Browsing by Author "Dexter, Paul R."

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    An Automated Line-of-Therapy Algorithm for Adults With Metastatic Non-Small Cell Lung Cancer: Validation Study Using Blinded Manual Chart Review
    (JMIR Publications, 2021-10-12) Meng, Weilin; Mosesso, Kelly M.; Lane, Kathleen A.; Roberts, Anna R.; Griffith, Ashley; Ou, Wanmei; Dexter, Paul R.; Biostatistics & Health Data Science, School of Medicine
    Background: Extraction of line-of-therapy (LOT) information from electronic health record and claims data is essential for determining longitudinal changes in systemic anticancer therapy in real-world clinical settings. Objective: The aim of this retrospective cohort analysis is to validate and refine our previously described open-source LOT algorithm by comparing the output of the algorithm with results obtained through blinded manual chart review. Methods: We used structured electronic health record data and clinical documents to identify 500 adult patients treated for metastatic non-small cell lung cancer with systemic anticancer therapy from 2011 to mid-2018; we assigned patients to training (n=350) and test (n=150) cohorts, randomly divided proportional to the overall ratio of simple:complex cases (n=254:246). Simple cases were patients who received one LOT and no maintenance therapy; complex cases were patients who received more than one LOT and/or maintenance therapy. Algorithmic changes were performed using the training cohort data, after which the refined algorithm was evaluated against the test cohort. Results: For simple cases, 16 instances of discordance between the LOT algorithm and chart review prerefinement were reduced to 8 instances postrefinement; in the test cohort, there was no discordance between algorithm and chart review. For complex cases, algorithm refinement reduced the discordance from 68 to 62 instances, with 37 instances in the test cohort. The percentage agreement between LOT algorithm output and chart review for patients who received one LOT was 89% prerefinement, 93% postrefinement, and 93% for the test cohort, whereas the likelihood of precise matching between algorithm output and chart review decreased with an increasing number of unique regimens. Several areas of discordance that arose from differing definitions of LOTs and maintenance therapy could not be objectively resolved because of a lack of precise definitions in the medical literature. Conclusions: Our findings identify common sources of discordance between the LOT algorithm and clinician documentation, providing the possibility of targeted algorithm refinement.
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    Automated pancreatic cyst screening using natural language processing: a new tool in the early detection of pancreatic cancer
    (Elsevier, 2015-05) Roch, Alexandra M.; Mehrabi, Saeed; Krishnan, Anand; Schmidt, Heidi E.; Kesterson, Joseph; Beesley, Chris; Dexter, Paul R.; Palakal, Matthew; Schmidt, C. Max; Department of Surgery, IU School of Medicine
    INTRODUCTION: As many as 3% of computed tomography (CT) scans detect pancreatic cysts. Because pancreatic cysts are incidental, ubiquitous and poorly understood, follow-up is often not performed. Pancreatic cysts may have a significant malignant potential and their identification represents a 'window of opportunity' for the early detection of pancreatic cancer. The purpose of this study was to implement an automated Natural Language Processing (NLP)-based pancreatic cyst identification system. METHOD: A multidisciplinary team was assembled. NLP-based identification algorithms were developed based on key words commonly used by physicians to describe pancreatic cysts and programmed for automated search of electronic medical records. A pilot study was conducted prospectively in a single institution. RESULTS: From March to September 2013, 566,233 reports belonging to 50,669 patients were analysed. The mean number of patients reported with a pancreatic cyst was 88/month (range 78-98). The mean sensitivity and specificity were 99.9% and 98.8%, respectively. CONCLUSION: NLP is an effective tool to automatically identify patients with pancreatic cysts based on electronic medical records (EMR). This highly accurate system can help capture patients 'at-risk' of pancreatic cancer in a registry.
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    Design and Rationale of GUARDD-US: A pragmatic, randomized trial of genetic testing for APOL1 and pharmacogenomic predictors of antihypertensive efficacy in patients with hypertension
    (Elsevier, 2022) Eadon, Michael T.; Cavanaugh, Kerri L.; Orlando, Lori A.; Christian, David; Chakraborty, Hrishikesh; Steen-Burrell, Kady-Ann; Merrill, Peter; Seo, Janet; Hauser, Diane; Singh, Rajbir; Maynor Beasley, Cherry; Fuloria, Jyotsna; Kitzman, Heather; Parker, Alexander S.; Ramos, Michelle; Ong, Henry H.; Elwood, Erica N.; Lynch, Sheryl E.; Clermont, Sabrina; Cicali, Emily J.; Starostik, Petr; Pratt, Victoria M.; Nguyen, Khoa A.; Rosenman, Marc B.; Calman, Neil S.; Robinson, Mimsie; Nadkarni, Girish N.; Madden, Ebony B.; Kucher, Natalie; Volpi, Simona; Dexter, Paul R.; Skaar, Todd C.; Johnson, Julie A.; Cooper-DeHoff, Rhonda M.; Horowitz, Carol R.; GUARDD-US Investigators; Medicine, School of Medicine
    Rationale and objective: APOL1 risk alleles are associated with increased cardiovascular and chronic kidney disease (CKD) risk. It is unknown whether knowledge of APOL1 risk status motivates patients and providers to attain recommended blood pressure (BP) targets to reduce cardiovascular disease. Study design: Multicenter, pragmatic, randomized controlled clinical trial. Setting and participants: 6650 individuals with African ancestry and hypertension from 13 health systems. Intervention: APOL1 genotyping with clinical decision support (CDS) results are returned to participants and providers immediately (intervention) or at 6 months (control). A subset of participants are re-randomized to pharmacogenomic testing for relevant antihypertensive medications (pharmacogenomic sub-study). CDS alerts encourage appropriate CKD screening and antihypertensive agent use. Outcomes: Blood pressure and surveys are assessed at baseline, 3 and 6 months. The primary outcome is change in systolic BP from enrollment to 3 months in individuals with two APOL1 risk alleles. Secondary outcomes include new diagnoses of CKD, systolic blood pressure at 6 months, diastolic BP, and survey results. The pharmacogenomic sub-study will evaluate the relationship of pharmacogenomic genotype and change in systolic BP between baseline and 3 months. Results: To date, the trial has enrolled 3423 participants. Conclusions: The effect of patient and provider knowledge of APOL1 genotype on systolic blood pressure has not been well-studied. GUARDD-US addresses whether blood pressure improves when patients and providers have this information. GUARDD-US provides a CDS framework for primary care and specialty clinics to incorporate APOL1 genetic risk and pharmacogenomic prescribing in the electronic health record.
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    Drug–gene and drug–drug interactions associated with tramadol and codeine therapy in the INGENIOUS trial
    (Future Medicine, 2019-04) Fulton, Cathy R.; Zang, Yong; Desta, Zeruesenay; Rosenman, Marc B.; Holmes, Ann M.; Decker, Brian S.; Zhang, Yifei; Callaghan, John T.; Pratt, Victoria M.; Levy, Kenneth D.; Gufford, Brandon T.; Dexter, Paul R.; Skaar, Todd C.; Eadon, Michael T.; Medicine, School of Medicine
    Background: Tramadol and codeine are metabolized by CYP2D6 and are subject to drug-gene and drug-drug interactions. Methods: This interim analysis examined prescribing behavior and efficacy in 102 individuals prescribed tramadol or codeine while receiving pharmaco-genotyping as part of the INGENIOUS trial (NCT02297126). Results: Within 60 days of receiving tramadol or codeine, clinicians more frequently prescribed an alternative opioid in ultrarapid and poor metabolizers (odds ratio: 19.0; 95% CI: 2.8-160.4) as compared with normal or indeterminate metabolizers (p = 0.01). After adjusting the CYP2D6 activity score for drug-drug interactions, uncontrolled pain was reported more frequently in individuals with reduced CYP2D6 activity (odds ratio: 0.50; 95% CI: 0.25-0.94). Conclusion: Phenoconversion for drug-drug and drug-gene interactions is an important consideration in pharmacogenomic implementation; drug-drug interactions may obscure the potential benefits of genotyping.
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    Establishing the value of genomics in medicine: the IGNITE Pragmatic Trials Network.
    (Springer, 2021-07) Ginsburg, Geoffrey S.; Cavallari, Larisa H.; Chakraborty, Hrishikesh; Cooper-DeHoff, Rhonda M.; Dexter, Paul R.; Eadon, Michael T.; Ferket, Bart S.; Horowitz, Carol R.; Johnson, Julie A.; Kannry, Joseph; Kucher, Natalie; Madden, Ebony B.; Orlando, Lori A.; Parker, Wanda; Peterson, Josh; Pratt, Victoria M.; Rakhra-Burris, Tejinder K.; Ramos, Michelle A.; Skaar, Todd C.; Sperber, Nina; Steen-Burrell, Kady-Ann; Van Driest, Sara L.; Voora, Deepak; Wiisanen, Kristin; Winterstein, Almut G.; Volpi, Simona
    PURPOSE: A critical gap in the adoption of genomic medicine into medical practice is the need for the rigorous evaluation of the utility of genomic medicine interventions. METHODS: The Implementing Genomics in Practice Pragmatic Trials Network (IGNITE PTN) was formed in 2018 to measure the clinical utility and cost-effectiveness of genomic medicine interventions, to assess approaches for real-world application of genomic medicine in diverse clinical settings, and to produce generalizable knowledge on clinical trials using genomic interventions. Five clinical sites and a coordinating center evaluated trial proposals and developed working groups to enable their implementation. RESULTS: Two pragmatic clinical trials (PCTs) have been initiated, one evaluating genetic risk APOL1 variants in African Americans in the management of their hypertension, and the other to evaluate the use of pharmacogenetic testing for medications to manage acute and chronic pain as well as depression. CONCLUSION: IGNITE PTN is a network that carries out PCTs in genomic medicine; it is focused on diversity and inclusion of underrepresented minority trial participants; it uses electronic health records and clinical decision support to deliver the interventions. IGNITE PTN will develop the evidence to support (or oppose) the adoption of genomic medicine interventions by patients, providers, and payers.
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    Feature engineering from medical notes: A case study of dementia detection
    (Elsevier, 2023-03-18) Ben Miled, Zina; Dexter, Paul R.; Grout, Randall W.; Boustani, Malaz; Electrical and Computer Engineering, School of Engineering and Technology
    Background and objectives: Medical notes are narratives that describe the health of the patient in free text format. These notes can be more informative than structured data such as the history of medications or disease conditions. They are routinely collected and can be used to evaluate the patient's risk for developing chronic diseases such as dementia. This study investigates different methodologies for transforming routine care notes into dementia risk classifiers and evaluates the generalizability of these classifiers to new patients and new health care institutions. Methods: The notes collected over the relevant history of the patient are lengthy. In this study, TF-ICF is used to select keywords with the highest discriminative ability between at risk dementia patients and healthy controls. The medical notes are then summarized in the form of occurrences of the selected keywords. Two different encodings of the summary are compared. The first encoding consists of the average of the vector embedding of each keyword occurrence as produced by the BERT or Clinical BERT pre-trained language models. The second encoding aggregates the keywords according to UMLS concepts and uses each concept as an exposure variable. For both encodings, misspellings of the selected keywords are also considered in an effort to improve the predictive performance of the classifiers. A neural network is developed over the first encoding and a gradient boosted trees model is applied to the second encoding. Patients from a single health care institution are used to develop all the classifiers which are then evaluated on held-out patients from the same health care institution as well as test patients from two other health care institutions. Results: The results indicate that it is possible to identify patients at risk for dementia one year ahead of the onset of the disease using medical notes with an AUC of 75% when a gradient boosted trees model is used in conjunction with exposure variables derived from UMLS concepts. However, this performance is not maintained with an embedded feature space and when the classifier is applied to patients from other health care institutions. Moreover, an analysis of the top predictors of the gradient boosted trees model indicates that different features inform the classification depending on whether or not spelling variants of the keywords are included. Conclusion: The present study demonstrates that medical notes can enable risk prediction models for complex chronic diseases such as dementia. However, additional research efforts are needed to improve the generalizability of these models. These efforts should take into consideration the length and localization of the medical notes; the availability of sufficient training data for each disease condition; and the variabilities resulting from different feature engineering techniques.
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    Golden opportunities for clinical decision support in an era of team-based healthcare
    (American Medical Informatics Association, 2022) Dexter, Paul R.; Schleyer, Titus; Medicine, School of Medicine
    Computerized clinical decision support (CDS) will be essential to ensuring the safety and efficiency of new care delivery models, such as the patient-centered medical home. CDS will help empower non-physician team members, coordinate overall team efforts, and facilitate physician oversight. In this article, we discuss common clinical scenarios that could benefit from CDS optimized for team-based healthcare, including (1) low-acuity episodic illness, (2) diagnostic workup of new onset symptoms, (3) chronic care, (4) preventive care, and (5) care coordination. CDS that maximally supports teams may be one of biomedical informatics' best opportunities to decrease health care costs, improve quality, and increase clinical capacity.
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    Identifying and Characterizing a Chronic Cough Cohort Through Electronic Health Records
    (Elsevier, 2021-06) Weiner, Michael; Dexter, Paul R.; Heithoff, Kim; Roberts, Anna R.; Liu, Ziyue; Griffith, Ashley; Hui, Siu; Schelfhout, Jonathan; Dicpinigaitis, Peter; Doshi, Ishita; Weaver, Jessica P.; Medicine, School of Medicine
    Background Chronic cough (CC) of 8 weeks or more affects about 10% of adults and may lead to expensive treatments and reduced quality of life. Incomplete diagnostic coding complicates identifying CC in electronic health records (EHRs). Natural language processing (NLP) of EHR text could improve detection. Research Question Can NLP be used to identify cough in EHRs, and to characterize adults and encounters with CC? Study Design and Methods A Midwestern EHR system identified patients aged 18 to 85 years during 2005 to 2015. NLP was used to evaluate text notes, except prescriptions and instructions, for mentions of cough. Two physicians and a biostatistician reviewed 12 sets of 50 encounters each, with iterative refinements, until the positive predictive value for cough encounters exceeded 90%. NLP, International Classification of Diseases, 10th revision, or medication was used to identify cough. Three encounters spanning 56 to 120 days defined CC. Descriptive statistics summarized patients and encounters, including referrals. Results Optimizing NLP required identifying and eliminating cough denials, instructions, and historical references. Of 235,457 cough encounters, 23% had a relevant diagnostic code or medication. Applying chronicity to cough encounters identified 23,371 patients (61% women) with CC. NLP alone identified 74% of these patients; diagnoses or medications alone identified 15%. The positive predictive value of NLP in the reviewed sample was 97%. Referrals for cough occurred for 3.0% of patients; pulmonary medicine was most common initially (64% of referrals). Limitations Some patients with diagnosis codes for cough, encounters at intervals greater than 4 months, or multiple acute cough episodes may have been misclassified. Interpretation NLP successfully identified a large cohort with CC. Most patients were identified through NLP alone, rather than diagnoses or medications. NLP improved detection of patients nearly sevenfold, addressing the gap in ability to identify and characterize CC disease burden. Nearly all cases appeared to be managed in primary care. Identifying these patients is important for characterizing treatment and unmet needs.
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    Implementing a pragmatic clinical trial to tailor opioids for acute pain on behalf of the IGNITE ADOPT PGx investigators.
    (Wiley, 2022-07-28) Cavallari, Larisa H.; Cicali, Emily; Wiisanen, Kristin; Fillingim, Roger B.; Chakraborty, Hrishikesh; Myers, Rachel A.; Blake, Kathryn V.; Asiyanbola, Bolanle; Baye, Jordan F.; Bronson, Wesley H.; Cook, Kelsey J.; Elwood, Erica N.; Gray, Chancellor F.; Gong, Yan; Hines, Lindsay; Kannry, Joseph; Kucher, Natalie; Lynch, Sheryl; Nguyen, Khoa A.; Obeng, Aniwaa Owusu; Pratt, Victoria M.; Prieto, Hernan A.; Ramos, Michelle; Sadeghpour, Azita; Singh, Rajbir; Rosenman, Marc; Starostik, Petr; Thomas, Cameron D.; Tillman, Emma; Dexter, Paul R.; Horowitz, Carol R.; Orlando, Lori A.; Peterson, Josh F.; Skaar, Todd C.; Van Driest, Sara L.; Volpi, Simona; Voora, Deepak; Parvataneni, Hari K.; Johnson, Julie A.
    Opioid prescribing for postoperative pain management is challenging because of inter-patient variability in opioid response and concern about opioid addiction. Tramadol, hydrocodone, and codeine depend on the cytochrome P450 2D6 (CYP2D6) enzyme for formation of highly potent metabolites. Individuals with reduced or absent CYP2D6 activity (i.e., intermediate metabolizers [IMs] or poor metabolizers [PMs], respectively) have lower concentrations of potent opioid metabolites and potentially inadequate pain control. The primary objective of this prospective, multicenter, randomized pragmatic trial is to determine the effect of postoperative CYP2D6-guided opioid prescribing on pain control and opioid usage. Up to 2020 participants, age ≥8 years, scheduled to undergo a surgical procedure will be enrolled and randomized to immediate pharmacogenetic testing with clinical decision support (CDS) for CYP2D6 phenotype-guided postoperative pain management (intervention arm) or delayed testing without CDS (control arm). CDS is provided through medical record alerts and/or a pharmacist consult note. For IMs and PM in the intervention arm, CDS includes recommendations to avoid hydrocodone, tramadol, and codeine. Patient-reported pain-related outcomes are collected 10 days and 1, 3, and 6 months after surgery. The primary outcome, a composite of pain intensity and opioid usage at 10 days postsurgery, will be compared in the subgroup of IMs and PMs in the intervention (n = 152) versus the control (n = 152) arm. Secondary end points include prescription pain medication misuse scores and opioid persistence at 6 months. This trial will provide data on the clinical utility of CYP2D6 phenotype-guided opioid selection for improving postoperative pain control and reducing opioid-related risks.
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    Implementing a pragmatic clinical trial to tailor opioids for chronic pain on behalf of the IGNITE ADOPT PGx investigators
    (Wiley, 2024) Skaar, Todd C.; Myers, Rachel A.; Fillingim, Roger B.; Callaghan, John T.; Cicali, Emily; Eadon, Michael T.; Elwood, Erica N.; Ginsburg, Geoffrey S.; Lynch, Sheryl; Nguyen, Khoa A.; Obeng, Aniwaa Owusu; Park, Haesuk; Pratt, Victoria M.; Rosenman, Marc; Sadeghpour, Azita; Shuman, Saskia; Singh, Rajbir; Tillman, Emma M.; Volpi, Simona; Wiisanen, Kristin; Winterstein, Almut G.; Horowitz, Carol R.; Voora, Deepak; Orlando, Lori; Chakraborty, Hrishikesh; Van Driest, Sara; Peterson, Josh F.; Cavallari, Larisa A.; Johnson, Julie A.; Dexter, Paul R.; IGNITE Pragmatic Trials Network; Medicine, School of Medicine
    Chronic pain is a prevalent condition with enormous economic burden. Opioids such as tramadol, codeine, and hydrocodone are commonly used to treat chronic pain; these drugs are activated to more potent opioid receptor agonists by the hepatic CYP2D6 enzyme. Results from clinical studies and mechanistic understandings suggest that CYP2D6-guided therapy will improve pain control and reduce adverse drug events. However, CYP2D6 is rarely used in clinical practice due in part to the demand for additional clinical trial evidence. Thus, we designed the ADOPT-PGx (A Depression and Opioid Pragmatic Trial in Pharmacogenetics) chronic pain study, a multicenter, pragmatic, randomized controlled clinical trial, to assess the effect of CYP2D6 testing on pain management. The study enrolled 1048 participants who are taking or being considered for treatment with CYP2D6-impacted opioids for their chronic pain. Participants were randomized to receive immediate or delayed (by 6 months) genotyping of CYP2D6 with clinical decision support (CDS). CDS encouraged the providers to follow the CYP2D6-guided trial recommendations. The primary study outcome is the 3-month absolute change in the composite pain intensity score assessed using Patient-Reported Outcomes Measurement Information System (PROMIS) measures. Follow-up will be completed in July 2024. Herein, we describe the design of this trial along with challenges encountered during enrollment.