2 research outputs found

    Bugs, Drugs and Data: Antibiotic Resistance, Prevalence and Prediction of Bug-Drug Mismatch using Electronic Health Records (EHR) Data

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    Title from PDF of title page viewed December 14, 2021Dissertation advisor: An-Lin ChengVitaIncludes bibliographical references (pages 92-130)Thesis (Ph.D.)--School of Medicine, School of Computing and Engineering, and School of Biological and Chemical Sciences. University of Missouri--Kansas City, 2021Bug-Drug Mismatch (BDM) occurrences are an important and modifiable category of inappropriate antibiotic therapy (IAAT) that increases adverse outcomes for patients and drives overall antibiotic resistance (AR). Surveillance of baseline AR, emerging trends in resistance among priority bacterial pathogens and prevalence of BDM with respect to the age of the patients and the type of health care-setting are required due to differences in antimicrobial need and use in these populations. Additionally, very little is known about the risk factors associated with BDM occurrence. We performed a retrospective study using de-identified, electronic health record (EHR) data in the Cerner Health Facts™ data warehouse. We assessed antibiotic susceptibility data between the years 2012 to 2017 and visualized the slope coefficient from linear regression to compare changes in resistance over time. We examined the prevalence of BDM for critically important antibiotics and clinically relevant pathogens between the year 2009 to 2017 in four groups of patients: adults; children; children treated in freestanding pediatric facilities and children treated in blended facilities (adults and children). We implemented multiple logistic regression as a reference model to identify risk factors for BDM occurrences and compared the predictive performance measure with 4 machine learning models (logistic regression with lasso regularization, random forest, gradient boosted decision tree and deep neural network). The trends in resistance rates to clinically relevant antibiotics were influenced by age and care setting. BDM prevalence for several critically important antibiotics differed between children and adults as well as within pediatric and blended facilities. Risk factors such as age of the patient, patient comorbidities and size of the facility were significantly associated with BDM occurrence. Additionally, the machine learning models developed in our study has a high predictive ability (C-statistic), higher sensitivity, specificity, positive predictive value and positive likelihood ratio to identify BDM occurrence than the reference model. This study describes the utility of data visualization to interpret large scale EHR data on the trends of AR, prevalence and risk factors of BDM which are critical in tailoring antibiotic stewardship efforts to improving appropriate antibiotic prescribing and ultimately reduce AR.Introduction -- Background -- Variation in antibiotic resistance patterns for children and adults treated at 166 non-affiliated facilities -- Differences in the prevalence of definitive bug-drug mismatch (BDM) therapy between adults and children by care setting -- Predicting bug-drug mismatch (BDM) occurrence in EHR data using machine Learning models -- Conclusio

    Use of National Electronic Health Record (EHR) Data Warehouse to Identify Inappropriate HbA1c Orders for Sickle cell Disease Patients

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    Title from PDF of title page viewed June 20, 2018Thesis advisor: An-Lin ChengVitaIncludes bibliographical references (pages 36-45)Thesis (M.S.)--School of Medicine. University of Missouri--Kansas City, 2018The glycated Hemoglobin (HbA1c) test is one of the most important diagnostic and prognostic strategies for monitoring diabetes. However, the clinical utility of this test is questionable for sickle cell disease patients, who are homozygous for a variant hemoglobin gene (HBB). While there have been analyses from individual provider organizations, no prior national level analysis of the HbA1c ordering practice for sickle cell disease patients has been performed. A national level assessment could serve as a baseline to evaluate this quality concern in individual health care settings. The main objective of this study was to evaluate the frequency of the inappropriate HbA1c test orders and the prevalence of the more appropriate fructosamine test orders as an alternative to HbA1c test, nationally and at Truman Medical Center (TMC) in Kansas City, MO. We analyzed de-identified, HIPAA compliant, electronic health record (EHR) data in the Cerner Health Facts™ (HF) data warehouse. We identified the frequency of inappropriate orders of HbA1c tests by comparing the 526 Sickle cell patients in TMC with 36,625 sickle cell patients from 393 national facilities in the data warehouse. The linear unbiased percentages estimated from the Generalized Linear Mixed Model (GLMM) was used to rank the TMC with other national hospitals based on the percentage of sickle cell patients with inappropriate HbA1c test. TMC had a significantly higher percentage of sickle cell patients with HbA1c tests when compared to the national hospital cohort (32% versus 11%). The results showed that TMC ranks in the bottom 25% quartile of the 393 qualifying facilities with respect to inappropriate HbA1c orders. Interestingly, TMC sickle cell patients were ten-fold more likely to have at least one fructosamine encounter when compared to the sickle cell patients in the other 10 national hospitals which had fructosamine encounters (11% versus 1%). However, there was still a significantly higher number of sickle cell disease patients in TMC than in other national hospitals who had only HbA1c tests (24% versus 10%). These findings indicate that inappropriate HbA1c orders in sickle cell patients is a potential quality concern at TMC which can be addressed with sustainable interventions so that overtreatment or under treatment of the diabetic condition in sickle cell patients are avoided.Introduction -- Review of literature -- Methodology -- Results -- Discussio
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