Adverse obstetric and neonatal outcomes among women with psychosis, particularly affective psychosis, has rarely been studied at the population level. We aimed to assess the risk of adverse obstetric and neonatal outcomes among women with psychosis (schizophrenia, affective psychosis, and other psychoses).
INTRODUCTION: The European Medical Information Framework consortium has assembled electronic health record (EHR) databases for dementia research. We calculated dementia prevalence and incidence in 25 million persons from 2004 to 2012.
METHODS: Six EHR databases (three primary care and three secondary care) from five countries were interrogated. Dementia was ascertained by consensus harmonization of clinical/diagnostic codes. Annual period prevalences and incidences by age and gender were calculated and meta-analyzed.
RESULTS: The six databases contained 138,625 dementia cases. Age-specific prevalences were around 30% of published estimates from community samples and incidences were around 50%. Pooled prevalences had increased from 2004 to 2012 in all age groups but pooled incidences only after age 75 years. Associations with age and gender were stable over time.
DISCUSSION: The European Medical Information Framework initiative supports EHR data on unprecedented number of people with dementia. Age-specific prevalences and incidences mirror estimates from community samples in pattern at levels that are lower but increasing over time.
OBJECTIVE: The effects of suicidal behavior on obstetric outcomes remain dangerously unquantified. We sought to report on the risk of adverse obstetric outcomes for US women with suicidal behavior at the time of delivery.
METHODS: We performed a cross-sectional analysis of delivery hospitalizations from 2007-2012 National (Nationwide) Inpatient Sample. From the same hospitalization record, International Classification of Diseases codes were used to identify suicidal behavior and adverse obstetric outcomes. Adjusted odds ratios (aOR) and 95% confidence intervals (CI) were obtained using logistic regression.
RESULTS: Of the 23,507,597 delivery hospitalizations, 2,180 were complicated by suicidal behavior. Women with suicidal behavior were at a heightened risk for outcomes including antepartum hemorrhage (aOR = 2.34; 95% CI: 1.47-3.74), placental abruption (aOR = 2.07; 95% CI: 1.17-3.66), postpartum hemorrhage (aOR = 2.33; 95% CI: 1.61-3.37), premature delivery (aOR = 3.08; 95% CI: 2.43-3.90), stillbirth (aOR = 10.73; 95% CI: 7.41-15.56), poor fetal growth (aOR = 1.70; 95% CI: 1.10-2.62), and fetal anomalies (aOR = 3.72; 95% CI: 2.57-5.40). No significant association was observed for maternal suicidal behavior with cesarean delivery, induction of labor, premature rupture of membranes, excessive fetal growth, and fetal distress. The mean length of stay was longer for women with suicidal behavior.
CONCLUSION: During delivery hospitalization, women with suicidal behavior are at increased risk for many adverse obstetric outcomes, highlighting the importance of screening for and providing appropriate clinical care for women with suicidal behavior during pregnancy.
Increasingly, biobanks are being developed to support organized collections of biological specimens and associated clinical information on broadly consented, diverse patient populations. We describe the implementation of a pediatric biobank, comprised of a fully-informed patient cohort linking specimens to phenotypic data derived from electronic health records (EHR). The Biobank was launched after multiple stakeholders' input and implemented initially in a pilot phase before hospital-wide expansion in 2016. In-person informed consent is obtained from all participants enrolling in the Biobank and provides permission to: (1) access EHR data for research; (2) collect and use residual specimens produced as by-products of routine care; and (3) share de-identified data and specimens outside of the institution. Participants are recruited throughout the hospital, across diverse clinical settings. We have enrolled 4900 patients to date, and 41% of these have an associated blood sample for DNA processing. Current efforts are focused on aligning the Biobank with other ongoing research efforts at our institution and extending our electronic consenting system to support remote enrollment. A number of pediatric-specific challenges and opportunities is reviewed, including the need to re-consent patients when they reach 18 years of age, the ability to enroll family members accompanying patients and alignment with disease-specific research efforts at our institution and other pediatric centers to increase cohort sizes, particularly for rare diseases.
Motivation: In the era of big data and precision medicine, the number of databases containing clinical, environmental, self-reported, and biochemical variables is increasing exponentially. Enabling the experts to focus on their research questions rather than on computational data management, access and analysis is one of the most significant challenges nowadays.
Results: We present Rcupcake, an R package that contains a variety of functions for leveraging different databases through the BD2K PIC-SURE RESTful API and facilitating its query, analysis and interpretation. The package offers a variety of analysis and visualization tools, including the study of the phenotype co-occurrence and prevalence, according to multiple layers of data, such as phenome, exposome or genome.
Availability: The package is implemented in R and is available under Mozilla v2 license from GitHub (https://github.com/hms-dbmi/Rcupcake). Two reproducible case studies are also available (https://github.com/hms-dbmi/Rcupcake-case-studies/blob/master/SSCcaseStu..., https://github.com/hms-dbmi/Rcupcake-case-studies/blob/master/NHANEScase...).
Supplementary information: Supplementary data are available at Bioinformatics online.
The first year of university is a particularly stressful period and can impact academic performance and students' health. The aim of this study was to evaluate the health and lifestyle of undergraduates and assess risk factors associated with psychiatric symptoms.
Between September 2012 and June 2013, we included all undergraduate students who underwent compulsory a medical visit at the university medical service in Nice (France) during which they were screened for potential diseases during a diagnostic interview. Data were collected prospectively in the CALCIUM database (Consultations Assistés par Logiciel pour les Centres Inter-Universitaire de Médecine) and included information about the students' lifestyle (living conditions, dietary behavior, physical activity, use of recreational drugs). The prevalence of psychiatric symptoms related to depression, anxiety and panic attacks was assessed and risk factors for these symptoms were analyzed using logistic regression.
A total of 4,184 undergraduates were included. Prevalence for depression, anxiety and panic attacks were 12.6%, 7.6% and 1.0%, respectively. During the 30 days preceding the evaluation, 0.6% of the students regularly drank alcohol, 6.3% were frequent-to-heavy tobacco smokers, and 10.0% smoked marijuana. Dealing with financial difficulties and having learning disabilities were associated with psychiatric symptoms. Students who were dissatisfied with their living conditions and those with poor dietary behavior were at risk of depression. Being a woman and living alone were associated with anxiety. Students who screened positively for any psychiatric disorder assessed were at a higher risk of having another psychiatric disorder concomitantly.
The prevalence of psychiatric disorders in undergraduate students is low but the rate of students at risk of developing chronic disease is far from being negligible. Understanding predictors for these symptoms may improve students' health by implementing targeted prevention campaigns. Further research in other French universities is necessary to confirm our results.
We are fortunate to be living in an era of twin biomedical data surges: a burgeoning representation of human phenotypes in the medical records of our healthcare systems, and high-throughput sequencing making rapid technological advances. The difficulty representing genomic data and its annotations has almost by itself led to the recognition of a biomedical “Big Data” challenge, and the complexity of healthcare data only compounds the problem to the point that coherent representation of both systems on the same platform seems insuperably difficult. We investigated the capability for complex, integrative genomic and clinical queries to be supported in the Informatics for Integrating Biology and the Bedside (i2b2) translational software package. Three different data integration approaches were developed: The first is based on Sequence Ontology, the second is based on the tranSMART engine, and the third on CouchDB. These novel methods for representing and querying complex genomic and clinical data on the i2b2 platform are available today for advancing precision medicine.
When developed jointly with clinical information systems, clinical data warehouses (CDWs) facilitate the reuse of healthcare data and leverage clinical research.
To describe both data access and use for clinical research, epidemiology and health serviceresearch of the “Hôpital Européen Georges Pompidou” (HEGP) CDW.
The CDW has been developed since 2008 using an i2b2 platform. It was made available to health professionals and researchers in October 2010. Procedures to access data have been implemented and different access levels have been distinguished according to the nature of queries.
As of July 2016, the CDW contained the consolidated data of over 860,000 patients followed since the opening of the HEGP hospital in July 2000. These data correspond to more than 122 million clinical item values, 124 million biological item values, and 3.7 million free text reports. The ethics committee of the hospital evaluates all CDW projects that generate secondary data marts. Characteristics of the 74 research projects validated between January 2011 and December 2015 are described.
The use of HEGP CDWs is a key facilitator for clinical research studies. It required however important methodological and organizational support efforts from a biomedical informatics department.
Assessment of drug and vaccine effects by combining information from different healthcare databases in the European Union requires extensive efforts in the harmonization of codes as different vocabularies are being used across countries. In this paper, we present a web application called CodeMapper, which assists in the mapping of case definitions to codes from different vocabularies, while keeping a transparent record of the complete mapping process.
CodeMapper builds upon coding vocabularies contained in the Metathesaurus of the Unified Medical Language System. The mapping approach consists of three phases. First, medical concepts are automatically identified in a free-text case definition. Second, the user revises the set of medical concepts by adding or removing concepts, or expanding them to related concepts that are more general or more specific. Finally, the selected concepts are projected to codes from the targeted coding vocabularies. We evaluated the application by comparing codes that were automatically generated from case definitions by applying CodeMapper's concept identification and successive concept expansion, with reference codes that were manually created in a previous epidemiological study.
Automated concept identification alone had a sensitivity of 0.246 and positive predictive value (PPV) of 0.420 for reproducing the reference codes. Three successive steps of concept expansion increased sensitivity to 0.953 and PPV to 0.616.
Automatic concept identification in the case definition alone was insufficient to reproduce the reference codes, but CodeMapper's operations for concept expansion provide an effective, efficient, and transparent way for reproducing the reference codes.
The heterogeneity of patient phenotype data are an impediment to the research into the origins and progression of neuropsychiatric disorders. This difficulty is compounded in the case of rare disorders such as Phelan-McDermid Syndrome (PMS) by the paucity of patient clinical data. PMS is a rare syndromic genetic cause of autism and intellectual deficiency. In this paper, we describe the Phelan-McDermid Syndrome Data Network (PMS_DN), a platform that facilitates research into phenotype–genotype correlation and progression of PMS by: a) integrating knowledge of patient phenotypes extracted from Patient Reported Outcomes (PRO) data and clinical notes—two heterogeneous, underutilized sources of knowledge about patient phenotypes—with curated genetic information from the same patient cohort and b) making this integrated knowledge, along with a suite of statistical tools, available free of charge to authorized investigators on a Web portal https://pmsdn.hms.harvard.edu. PMS_DN is a Patient Centric Outcomes Research Initiative (PCORI) where patients and their families are involved in all aspects of the management of patient data in driving research into PMS. To foster collaborative research, PMS_DN also makes patient aggregates from this knowledge available to authorized investigators using distributed research networks such as the PCORnet PopMedNet. PMS_DN is hosted on a scalable cloud based environment and complies with all patient data privacy regulations. As of October 31, 2016, PMS_DN integrates high-quality knowledge extracted from the clinical notes of 112 patients and curated genetic reports of 176 patients with preprocessed PRO data from 415 patients.
The National Health and Nutrition Examination Survey (NHANES) is a population survey implemented by the Centers for Disease Control and Prevention (CDC) to monitor the health of the United States whose data is publicly available in hundreds of files. This Data Descriptor describes a single unified and universally accessible data file, merging across 255 separate files and stitching data across 4 surveys, encompassing 41,474 individuals and 1,191 variables. The variables consist of phenotype and environmental exposure information on each individual, specifically (1) demographic information, physical exam results (e.g., height, body mass index), laboratory results (e.g., cholesterol, glucose, and environmental exposures), and (4) questionnaire items. Second, the data descriptor describes a dictionary to enable analysts find variables by category and human-readable description. The datasets are available on DataDryad and a hands-on analytics tutorial is available on GitHub. Through a new big data platform, BD2K Patient Centered Information Commons (http://pic-sure.org), we provide a new way to browse the dataset via a web browser (https://nhanes.hms.harvard.edu) and provide application programming interface for programmatic access.
The recent announcement of the Precision Medicine Initiative by President Obama has brought precision medicine (PM) to the forefront for healthcare providers, researchers, regulators, innovators, and funders alike. As technologies continue to evolve and datasets grow in magnitude, a strong computational infrastructure will be essential to realize PM's vision of improved healthcare derived from personal data. In addition, informatics research and innovation affords a tremendous opportunity to drive the science underlying PM. The informatics community must lead the development of technologies and methodologies that will increase the discovery and application of biomedical knowledge through close collaboration between researchers, clinicians, and patients. This perspective highlights seven key areas that are in need of further informatics research and innovation to support the realization of PM.
INTRODUCTION: We see increased use of existing observational data in order to achieve fast and transparent production of empirical evidence in health care research. Multiple databases are often used to increase power, to assess rare exposures or outcomes, or to study diverse populations. For privacy and sociological reasons, original data on individual subjects can't be shared, requiring a distributed network approach where data processing is performed prior to data sharing.
CASE DESCRIPTIONS AND VARIATION AMONG SITES: We created a conceptual framework distinguishing three steps in local data processing: (1) data reorganization into a data structure common across the network; (2) derivation of study variables not present in original data; and (3) application of study design to transform longitudinal data into aggregated data sets for statistical analysis. We applied this framework to four case studies to identify similarities and differences in the United States and Europe: Exploring and Understanding Adverse Drug Reactions by Integrative Mining of Clinical Records and Biomedical Knowledge (EU-ADR), Observational Medical Outcomes Partnership (OMOP), the Food and Drug Administration's (FDA's) Mini-Sentinel, and the Italian network-the Integration of Content Management Information on the Territory of Patients with Complex Diseases or with Chronic Conditions (MATRICE).
FINDINGS: National networks (OMOP, Mini-Sentinel, MATRICE) all adopted shared procedures for local data reorganization. The multinational EU-ADR network needed locally defined procedures to reorganize its heterogeneous data into a common structure. Derivation of new data elements was centrally defined in all networks but the procedure was not shared in EU-ADR. Application of study design was a common and shared procedure in all the case studies. Computer procedures were embodied in different programming languages, including SAS, R, SQL, Java, and C++.
CONCLUSION: Using our conceptual framework we found several areas that would benefit from research to identify optimal standards for production of empirical knowledge from existing databases.an opportunity to advance evidence-based care management. In addition, formalized CM outcomes assessment methodologies will enable us to compare CM effectiveness across health delivery settings.
Due to the heterogeneity of existing European sources of observational healthcare data, data source-tailored choices are needed to execute multi-data source, multi-national epidemiological studies. This makes transparent documentation paramount. In this proof-of-concept study, a novel standard data derivation procedure was tested in a set of heterogeneous data sources. Identification of subjects with type 2 diabetes (T2DM) was the test case. We included three primary care data sources (PCDs), three record linkage of administrative and/or registry data sources (RLDs), one hospital and one biobank. Overall, data from 12 million subjects from six European countries were extracted. Based on a shared event definition, sixteeen standard algorithms (components) useful to identify T2DM cases were generated through a top-down/bottom-up iterative approach. Each component was based on one single data domain among diagnoses, drugs, diagnostic test utilization and laboratory results. Diagnoses-based components were subclassified considering the healthcare setting (primary, secondary, inpatient care). The Unified Medical Language System was used for semantic harmonization within data domains. Individual components were extracted and proportion of population identified was compared across data sources. Drug-based components performed similarly in RLDs and PCDs, unlike diagnoses-based components. Using components as building blocks, logical combinations with AND, OR, AND NOT were tested and local experts recommended their preferred data source-tailored combination. The population identified per data sources by resulting algorithms varied from 3.5% to 15.7%, however, age-specific results were fairly comparable. The impact of individual components was assessed: diagnoses-based components identified the majority of cases in PCDs (93-100%), while drug-based components were the main contributors in RLDs (81-100%). The proposed data derivation procedure allowed the generation of data source-tailored case-finding algorithms in a standardized fashion, facilitated transparent documentation of the process and benchmarking of data sources, and provided bases for interpretation of possible inter-data source inconsistency of findings in future studies.
OBJECTIVE: The purpose of this study is to determine whether the posterior radioscaphoid angle, a marker of posterior displacement of the scaphoid, is associated with degenerative joint disease in patients with scapholunate ligament tears.
MATERIALS AND METHODS: Images from 150 patients with wrist pain who underwent CT arthrography and radiography were retrospectively evaluated. Patients with and without scapholunate ligament ruptures were divided into two groups according to CT arthrography findings. The presence of degenerative changes (scapholunate advanced collapse [SLAC] wrist) was evaluated and graded on conventional radiographs. Images were evaluated by two readers independently, and an adjudicator analyzed the discordant cases. Posterior radioscaphoid angle values were correlated with CT arthrography and radiographic findings. The association between posterior radioscaphoid angle and degenerative joint disease was evaluated. Scapholunate and radiolunate angles were considered in the analysis.
RESULTS: The posterior radioscaphoid angle was measurable in all patients, with substantial interobserver agreement (intraclass correlation coefficient, 0.75). The posterior radioscaphoid angle performed better than did the scapholunate and radiolunate angles in the differentiation of patients with and without SLAC wrist (p < 0.02). Posterior radioscaphoid angles greater than 114° presented an 80.0% sensitivity and 89.7% specificity for the detection of SLAC wrist.
CONCLUSION: Posterior radioscaphoid angles were strongly associated with degenerative wrist disease, with potential prognostic implications in patients with wrist trauma and scapholunate ligament ruptures.
The worldwide incidence of melanoma is rising faster than any other cancer, and prognosis for patients with metastatic disease is poor. Current targeted therapies are limited in their durability and/or effect size in certain patient populations due to acquired mechanisms of resistance. Thus, the development of synergistic combinatorial treatment regimens holds great promise to improve patient outcomes. We have previously shown that a model for in-silico knowledge discovery, Translational Ontology-anchored Knowledge Discovery Engine (TOKEn), is able to generate valid relationships between bimolecular and clinical phenotypes. In this study, we have aggregated observational and canonical knowledge consisting of melanoma-related biomolecular entities and targeted therapeutics in a computationally tractable model. We demonstrate here that the explicit linkage of therapeutic modalities with biomolecular underpinnings of melanoma utilizing the TOKEn pipeline yield a set of informed relationships that have the potential to generate combination therapy strategies.
OBJECTIVE: To evaluate the impact of computerized provider order entry (CPOE) at the bedside on medical students training.
MATERIALS AND METHODS: We conducted a randomized cross-controlled educational trial on medical students during two clerkship rotations in three departments, assessing the impact of the use of CPOE on their ability to place adequate monitoring and therapeutic orders using a written test before and after each rotation. Students' satisfaction with their practice and the order placement system was surveyed. A multivariate mixed model was used to take individual students and chief resident (CR) effects into account. Factorial analysis was applied on the satisfaction questionnaire to identify dimensions, and scores were compared on these dimensions.
RESULTS: Thirty-six students show no better progress (beginning and final test means = 69.87 and 80.98 points out of 176 for the control group, 64.60 and 78.11 for the CPOE group, p = 0.556) during their rotation in either group, even after adjusting for each student and CR, but show a better satisfaction with patient care and greater involvement in the medical team in the CPOE group (p = 0.035*). Both groups have a favorable opinion regarding CPOE as an educational tool, especially because of the order reviewing by the supervisor.
CONCLUSION: This is the first randomized controlled trial assessing the performance of CPOE in both the progress in prescriptions ability and satisfaction of the students. The absence of effect on the medical skills must be weighted by the small time scale and low sample size. However, students are more satisfied when using CPOE rather than usual training.
Graft-versus-host disease (GVHD) is a known risk factor for invasive aspergillosis (IA), but remains poorly studied in relation to Clostridium difficile infection (CDI). We report a case of a 58-years-old patient who developed an IA within a protected room, CDI and GVHD after allogeneic allogeneic peripheral blood stem cell transplantation (PBSCT). Factors associated with this complex condition in patients receiving allogeneic PBSCT need to be identified.
This work proposes an integrated workflow for secondary use of medical data to serve feasibility studies, and the prescreening and monitoring of research studies. All research issues are initially addressed by the Clinical Research Office through a research portal and subsequently redirected to relevant experts in the determined field of concentration. For secondary use of data, the workflow is then based on the clinical data warehouse of the hospital. A datamart with potentially eligible research candidates is constructed. Datamarts can either produce aggregated data, de-identified data, or identified data, according to the kind of study being treated. In conclusion, integrating the secondary use of data process into a general research workflow allows visibility of information technologies and improves the accessability of clinical data.