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). Documented ADRs in structured datasets associated with an ACEi or ARB were categorized into 17 pre-specified groups. Logistic regression assessed associations of documented ADRs with treatment discontinuation. RESULTS: There were 882,441 (73.0%) individuals in the current user group and 326,794 (27.0%) in the discontinued group. There were 26,434 documented ADRs, with at least one documented ADR in 7,520 (0.9%) current users and 9,569 (2.9%) of the discontinued group. ADR presence was associated with treatment discontinuation, aOR 4.16 (95% CI: 4.03, 4.29). The most common documented ADRs were cough (37.3%), angioedema (14.2%), and allergic reaction (10.4%). ADRs related to angioedema (aOR 3.81, 95% CI: 3.47, 4.17), hyperkalemia (aOR 2.03, 95% CI: 1.84, 2.24), peripheral edema (aOR 1.53, 95% CI: 1.33, 1.77), or acute kidney injury (aOR 1.32, 95% CI: 1.15, 1.51) were associated with treatment discontinuation. CONCLUSION: ADRs leading to drug discontinuation were infrequently documented. ADR types were differentially associated with treatment discontinuation. An understanding of which ADRs lead to treatment discontinuation provides an opportunity to address them at a healthcare system level.

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. The facility rates for SASE reporting were expressed as cases per 1000 patients with ASCVD. Facility-level variation was determined using hierarchical regression analysis to calculate median rate ratios (MRR [95% confidence interval]) by first using an unadjusted model and then adjusting for patient, provider, and facility characteristics. RESULTS: Of the 1,248,158 patients with ASCVD included in our study across 130 facilities, 13.7% had at least one SASE reported. Individuals with a history of SASE were less likely to be on a statin at follow-up compared with those without SASE (72.0% vs 80.8%, p < 0.01). The median (interquartile range) facility rate of SASE reported was 140.5 (109.4-167.7) cases per 1000 patients with ASCVD. Significant facility-level variation in the rate of SASE reported was observed: MRR 1.38 (1.33-1.44) in the unadjusted model and MRR 1.56 (1.47-1.65) in the adjusted model. CONCLUSION: Significant facility-level variation in SASE reporting was found within the VA healthcare system suggesting room for improvement in standardized documentation of SASEs among medical facilities. This has the potential to lead to improvement in statin utilization.

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. We developed a transformation of the SDoH outputs of the tool into the OMOP common data model (CDM) for re-use across many potential use cases, yielding performance measures across 8 SDoH classes of precision 0.83 recall 0.74 and F-measure of 0.78.

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. demonstrate that NLP is sensitive for identifying symptoms in hemodialysis patients. These findings highlight the benefit NLP may bring to nephrology and should prompt discussion of important considerations for NLP system design and implementation.

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. Two reviewers independently categorized each report for the presence of critical data elements essential to describing post-test ischemic risk. RESULTS: Few stress MPI reports provided a formal assessment of post-test risk within the impression section (3%) or the entire document (4%). In most cases, risk was determinable by combining critical data elements (74% impression, 98% whole). If ischemic risk was not determinable (25% impression, 2% whole), inadequate description of systolic function (9% impression, 1% whole) and inadequate description of ischemia (5% impression, 1% whole) were most commonly implicated. CONCLUSIONS: Post-test ischemic risk was determinable but rarely reported in this sample of stress MPI reports. This supports the potential use of NLP to help clarify risk. Further study of NLP in this context is needed.

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. It was also assessed on the manually annotated, 2010 i2b2 challenge data. Performance was established with regard to precision, recall, and F-measure for each of the concepts within the VA documents using bootstrapping. Within that corpus, concepts identified by MCVS were broadly distributed throughout SNOMED CT, and the token-order-specific language model achieved better performance based on precision, recall, and F-measure (0.95±0.15, 0.96±0.16, and 0.95±0.16, respectively; mean±SD) than the bag-of-words based, naïve Bayes model (0.64±0.45, 0.61±0.46, and 0.60±0.45, respectively) that has previously been used for concept mapping. Precision, recall, and F-measure on the i2b2 test set were 92.9%, 85.9%, and 89.2% respectively, using the token-order-specific model. RapTAT required just 7.2ms to map all phrases within a single discharge summary, and mapping rate did not decrease as the number of processed documents increased. The high performance attained by the tool in terms of both accuracy and speed was encouraging, and the mapping rate should be sufficient to support near-real-time, interactive annotation of medical narratives. These results demonstrate the feasibility of rapidly and accurately mapping phrases to a wide range of medical concepts based on a token-order-specific naïve Bayes model and machine learning.

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. METHODS: A multi-profession sample of health care clinicians and patients with documented ACEI/ARB-associated side effects in the past 6 months. Participants were recruited from 2 Veterans Affairs healthcare systems in Texas and Tennessee. A total of 15 clinicians and 10 patients completed interviews. We used inductive and deductive qualitative data analysis approaches to identify themes related to clinician and patient experiences with ACEI/ARB. Thematic analysis focused on prescribing decisions and practices, clinical guidelines, and perception of side effects. Data were analyzed as they amassed, and recruitment was stopped at the point of thematic saturation. RESULTS: Clinicians prescribe ACEI/ARB for blood pressure control and kidney protection and underscored the importance of these medications in patients with diabetes. While clinicians described providing comprehensive patient education about ACEI/ARB in CKD, patient interviews revealed significant knowledge gaps about CKD and ACEI/ARB use. Many patients were unaware of their CKD status, and some did not know why they were prescribed ACEI/ARB. Clinicians' drug management strategies varied widely, as did their understanding of prescribing guidelines. They identified structural and patient-level barriers to prescribing and many endorsed the development of a decision support tool to facilitate ACEI/ARB prescribing and management. DISCUSSION/CONCLUSION: Our qualitative study of clinicians and providers identified key target areas for improvement to increase ACEI/ARB utilization in patients with CKD with the goal to improve long-term outcomes in high-risk patients. These findings will also inform the development of a decision support tool to assist with prescribing ACEI/ARBs for patients with CKD.

Deploying a national clinical text processing infrastructure.

OBJECTIVES: Clinical text processing offers a promising avenue for improving multiple aspects of healthcare, though operational deployment remains a substantial challenge. This case report details the implementation of a national clinical text processing infrastructure within the Department of Veterans Affairs (VA). METHODS: Two foundational use cases, cancer case management and suicide and overdose prevention, illustrate how text processing can be practically implemented at scale for diverse clinical applications using shared services. RESULTS: Insights from these use cases underline both commonalities and differences, providing a replicable model for future text processing applications. CONCLUSIONS: This project enables more efficient initiation, testing, and future deployment of text processing models, streamlining the integration of these use cases into healthcare operations. This project implementation is in a large integrated health delivery system in the United States, but we expect the lessons learned to be relevant to any health system, including smaller local and regional health systems in the United States.

Leveraging Natural Language Processing to Improve Electronic Health Record Suicide Risk Prediction for Veterans Health Administration Users.

Background: Suicide risk prediction models frequently rely on structured electronic health record (EHR) data, including patient demographics and health care usage variables. Unstructured EHR data, such as clinical notes, may improve predictive accuracy by allowing access to detailed information that does not exist in structured data fields. To assess comparative benefits of including unstructured data, we developed a large case-control dataset matched on a state-of-the-art structured EHR suicide risk algorithm, utilized natural language processing (NLP) to derive a clinical note predictive model, and evaluated to what extent this model provided predictive accuracy over and above existing predictive thresholds.Methods: We developed a matched case-control sample of Veterans Health Administration (VHA) patients in 2017 and 2018. Each case (all patients that died by suicide in that interval, n = 4,584) was matched with 5 controls (patients who remained alive during treatment year) who shared the same suicide risk percentile. All sample EHR notes were selected and abstracted using NLP methods. We applied machine-learning classification algorithms to NLP output to develop predictive models. We calculated area under the curve (AUC) and suicide risk concentration to evaluate predictive accuracy overall and for high-risk patients.Results: The best performing NLP-derived models provided 19% overall additional predictive accuracy (AUC = 0.69; 95% CI, 0.67, 0.72) and 6-fold additional risk concentration for patients at the highest risk tier (top 0.1%), relative to the structured EHR model.Conclusions: The NLP-supplemented predictive models provided considerable benefit when compared to conventional structured EHR models. Results support future structured and unstructured EHR risk model integrations.

Leveraging unstructured electronic medical record notes to derive population-specific suicide risk models.

Electronic medical record (EMR)-based suicide risk prediction methods typically rely on analysis of structured variables such as demographics, visit history, and prescription data. Leveraging unstructured EMR notes may improve predictive accuracy by allowing access to nuanced clinical information. We utilized natural language processing (NLP) to analyze a large EMR note corpus to develop a data-driven suicide risk prediction model. We developed a matched case-control sample of U.S. Department of Veterans Affairs (VA) patients in 2015 and 2016. We randomly matched each case (all patients that died by suicide in that interval, n = 5029) with five controls (patients that remained alive). We processed note corpus using NLP methods and applied machine-learning classification algorithms to output. We calculated area under the curve (AUC) and risk tiers to determine predictive accuracy. NLP-derived models demonstrated strong predictive accuracy. Patients that scored within top 10% of risk model accounted for up to 29% of suicide decedents. NLP-derived model compares positively to other leading prediction methods. Our approach is highly implementable, only requiring access to text data and open-source software. Additional studies should evaluate ensemble models incorporating NLP-derived information alongside more typical structured variables.

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. Negative predictive value (NPV), sensitivity, specificity and Area Under the Curve (AUC) were used to assess accuracy at detecting documents containing reasons when using structured data, NLP-extracted unstructured data, or both data sources combined. RESULTS: At least one documented reason for not prescribing HIST occurred in 47% of notes. The most frequent reasons were SASEs (41%) and general intolerance (20%). When identifying notes containing any documented reason for not using HIST, adding NLP-extracted, unstructured data significantly (p<0.05) increased sensitivity (0.69 (95% confidence interval [CI] 0.60-0.76) to 0.89 (95% CI 0.81-0.93)), NPV (0.90 (95% CI 0.87 to 0.93) to 0.96 (95% CI 0.93-0.98)), and AUC (0.84 (95% confidence interval [CI] 0.81-0.88) to 0.91 (95% CI 0.90-0.93)) compared to structured data alone. CONCLUSIONS: NLP extraction of data from unstructured text can improve identification of reasons for patients not being on HIST over structured data alone. The additional information provided through NLP of unstructured free text should help in tailoring and implementing system-level interventions to improve HIST use in patients with ASCVD.

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. A random sample of 800 reports from 94 Veterans Affairs facilities during 2015 to 2017 was selected and annotated by clinical experts. We trained the NLP system using random forest models and optimized it through sequential iterations of 10-fold cross-validation and error analysis on 600 test reports and evaluated its final performance on a separate set of 200 reports. We also assessed the accuracy of NLP-extracted ABI and toe-brachial index values for identifying patients with PAD in a separate cohort undergoing ABI testing. RESULTS: The NLP system had an overall precision (positive predictive value) of 0.85, recall (sensitivity) of 0.93, and F1 measure (accuracy) of 0.89 to correctly identify ABI/toe-brachial index values and laterality. Among 261 patients with ABI testing (49% PAD), the NLP system achieved a positive predictive value of 92.3%, sensitivity of 83.1%, and specificity of 93.1% to identify PAD when compared with a structured chart review. The above findings were consistent in a range of sensitivity analysis. CONCLUSIONS: We successfully developed and validated an NLP system for identifying patients with PAD within the Veterans Affairs electronic health record. Our findings have broad implications for PAD research and quality improvement.

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. Multivariable logistic regression analyses were performed to determine (a) factors associated with SASE and (b) factors associated with statin use in those with SASE. RESULTS: Our cohort included 1,225,576 patients with ASCVD. Of these, 171,189 (13.7%) had at least 1 reported SASE since year 2000. The most significant odds for SASEs were observed with female sex (odds ratio [OR] 1.40, 95% confidence interval [CI] 1.36, 1.45), White race (OR 1.43, 95% CI 1.41, 1.45), hypertension (OR 1.37, 95% CI 1.33, 1.41) and ischemic heart disease (IHD: OR 1.45, 95% CI 1.43, 1.47). Lower odds were noted with care at a teaching facility (OR 0.89, 95% CI 0.88, 0.90). Factors most associated with being on a statin among patients with SASE included having diabetes (OR 1.18, 95% CI 1.15, 1.20), IHD (OR 1.39, 95% CI 1.35, 1.43) and a higher number of cardiology visits (OR 1.08, 95% CI 1.07, 1.09), while female sex was associated with lower odds (OR 0.65, 95% CI 0.61, 0.69). CONCLUSION: There are significant disparities in statin use by sex, ASCVD type, and comorbidities among secondary prevention patients with SASE, which represent areas for improvement in optimizing statin utilization.

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. The association of time to constipation with opioid use was analyzed using Cox proportional hazard regression adjusted for patient characteristics, concomitant medications, laboratory tests, and comorbidities. Results: The prevalence of OIC was 12.6%. Twelve positively associated covariates were identified with the largest associations for prior constipation and prevalent laxative (any laxative that continued into the first day of opioid exposure). Among the 17 negatively associated covariates, the largest associations were for erythromycins, androgens/anabolics, and unknown race. Conclusions: There were several novel covariates found that are seen in the all-cause chronic constipation literature but have not been reported for opioid-induced constipation. Some are modifiable covariates, particularly medication coadministration, which may assist clinicians and researchers in risk stratification efforts when initiating opioid medications. The integration of CMS data supports the robustness of the analysis and may be of interest in the elderly population warranting future examination.

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. Using an ADR data repository, we identified SASEs in 15 major symptom categories. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were assessed using a chart review of 256 ASCVD patients with identified SASEs, who were not on high-intensity statin therapy. RESULTS: We identified 171,189 patients (13.71%) with documented SASEs over a 15-year period (9.9%, 2.7%, and 1.1% to 1, 2, or >2 statins, respectively). Statin use, high-intensity statin use, low-density lipoprotein cholesterol, and non-high-density lipoprotein cholesterol levels were 72%, 28.1%, 99 mg/dL, and 129 mg/dL among those with vs 81%, 31.1%, 84 mg/dL, and 111 mg/dL among those without SASEs. Progressively lower statin and high-intensity statin use, and higher low-density lipoprotein cholesterol and non-high-density lipoprotein cholesterol levels were noted among those with SASEs to 1, 2, or >2 statins. Two-thirds of SASEs were related to muscle symptoms. Sensitivity, specificity, PPV, NPV compared with manual chart review were 63.4%, 100%, 100%, and 85.3%, respectively. CONCLUSION: A strategy of using ADR entry in the electronic medical record is feasible to identify SASEs with modest sensitivity and NPV but high specificity and PPV. Health care systems can use this strategy to identify ASCVD patients with SASEs and operationalize efforts to improve guideline-concordant lipid-lowering therapy use in such patients. The sensitivity of this approach can be further enhanced by the use of unstructured text data.

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. We used documents from 1083 unique inpatients from eight VA medical centers to develop a reference standard (RS) to train (n=314) and test (n=769) the Congestive Heart Failure Information Extraction Framework (CHIEF). We also conducted semi-structured interviews (n=15) for stakeholder feedback on implementation of the CHIEF. RESULTS: The CHIEF classified each hospitalization in the test set with a sensitivity (SN) of 98.9% and positive predictive value of 98.7%, compared with an RS and SN of 98.5% for available External Peer Review Program assessments. Of the 1083 patients available for the NLP system, the CHIEF evaluated and classified 100% of cases. Stakeholders identified potential implementation facilitators and clinical uses of the CHIEF. CONCLUSIONS: The CHIEF provided complete data for all patients in the cohort and could potentially improve the efficiency, timeliness, and utility of HF quality measurements.

Survival of transplanted neural progenitor cells enhanced by brain irradiation.

OBJECT: Authors of previous studies have reported that adult transplanted neural progenitor cells (NPCs) are suitable for brain cell replacement or gene delivery. In this study, the authors evaluated survival and integration of adult rat-derived NPCs after transplantation and explored the potential impact on transplant survival of various mechanical and biological factors of clinical importance. METHODS: Adult female Fischer 344 rats were used both as a source and recipient of transplanted NPCs. Both 9L and RG2 rat glioma cells were used to generate in vivo brain tumor models. On the 5th day after tumor implantation, NPCs expressing green fluorescent protein (GFP) were administered either intravenously (3.5 x 10(7) cells) or by stereotactic injection (1 x 10(4)-1 x 10(6) cells) into normal or tumor-bearing brain. The authors evaluated the effect of delivery method (sharp compared with blunt needles, normal compared with zero-volume needles, phosphate-buffered saline compared with medium as vehicle), delivery sites (intravenous compared with intratumoral compared with intraparenchymal), and pretreatment with an immunosuppressive agent (cyclosporin) or brain irradiation (20-40 Gy) on survival and integration of transplanted NPCs. RESULTS: Very few cells survived when less than 10(5) cells were transplanted. When 10(5) cells or more were transplanted, only previously administered brain irradiation significantly affected survival and integration of NPCs. Although GFP-containing NPCs could be readily detected 1 day after injection, few cells survived 4 days to 1 week unless preceded by whole-brain radiation (20 or 40 Gy in a single fraction), which increased the number of GFP-containing NPCs within the tissue more than fivefold. CONCLUSIONS: The authors' findings indicate that most NPCs, including those from a syngeneic autologous source, do not survive at the site of implantation, but that brain irradiation can facilitate subsequent survival in both normal and tumor-bearing brain. An understanding of the mechanisms of this effect could lead to improved survival and clinical utility of transplanted NPCs.

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. The long-term goal is to assist veterans with SCI and their providers in preventing PrUs through an automated system of risk assessment integrated into the veteran's electronic health record (EHR). METHODS: This 5-year longitudinal, retrospective, cohort study targets 12,344 veterans with SCI who were cared for in the Veterans Health Administration (VHA) in fiscal year (FY) 2009 and had no record of a PrU in the prior 12 months. Potential risk factors identified in the literature were reviewed by an expert panel that prioritized factors and determined if these were found in structured data or unstructured form in narrative clinical notes for FY 2009-2013. These data are from the VHA enterprise Corporate Data Warehouse that is derived from the EHR structured (ie, coded in database/table) or narrative (ie, text in clinical notes) data for FY 2009-2013. RESULTS: This study is ongoing and final results are expected in 2017. Thus far, the expert panel reviewed the initial list of risk factors extracted from the literature; the panel recommended additions and omissions and provided insights about the format in which the documentation of the risk factors might exist in the EHR. This list was then iteratively refined through review and discussed with individual experts in the field. The cohort for the study was then identified, and all structured, unstructured, and semistructured data were extracted. Annotation schemas were developed, samples of documents were extracted, and annotations are ongoing. Operational definitions of structured data elements have been created and steps to create an analytic dataset are underway. CONCLUSIONS: To our knowledge, this is the largest cohort employed to identify PrU risk factors in the United States. It also represents the first time natural language processing and statistical text mining will be used to expand the number of variables available for analysis. A major strength of this quantitative study is that all VHA SCI centers were included in the analysis, reducing potential for selection bias and providing increased power for complex statistical analyses. This longitudinal study will eventually result in a risk prediction tool to assess PrU risk that is reliable and valid, and that is sensitive to this vulnerable population.

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. Domain experts manually annotated these notes to create our reference standard. Metrics used included recall, precision, and the F 1 -measure. RESULTS: In general, CHIEF extracted CHF medications with high recall (>0.990) and good precision (0.960-0.978). Mentions of Left Ventricular Ejection Fraction were also extracted with high recall (0.978-0.986) and precision (0.986-0.994), and quantitative values of Left Ventricular Ejection Fraction were found with 0.910-0.945 recall and with high precision (0.939-0.976). Reasons for not prescribing CHF medications were more difficult to extract, only reaching fair accuracy with about 0.310-0.400 recall and 0.250-0.320 precision. CONCLUSION: This study demonstrated that applying natural language processing to unlock the rich and detailed clinical information found in clinical narrative text notes makes fast and scalable quality improvement approaches possible, eventually improving management and outpatient treatment of patients suffering from CHF.