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Article ; Online: Evaluating robustness of a generalized linear model when applied to electronic health record data accessed using an Open API.

Sharma, Priya / Haaland, Perry / Krishnamurthy, Ashok / Lan, Bo / Schmitt, Patrick L / Sinha, Meghamala / Xu, Hao / Fecho, Karamarie

Health informatics journal

2023 Volume 29, Issue 2, Page(s) 14604582231170892

Abstract: The Integrated Clinical and Environmental Exposures Service (ICEES) provides open regulatory-compliant access to clinical data, including electronic health record data, that have been integrated with environmental exposures data. While ICEES has been ... ...

Abstract	The Integrated Clinical and Environmental Exposures Service (ICEES) provides open regulatory-compliant access to clinical data, including electronic health record data, that have been integrated with environmental exposures data. While ICEES has been validated in the context of an asthma use case and several other use cases, the regulatory constraints on the ICEES open application programming interface (OpenAPI) result in data loss when using the service for multivariate analysis. In this study, we investigated the robustness of the ICEES OpenAPI through a comparative analysis, in which we applied a generalized linear model (GLM) to the OpenAPI data and the constraint-free source data to examine factors predictive of asthma exacerbations. Consistent with previous studies, we found that the main predictors identified by both analyses were sex, prednisone, race, obesity, and airborne particulate exposure. Comparison of GLM model fit revealed that data loss impacts model quality, but only with select interaction terms. We conclude that the ICEES OpenAPI supports multivariate analysis, albeit with potential data loss that users should be aware of.
MeSH term(s)	Humans ; Linear Models ; Electronic Health Records ; Environmental Exposure ; Software ; Asthma/epidemiology
Language	English
Publishing date	2023-04-11
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	2213115-2
ISSN	1741-2811 ; 1460-4582
ISSN (online)	1741-2811
ISSN	1460-4582
DOI	10.1177/14604582231170892
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Article ; Online: Development and Application of an Open Tool for Sharing and Analyzing Integrated Clinical and Environmental Exposures Data: Asthma Use Case.

Fecho, Karamarie / Ahalt, Stanley C / Appold, Stephen / Arunachalam, Saravanan / Pfaff, Emily / Stillwell, Lisa / Valencia, Alejandro / Xu, Hao / Peden, David B

JMIR formative research

2022 Volume 6, Issue 4, Page(s) e32357

Abstract: Background: The Integrated Clinical and Environmental Exposures Service (ICEES) serves as an open-source, disease-agnostic, regulatory-compliant framework and approach for openly exposing and exploring clinical data that have been integrated at the ... ...

Abstract	Background: The Integrated Clinical and Environmental Exposures Service (ICEES) serves as an open-source, disease-agnostic, regulatory-compliant framework and approach for openly exposing and exploring clinical data that have been integrated at the patient level with a variety of environmental exposures data. ICEES is equipped with tools to support basic statistical exploration of the integrated data in a completely open manner. Objective: This study aims to further develop and apply ICEES as a novel tool for openly exposing and exploring integrated clinical and environmental data. We focus on an asthma use case. Methods: We queried the ICEES open application programming interface (OpenAPI) using a functionality that supports chi-square tests between feature variables and a primary outcome measure, with a Bonferroni correction for multiple comparisons (α=.001). We focused on 2 primary outcomes that are indicative of asthma exacerbations: annual emergency department (ED) or inpatient visits for respiratory issues; and annual prescriptions for prednisone. Results: Of the 157,410 patients within the asthma cohort, 26,332 (16.73%) had 1 or more annual ED or inpatient visits for respiratory issues, and 17,056 (10.84%) had 1 or more annual prescriptions for prednisone. We found that close proximity to a major roadway or highway, exposure to high levels of particulate matter ≤2.5 μm (PM Conclusions: Our results demonstrate that the open-source ICEES can be used to replicate and extend published findings on factors that influence asthma exacerbations. As a disease-agnostic, open-source approach for integrating, exposing, and exploring patient-level clinical and environmental exposures data, we believe that ICEES will have broad adoption by other institutions and application in environmental health and other biomedical fields.
Language	English
Publishing date	2022-04-01
Publishing country	Canada
Document type	Journal Article
ISSN	2561-326X
ISSN (online)	2561-326X
DOI	10.2196/32357
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Article ; Online: Leveraging Open Electronic Health Record Data and Environmental Exposures Data to Derive Insights Into Rare Pulmonary Disease.

Fecho, Karamarie / Ahalt, Stanley C / Knowles, Michael / Krishnamurthy, Ashok / Leigh, Margaret / Morton, Kenneth / Pfaff, Emily / Wang, Max / Yi, Hong

Frontiers in artificial intelligence

2022 Volume 5, Page(s) 918888

Abstract: Research on rare diseases has received increasing attention, in part due to the realized profitability of orphan drugs. Biomedical informatics holds promise in accelerating translational research on rare disease, yet challenges remain, including the lack ...

Abstract	Research on rare diseases has received increasing attention, in part due to the realized profitability of orphan drugs. Biomedical informatics holds promise in accelerating translational research on rare disease, yet challenges remain, including the lack of diagnostic codes for rare diseases and privacy concerns that prevent research access to electronic health records when few patients exist. The Integrated Clinical and Environmental Exposures Service (ICEES) provides regulatory-compliant open access to electronic health record data that have been integrated with environmental exposures data, as well as analytic tools to explore the integrated data. We describe a proof-of-concept application of ICEES to examine demographics, clinical characteristics, environmental exposures, and health outcomes among a cohort of patients enriched for phenotypes associated with cystic fibrosis (CF), idiopathic bronchiectasis (IB), and primary ciliary dyskinesia (PCD). We then focus on a subset of patients with CF, leveraging the availability of a diagnostic code for CF and serving as a benchmark for our development work. We use ICEES to examine select demographics, co-diagnoses, and environmental exposures that may contribute to poor health outcomes among patients with CF, defined as emergency department or inpatient visits for respiratory issues. We replicate current understanding of the pathogenesis and clinical manifestations of CF by identifying co-diagnoses of asthma, chronic nasal congestion, cough, middle ear disease, and pneumonia as factors that differentiate patients with poor health outcomes from those with better health outcomes. We conclude by discussing our preliminary findings in relation to other published work, the strengths and limitations of our approach, and our future directions.
Language	English
Publishing date	2022-06-28
Publishing country	Switzerland
Document type	Journal Article
ISSN	2624-8212
ISSN (online)	2624-8212
DOI	10.3389/frai.2022.918888
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Article: A Biomedical Knowledge Graph System to Propose Mechanistic Hypotheses for Real-World Environmental Health Observations: Cohort Study and Informatics Application.

Fecho, Karamarie / Bizon, Chris / Miller, Frederick / Schurman, Shepherd / Schmitt, Charles / Xue, William / Morton, Kenneth / Wang, Patrick / Tropsha, Alexander

JMIR medical informatics

2021 Volume 9, Issue 7, Page(s) e26714

Abstract: Background: Knowledge graphs are a common form of knowledge representation in biomedicine and many other fields. We developed an open biomedical knowledge graph-based system termed Reasoning Over Biomedical Objects linked in Knowledge Oriented Pathways ( ...

Abstract	Background: Knowledge graphs are a common form of knowledge representation in biomedicine and many other fields. We developed an open biomedical knowledge graph-based system termed Reasoning Over Biomedical Objects linked in Knowledge Oriented Pathways (ROBOKOP). ROBOKOP consists of both a front-end user interface and a back-end knowledge graph. The ROBOKOP user interface allows users to posit questions and explore answer subgraphs. Users can also posit questions through direct Cypher query of the underlying knowledge graph, which currently contains roughly 6 million nodes or biomedical entities and 140 million edges or predicates describing the relationship between nodes, drawn from over 30 curated data sources. Objective: We aimed to apply ROBOKOP to survey data on workplace exposures and immune-mediated diseases from the Environmental Polymorphisms Registry (EPR) within the National Institute of Environmental Health Sciences. Methods: We analyzed EPR survey data and identified 45 associations between workplace chemical exposures and immune-mediated diseases, as self-reported by study participants (n= 4574), with 20 associations significant at P<.05 after false discovery rate correction. We then used ROBOKOP to (1) validate the associations by determining whether plausible connections exist within the ROBOKOP knowledge graph and (2) propose biological mechanisms that might explain them and serve as hypotheses for subsequent testing. We highlight the following three exemplar associations: carbon monoxide-multiple sclerosis, ammonia-asthma, and isopropanol-allergic disease. Results: ROBOKOP successfully returned answer sets for three queries that were posed in the context of the driving examples. The answer sets included potential intermediary genes, as well as supporting evidence that might explain the observed associations. Conclusions: We demonstrate real-world application of ROBOKOP to generate mechanistic hypotheses for associations between workplace chemical exposures and immune-mediated diseases. We expect that ROBOKOP will find broad application across many biomedical fields and other scientific disciplines due to its generalizability, speed to discovery and generation of mechanistic hypotheses, and open nature.
Language	English
Publishing date	2021-07-20
Publishing country	Canada
Document type	Journal Article
ZDB-ID	2798261-0
ISSN	2291-9694
ISSN	2291-9694
DOI	10.2196/26714
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Article ; Online: Enabling Longitudinal Exploratory Analysis of Clinical COVID Data.

Borland, David / Brain, Irena / Fecho, Karamarie / Pfaff, Emily / Xu, Hao / Champion, James / Bizon, Chris / Gotz, David

ArXiv

2021

Abstract: As the COVID-19 pandemic continues to impact the world, data is being gathered and analyzed to better understand the disease. Recognizing the potential for visual analytics technologies to support exploratory analysis and hypothesis generation from ... ...

Abstract	As the COVID-19 pandemic continues to impact the world, data is being gathered and analyzed to better understand the disease. Recognizing the potential for visual analytics technologies to support exploratory analysis and hypothesis generation from longitudinal clinical data, a team of collaborators worked to apply existing event sequence visual analytics technologies to a longitudinal clinical data from a cohort of 998 patients with high rates of COVID-19 infection. This paper describes the initial steps toward this goal, including: (1) the data transformation and processing work required to prepare the data for visual analysis, (2) initial findings and observations, and (3) qualitative feedback and lessons learned which highlight key features as well as limitations to address in future work.
Language	English
Publishing date	2021-08-25
Publishing country	United States
Document type	Preprint
ISSN	2331-8422
ISSN (online)	2331-8422
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: An approach for open multivariate analysis of integrated clinical and environmental exposures data.

Fecho, Karamarie / Haaland, Perry / Krishnamurthy, Ashok / Lan, Bo / Ramsey, Stephen A / Schmitt, Patrick L / Sharma, Priya / Sinha, Meghamala / Xu, Hao

Informatics in medicine unlocked

2021 Volume 26

Abstract: The Integrated Clinical and Environmental Exposures Service (ICEES) provides regulatory-compliant open access to sensitive patient data that have been integrated with public exposures data. ICEES was designed initially to support dynamic cohort creation ... ...

Abstract	The Integrated Clinical and Environmental Exposures Service (ICEES) provides regulatory-compliant open access to sensitive patient data that have been integrated with public exposures data. ICEES was designed initially to support dynamic cohort creation and bivariate contingency tests. The objective of the present study was to develop an open approach to support multivariate analyses using existing ICEES functionalities and abiding by all regulatory constraints. We first developed an open approach for generating a multivariate table that maintains contingencies between clinical and environmental variables using programmatic calls to the open ICEES application programming interface. We then applied the approach to data on a large cohort (N = 22,365) of patients with asthma or related conditions and generated an eight-feature table. Due to regulatory constraints, data loss was incurred with the incorporation of each successive feature variable, from a starting sample size of N = 22,365 to a final sample size of N = 4,556 (20.4%), but data loss was < 10% until the addition of the final two feature variables. We then applied a generalized linear model to the subsequent dataset and focused on the impact of seven select feature variables on asthma exacerbations, defined as annual emergency department or inpatient visits for respiratory issues. We identified five feature variables-sex, race, obesity, prednisone, and airborne particulate exposure-as significant predictors of asthma exacerbations. We discuss the advantages and disadvantages of ICEES open multivariate analysis and conclude that, despite limitations, ICEES can provide a valuable resource for open multivariate analysis and can serve as an exemplar for regulatory-compliant informatic solutions to open patient data, with capabilities to explore the impact of environmental exposures on health outcomes.
Language	English
Publishing date	2021-09-20
Publishing country	England
Document type	Journal Article
ISSN	2352-9148
ISSN	2352-9148
DOI	10.1016/j.imu.2021.100733
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Article ; Online: Open Application of Statistical and Machine Learning Models to Explore the Impact of Environmental Exposures on Health and Disease: An Asthma Use Case.

Lan, Bo / Haaland, Perry / Krishnamurthy, Ashok / Peden, David B / Schmitt, Patrick L / Sharma, Priya / Sinha, Meghamala / Xu, Hao / Fecho, Karamarie

International journal of environmental research and public health

2021 Volume 18, Issue 21

Abstract: ICEES (Integrated Clinical and Environmental Exposures Service) provides a disease-agnostic, regulatory-compliant approach for openly exposing and analyzing clinical data that have been integrated at the patient level with environmental exposures data. ... ...

Abstract	ICEES (Integrated Clinical and Environmental Exposures Service) provides a disease-agnostic, regulatory-compliant approach for openly exposing and analyzing clinical data that have been integrated at the patient level with environmental exposures data. ICEES is equipped with basic features to support exploratory analysis using statistical approaches, such as bivariate chi-square tests. We recently developed a method for using ICEES to generate multivariate tables for subsequent application of machine learning and statistical models. The objective of the present study was to use this approach to identify predictors of asthma exacerbations through the application of three multivariate methods: conditional random forest, conditional tree, and generalized linear model. Among seven potential predictor variables, we found five to be of significant importance using both conditional random forest and conditional tree: prednisone, race, airborne particulate exposure, obesity, and sex. The conditional tree method additionally identified several significant two-way and three-way interactions among the same variables. When we applied a generalized linear model, we identified four significant predictor variables, namely prednisone, race, airborne particulate exposure, and obesity. When ranked in order by effect size, the results were in agreement with the results from the conditional random forest and conditional tree methods as well as the published literature. Our results suggest that the open multivariate analytic capabilities provided by ICEES are valid in the context of an asthma use case and likely will have broad value in advancing open research in environmental and public health.
MeSH term(s)	Asthma/epidemiology ; Asthma/etiology ; Environmental Exposure ; Humans ; Machine Learning ; Models, Statistical
Language	English
Publishing date	2021-10-29
Publishing country	Switzerland
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, U.S. Gov't, Non-P.H.S.
ZDB-ID	2175195-X
ISSN	1660-4601 ; 1661-7827
ISSN (online)	1660-4601
ISSN	1661-7827
DOI	10.3390/ijerph182111398
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Article ; Online: FHIR PIT: an open software application for spatiotemporal integration of clinical data and environmental exposures data.

Xu, Hao / Cox, Steven / Stillwell, Lisa / Pfaff, Emily / Champion, James / Ahalt, Stanley C / Fecho, Karamarie

BMC medical informatics and decision making

2020 Volume 20, Issue 1, Page(s) 53

Abstract: Background: Informatics tools to support the integration and subsequent interrogation of spatiotemporal data such as clinical data and environmental exposures data are lacking. Such tools are needed to support research in environmental health and any ... ...

Abstract	Background: Informatics tools to support the integration and subsequent interrogation of spatiotemporal data such as clinical data and environmental exposures data are lacking. Such tools are needed to support research in environmental health and any biomedical field that is challenged by the need for integrated spatiotemporal data to examine individual-level determinants of health and disease. Results: We have developed an open-source software application-FHIR PIT (Health Level 7 Fast Healthcare Interoperability Resources Patient data Integration Tool)-to enable studies on the impact of individual-level environmental exposures on health and disease. FHIR PIT was motivated by the need to integrate patient data derived from our institution's clinical warehouse with a variety of public data sources on environmental exposures and then openly expose the data via ICEES (Integrated Clinical and Environmental Exposures Service). FHIR PIT consists of transformation steps or building blocks that can be chained together to form a transformation and integration workflow. Several transformation steps are generic and thus can be reused. As such, new types of data can be incorporated into the modular FHIR PIT pipeline by simply reusing generic steps or adding new ones. We validated FHIR PIT in the context of a driving use case designed to investigate the impact of airborne pollutant exposures on asthma. Specifically, we replicated published findings demonstrating racial disparities in the impact of airborne pollutants on asthma exacerbations. Conclusions: While FHIR PIT was developed to support our driving use case on asthma, the software can be used to integrate any type and number of spatiotemporal data sources at a level of granularity that enables individual-level study. We expect FHIR PIT to facilitate research in environmental health and numerous other biomedical disciplines.
MeSH term(s)	Electronic Health Records ; Environmental Exposure ; Health Information Interoperability/standards ; Health Level Seven ; Humans ; Software ; Software Design ; Spatio-Temporal Analysis ; Systems Integration ; Workflow
Language	English
Publishing date	2020-03-11
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural
ISSN	1472-6947
ISSN (online)	1472-6947
DOI	10.1186/s12911-020-1056-9
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Article ; Online: Lateral antebrachial cutaneous neuropathy as a result of positioning while under general anesthesia.

Judge, Amy / Fecho, Karamarie

Anesthesia and analgesia

2010 Volume 110, Issue 1, Page(s) 122–124

Abstract: As the second most common cause for professional liability in anesthetic practice, nerve injuries are a well-recognized complication. We present a case of lateral antebrachial cutaneous neuropathy after prolonged general anesthesia for left medial ... ...

Abstract	As the second most common cause for professional liability in anesthetic practice, nerve injuries are a well-recognized complication. We present a case of lateral antebrachial cutaneous neuropathy after prolonged general anesthesia for left medial meniscal transplant and microfracture surgery. In the orthopedic and sports medicine literature, <100 cases have been described. We discuss the possible causes and propose surgical positioning as the most likely cause of postoperative lateral antebrachial cutaneous neuropathy in our patient.
MeSH term(s)	Adult ; Anesthesia, General/adverse effects ; Arm/innervation ; Arm Injuries/etiology ; Athletic Injuries/surgery ; Cartilage/transplantation ; Fractures, Bone/surgery ; Humans ; Male ; Peripheral Nervous System Diseases/etiology ; Recovery of Function ; Sensation Disorders/etiology ; Skin/injuries ; Supine Position/physiology ; Transplantation, Autologous
Language	English
Publishing date	2010-01-01
Publishing country	United States
Document type	Journal Article
ZDB-ID	80032-6
ISSN	1526-7598 ; 0003-2999
ISSN (online)	1526-7598
ISSN	0003-2999
DOI	10.1213/ANE.0b013e3181c4baa3
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Article ; Online: ROBOKOP KG and KGB: Integrated Knowledge Graphs from Federated Sources.

Bizon, Chris / Cox, Steven / Balhoff, James / Kebede, Yaphet / Wang, Patrick / Morton, Kenneth / Fecho, Karamarie / Tropsha, Alexander

Journal of chemical information and modeling

2019 Volume 59, Issue 12, Page(s) 4968–4973

Abstract: A proliferation of data sources has led to the notional existence of an implicit Knowledge Graph (KG) that contains vast amounts of biological knowledge contributed by distributed Application Programming Interfaces (APIs). However, challenges arise when ... ...

Abstract	A proliferation of data sources has led to the notional existence of an implicit Knowledge Graph (KG) that contains vast amounts of biological knowledge contributed by distributed Application Programming Interfaces (APIs). However, challenges arise when integrating data across multiple APIs due to incompatible semantic types, identifier schemes, and data formats. We present ROBOKOP KG ( http://robokopkg.renci.org ), which is a KG that was initially built to support the open biomedical question-answering application, ROBOKOP (
MeSH term(s)	Computer Graphics ; Data Mining/methods ; Databases, Factual ; Knowledge Bases ; User-Computer Interface
Language	English
Publishing date	2019-12-12
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	190019-5
ISSN	1549-960X ; 0095-2338
ISSN (online)	1549-960X
ISSN	0095-2338
DOI	10.1021/acs.jcim.9b00683
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