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.

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.

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.

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.

Ischemic preconditioning in the rat brain enhances the repair of endogenous oxidative DNA damage by activating the base-excision repair pathway.

The development of ischemic tolerance in the brain, whereby a brief period of sublethal 'preconditioning' ischemia attenuates injury from subsequent severe ischemia, may involve the activation of multiple intracellular signaling events that promote neuronal survival. In this study, the potential role of inducible DNA base-excision repair (BER), an endogenous adaptive response that prevents the detrimental effect of oxidative DNA damage, has been studied in the rat model of ischemic tolerance produced by three episodes of ischemic preconditioning (IP). This paradigm of IP, when applied 2 and 5 days before 2-h middle cerebral artery occlusion (MCAO), significantly decreased infarct volume in the frontal-parietal cortex 72 h later. Correlated with this protective effect, IP markedly attenuated the nuclear accumulations of several oxidative DNA lesions, including 8-oxodG, AP sites, and DNA strand breaks, after 2-h MCAO. Consequently, harmful DNA damage-responsive events, including NAD depletion and p53 activation, were reduced during postischemic reperfusion in preconditioned brains. The mechanism underlying the decreased DNA damage in preconditioned brain was then investigated by measuring BER activities in nuclear extracts. Beta-polymerase-mediated BER activity was markedly increased after IP, and this activation occurred before (24 h) and during the course of ischemic tolerance (48 to 72 h). In similar patterns, the activities for AP site and 8-oxodG incisions were also upregulated after IP. The upregulation of BER activities after IP was likely because of increased expression of repair enzymes beta-polymerase, AP endonuclease, and OGG1. These results suggest that the activation of the BER pathway may contribute to IP-induced neuroprotection by enhancing the repair of endogenous oxidative DNA damage after ischemic injury.

Long-term cultivation of multipotential neural stem cells from adult rat subependyma.

Cultivation of adult rat neural stem cells (RNSCs) from the ventricular subependyma has been reported to be more difficult than growth of mouse neural stem cells. This is unfortunate, because rats provide useful models of brain function and disease, and implantation of RNSCs in these models could provide critical information on allograft behavior. Growing the cells in an appropriate medium (NS-A+B27 supplement), plating at sufficient densities (>5 cells per mm(2)), and minimizing opportunities for detachment from the substratum made it possible to isolate and cultivate these cells for over 6 months for >50 passages with no apparent change in phenotype. Single clones could be expanded indefinitely and differentiated to form astrocytes, oligodendrocytes, and neurons, demonstrating that the cultures did indeed contain neural stem cells. The cells had a much shorter cell cycle time ( approximately 13 h) than doubling time ( approximately 35 h), suggesting that these cells produce post-mitotic cells in approximately two of three divisions, thus making expansion difficult. The optimization of methods to grow adult RNSCs and identification of characteristics that limit their growth should prove useful in increasing the use of RNSCs for studies of their potential role in brain health and disease.

Experimental radiobiological investigations into radiosurgery: present understanding and future directions.

LARS LEKSELL BEGAN radiobiological investigations to study the effect of high-dose focused radiation on the central nervous system more than 5 decades ago. Although the effects of radiosurgery on the brain tumor microenvironment are still under investigation, radiosurgery has become a preferred management modality for many intracranial tumors and vascular malformations. The effects and the pathogenesis of biological effects after radiosurgery may be unique. The need for basic research concerning the radiobiological effects of high-dose, single-fraction, ionizing radiation on nervous system tissue is crucial. Information from those studies would be useful in devising strategies to avoid, prevent, or ameliorate damage to normal tissue without compromising treatment efficacy. The development of future applications of radiosurgery will depend on an increase in our understanding of the radiobiology of radiosurgery, which in turn will affect the efficacy of treatment. This article analyzes the current state of radiosurgery research with regard to the nature of central nervous system effects, the techniques developed to increase therapeutic efficacy, investigations into the use of radiosurgery for functional disorders, radiosurgery as a tool for investigations into basic central nervous system biology, and the additional areas that require further investigation.

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. Notes were divided into 20 batches of 19-21 documents for iterative annotation and training. RESULTS: The number of correct RapTAT pre-annotations increased significantly and annotation time per batch decreased by ~50% over the course of annotation. Annotation rate increased from batch to batch for assisted but not manual reviewers. Pre-annotation F-measure increased from 0.5 to 0.6 to >0.80 (relative to both assisted reviewer and reference annotations) over the first three batches and more slowly thereafter. Overall inter-annotator agreement was significantly higher between RapTAT-assisted reviewers (0.89) than between manual reviewers (0.85). DISCUSSION: The tool reduced workload by decreasing the number of annotations needing to be added and helping reviewers to annotate at an increased rate. Agreement between the pre-annotations and reference standard, and agreement between the pre-annotations and assisted annotations, were similar throughout the annotation process, which suggests that pre-annotation did not introduce bias. CONCLUSIONS: Pre-annotations generated by a tool capable of interactive training can reduce the time required to create an annotated document corpus by up to 50%.