remotely assess collected samples. Though the initial cost of setting up a telepathology facility is high, its overall benefits far outweigh the cost. Its usefulness as a quality assurance measure, as a permanent image data storage system, in reducing costs associated with repeated slide preparations, reducing turn-around time of pathology reports, in collaborative research and in teaching has been well documented. This paper highlights the experiences, gains and challenges encountered in the deployment of telepathology in two resource-constrained settings in Nigeria. Overcoming the challenges associated with setting up a telepathology service in sub-Saharan Africa is important as it has the potential to improve overall health outcomes in a medically underserved region while ensuring technology and knowledge transfer are achieved., Competing Interests: The authors have no competing interests to declare., (Copyright: © 2022 The Author(s).)

Despite the strong prognostic stratification of circulating tumor cells (CTCs) enumeration in metastatic breast cancer (MBC), current clinical trials usually do not include a baseline CTCs in their design. This study aimed to generate a classifier for CTCs prognostic simulation in existing datasets for hypothesis generation in patients with MBC. A K-nearest neighbor machine learning algorithm was trained on a pooled dataset comprising 2436 individual MBC patients from the European Pooled Analysis Consortium and the MD Anderson Cancer Center to identify patients likely to have CTCs ≥ 5/7 mL blood (StageIVaggressive vs StageIVindolent). The model had a 65.1% accuracy and its prognostic impact resulted in a hazard ratio (HR) of 1.89 (Simulatedaggressive vs SimulatedindolentP < .001), similar to patients with actual CTCs enumeration (HR 2.76; P < .001). The classifier's performance was then tested on an independent retrospective database comprising 446 consecutive hormone receptor (HR)-positive HER2-negative MBC patients. The model further stratified clinical subgroups usually considered prognostically homogeneous such as patients with bone-only or liver metastases. Bone-only disease classified as Simulatedaggressive had a significantly worse overall survival (OS; P < .0001), while patients with liver metastases classified as Simulatedindolent had a significantly better prognosis (P < .0001). Consistent results were observed for patients who had undergone CTCs enumeration in the pooled population. The differential prognostic impact of endocrine- (ET) and chemotherapy (CT) was explored across the simulated subgroups. No significant differences were observed between ET and CT in the overall population, both in terms of progression-free survival (PFS) and OS. In contrast, a statistically significant difference, favoring CT over ET was observed among Simulatedaggressive patients (HR: 0.62; P = .030 and HR: 0.60; P = .037, respectively, for PFS and OS)., (© The Author(s) 2022. Published by Oxford University Press.)

Background: Food allergy (FA) data lacks a common base of terminology and hinders data exchange among institutions., Objective: To examine the current FA concept coverage by clinical terminologies and to develop and evaluate a Food Allergy Data Dictionary (FADD)., Methods: Allergy/immunology templates and patient intake forms from 4 academic medical centers with expertise in FA were systematically reviewed, and in-depth discussions with a panel of FA experts were conducted to identify important FA clinical concepts and data elements. The candidate ontology was iteratively refined through a series of virtual meetings. The concepts were mapped to existing clinical terminologies manually with the ATHENA vocabulary browser. Finally, the revised dictionary document was vetted with experts across 22 academic FA centers and 3 industry partners., Results: A consensus version 1.0 FADD was finalized in November 2020. The FADD v1.0 contained 936 discrete FA concepts that were grouped into 14 categories. The categories included both FA-specific concepts, such as foods triggering reactions, and general health care categories, such as medications. Although many FA concepts are included in existing clinical terminologies, some critical concepts are missing., Conclusions: The FADD provides a pragmatic tool that can enable improved structured coding of FA data for both research and clinical uses, as well as lay the foundation for the development of standardized FA structured data entry forms., (Copyright © 2022 American Academy of Allergy, Asthma & Immunology. Published by Elsevier Inc. All rights reserved.)

Patients with a history of malignancy have been shown to be at an increased risk of COVID-19-related morbidity and mortality. Poorer clinical outcomes in that patient population are likely due to the underlying systemic illness, comorbidities, and the cytotoxic and immunosuppressive anti-tumor treatments they are subjected to. We identified 416 cancer patients with SARS-CoV-2 infection being managed for their malignancy at Northwestern Medicine in Chicago, Illinois, between March and July of 2020. Seventy-five (18.0%) patients died due to COVID-related complications. Older age (>60), male gender, and current treatment with immunotherapy were associated with shorter overall survival. Laboratory findings showed that higher platelet counts, ALC, and hemoglobin were protective against critical illness and death from COVID-19. Conversely, elevated inflammatory markers such as ferritin, d-dimer, procalcitonin, CRP, and LDH led to worse clinical outcomes. Our findings suggest that a thorough clinical and laboratory assessment of infected patients with cancer might help identify a more vulnerable population and implement more aggressive proactive strategies.

The challenges of reliably collecting, storing, organizing, and analyzing research data are critical in low- and middle-income countries (LMICs), particularly in Sub-Saharan Africa where several healthcare and biomedical research organizations have limited data infrastructure. The Research Electronic Data Capture (REDCap) System has been widely used by many institutions and hospitals in the USA for data collection, entry, and management and could help solve this problem. This study reports on the experiences, challenges, and lessons learned from establishing and applying REDCap for a large US-Nigeria research partnership that includes two sites in Nigeria, (the College of Medicine of the University of Lagos (CMUL) and Jos University Teaching Hospital (JUTH)) and Northwestern University (NU) in Chicago, Illinois in the United States. The largest challenges to this implementation were significant technical obstacles: the lack of REDCap-trained personnel, transient electrical power supply, and slow/intermittent internet connectivity. However, asynchronous communication and on-site hands-on collaboration between the Nigerian sites and NU led to the successful installation and configuration of REDCap to meet the needs of the Nigerian sites. An example of one lesson learned is the use of Virtual Private Network (VPN) as a solution to poor internet connectivity at one of the sites, and its adoption is underway at the other. Virtual Private Servers (VPS) or shared online hosting were also evaluated and offer alternative solutions. Installing and using REDCap in LMIC institutions for research data management is feasible; however, planning for trained personnel and addressing electrical and internet infrastructural requirements are essential to optimize its use. Building this fundamental research capacity within LMICs across Africa could substantially enhance the potential for more cross-institutional and cross-country collaboration in future research endeavors., Competing Interests: Disclosure of potential conflicts of interest None to declare.

Background: Data on patients with COVID-19 who have cancer are lacking. Here we characterise the outcomes of a cohort of patients with cancer and COVID-19 and identify potential prognostic factors for mortality and severe illness., Methods: In this cohort study, we collected de-identified data on patients with active or previous malignancy, aged 18 years and older, with confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection from the USA, Canada, and Spain from the COVID-19 and Cancer Consortium (CCC19) database for whom baseline data were added between March 17 and April 16, 2020. We collected data on baseline clinical conditions, medications, cancer diagnosis and treatment, and COVID-19 disease course. The primary endpoint was all-cause mortality within 30 days of diagnosis of COVID-19. We assessed the association between the outcome and potential prognostic variables using logistic regression analyses, partially adjusted for age, sex, smoking status, and obesity. This study is registered with ClinicalTrials.gov, NCT04354701, and is ongoing., Findings: Of 1035 records entered into the CCC19 database during the study period, 928 patients met inclusion criteria for our analysis. Median age was 66 years (IQR 57-76), 279 (30%) were aged 75 years or older, and 468 (50%) patients were male. The most prevalent malignancies were breast (191 [21%]) and prostate (152 [16%]). 366 (39%) patients were on active anticancer treatment, and 396 (43%) had active (measurable) cancer. At analysis (May 7, 2020), 121 (13%) patients had died. In logistic regression analysis, independent factors associated with increased 30-day mortality, after partial adjustment, were: increased age (per 10 years; partially adjusted odds ratio 1·84, 95% CI 1·53-2·21), male sex (1·63, 1·07-2·48), smoking status (former smoker vs never smoked: 1·60, 1·03-2·47), number of comorbidities (two vs none: 4·50, 1·33-15·28), Eastern Cooperative Oncology Group performance status of 2 or higher (status of 2 vs 0 or 1: 3·89, 2·11-7·18), active cancer (progressing vs remission: 5·20, 2·77-9·77), and receipt of azithromycin plus hydroxychloroquine (vs treatment with neither: 2·93, 1·79-4·79; confounding by indication cannot be excluded). Compared with residence in the US-Northeast, residence in Canada (0·24, 0·07-0·84) or the US-Midwest (0·50, 0·28-0·90) were associated with decreased 30-day all-cause mortality. Race and ethnicity, obesity status, cancer type, type of anticancer therapy, and recent surgery were not associated with mortality., Interpretation: Among patients with cancer and COVID-19, 30-day all-cause mortality was high and associated with general risk factors and risk factors unique to patients with cancer. Longer follow-up is needed to better understand the effect of COVID-19 on outcomes in patients with cancer, including the ability to continue specific cancer treatments., Funding: American Cancer Society, National Institutes of Health, and Hope Foundation for Cancer Research., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Existing approaches to managing genetic and genomic test results from external laboratories typically include filing of text reports within the electronic health record, making them unavailable in many cases for clinical decision support. Even when structured computable results are available, the lack of adopted standards requires considerations for processing the results into actionable knowledge, in addition to storage and management of the data. Here, we describe the design and implementation of an ancillary genomics system used to receive and process heterogeneous results from external laboratories, which returns a descriptive phenotype to the electronic health record in support of pharmacogenetic clinical decision support., (© The Author(s) 2019. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

The eMERGE Network is establishing methods for electronic transmittal of patient genetic test results from laboratories to healthcare providers across organizational boundaries. We surveyed the capabilities and needs of different network participants, established a common transfer format, and implemented transfer mechanisms based on this format. The interfaces we created are examples of the connectivity that must be instantiated before electronic genetic and genomic clinical decision support can be effectively built at the point of care. This work serves as a case example for both standards bodies and other organizations working to build the infrastructure required to provide better electronic clinical decision support for clinicians.

A robust biomedical informatics infrastructure is essential for academic health centers engaged in translational research. There are no templates for what such an infrastructure encompasses or how it is funded. An informatics workgroup within the Clinical and Translational Science Awards network conducted an analysis to identify the scope, governance, and funding of this infrastructure. After we identified the essential components of an informatics infrastructure, we surveyed informatics leaders at network institutions about the governance and sustainability of the different components. Results from 42 survey respondents showed significant variations in governance and sustainability; however, some trends also emerged. Core informatics components such as electronic data capture systems, electronic health records data repositories, and related tools had mixed models of funding including, fee-for-service, extramural grants, and institutional support. Several key components such as regulatory systems (e.g., electronic Institutional Review Board [IRB] systems, grants, and contracts), security systems, data warehouses, and clinical trials management systems were overwhelmingly supported as institutional infrastructure. The findings highlighted in this report are worth noting for academic health centers and funding agencies involved in planning current and future informatics infrastructure, which provides the foundation for a robust, data-driven clinical and translational research program.

Purpose: Liquid biopsy provides a real-time assessment of metastatic breast cancer (MBC). We evaluated the utility of combining circulating tumor cells (CTC) and circulating tumor DNA (ctDNA) to predict prognosis in MBC. Experimental Design: We conducted a retrospective study of 91 patients with locally advanced breast cancer and MBC. CTCs were enumerated by CellSearch; the plasma-based assay was performed utilizing Guardant360 and the survival analysis using Kaplan-Meier curves. Results: Eighty-four patients had stage IV cancer, and 7 patients had no metastases. Eighty patients had CTC analysis: median number 2 (0-5,612). Blood samples [232 of 277 (84%)] had mutations. The average ctDNA fraction was 4.5% (0-88.2%) and number of alterations 3 (0-27); the most commonly mutated genes were TP53 (52%), PIK3CA (40%), and ERBB2 (20%). At the time of analysis, 36 patients (39.6%) were dead. The median follow-up for CTCs was 9 months; for ctDNA, it was 9.9 months. For CTCs and ctDNA, respectively, progression-free survival (PFS) was 4.2 and 5.2 months and overall survival (OS) was 18.7 and 21.5 months. There was a statistically significant difference in PFS and OS for baseline CTCs < 5 versus CTCs ≥ 5 ( P = 0.021 and P = 0.0004, respectively); %ctDNA < 0.5 versus ≥ 0.5 ( P = 0.003 and P = 0.012); number of alterations < 2 versus ≥ 2 ( P = 0.059 borderline and P = 0.0015). A significant association by Fisher exact test was found between the number of alterations and the %ctDNA in the baseline sample ( P < 0.0001). Conclusions: The study demonstrated that liquid biopsy is an effective prognostic tool. Clin Cancer Res; 24(3); 560-8. ©2017 AACR ., (©2017 American Association for Cancer Research.)

Purpose: To inform goals of care discussions at the time of palliative radiation therapy (RT) consultation, we sought to characterize intensive care unit (ICU) outcomes for patients treated with palliative RT compared to all other patients with metastatic cancer admitted to the ICU., Methods and Materials: We conducted a retrospective cohort study of patients with metastatic cancer admitted to an ICU in a tertiary medical center from January 2010 to September 2015. We compared in-hospital mortality between patients who received palliative RT in the 12 months before admission and all other patients with metastatic cancer. We used multivariable logistic regression to evaluate the association between receipt of palliative RT and in-hospital mortality, adjusting for patient characteristics and acute illness severity., Results: Among 1424 patients with metastatic cancer, 11.3% (n=161) received palliative RT before ICU admission. In-hospital mortality was 36.7% for palliative RT patients, compared with 16.6% for other patients with metastatic cancer (P<.001). Receipt of palliative RT was associated with increased in-hospital mortality (odds ratio 2.08, 95% confidence interval 1.34-3.21, P=.001), after adjusting for patient characteristics and severity of critical illness. Only 34 patients (21.1%) treated with palliative RT received additional cancer-directed treatment after ICU admission., Conclusions: For patients with metastatic cancer, prior treatment with palliative RT is associated with increased in-hospital mortality after ICU admission. Nearly half of patients previously treated with palliative RT either died during hospitalization or were discharged with hospice care, and few received further cancer-directed therapy. Palliative RT referral may represent an opportunity to discuss end-of-life treatment preferences with patients and families., (Copyright © 2017 Elsevier Inc. All rights reserved.)

Objective: To develop and disseminate tools for interactive visualization of HIV cohort data., Design and Methods: If a picture is worth a thousand words, then an interactive video, composed of a long string of pictures, can produce an even richer presentation of HIV population dynamics. We developed an HIV cohort data visualization tool using open-source software (R statistical language). The tool requires that the data structure conform to the HIV Cohort Data Exchange Protocol (HICDEP), and our implementation utilized Caribbean, Central and South America network (CCASAnet) data., Results: This tool currently presents patient-level data in three classes of plots: (1) Longitudinal plots showing changes in measurements viewed alongside event probability curves allowing for simultaneous inspection of outcomes by relevant patient classes. (2) Bubble plots showing changes in indicators over time allowing for observation of group level dynamics. (3) Heat maps of levels of indicators changing over time allowing for observation of spatial-temporal dynamics. Examples of each class of plot are given using CCASAnet data investigating trends in CD4 count and AIDS at antiretroviral therapy (ART) initiation, CD4 trajectories after ART initiation, and mortality., Conclusions: We invite researchers interested in this data visualization effort to use these tools and to suggest new classes of data visualization. We aim to contribute additional shareable tools in the spirit of open scientific collaboration and hope that these tools further the participation in open data standards like HICDEP by the HIV research community.

Background: Electronic health records (EHRs) are increasingly used for clinical and translational research through the creation of phenotype algorithms. Currently, phenotype algorithms are most commonly represented as noncomputable descriptive documents and knowledge artifacts that detail the protocols for querying diagnoses, symptoms, procedures, medications, and/or text-driven medical concepts, and are primarily meant for human comprehension. We present desiderata for developing a computable phenotype representation model (PheRM)., Methods: A team of clinicians and informaticians reviewed common features for multisite phenotype algorithms published in PheKB.org and existing phenotype representation platforms. We also evaluated well-known diagnostic criteria and clinical decision-making guidelines to encompass a broader category of algorithms., Results: We propose 10 desired characteristics for a flexible, computable PheRM: (1) structure clinical data into queryable forms; (2) recommend use of a common data model, but also support customization for the variability and availability of EHR data among sites; (3) support both human-readable and computable representations of phenotype algorithms; (4) implement set operations and relational algebra for modeling phenotype algorithms; (5) represent phenotype criteria with structured rules; (6) support defining temporal relations between events; (7) use standardized terminologies and ontologies, and facilitate reuse of value sets; (8) define representations for text searching and natural language processing; (9) provide interfaces for external software algorithms; and (10) maintain backward compatibility., Conclusion: A computable PheRM is needed for true phenotype portability and reliability across different EHR products and healthcare systems. These desiderata are a guide to inform the establishment and evolution of EHR phenotype algorithm authoring platforms and languages., (© The Author 2015. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

Background: Antiretroviral therapy (ART) decreases mortality risk in HIV-infected tuberculosis patients, but the effect of the duration of anti-tuberculosis therapy and timing of anti-tuberculosis therapy initiation in relation to ART initiation on mortality, is unclear., Methods: We conducted a retrospective observational multi-center cohort study among HIV-infected persons concomitantly treated with Rifamycin-based anti-tuberculosis therapy and ART in Latin America. The study population included persons for whom 6 months of anti-tuberculosis therapy is recommended., Results: Of 253 patients who met inclusion criteria, median CD4+ lymphocyte count at ART initiation was 64 cells/mm(3), 171 (68%) received >180 days of anti-tuberculosis therapy, 168 (66%) initiated anti-tuberculosis therapy before ART, and 43 (17%) died. In a multivariate Cox proportional hazards model that adjusted for CD4+ lymphocytes and HIV-1 RNA, tuberculosis diagnosed after ART initiation was associated with an increased risk of death compared to tuberculosis diagnosis before ART initiation (HR 2.40; 95% CI 1.15, 5.02; P = 0.02). In a separate model among patients surviving >6 months after tuberculosis diagnosis, after adjusting for CD4+ lymphocytes, HIV-1 RNA, and timing of ART initiation relative to tuberculosis diagnosis, receipt of >6 months of anti-tuberculosis therapy was associated with a decreased risk of death (HR 0.23; 95% CI 0.08, 0.66; P=0.007)., Conclusions: The increased risk of death among persons diagnosed with tuberculosis after ART initiation highlights the importance of screening for tuberculosis before ART initiation. The decreased risk of death among persons receiving > 6 months of anti-tuberculosis therapy suggests that current anti-tuberculosis treatment duration guidelines should be re-evaluated.

Clinical data auditing often requires validating the contents of clinical research databases against source documents available in health care settings. Currently available data audit software, however, does not provide features necessary to compare the contents of such databases to source data in paper medical records. This work enumerates the primary weaknesses of using paper forms for clinical data audits and identifies the shortcomings of existing data audit software, as informed by the experiences of an audit team evaluating data quality for an international research consortium. The authors propose a set of attributes to guide the development of a computer-assisted clinical data audit tool to simplify and standardize the audit process.

Significant research has been devoted to predicting diagnosis, prognosis, and response to treatment using high-throughput assays. Rapid translation into clinical results hinges upon efficient access to up-to-date and high-quality molecular medicine modalities. We first explain why this goal is inadequately supported by existing databases and portals and then introduce a novel semantic indexing and information retrieval model for clinical bioinformatics. The formalism provides the means for indexing a variety of relevant objects (e.g. papers, algorithms, signatures, datasets) and includes a model of the research processes that creates and validates these objects in order to support their systematic presentation once retrieved.We test the applicability of the model by constructing proof-of-concept encodings and visual presentations of evidence and modalities in molecular profiling and prognosis of: (a) diffuse large B-cell lymphoma (DLBCL) and (b) breast cancer.

Many medical schools currently provide electronic access to their medical curriculum. In order to better develop and evaluate online curricular databases, knowledge of the interaction of students and faculty with such systems is required. The KnowledeMap application provides a web interface for students, faculty and administrators to perform NLP-assisted searches for documents from the entire medical curriculum. The pilot implementation of KM in a first year anatomy course was evaluated. Data was collected from the web-server log files over two years, a paper survey at the end of the course, and structured interviews with students and faculty members. The data showed complete adoption of KM. Analysis of usage patterns showed that most of the students chose to browse for current course material rather than to search for related medical concepts in future courses. Analysis of the interviews identified key concepts relating to the students' utilization of KM for their learning tasks. The impact of KM on medical pedagogy is discussed in light of our results.

User surveys are often used to estimate usage of online systems. We asked medical student to estimate their weekly use of KnowledgeMap, an online medical education system, during the previous semester. The information was validated against server log files. The average number of log-on days was significantly different across four categories of self-reported use. Self-reported frequency scales may be used to correctly segregate users into discrete ordinal usage groups.

We developed the KnowledgeMap (KM) system as an online, concept-based database of medical school curriculum documents. It uses the KM concept indexer to map full-text documents and match search queries to concepts in the Unified Medical Language System (UMLS). In this paper, we describe the design of KM and report the first seven months of its implementation into a medical school. Despite being emphasized in only two first year courses and one fourth year course, students from all four classes used KM to search and browse documents. All faculty members involved with courses piloting KM used the system to upload and manage lecture documents. Currently, we are working with eight course directors to transition their courses to KM for next year.

KM is a Web-accessible, comprehensive database that organizes course materials (at the level of full lectures, not just outlines or syllabi) from the Vanderbilt School of Medicine curriculum. KM uses natural language processing techniques to analyze educational documents for biomedical concepts. Lecture handouts and Microsoft PowerPoint presentations are indexed and available online for students, faculty and administrators to search for individual or interrelated concepts across the medical school curriculum.