scholarly journals Studying pediatric health outcomes with electronic health records using Bayesian clustering and trajectory analysis

2021 ◽  
Vol 113 ◽  
pp. 103654
Author(s):  
Rebecca A. Hubbard ◽  
Jinyu Xu ◽  
Robert Siegel ◽  
Yong Chen ◽  
Ihuoma Eneli
Keyword(s):  
Electronic Health Records ◽  
Health Outcomes ◽  
Trajectory Analysis ◽  
Bayesian Clustering ◽  
Health Records ◽  
Pediatric Health ◽  
Electronic Health

scholarly journals The integration of genetically-regulated transcriptomics and electronic health records highlights a pattern of medical outcomes related to increased hepatic Transthyretin expression

2021 ◽  
Author(s):  
Gita A Pathak ◽  
Antonella De Lillo ◽  
Frank Wendt ◽  
Flavio De Angelis ◽  
Dora Koller ◽  
...  
Keyword(s):  
Electronic Health Records ◽  
Health Outcomes ◽  
Bone Fracture ◽  
Neuronal Response ◽  
Genetic Regulation ◽  
Uk Biobank ◽  
Health Records ◽  
Hepatic Expression ◽  
Electronic Health ◽  
The Uk

Background: Transthyretin (TTR) is a multi-function protein involved in the systemic transport of retinol and thyroxine. It also participates in the neuronal response to stress and proteolysis of few specific substrates. TTR is also the precursor of the fibrils that compromise organ function in the familial and sporadic systemic amyloidoses (ATTR). RNA-interference and anti-sense therapeutics targeting TTR hepatic transcription have been shown to reduce TTR amyloid formation. The goal of our study was to investigate the role of genetic regulation of TTR transcriptomic variation in human traits and diseases. Methods and Findings: We leveraged genetic and phenotypic information from the UK Biobank and transcriptomic profiles from the GTEx (Genotype-Tissue Expression) project to test the association of genetically regulated TTR gene expression with 7,149 traits assessed in 420,531 individuals. We conducted a joint multi-tissue analysis of TTR transcription regulation and identified an association with a specific operational procedure related to secondary open reduction of fracture of bone (p=5.46x10-6, false discovery rate q=0.039). Using tissue-specific TTR cis expression quantitative trait loci, we demonstrated that the association is driven by the genetic regulation of TTR hepatic expression (odds ratio [OR] = 3.46, 95% confidence interval [CI] = 1.85-6.44, p = 9.51x10-5). Although there is an established relationship of retinol and thyroxine abnormalities with bone loss and the risk of bone fracture, this is the first evidence of a possible effect of TTR transcriptomic regulation. Investigating the UK Biobank electronic health records available, we investigated the comorbidities affecting individuals undergoing the specific surgical procedure. Excluding medical codes related to bone fracture events, we identified a pattern of health outcomes that have been previously associated with ATTR manifestations. These included osteoarthritis (OR=3.18, 95%CI=1.93-4.25, p=9.18x10-8), carpal tunnel syndrome (OR=2.15, 95%CI=1.33-3.48, p=0.002), and a history of gastrointestinal diseases (OR=2.01, 95%CI=1.33-3.01, p=8.07x10-4). Conclusions: The present study supports the notion that TTR hepatic expression can affect health outcomes linked to physiological and pathological processes presumably related to the encoded protein. Our findings highlight how the integration of omics information and electronic health records can successfully dissect the complexity of multi-function proteins such as TTR.

scholarly journals Multimorbidity patterns in patients with heart failure: an observational Spanish study based on electronic health records

BMJ Open ◽  
2019 ◽  
Vol 9 (12) ◽  
pp. e033174 ◽  
Author(s):  
Antonio Gimeno-Miguel ◽  
Anyuli Gracia Gutiérrez ◽  
Beatriz Poblador-Plou ◽  
Carlos Coscollar-Santaliestra ◽  
J Ignacio Pérez-Calvo ◽  
...  
Keyword(s):  
Heart Failure ◽  
Electronic Health Records ◽  
Health Outcomes ◽  
Chronic Diseases ◽  
Large Scale ◽  
Health Records ◽  
Men And Women ◽  
Patients With Heart Failure ◽  
Electronic Health ◽  
The Impact

ObjectivesTo characterise the comorbidities of heart failure (HF) in men and women, to explore their clustering into multimorbidity patterns, and to measure the impact of such patterns on the risk of hospitalisation and mortality.DesignObservational retrospective population study based on electronic health records.SettingEpiChron Cohort (Aragón, Spain).ParticipantsAll the primary and hospital care patients of the EpiChron Cohort with a diagnosis of HF on 1 January 2011 (ie, 8488 women and 6182 men). We analysed all the chronic diseases registered in patients’ electronic health records until 31 December 2011.Primary outcomeWe performed an exploratory factor analysis to identify the multimorbidity patterns in men and women, and logistic and Cox proportional-hazards regressions to investigate the association between the patterns and the risk of hospitalisation in 2012, and of 3-year mortality.ResultsAlmost all HF patients (98%) had multimorbidity, with an average of 7.8 chronic diseases per patient. We identified six different multimorbidity patterns, named cardiovascular, neurovascular, coronary, metabolic, degenerative and respiratory. The most prevalent were the degenerative (64.0%) and cardiovascular (29.9%) patterns in women, and the metabolic (49.3%) and cardiovascular (43.2%) patterns in men. Every pattern was associated with higher hospitalisation risks; and the cardiovascular, neurovascular and respiratory patterns significantly increased the likelihood of 3-year mortality.ConclusionsMultimorbidity is the norm rather than the exception in patients with heart failure, whose comorbidities tend to cluster together beyond simple chance in the form of multimorbidity patterns that have different impact on health outcomes. This knowledge could be useful to better understand common pathophysiological pathways underlying this condition and its comorbidities, and the factors influencing the prognosis of men and women with HF. Further large scale longitudinal studies are encouraged to confirm the existence of these patterns as well as their differential impact on health outcomes.

scholarly journals ‘Warning: allergic to penicillin’: association between penicillin allergy status in 2.3 million NHS general practice electronic health records, antibiotic prescribing and health outcomes

2019 ◽  
Vol 74 (7) ◽  
pp. 2075-2082 ◽  
Author(s):  
R M West ◽  
C J Smith ◽  
S H Pavitt ◽  
C C Butler ◽  
P Howard ◽  
...  
Keyword(s):  
General Population ◽  
Electronic Health Records ◽  
Health Outcomes ◽  
Penicillin Allergy ◽  
Antibiotic Prescribing ◽  
Exact Matching ◽  
Health Records ◽  
Mrsa Infection ◽  
Electronic Health ◽  
The Impact

Abstract Background The prevalence of reported penicillin allergy (PenA) and the impact these records have on health outcomes in the UK general population are unknown. Without such data, justifying and planning enhanced allergy services is challenging. Objectives To determine: (i) prevalence of PenA records; (ii) patient characteristics associated with PenA records; and (iii) impact of PenA records on antibiotic prescribing/health outcomes in primary care. Methods We carried out cross-sectional/retrospective cohort studies using patient-level data from electronic health records. Cohort study: exact matching across confounders identified as affecting PenA records. Setting: English NHS general practices between 1 April 2013 and 31 March 2014. Participants: 2.3 million adult patients. Outcome measures: prevalence of PenA, antibiotic prescribing, mortality, MRSA infection/colonization and Clostridioides difficile infection. Results PenA prevalence was 5.9% (IQR = 3.8%–8.2%). PenA records were more common in older people, females and those with a comorbidity, and were affected by GP practice. Antibiotic prescribing varied significantly: penicillins were prescribed less frequently in those with a PenA record [relative risk (RR)  = 0.15], and macrolides (RR = 4.03), tetracyclines (RR = 1.91) nitrofurantoin (RR = 1.09), trimethoprim (RR = 1.04), cephalosporins (RR = 2.05), quinolones (RR = 2.10), clindamycin (RR = 5.47) and total number of prescriptions were increased in patients with a PenA record. Risk of re-prescription of a new antibiotic class within 28 days (RR = 1.32), MRSA infection/colonization (RR = 1.90) and death during the year subsequent to 1 April 2013 (RR = 1.08) increased in those with PenA records. Conclusions PenA records are common in the general population and associated with increased/altered antibiotic prescribing and worse health outcomes. We estimate that incorrect PenA records affect 2.7 million people in England. Establishing true PenA status (e.g. oral challenge testing) would allow more people to be prescribed first-line antibiotics, potentially improving health outcomes.

scholarly journals Social and Behavioral Determinants of Health in the Era of Artificial Intelligence with Electronic Health Records: A Scoping Review

2021 ◽  
Vol 2021 ◽  
pp. 1-19
Author(s):  
Anusha Bompelli ◽  
Yanshan Wang ◽  
Ruyuan Wan ◽  
Esha Singh ◽  
Yuqi Zhou ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Electronic Health Records ◽  
Health Outcomes ◽  
Predictive Models ◽  
Determinants Of Health ◽  
Unstructured Data ◽  
Health Records ◽  
Behavioral Determinants ◽  
Wide Range ◽  
Electronic Health

Background. There is growing evidence that social and behavioral determinants of health (SBDH) play a substantial effect in a wide range of health outcomes. Electronic health records (EHRs) have been widely employed to conduct observational studies in the age of artificial intelligence (AI). However, there has been limited review into how to make the most of SBDH information from EHRs using AI approaches. Methods. A systematic search was conducted in six databases to find relevant peer-reviewed publications that had recently been published. Relevance was determined by screening and evaluating the articles. Based on selected relevant studies, a methodological analysis of AI algorithms leveraging SBDH information in EHR data was provided. Results. Our synthesis was driven by an analysis of SBDH categories, the relationship between SBDH and healthcare-related statuses, natural language processing (NLP) approaches for extracting SBDH from clinical notes, and predictive models using SBDH for health outcomes. Discussion. The associations between SBDH and health outcomes are complicated and diverse; several pathways may be involved. Using NLP technology to support the extraction of SBDH and other clinical ideas simplifies the identification and extraction of essential concepts from clinical data, efficiently unlocks unstructured data, and aids in the resolution of unstructured data-related issues. Conclusion. Despite known associations between SBDH and diseases, SBDH factors are rarely investigated as interventions to improve patient outcomes. Gaining knowledge about SBDH and how SBDH data can be collected from EHRs using NLP approaches and predictive models improves the chances of influencing health policy change for patient wellness, ultimately promoting health and health equity.

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