Publications by authors named "Zhaoyi Chen"

24 Publications

  • Page 1 of 1

Challenges in replicating secondary analysis of electronic health records data with multiple computable phenotypes: A case study on methicillin-resistant Staphylococcus aureus bacteremia infections.

Int J Med Inform 2021 09 16;153:104531. Epub 2021 Jul 16.

Department of Epidemiology, College of Public Health and Health Professions and College of Medicine, University of Florida, Gainesville, FL, USA. Electronic address:

Background: Replication of prediction modeling using electronic health records (EHR) is challenging because of the necessity to compute phenotypes including study cohort, outcomes, and covariates. However, some phenotypes may not be easily replicated across EHR data sources due to a variety of reasons such as the lack of gold standard definitions and documentation variations across systems, which may lead to measurement error and potential bias. Methicillin-resistant Staphylococcus aureus (MRSA) infections are responsible for high mortality worldwide. With limited treatment options for the infection, the ability to predict MRSA outcome is of interest. However, replicating these MRSA outcome prediction models using EHR data is problematic due to the lack of well-defined computable phenotypes for many of the predictors as well as study inclusion and outcome criteria.

Objective: In this study, we aimed to evaluate a prediction model for 30-day mortality after MRSA bacteremia infection diagnosis with reduced vancomycin susceptibility (MRSA-RVS) considering multiple computable phenotypes using EHR data.

Methods: We used EHR data from a large academic health center in the United States to replicate the original study conducted in Taiwan. We derived multiple computable phenotypes of risk factors and predictors used in the original study, reported stratified descriptive statistics, and assessed the performance of the prediction model.

Results: In our replication study, it was possible to (re)compute most of the original variables. Nevertheless, for certain variables, their computable phenotypes can only be approximated by proxy with structured EHR data items, especially the composite clinical indices such as the Pitt bacteremia score. Even computable phenotype for the outcome variable was subject to variation on the basis of the admission/discharge windows. The replicated prediction model exhibited only a mild discriminatory ability.

Conclusion: Despite the rich information in EHR data, replication of prediction models involving complex predictors is still challenging, often due to the limited availability of validated computable phenotypes. On the other hand, it is often possible to derive proxy computable phenotypes that can be further validated and calibrated.
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http://dx.doi.org/10.1016/j.ijmedinf.2021.104531DOI Listing
September 2021

Examination of Early CNS Symptoms and Severe Coronavirus Disease 2019: A Multicenter Observational Case Series.

Crit Care Explor 2021 Jun 11;3(6):e0456. Epub 2021 Jun 11.

Department of Clinical and Health Psychology, University of Florida, Gainesville, FL.

To determine if early CNS symptoms are associated with severe coronavirus disease 2019.

Design: A retrospective, observational case series study design.

Setting: Electronic health records were reviewed for patients from five healthcare systems across the state of Florida, United States.

Patients: A clinical sample ( = 36,615) of patients with confirmed diagnosis of coronavirus disease 2019 were included. Twelve percent ( = 4,417) of the sample developed severe coronavirus disease 2019, defined as requiring critical care, mechanical ventilation, or diagnosis of acute respiratory distress syndrome, sepsis, or severe inflammatory response syndrome.

Interventions: None.

Measurement And Main Results: We reviewed the electronic health record for diagnosis of early CNS symptoms (encephalopathy, headache, ageusia, anosmia, dizziness, acute cerebrovascular disease) between 14 days before the diagnosis of coronavirus disease 2019 and 8 days after the diagnosis of coronavirus disease 2019, or before the date of severe coronavirus disease 2019 diagnosis, whichever came first. Hierarchal logistic regression models were used to examine the odds of developing severe coronavirus disease 2019 based on diagnosis of early CNS symptoms. Severe coronavirus disease 2019 patients were significantly more likely to have early CNS symptoms (32.8%) compared with nonsevere patients (6.11%; χ[1] = 3,266.08, < 0.0001, φ = 0.29). After adjusting for demographic variables and pertinent comorbidities, early CNS symptoms were significantly associated with severe coronavirus disease 2019 (odds ratio = 3.21). Diagnosis of encephalopathy (odds ratio = 14.38) was associated with greater odds of severe coronavirus disease 2019; whereas diagnosis of anosmia (odds ratio = 0.45), ageusia (odds ratio = 0.46), and headache (odds ratio = 0.63) were associated with reduced odds of severe coronavirus disease 2019.

Conclusions: Early CNS symptoms, and specifically encephalopathy, are differentially associated with risk of severe coronavirus disease 2019 and may serve as an early marker for differences in clinical disease course. Therapies for early coronavirus disease 2019 are scarce, and further identification of subgroups at risk may help to advance understanding of the severity trajectories and enable focused treatment.
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http://dx.doi.org/10.1097/CCE.0000000000000456DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8202548PMC
June 2021

Exploring the feasibility of using real-world data from a large clinical data research network to simulate clinical trials of Alzheimer's disease.

NPJ Digit Med 2021 May 14;4(1):84. Epub 2021 May 14.

Health Outcomes & Biomedical Informatics, University of Florida, Gainesville, FL, USA.

In this study, we explored the feasibility of using real-world data (RWD) from a large clinical research network to simulate real-world clinical trials of Alzheimer's disease (AD). The target trial (i.e., NCT00478205) is a Phase III double-blind, parallel-group trial that compared the 23 mg donepezil sustained release with the 10 mg donepezil immediate release formulation in patients with moderate to severe AD. We followed the target trial's study protocol to identify the study population, treatment regimen assignments and outcome assessments, and to set up a number of different simulation scenarios and parameters. We considered two main scenarios: (1) a one-arm simulation: simulating a standard-of-care (SOC) arm that can serve as an external control arm; and (2) a two-arm simulation: simulating both intervention and control arms with proper patient matching algorithms for comparative effectiveness analysis. In the two-arm simulation scenario, we used propensity score matching controlling for baseline characteristics to simulate the randomization process. In the two-arm simulation, higher serious adverse event (SAE) rates were observed in the simulated trials than the rates reported in original trial, and a higher SAE rate was observed in the 23 mg arm than in the 10 mg SOC arm. In the one-arm simulation scenario, similar estimates of SAE rates were observed when proportional sampling was used to control demographic variables. In conclusion, trial simulation using RWD is feasible in this example of AD trial in terms of safety evaluation. Trial simulation using RWD could be a valuable tool for post-market comparative effectiveness studies and for informing future trials' design. Nevertheless, such an approach may be limited, for example, by the availability of RWD that matches the target trials of interest, and further investigations are warranted.
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http://dx.doi.org/10.1038/s41746-021-00452-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8121837PMC
May 2021

Identifying Clinical Risk Factors for Opioid Use Disorder using a Distributed Algorithm to Combine Real-World Data from a Large Clinical Data Research Network.

AMIA Annu Symp Proc 2020 25;2020:1220-1229. Epub 2021 Jan 25.

Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, The University of Pennsylvania, Philadelphia, PA, USA.

Because they contain detailed individual-level data on various patient characteristics including their medical conditions and treatment histories, electronic health record (EHR) systems have been widely adopted as an efficient source for health research. Compared to data from a single health system, real-world data (RWD) from multiple clinical sites provide a larger and more generalizable population for accurate estimation, leading to better decision making for health care. However, due to concerns over protecting patient privacy, it is challenging to share individual patient-level data across sites in practice. To tackle this issue, many distributed algorithms have been developed to transfer summary-level statistics to derive accurate estimates. Nevertheless, many of these algorithms require multiple rounds of communication to exchange intermediate results across different sites. Among them, the One-shot Distributed Algorithm for Logistic regression (termed ODAL) was developed to reduce communication overhead while protecting patient privacy. In this paper, we applied the ODAL algorithm to RWD from a large clinical data research network-the OneFlorida Clinical Research Consortium and estimated the associations between risk factors and the diagnosis of opioid use disorder (OUD) among individuals who received at least one opioid prescription. The ODAL algorithm provided consistent findings of the associated risk factors and yielded better estimates than meta-analysis.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8075517PMC
June 2021

Developing and Validating a Computable Phenotype for the Identification of Transgender and Gender Nonconforming Individuals and Subgroups.

AMIA Annu Symp Proc 2020 25;2020:514-523. Epub 2021 Jan 25.

University of Florida, Gainesville, Florida, USA.

Transgender and gender nonconforming (TGNC) individuals face significant marginalization, stigma, and discrimination. Under-reporting of TGNC individuals is common since they are often unwilling to self-identify. Meanwhile, the rapid adoption of electronic health record (EHR) systems has made large-scale, longitudinal real-world clinical data available to research and provided a unique opportunity to identify TGNC individuals using their EHRs, contributing to a promising routine health surveillance approach. Built upon existing work, we developed and validated a computable phenotype (CP) algorithm for identifying TGNC individuals and their natal sex (i.e., male-to-female or female-to-male) using both structured EHR data and unstructured clinical notes. Our CP algorithm achieved a 0.955 F1-score on the training data and a perfect F1-score on the independent testing data. Consistent with the literature, we observed an increasing percentage of TGNC individuals and a disproportionate burden of adverse health outcomes, especially sexually transmitted infections and mental health distress, in this population.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8075543PMC
June 2021

Leverage Real-world Longitudinal Data in Large Clinical Research Networks for Alzheimer's Disease and Related Dementia (ADRD).

AMIA Annu Symp Proc 2020 25;2020:393-401. Epub 2021 Jan 25.

Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, The University of Pennsylvania, Philadelphia, PA, USA.

With vast amounts ofpatients' medical information, electronic health records (EHRs) are becoming one of the most important data sources in biomedical and health care research. Effectively integrating data from multiple clinical sites can help provide more generalized real-world evidence that is clinically meaningful. To analyze the clinical data from multiple sites, distributed algorithms are developed to protect patient privacy without sharing individual-level medical information. In this paper, we applied the One-shot Distributed Algorithm for Cox proportional hazard model (ODAC) to the longitudinal data from the OneFlorida Clinical Research Consortium to demonstrate the feasibility of implementing the distributed algorithms in large research networks. We studied the associations between the clinical risk factors and Alzheimer's disease and related dementia (ADRD) onsets to advance clinical research on our understanding of the complex risk factors of ADRD and ultimately improve the care of ADRD patients.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8075520PMC
June 2021

Applications of artificial intelligence in drug development using real-world data.

Drug Discov Today 2021 05 24;26(5):1256-1264. Epub 2020 Dec 24.

Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL 32610-0177, USA. Electronic address:

The US Food and Drug Administration (FDA) has been actively promoting the use of real-world data (RWD) in drug development. RWD can generate important real-world evidence reflecting the real-world clinical environment where the treatments are used. Meanwhile, artificial intelligence (AI), especially machine- and deep-learning (ML/DL) methods, have been increasingly used across many stages of the drug development process. Advancements in AI have also provided new strategies to analyze large, multidimensional RWD. Thus, we conducted a rapid review of articles from the past 20 years, to provide an overview of the drug development studies that use both AI and RWD. We found that the most popular applications were adverse event detection, trial recruitment, and drug repurposing. Here, we also discuss current research gaps and future opportunities.
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http://dx.doi.org/10.1016/j.drudis.2020.12.013DOI Listing
May 2021

International Classification of Diseases, Tenth Revision, Clinical Modification social determinants of health codes are poorly used in electronic health records.

Medicine (Baltimore) 2020 Dec;99(52):e23818

Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida.

Abstract: There have been increasing calls for clinicians to document social determinants of health (SDOH) in electronic health records (EHRs). One potential source of SDOH in the EHRs is in the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) Z codes (Z55-Z65). In February 2018, ICD-10-CM Official Guidelines for Coding and Reporting approved that all clinicians, not just the physicians, involved in the care of a patient can document SDOH using these Z codes.To examine the utilization rate of the ICD-10-CM Z codes using data from a large network of EHRs.We conducted a retrospective analysis of EHR data between 2015 to 2018 in the OneFlorida Clinical Research Consortium, 1 of the 13 Clinical Data Research Networks funded by Patient-Centered Outcomes Research Institute. We calculated the Z code utilization rate at both the encounter and patient levels.We found a low rate of utilization for these Z codes (270.61 per 100,000 at the encounter level and 2.03% at the patient level). We also found that the rate of utilization for these Z codes increased (from 255.62 to 292.79 per 100,000) since the official approval of Z code reporting from all clinicians by the American Hospital Association Coding Clinic and ICD-10-CM Official Guidelines for Coding and Reporting became effective in February 2018.The SDOH Z codes are rarely used by clinicians. Providing clear guidelines and incentives for documenting the Z codes can promote their use in EHRs. Improvements in the EHR systems are probably needed to better document SDOH.
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http://dx.doi.org/10.1097/MD.0000000000023818DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7769291PMC
December 2020

Identification of important factors in an inpatient fall risk prediction model to improve the quality of care using EHR and electronic administrative data: A machine-learning approach.

Int J Med Inform 2020 11 15;143:104272. Epub 2020 Sep 15.

Department of Family, Community, and Health Systems Science, College of Nursing, University of Florida, United States.

Background: Inpatient falls, many resulting in injury or death, are a serious problem in hospital settings. Existing falls risk assessment tools, such as the Morse Fall Scale, give a risk score based on a set of factors, but don't necessarily signal which factors are most important for predicting falls. Artificial intelligence (AI) methods provide an opportunity to improve predictive performance while also identifying the most important risk factors associated with hospital-acquired falls. We can glean insight into these risk factors by applying classification tree, bagging, random forest, and adaptive boosting methods applied to Electronic Health Record (EHR) data.

Objective: The purpose of this study was to use tree-based machine learning methods to determine the most important predictors of inpatient falls, while also validating each via cross-validation.

Materials And Methods: A case-control study was designed using EHR and electronic administrative data collected between January 1, 2013 to October 31, 2013 in 14 medical surgical units. The data contained 38 predictor variables which comprised of patient characteristics, admission information, assessment information, clinical data, and organizational characteristics. Classification tree, bagging, random forest, and adaptive boosting methods were used to identify the most important factors of inpatient fall-risk through variable importance measures. Sensitivity, specificity, and area under the ROC curve were computed via ten-fold cross validation and compared via pairwise t-tests. These methods were also compared to a univariate logistic regression of the Morse Fall Scale total score.

Results: In terms of AUROC, bagging (0.89), random forest (0.90), and boosting (0.89) all outperformed the Morse Fall Scale (0.86) and the classification tree (0.85), but no differences were measured between bagging, random forest, and adaptive boosting, at a p-value of 0.05. History of Falls, Age, Morse Fall Scale total score, quality of gait, unit type, mental status, and number of high fall risk increasing drugs (FRIDs) were considered the most important features for predicting inpatient fall risk.

Conclusions: Machine learning methods have the potential to identify the most relevant and novel factors for the detection of hospitalized patients at risk of falling, which would improve the quality of patient care, and to more fully support healthcare provider and organizational leadership decision-making. Nurses would be able to enhance their judgement to caring for patients at risk for falls. Our study may also serve as a reference for the development of AI-based prediction models of other iatrogenic conditions. To our knowledge, this is the first study to report the importance of patient, clinical, and organizational features based on the use of AI approaches.
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http://dx.doi.org/10.1016/j.ijmedinf.2020.104272DOI Listing
November 2020

MicroRNA predicts cognitive performance in healthy older adults.

Neurobiol Aging 2020 11 3;95:186-194. Epub 2020 Aug 3.

Center for Cognitive Aging and Memory, Department of Clinical & Health Psychology, University of Florida, Gainesville, FL, USA.

The expression of microRNA (miRNA) is influenced by ongoing biological processes, including aging, and has begun to play a role in the measurement of neurodegenerative processes in central nervous system. The purpose of this study is to utilize machine learning approaches to determine whether miRNA can be utilized as a blood-based biomarker of cognitive aging. A random forest regression combining miRNA with biological (brain volume), clinical (comorbid conditions), and demographic variables in 115 typically aging older adults explained the greatest level of variance in cognitive performance compared to the other machine learning models explored. Three miRNA (miR-140-5p, miR-197-3p, and miR-501-3p) were top-ranked predictors of multiple cognitive outcomes (Fluid, Crystallized, and Overall Cognition) and past studies of these miRNA link them to cellular senescence, inflammatory signals for atherosclerotic formation, and potential development of neurodegenerative disorders (e.g., Alzheimer's disease). Several novel miRNAs were also linked to age and multiple cognitive functions, findings which together warrant further exploration linking these miRNAs to brain-derived metrics of neurodegeneration in typically aging older adults.
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http://dx.doi.org/10.1016/j.neurobiolaging.2020.07.023DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7606424PMC
November 2020

Explainable artificial intelligence models using real-world electronic health record data: a systematic scoping review.

J Am Med Inform Assoc 2020 07;27(7):1173-1185

School of Information, Florida State University, Tallahassee, Florida, USA.

Objective: To conduct a systematic scoping review of explainable artificial intelligence (XAI) models that use real-world electronic health record data, categorize these techniques according to different biomedical applications, identify gaps of current studies, and suggest future research directions.

Materials And Methods: We searched MEDLINE, IEEE Xplore, and the Association for Computing Machinery (ACM) Digital Library to identify relevant papers published between January 1, 2009 and May 1, 2019. We summarized these studies based on the year of publication, prediction tasks, machine learning algorithm, dataset(s) used to build the models, the scope, category, and evaluation of the XAI methods. We further assessed the reproducibility of the studies in terms of the availability of data and code and discussed open issues and challenges.

Results: Forty-two articles were included in this review. We reported the research trend and most-studied diseases. We grouped XAI methods into 5 categories: knowledge distillation and rule extraction (N = 13), intrinsically interpretable models (N = 9), data dimensionality reduction (N = 8), attention mechanism (N = 7), and feature interaction and importance (N = 5).

Discussion: XAI evaluation is an open issue that requires a deeper focus in the case of medical applications. We also discuss the importance of reproducibility of research work in this field, as well as the challenges and opportunities of XAI from 2 medical professionals' point of view.

Conclusion: Based on our review, we found that XAI evaluation in medicine has not been adequately and formally practiced. Reproducibility remains a critical concern. Ample opportunities exist to advance XAI research in medicine.
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http://dx.doi.org/10.1093/jamia/ocaa053DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7647281PMC
July 2020

Design and methodology challenges of environment-wide association studies: A systematic review.

Environ Res 2020 04 19;183:109275. Epub 2020 Feb 19.

Department of Epidemiology, College of Public Health and Health Professions and College of Medicine, University of Florida, Gainesville, FL, USA. Electronic address:

Environment-wide association studies (EWAS) are an untargeted, agnostic, and hypothesis-generating approach to exploring environmental factors associated with health outcomes, akin to genome-wide association studies (GWAS). While design, methodology, and replicability standards for GWAS are established, EWAS pose many challenges. We systematically reviewed published literature on EWAS to categorize scope, impact, types of analytical approaches, and open challenges in designs and methodologies. The Web of Science and PubMed databases were searched through multiple queries to identify EWAS articles between January 2010 and December 2018, and a systematic review was conducted following the Preferred Reporting Item for Systematic Reviews and Meta-Analyses (PRISMA) reporting standard. Twenty-three articles met our inclusion criteria and were included. For each study, we categorized the data sources, the definitions of study outcomes, the sets of environmental variables, and the data engineering/analytical approaches, e.g. neighborhood definition, variable standardization, handling of multiple hypothesis testing, model selection, and validation. We identified limited exploitation of data sources, high heterogeneity in analytical approaches, and lack of replication. Despite of the promising utility of EWAS, further development of EWAS will require improved data sources, standardization of study designs, and rigorous testing of methodologies.
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http://dx.doi.org/10.1016/j.envres.2020.109275DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7346707PMC
April 2020

Clinical correlates of workplace injury occurrence and recurrence in adults.

PLoS One 2019 12;14(9):e0222603. Epub 2019 Sep 12.

School of Business Administration, Zhejiang Gongshang University, Hangzhou, China.

Objectives: To examine the morbidities associated with workplace injury and to explore how clinical variables modify the risk of injury recurrence.

Methods: A case-control study was designed using Florida's statewide inpatient, outpatient, and emergency visits data obtained from the Healthcare Cost and Utilization Project. We included adults who were admitted for a workplace injury (WPI) or injury at other places (IOP), and a matched population of random controls without WPI/IOP. The associations between WPI and clinical morbidities were assessed by univariate and multivariable regression, ranking predictors by information gain, area under the receiver operating characteristic (AUROC), and odds ratios. We analyzed WPI recurrence using survival methods (Kaplan-Meier, Cox regression, survival decision trees) and developed prediction models via regularized logistic regression, random forest, and AdTree. Performance was assessed by 10-fold cross-validation comparing AUROC, sensitivity, specificity, and Harrell's c-index.

Results: A total of 80,712 WPI, 161,424 IOP, and 161,424 control patients were included; 485 distinct clinical diagnostic and 160 procedure codes were analyzed after filtering. Acute bronchitis and bronchiolitis, sprains and strains of shoulder and upper arm, ankle and foot, or other and unspecified parts of back, accidents caused by cutting and piercing instruments or objects, and overexertion and strenuous movements were identified as important consequences of WPI. The prediction models of injury recurrence identified several key factors, such as insurance type and prior injury events, although none of the models exhibited high predictive performance (best AUROC = 0.60, best c-index = 0.62).

Conclusions: WPI is associated to diverse serious physical comorbidity burden. There are demographic, social and clinical comorbidity components associated to the risk of WPI recurrence, although their predictive value is moderate, which warrants future investigation in other information source domains, e.g. deepening into the environmental and societal sphere.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0222603PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6742381PMC
March 2020

Prodromal clinical, demographic, and socio-ecological correlates of asthma in adults: a 10-year statewide big data multi-domain analysis.

J Asthma 2020 11 26;57(11):1155-1167. Epub 2019 Jul 26.

Department of Epidemiology, College of Medicine & College of Public Health and Health Professions, University of Florida, Gainesville, FL, USA.

To identify prodromal correlates of asthma as compared to chronic obstructive pulmonary disease and allied-conditions (COPDAC) using a multi domain analysis of socio-ecological, clinical, and demographic domains. This is a retrospective case-risk-control study using data from Florida's statewide Healthcare Cost and Utilization Project (HCUP). Patients were grouped into three groups: asthma, COPDAC (without asthma), and neither asthma nor COPDAC. To identify socio-ecological, clinical, demographic, and clinical predictors of asthma and COPDAC, we used univariate analysis, feature ranking by bootstrapped information gain ratio, multivariable logistic regression with LogitBoost selection, decision trees, and random forests. A total of 141,729 patients met inclusion criteria, of whom 56,052 were diagnosed with asthma, 85,677 with COPDAC, and 84,737 with neither asthma nor COPDAC. The multi-domain approach proved superior in distinguishing asthma versus COPDAC and non-asthma/non-COPDAC controls (area under the curve (AUROC) 84%). The best domain to distinguish asthma from COPDAC without controls was prior clinical diagnoses (AUROC 82%). Ranking variables from all the domains found the most important predictors for the asthma versus COPDAC and controls were primarily socio-ecological variables, while for asthma versus COPDAC without controls, demographic and clinical variables such as age, CCI, and prior clinical diagnoses, scored better. In this large statewide study using a machine learning approach, we found that a multi-domain approach with demographics, clinical, and socio-ecological variables best predicted an asthma diagnosis. Future work should focus on integrating machine learning-generated predictive models into clinical practice to improve early detection of those common respiratory diseases.
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http://dx.doi.org/10.1080/02770903.2019.1642352DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6982549PMC
November 2020

The Common Antidiabetic Drug Metformin Reduces Odds of Developing Age-Related Macular Degeneration.

Invest Ophthalmol Vis Sci 2019 04;60(5):1470-1477

Department of Ophthalmology, College of Medicine, University of Florida, Gainesville, Florida, United States.

Purpose: AMD is the leading cause of irreversible blindness in older individuals in the Western world, and there are currently no therapies to halt disease progression. Studies suggest that the commonly prescribed antidiabetic drug, metformin, is associated with decreased risk of several ocular diseases, but no work has investigated the effect of metformin use on development of AMD. Thus, we aim to investigate whether metformin use is associated with decreased risk of developing AMD.

Methods: In this retrospective case-control study, we used medical records from patients older than 55 who have visited a University of Florida health clinic. Three controls were matched for every AMD case, defined by International Classification of Diseases, Ninth Revision code, based on the Charlson Comorbidity Index to ensure comparable baseline overall health status. Univariate and conditional multivariable logistic regressions were used to determine the association between a variety of covariates, including metformin use, and AMD diagnosis.

Results: Metformin use was associated with decreased odds of developing AMD, independently of the other covariates investigated, with an odds ratio of 0.58 and a 95% confidence interval of 0.43 to 0.79. Other medications assessed were not associated with decreased odds of developing AMD.

Conclusions: Patients who had taken metformin had decreased odds of developing AMD, suggesting that metformin may have a therapeutic role in AMD development or progression in those who are at risk. Further work should include clinical trials to investigate prospectively whether metformin has a protective effect in those at risk for developing AMD.
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http://dx.doi.org/10.1167/iovs.18-26422DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6736343PMC
April 2019

Risk of health morbidity for the uninsured: 10-year evidence from a large hospital center in Boston, Massachusetts.

Int J Qual Health Care 2019 Jun;31(5):325-330

Department of Epidemiology, College of Public Health and Health Professions & College of Medicine, University of Florida, 2004 Mowry Road, Gainesville, Florida, USA.

Objective: To investigate the independent contribution of insurance status toward the risk of diagnosis of specific clinical comorbidities for individuals admitted to intensive care unit (ICU).

Design: Retrospective analysis of secondary database.

Setting: Ten years of public de-identified ICU electronic medical records from a large hospital in USA.

Participants: Patients (18-65 years old) who had private insurance or no insurance were extracted from the database.

Main Outcome Measures: Independent association of insurance status (uninsured vs. privately insured) with the risk of diagnosis of specific clinical comorbidities.

Results: Among 14 268 (from 11 753 patients) admissions to ICU between 2001 and 2012, 96% of them were covered by private insurance. Patients with private insurance had higher proportion of females, married, White race, longer ICU stay and more procedures during stay, and fewer deaths. A lower CCI was observed in uninsured patients. At multivariable analysis, uninsured patients had higher odds of death and of admissions for accidental falls, substance or alcohol abuse.

Conclusions: Patients with no insurance coverage were at higher risk of death and of admission for physical and substance-related injury. We did not observe a higher risk for acute life-threatening diseases such as myocardial infarction or kidney failure. The lower CCI observed in the uninsured may be explained by under diagnosis or voluntary withdrawal from coverage in the pre-Affordable Care Act era. Replication of findings is warranted in other populations, among those with government-subsidized insurance and in the procedure/prescription domains.
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http://dx.doi.org/10.1093/intqhc/mzy175DOI Listing
June 2019

Development of a personalized diagnostic model for kidney stone disease tailored to acute care by integrating large clinical, demographics and laboratory data: the diagnostic acute care algorithm - kidney stones (DACA-KS).

BMC Med Inform Decis Mak 2018 08 17;18(1):72. Epub 2018 Aug 17.

Department of Epidemiology, College of Public Health and Health Professions & College of Medicine, University of Florida, 2004 Mowry Road, PO Box 100231, Gainesville, Florida, 32610-0231, USA.

Background: Kidney stone (KS) disease has high, increasing prevalence in the United States and poses a massive economic burden. Diagnostics algorithms of KS only use a few variables with a limited sensitivity and specificity. In this study, we tested a big data approach to infer and validate a 'multi-domain' personalized diagnostic acute care algorithm for KS (DACA-KS), merging demographic, vital signs, clinical, and laboratory information.

Methods: We utilized a large, single-center database of patients admitted to acute care units in a large tertiary care hospital. Patients diagnosed with KS were compared to groups of patients with acute abdominal/flank/groin pain, genitourinary diseases, and other conditions. We analyzed multiple information domains (several thousands of variables) using a collection of statistical and machine learning models with feature selectors. We compared sensitivity, specificity and area under the receiver operating characteristic (AUROC) of our approach with the STONE score, using cross-validation.

Results: Thirty eight thousand five hundred and ninety-seven distinct adult patients were admitted to critical care between 2001 and 2012, of which 217 were diagnosed with KS, and 7446 with acute pain (non-KS). The multi-domain approach using logistic regression yielded an AUROC of 0.86 and a sensitivity/specificity of 0.81/0.82 in cross-validation. Increase in performance was obtained by fitting a super-learner, at the price of lower interpretability. We discussed in detail comorbidity and lab marker variables independently associated with KS (e.g. blood chloride, candidiasis, sleep disorders).

Conclusions: Although external validation is warranted, DACA-KS could be integrated into electronic health systems; the algorithm has the potential used as an effective tool to help nurses and healthcare personnel during triage or clinicians making a diagnosis, streamlining patients' management in acute care.
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http://dx.doi.org/10.1186/s12911-018-0652-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6098647PMC
August 2018

Reprogramming progeria fibroblasts re-establishes a normal epigenetic landscape.

Aging Cell 2017 08 8;16(4):870-887. Epub 2017 Jun 8.

The Sprott Centre for Stem Cell Research, Regenerative Medicine Program, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada, K1H 8L6.

Ideally, disease modeling using patient-derived induced pluripotent stem cells (iPSCs) enables analysis of disease initiation and progression. This requires any pathological features of the patient cells used for reprogramming to be eliminated during iPSC generation. Hutchinson-Gilford progeria syndrome (HGPS) is a segmental premature aging disorder caused by the accumulation of the truncated form of Lamin A known as Progerin within the nuclear lamina. Cellular hallmarks of HGPS include nuclear blebbing, loss of peripheral heterochromatin, defective epigenetic inheritance, altered gene expression, and senescence. To model HGPS using iPSCs, detailed genome-wide and structural analysis of the epigenetic landscape is required to assess the initiation and progression of the disease. We generated a library of iPSC lines from fibroblasts of patients with HGPS and controls, including one family trio. HGPS patient-derived iPSCs are nearly indistinguishable from controls in terms of pluripotency, nuclear membrane integrity, as well as transcriptional and epigenetic profiles, and can differentiate into affected cell lineages recapitulating disease progression, despite the nuclear aberrations, altered gene expression, and epigenetic landscape inherent to the donor fibroblasts. These analyses demonstrate the power of iPSC reprogramming to reset the epigenetic landscape to a revitalized pluripotent state in the face of widespread epigenetic defects, validating their use to model the initiation and progression of disease in affected cell lineages.
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http://dx.doi.org/10.1111/acel.12621DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5506428PMC
August 2017

Trends in Gene Expression Profiling for Prostate Cancer Risk Assessment: A Systematic Review.

Biomed Hub 2017 May-Aug;2(2):1-15. Epub 2017 May 17.

Department of Epidemiology, College of Public Health and Health Professions, College of Medicine, University of Florida, Gainesville, FL, USA.

Objectives: The aim of the study is to review biotechnology advances in gene expression profiling on prostate cancer (PCa), focusing on experimental platform development and gene discovery, in relation to different study designs and outcomes in order to understand how they can be exploited to improve PCa diagnosis and clinical management.

Methods: We conducted a systematic literature review on gene expression profiling studies through PubMed/MEDLINE and Web of Science between 2000 and 2016. Tissue biopsy and clinical gene profiling studies with different outcomes (e.g., recurrence, survival) were included.

Results: Over 3,000 papers were screened and 137 full-text articles were selected. In terms of technology used, microarray is still the most popular technique, increasing from 50 to 70% between 2010 and 2015, but there has been a rise in the number of studies using RNA sequencing (13% in 2015). Sample sizes have increased, as well as the number of genes that can be screened all at once, but we have also observed more focused targeting in more recent studies. Qualitative analysis on the specific genes found associated with PCa risk or clinical outcomes revealed a large variety of gene candidates, with a few consistent cross-studies.

Conclusions: The last 15 years of research in gene expression in PCa have brought a large volume of data and information that has been decoded only in part, but advancements in high-throughput sequencing technology are increasing the amount of data that can be generated. The variety of findings warrants the execution of both validation studies and meta-analyses. Genetic biomarkers have tremendous potential for early diagnosis of PCa and, if coupled with other diagnostics (e.g., imaging), can effectively be used to concretize less-invasive, personalized prediction of PCa risk and progression.
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http://dx.doi.org/10.1159/000472146DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6945900PMC
May 2017

Re: Potassium-sodium citrate prevents the development of renal microcalculi into symptomatic stones in calcium stone-forming patients.

Authors:
Zhaoyi Chen

Int J Urol 2017 04 10;24(4):334. Epub 2017 Jan 10.

Department of Epidemiology, College of Public Health and Health Professions & College of Medicine, University of Florida, Gainesville, Florida, USA.

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http://dx.doi.org/10.1111/iju.13277DOI Listing
April 2017

Integrative genomics positions MKRN1 as a novel ribonucleoprotein within the embryonic stem cell gene regulatory network.

EMBO Rep 2015 Oct 11;16(10):1334-57. Epub 2015 Aug 11.

Institute of Medical Science University of Toronto, Toronto, ON, Canada Collaborative Program in Genome Biology and Bioinformatics, University of Toronto, Toronto, ON, Canada Sprott Centre for Stem Cell Research, Ottawa Hospital Research Institute, Ottawa, ON, Canada Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, ON, Canada Department of Cellular and Molecular Medicine, University of Ottawa, Ottawa, ON, Canada Department of Biochemistry, Microbiology & Immunology, University of Ottawa, Ottawa, ON, Canada Ottawa Institute of Systems Biology, Ottawa, ON, Canada

In embryonic stem cells (ESCs), gene regulatory networks (GRNs) coordinate gene expression to maintain ESC identity; however, the complete repertoire of factors regulating the ESC state is not fully understood. Our previous temporal microarray analysis of ESC commitment identified the E3 ubiquitin ligase protein Makorin-1 (MKRN1) as a potential novel component of the ESC GRN. Here, using multilayered systems-level analyses, we compiled a MKRN1-centered interactome in undifferentiated ESCs at the proteomic and ribonomic level. Proteomic analyses in undifferentiated ESCs revealed that MKRN1 associates with RNA-binding proteins, and ensuing RIP-chip analysis determined that MKRN1 associates with mRNAs encoding functionally related proteins including proteins that function during cellular stress. Subsequent biological validation identified MKRN1 as a novel stress granule-resident protein, although MKRN1 is not required for stress granule formation, or survival of unstressed ESCs. Thus, our unbiased systems-level analyses support a role for the E3 ligase MKRN1 as a ribonucleoprotein within the ESC GRN.
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http://dx.doi.org/10.15252/embr.201540974DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4670460PMC
October 2015

pH and ligand dependent assembly of Well-Dawson arsenomolybdate capped architectures.

Inorg Chem 2014 Dec 14;53(23):12337-47. Epub 2014 Nov 14.

Key Laboratory for Photonic and Electronic Bandgap Materials, Ministry of Education, School of Chemistry annd Chemical Enginerring, Harbin Normal University , No.1 South of shida Road Limin Development Zone, Harbin City Helongjiang Province, Harbin 150025, China.

Five Well-Dawson-type arsenomolybdates, formulated as [Cu(2,2'-bpy)2][{Cu(2,2'-bpy)}3{As2(V)Mo2(V)Mo16(VI)O62}]·4H2O (1), [H2(4,4'-bpy)]2.5[As(III)(As2(V)Mo2(V)Mo16(VI)O62)]·5H2O (2), (pyr)(imi)(Himi)3[As2(III)(As2(V)Mo3(V)Mo15(VI)O62)]·3H2O (3), [As3(III)(As2(V)Mo3(V)Mo15(VI)O62)]·4H2O (4), and (H2btp)3[As2(V)Mo18(VI)O62]·6H2O (5) (bpy = bipyridine, pyr = pyrazine, imi = imidazole, btp = 1,5-bis(triazol)pentane), have been hydrothermally synthesized and structurally characterized by the elemental analysis, TG, IR, UV-vis-NIR, XPS, XRD, and single-crystal X-ray diffraction. The structural analysis indicates that compounds 1-4 contain rare reduced Dawson {As2Mo18O62} (abbreviated as {As2Mo18}) anions as parent cluster unit, which are capped by a certain number of Cu(II) or As(III) species on different coordination positions via altering pH values and organic ligand of the reaction system. Compounds 1 and 2 are asymmetric tricopper and monoarsenate(III) capped assemble by three {Cu(bpy)}(2+) and a {AsO3} fragments, respectively. Compounds 3 and 4 are symmetric biarsenate(III) and triarsenate(III) capped cluster by four and six half occupancy {AsO3} units, respectively. Compound 5 is uncapped {As2Mo18} structures. Compounds 1-4 represent infrequent Dawson arsenomolybdate capped architectures, especially 2-4, as arsenate(III) capped Dawson-type assemblies are observed for the first time. Compounds 1-5 display good electrocatalytic activity on reduction of nitrite. Compounds 1, 2, 3, and 5 exhibit fluorescent properties in the solid state at room temperature. In addition, magnetic properties of 1-4 have been investigated in detail.
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http://dx.doi.org/10.1021/ic5014973DOI Listing
December 2014

Feeder-independent derivation of induced-pluripotent stem cells from peripheral blood endothelial progenitor cells.

Stem Cell Res 2013 Mar 3;10(2):195-202. Epub 2012 Dec 3.

Sprott Centre for Stem Cell Research, Regenerative Medicine Program, Ottawa Hospital Research Institute, Ottawa, Canada.

Induced-pluripotent stem cells (iPSCs) are a potential alternative cell source in regenerative medicine, which includes the use of differentiated iPSCs for cell therapies to treat coronary artery and/or peripheral arterial diseases. Late-outgrowth endothelial progenitor cells (late-EPCs) are a unique primary cell present in peripheral blood that exhibit high proliferative capacity, are being used in a wide variety of clinical trials, and have the ability to differentiate into mature endothelial cells. The objective of this study was to reprogram peripheral blood-derived late-EPCs to a pluripotent state under feeder-free and defined culture conditions. Late-EPCs that were retrovirally transduced with OCT4, SOX2, KLF4, c-MYC, and iPSC colonies were derived in feeder-free and defined media conditions. EPC-iPSCs expressed pluripotent markers, were capable of differentiating to cells from all three germ-layers, and retained a normal karyotype. Transcriptome analyses demonstrated that EPC-iPSCs exhibit a global gene expression profile similar to human embryonic stem cells (hESCs). We have generated iPSCs from late-EPCs under feeder-free conditions. Thus, peripheral blood-derived late-outgrowth EPCs represent an alternative cell source for generating iPSCs.
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http://dx.doi.org/10.1016/j.scr.2012.11.006DOI Listing
March 2013

[Portosystemic shunt via the left branch of portal vein for the prevention of encephalopathy following transjugular intrahepatic portosystemic shunt].

Zhonghua Gan Zang Bing Za Zhi 2002 Dec;10(6):437-40

Department of Radiology, General Hospital of Air Force of PLA, Beijing 100036, China.

Objective: To determine and analyze plasma ammonia concentration difference of the portal vein system and ramifications of rabbits and consequently guide selection of the portal vein in transjugular intrahepatic portosystemic shunt (TIPS) so that reduce shunt-induced hepatic encephalopathic incidence. To evaluate clinical significance of transjugular intrahepatic left branch of portal vein portosystemic shunt (TILPS) and to analyse hemodynamics of both branches of the portal vein and to observe long-term results in the prevention of encephalopathy.

Methods: Blood samples in different portal vein branches of rabbits were collected and the plasma ammonia concentration was assayed and compared. The left branch of portal vein was used as the puncture site to perform TILPS and to keep away from the right branch of portal vein blood that contains nutrition and toxin.

Results: Plasma ammonia content was superior in the mesenteric vein and higher than the portal vein branch, the splenic vein, and the vena cava. The right portal vein was above the left. Encephalopathy did not occur in all patients within 3 months. Of the 341 patients undergoing TILPS, encephalopathy occurred in only 5 patients (1.47%) and shunt abnormalities in 19 patients (5.57%) verified by venography during overall follow-up period.

Conclusions: Selective left branch of the portal vein portosystemic shunt can decrease encephalopathy obviously and protect liver function.
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December 2002
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