Understanding Prescribing Practices and Patient Experiences with Renin Angiotensin System Inhibitors Use in Chronic Kidney Disease: A Qualitative Study.

Introduction: Angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin receptor blockers (ARBs) improve outcomes but are underutilized in patients with chronic kidney disease (CKD). Little is known about reasons for discontinuation and lack of reinitiating these medications. We aimed to explore clinicians' and patients' experiences and perceptions of ACEI/ARB use in CKD.

Documented Adverse Drug Reactions and Discontinuation of Angiotensin-Converting Enzyme Inhibitors and Angiotensin Receptor Blockers in Chronic Kidney Disease.

Introduction: Angiotensin-converting enzyme inhibitors (ACEis) and angiotensin receptor blockers (ARBs) are frequently discontinued in patients with chronic kidney disease (CKD). Documented adverse drug reactions (ADRs) in medical records may provide insight into the reasons for treatment discontinuation.

Methods: In this retrospective cohort of US veterans from 2005 to 2019, we identified individuals with CKD and a current prescription for an ACEi or ARB (current user group) or a discontinued prescription within the preceding 5 years (discontinued group).

Information Extraction From Electronic Health Records to Predict Readmission Following Acute Myocardial Infarction: Does Natural Language Processing Using Clinical Notes Improve Prediction of Readmission?

Background Social risk factors influence rehospitalization rates yet are challenging to incorporate into prediction models. Integration of social risk factors using natural language processing (NLP) and machine learning could improve risk prediction of 30-day readmission following an acute myocardial infarction. Methods and Results Patients were enrolled into derivation and validation cohorts.

Ankle- and Toe-Brachial Index for Peripheral Artery Disease Identification: Unlocking Clinical Data Through Novel Methods.

Background: Despite its high prevalence and clinical impact, research on peripheral artery disease (PAD) remains limited due to poor accuracy of billing codes. Ankle-brachial index (ABI) and toe-brachial index can be used to identify PAD patients with high accuracy within electronic health records.

Methods: We developed a novel natural language processing (NLP) algorithm for extracting ABI and toe-brachial index values and laterality (right or left) from ABI reports.

Leveraging structured and unstructured electronic health record data to detect reasons for suboptimal statin therapy use in patients with atherosclerotic cardiovascular disease.

Objective: To determine whether natural language processing (NLP) of unstructured medical text can improve identification of ASCVD patients not using high-intensity statin therapy (HIST) due to statin-associated side effects (SASEs) and other reasons.

Methods: Reviewers annotated reasons for not prescribing HIST in notes of 1152 randomly selected patients from across the VA healthcare system treated for ASCVD but not receiving HIST. Developers used reviewer annotations to train the Canary NLP tool to detect and extract notes containing one or more of these reasons.

Association of patient, provider and facility related characteristics with statin associated side effects and statin use: Insight from the Veteran's Affairs healthcare system.

Background: Statin associated side effects (SASE) are a leading cause of statin discontinuation.

Objective: We evaluated patient, provider, and facility characteristics associated with SASEs and whether these characteristics impact statin utilization.

Methods: Patients with atherosclerotic cardiovascular disease (ASCVD) receiving care across the Veterans Affairs healthcare system from October 1, 2014 to September 30, 2015 were included.

Adaptation of an NLP system to a new healthcare environment to identify social determinants of health.

Social determinants of health (SDoH) are increasingly important factors for population health, healthcare outcomes, and care delivery. However, many of these factors are not reliably captured within structured electronic health record (EHR) data. In this work, we evaluated and adapted a previously published NLP tool to include additional social risk factors for deployment at Vanderbilt University Medical Center in an Acute Myocardial Infarction cohort.

Facility-Level Variation in Reported Statin-Associated Side Effects Among Patients with Atherosclerotic Cardiovascular Disease-Perspective from the Veterans Affair Healthcare System.

Purpose: Statin-associated side effects (SASEs) can limit statin adherence and present a potential barrier to optimal statin utilization. How standardized reporting of SASEs varies across medical facilities has not been well characterized.

Methods: We assessed facility-level variation in SASE reporting among patients with atherosclerotic cardiovascular disease receiving care across the Veterans Affairs (VA) healthcare system from October 1, 2014, to September 30, 2015.

Prevalence and Risk Factors for Opioid-Induced Constipation in an Older National Veteran Cohort.

Objectives: This research describes the prevalence and covariates associated with opioid-induced constipation (OIC) in an observational cohort study utilizing a national veteran cohort and integrated data from the Center for Medicare and Medicaid Services (CMS).

Methods: A cohort of 152,904 veterans with encounters between 1 January 2008 and 30 November 2010, an exposure to opioids of 30 days or more, and no exposure in the prior year was developed to establish existing conditions and medications at the start of the opioid exposure and determining outcomes through the end of exposure. OIC was identified through additions/changes in laxative prescriptions, all-cause constipation identification through diagnosis, or constipation related procedures in the presence of opioid exposure.

Considerations for advancing nephrology research and practice through natural language processing.

Much of medical data is buried in the free text of clinical notes and not captured by structured data, such as administrative codes. Natural language processing (NLP) can locate and use information that resides in unstructured free text. Chan et al.

The use of structured data elements to identify ASCVD patients with statin-associated side effects: Insights from the Department of Veterans Affairs.

Background: Accurate identification of patients with statin-associated side effects (SASEs) is critical for health care systems to institute strategies to improve guideline-concordant statin use.

Objective: The objective of this study was to determine whether adverse drug reaction (ADR) entry by clinicians in the electronic medical record can accurately identify SASEs.

Methods: We identified 1,248,214 atherosclerotic cardiovascular disease (ASCVD) patients seeking care in the Department of Veterans Affairs.

Determining post-test risk in a national sample of stress nuclear myocardial perfusion imaging reports: Implications for natural language processing tools.

Background: Reporting standards promote clarity and consistency of stress myocardial perfusion imaging (MPI) reports, but do not require an assessment of post-test risk. Natural Language Processing (NLP) tools could potentially help estimate this risk, yet it is unknown whether reports contain adequate descriptive data to use NLP.

Methods: Among VA patients who underwent stress MPI and coronary angiography between January 1, 2009 and December 31, 2011, 99 stress test reports were randomly selected for analysis.

Automating Quality Measures for Heart Failure Using Natural Language Processing: A Descriptive Study in the Department of Veterans Affairs.

Background: We developed an accurate, stakeholder-informed, automated, natural language processing (NLP) system to measure the quality of heart failure (HF) inpatient care, and explored the potential for adoption of this system within an integrated health care system.

Objective: To accurately automate a United States Department of Veterans Affairs (VA) quality measure for inpatients with HF.

Methods: We automated the HF quality measure Congestive Heart Failure Inpatient Measure 19 (CHI19) that identifies whether a given patient has left ventricular ejection fraction (LVEF) <40%, and if so, whether an angiotensin-converting enzyme inhibitor or angiotensin-receptor blocker was prescribed at discharge if there were no contraindications.

Leveraging Electronic Health Care Record Information to Measure Pressure Ulcer Risk in Veterans With Spinal Cord Injury: A Longitudinal Study Protocol.

Background: Pressure ulcers (PrUs) are a frequent, serious, and costly complication for veterans with spinal cord injury (SCI). The health care team should periodically identify PrU risk, although there is no tool in the literature that has been found to be reliable, valid, and sensitive enough to assess risk in this vulnerable population.

Objective: The immediate goal is to develop a risk assessment model that validly estimates the probability of developing a PrU.

Congestive heart failure information extraction framework for automated treatment performance measures assessment.

Objective: This paper describes a new congestive heart failure (CHF) treatment performance measure information extraction system - CHIEF - developed as part of the Automated Data Acquisition for Heart Failure project, a Veterans Health Administration project aiming at improving the detection of patients not receiving recommended care for CHF.

Design: CHIEF is based on the Apache Unstructured Information Management Architecture framework, and uses a combination of rules, dictionaries, and machine learning methods to extract left ventricular function mentions and values, CHF medications, and documented reasons for a patient not receiving these medications.

Measurements: The training and evaluation of CHIEF were based on subsets of a reference standard of various clinical notes from 1083 Veterans Health Administration patients.

Assisted annotation of medical free text using RapTAT.

Objective: To determine whether assisted annotation using interactive training can reduce the time required to annotate a clinical document corpus without introducing bias.

Materials And Methods: A tool, RapTAT, was designed to assist annotation by iteratively pre-annotating probable phrases of interest within a document, presenting the annotations to a reviewer for correction, and then using the corrected annotations for further machine learning-based training before pre-annotating subsequent documents. Annotators reviewed 404 clinical notes either manually or using RapTAT assistance for concepts related to quality of care during heart failure treatment.

Development and evaluation of RapTAT: a machine learning system for concept mapping of phrases from medical narratives.

Rapid, automated determination of the mapping of free text phrases to pre-defined concepts could assist in the annotation of clinical notes and increase the speed of natural language processing systems. The aim of this study was to design and evaluate a token-order-specific naïve Bayes-based machine learning system (RapTAT) to predict associations between phrases and concepts. Performance was assessed using a reference standard generated from 2860 VA discharge summaries containing 567,520 phrases that had been mapped to 12,056 distinct Systematized Nomenclature of Medicine - Clinical Terms (SNOMED CT) concepts by the MCVS natural language processing system.

Who said it? Establishing professional attribution among authors of Veterans' Electronic Health Records.

Background: A practical data point for assessing information quality and value in the Electronic Health Record (EHR) is the professional category of the EHR author. We evaluated and compared free form electronic signatures against LOINC note titles in categorizing the profession of EHR authors.

Methods: A random 1000 clinical document sample was selected and divided into 500 document sets for training and testing.

Detecting temporal expressions in medical narratives.

Background: Clinical practice and epidemiological information aggregation require knowing when, how long, and in what sequence medically relevant events occur. The Temporal Awareness and Reasoning Systems for Question Interpretation (TARSQI) Toolkit (TTK) is a complete, open source software package for the temporal ordering of events within narrative text documents. TTK was developed on newspaper articles.

Impact of decaying dose rate in gamma knife radiosurgery: study on 9L rat gliosarcoma cells.

Purpose: Leksell Gamma Knife (LGK) installations replace their Co-60 sources every 5-10 years corresponding to one two Co-60 half-lives. Between source replacements the dose rate gradually declines. The purpose of this study was to assess whether the decreasing dose rates associated with radioactive decay of Co-60 may affect the radiobiological response of a given dose delivered to 9L rat gliosarcoma cells.