Virtual multi-institutional tumor board: a strategy for personalized diagnoses and management of rare CNS tumors.

PURPOSE: Multidisciplinary tumor boards (MTBs) integrate clinical, molecular, and radiological information and facilitate coordination of neuro-oncology care. During the COVID-19 pandemic, our MTB transitioned to a virtual and multi-institutional format. We hypothesized that this expansion would allow expert review of challenging neuro-oncology cases and contribute to the care of patients with limited access to specialized centers. METHODS: We retrospectively reviewed records from virtual MTBs held between 04/2020-03/2021. Data collected included measures of potential clinical impact, including referrals to observational or therapeutic studies, referrals for specialized neuropathology analysis, and whether molecular findings led to a change in diagnosis and/or guided management suggestions. RESULTS: During 25 meetings, 32 presenters discussed 44 cases. Approximately half (n = 20; 48%) involved a rare central nervous system (CNS) tumor. In 21% (n = 9) the diagnosis was changed or refined based on molecular profiling obtained at the NIH and in 36% (n = 15) molecular findings guided management. Clinical trial suggestions were offered to 31% (n = 13), enrollment in the observational NCI Natural History Study to 21% (n = 9), neuropathology review and molecular testing at the NIH to 17% (n = 7), and all received management suggestions. CONCLUSION: Virtual multi-institutional MTBs enable remote expert review of CNS tumors. We propose them as a strategy to facilitate expert opinions from specialized centers, especially for rare CNS tumors, helping mitigate geographic barriers to patient care and serving as a pre-screening tool for studies. Advanced molecular testing is key to obtaining a precise diagnosis, discovering potentially actionable targets, and guiding management.

Cost considerations for vestibular schwannoma screening and imaging: a systematic review.

Vestibular schwannomas (VS) account for approximately 8% of all intracranial neoplasms. Importantly, the cost of the diagnostic workup for VS, including the screening modalities most commonly used, has not been thoroughly investigated. Our aim is to conduct a systematic review of the published literature on costs associated with VS screening. A systematic review of the literature for cost of VS treatment was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The terms "vestibular schwannoma," "acoustic neuroma," and "cost" were queried using the PubMed and Embase databases. Studies from all countries were considered. Cost was then corrected for inflation using the US Bureau of Labor Statistics Inflation Calculator, correcting to April 2022. The search resulted in an initial review of 483 articles, of which 12 articles were included in the final analysis. Screening criteria were used for non-neurofibromatosis type I and II patients who complained of asymmetric hearing loss, tinnitus, or vertigo. Patients included in the studies ranged from 72 to 1249. The currency and inflation-adjusted mean cost was $418.40 (range, $21.81 to $487.03, n = 5) for auditory brainstem reflex and $1433.87 (range, $511.64 to $1762.15, n = 3) for non-contrasted computed tomography. A contrasted magnetic resonance imaging (MRI) scan was found to have a median cost of $913.27 (range, $172.25-$2733.99; n = 8) whereas a non-contrasted MRI was found to have a median cost of $478.62 (range, $116.61-$3256.38, n = 4). In terms of cost reporting, of the 12 articles, 1 (8.3%) of them separated out the cost elements, and 10 (83%) of them used local prices, which include institutional costs and/or average costs of multiple institutions. Our findings describe the limited data on published costs for screening and imaging of VS. The paucity of data and significant variability of costs between studies indicates that this endpoint is relatively unexplored, and the cost of screening is poorly understood.

High resolution dermal ultrasound (US) combined with superficial radiation therapy (SRT) versus non-image guided SRT or external beam radiotherapy (XRT) in early-stage epithelial cancer: a comparison of studies.

BACKGROUND: To compare the effectiveness of high-resolution dermal ultrasound (US) guided superficial radiotherapy (SRT) to non-image-guided radiotherapy in the treatment of early-stage Non-Melanoma Skin Cancer (NMSC). METHODS: A high-resolution dermal ultrasound (US) image guided form of superficial radiation therapy (designated here as US-SRT) was developed in 2013 where the tumor configuration and depth can be visualized prior to, during, and subsequent to treatments, using a 22 megahertz (MHz) dermal ultrasound (US) with a doppler component. We previously published the results using this technology to treat 2917 early-stage epithelial cancers showing a high local control (LC) rate of 99.3%. We compared these results with similar American studies from a comprehensive literature search used in an article/guideline published by American Society of Radiation Oncology (ASTRO) on curative radiation treatment of basal cell carcinoma (BCC), squamous cell carcinoma (SCC) and squamous cell carcinoma in-situ (SCCIS) lesions from 1988 to 2018. Only U.S. based studies with greater than 100 cases with similar patient/lesion characteristics and stages treated by external beam, electron, or superficial/orthovoltage radiation therapy were included in the criteria for selection. The resultant 4 studies had appropriate comparable cases identified and the data analyzed/calculated with regard to local control. Logistic regression analysis was performed comparing each study to US-SRT individually and collectively with stratification by histology (BCC, SCC, and SCCIS). RESULTS: US-SRT LC was found to be statistically superior to each of the 4 non-image-guided radiation therapy studies individually and collectively (as well as stratified by histology subtype) with p-values ranging from p < 0.0001 to p = 0.0438. CONCLUSIONS: Results of US-SRT in local control were statistically significantly superior across the board versus non-image-guided radiation modalities in treatment of epithelial NMSC and should be considered a new gold standard for treatment of early-stage cutaneous BCC, SCC, and SCCIS.

Evaluating the state of non-invasive imaging biomarkers for traumatic brain injury.

Non-invasive imaging biomarkers are useful for prognostication in patients with traumatic brain injury (TBI) at high risk for morbidity with invasive procedures. The authors present findings from a scoping review discussing the pertinent biomarkers. Embase, Ovid-MEDLINE, and Scopus were queried for original research on imaging biomarkers for prognostication of TBI in adult patients. Two reviewers independently screened articles, extracted data, and evaluated risk of bias. Data was synthesized and confidence evaluated with the linked evidence according to the Grades of Recommendation, Assessment, Development, and Evaluation (GRADE) approach. Our search yielded 3104 unique citations, 44 of which were included in this review. Study populations varied in TBI severity, as defined by Glasgow Coma Scale (GCS), including: mild (n=9), mild and moderate (n=3), moderate and severe (n=7), severe (n=6), and all GCS scores (n=17). Diverse imaging modalities were used for prognostication, predominantly computed tomography (CT) only (n=11), magnetic resonance imaging (MRI) only (n=9), and diffusion tensor imaging (DTI) (N=9). The biomarkers included diffusion coefficient mapping, metabolic characteristics, optic nerve sheath diameter, T1-weighted signal changes, cortical cerebral blood flow, axial versus extra-axial lesions, T2-weighted gradient versus spin echo, translocator protein levels, and trauma imaging of brainstem areas. The majority (93%) of studies identified that the imaging biomarker of interest had a statistically significant prognostic value; however, these are based on a very low to low level of quality of evidence. No study directly compared the effects on specific TBI treatments on the temporal course of imaging biomarkers. The current literature is insufficient to make a strong recommendation about a preferred imaging biomarker for TBI, especially considering GRADE criteria revealing low quality of evidence. Rigorous prospective research of imaging biomarkers of TBI is warranted to improve the understanding of TBI severity.

Biocoating-A Critical Step Governing the Oral Delivery of Polymeric Nanoparticles.

Decades of research into the topic of oral nanoparticle (NP) delivery has still not provided a clear consensus regarding which properties produce an effective oral drug delivery system. The surface properties-charge and bioadhesiveness-as well as in vitro and in vivo correlation seem to generate the greatest number of disagreements within the field. Herein, a mechanism underlying the in vivo behavior of NPs is proposed, which bridges the gaps between these disagreements. The mechanism relies on the idea of biocoating-the coating of NPs with mucus-which alters their surface properties, and ultimately their systemic uptake. Utilizing this mechanism, several coated NPs are tested in vitro, ex vivo, and in vivo, and biocoating is found to affect NPs size, zeta-potential, mucosal diffusion coefficient, the extent of aggregation, and in vivo/in vitro/ex vivo correlation. Based on these results, low molecular weight polylactic acid exhibits a 21-fold increase in mucosal diffusion coefficient after precoating as compared to uncoated particles, as well as 20% less aggregation, and about 30% uptake to the blood in vivo. These discoveries suggest that biocoating reduces negative NP charge which results in an enhanced mucosal diffusion rate, increased gastrointestinal retention time, and high systemic uptake.

Leveraging electronic health record data for clinical trial planning by assessing eligibility criteria's impact on patient count and safety.

OBJECTIVE: To present an approach on using electronic health record (EHR) data that assesses how different eligibility criteria, either individually or in combination, can impact patient count and safety (exemplified by all-cause hospitalization risk) and further assist with criteria selection for prospective clinical trials. MATERIALS AND METHODS: Trials in three disease domains - relapsed/refractory (r/r) lymphoma/leukemia; hepatitis C virus (HCV); stages 3 and 4 chronic kidney disease (CKD) - were analyzed as case studies for this approach. For each disease domain, criteria were identified and all criteria combinations were used to create EHR cohorts. Per combination, two values were derived: (1) number of eligible patients meeting the selected criteria; (2) hospitalization risk, measured as the hazard ratio between those that qualified and those that did not. From these values, k-means clustering was applied to derive which criteria combinations maximized patient counts but minimized hospitalization risk. RESULTS: Criteria combinations that reduced hospitalization risk without substantial reductions on patient counts were as follows: for r/r lymphoma/leukemia (23 trials; 9 criteria; 623 patients), applying no infection and adequate absolute neutrophil count while forgoing no prior malignancy; for HCV (15; 7; 751), applying no human immunodeficiency virus and no hepatocellular carcinoma while forgoing no decompensated liver disease/cirrhosis; for CKD (10; 9; 23893), applying no congestive heart failure. CONCLUSIONS: Within each disease domain, the more drastic effects were generally driven by a few criteria. Similar criteria across different disease domains introduce different changes. Although results are contingent on the trial sample and the EHR data used, this approach demonstrates how EHR data can inform the impact on safety and available patients when exploring different criteria combinations for designing clinical trials.

Deep learning for rare disease: A scoping review.

Although individually rare, collectively more than 7,000 rare diseases affect about 10% of patients. Each of the rare diseases impacts the quality of life for patients and their families, and incurs significant societal costs. The low prevalence of each rare disease causes formidable challenges in accurately diagnosing and caring for these patients and engaging participants in research to advance treatments. Deep learning has advanced many scientific fields and has been applied to many healthcare tasks. This study reviewed the current uses of deep learning to advance rare disease research. Among the 332 reviewed articles, we found that deep learning has been actively used for rare neoplastic diseases (250/332), followed by rare genetic diseases (170/332) and rare neurological diseases (127/332). Convolutional neural networks (307/332) were the most frequently used deep learning architecture, presumably because image data were the most commonly available data type in rare disease research. Diagnosis is the main focus of rare disease research using deep learning (263/332). We summarized the challenges and future research directions for leveraging deep learning to advance rare disease research.

Clinical comparison between trial participants and potentially eligible patients using electronic health record data: A generalizability assessment method.

OBJECTIVE: To present a generalizability assessment method that compares baseline clinical characteristics of trial participants (TP) to potentially eligible (PE) patients as presented in their electronic health record (EHR) data while controlling for clinical setting and recruitment period. METHODS: For each clinical trial, a clinical event was defined to identify patients of interest using available EHR data from one clinical setting during the trial's recruitment timeframe. The trial's eligibility criteria were then applied and patients were separated into two mutually exclusive groups: (1) TP, which were patients that participated in the trial per trial enrollment data; (2) PE, the remaining patients. The primary outcome was standardized differences in clinical characteristics between TP and PE per trial. A standardized difference was considered prominent if its absolute value was greater than or equal to 0.1. The secondary outcome was the difference in mean propensity scores (PS) between TP and PE per trial, in which the PS represented prediction for a patient to be in the trial. Three diverse trials were selected for illustration: one focused on hepatitis C virus (HCV) patients receiving a liver transplantation; one focused on leukemia patients and lymphoma patients; and one focused on appendicitis patients. RESULTS: For the HCV trial, 43 TP and 83 PE were found, with 61 characteristics evaluated. Prominent differences were found among 69% of characteristics, with a mean PS difference of 0.13. For the leukemia/lymphoma trial, 23 TP and 23 PE were found, with 39 characteristics evaluated. Prominent differences were found among 82% of characteristics, with a mean PS difference of 0.76. For the appendicitis trial, 123 TP and 242 PE were found, with 52 characteristics evaluated. Prominent differences were found among 52% of characteristics, with a mean PS difference of 0.15. CONCLUSIONS: Differences in clinical characteristics were observed between TP and PE among all three trials. In two of the three trials, not all of the differences necessarily compromised trial generalizability and subsets of PE could be considered similar to their corresponding TP. In the remaining trial, lack of generalizability appeared present, but may be a result of other factors such as small sample size or site recruitment strategy. These inconsistent findings suggest eligibility criteria alone are sometimes insufficient in defining a target group to generalize to. With caveats in limited scalability, EHR data quality, and lack of patient perspective on trial participation, this generalizability assessment method that incorporates control for temporality and clinical setting promise to better pinpoint clinical patterns and trial considerations.

Biochemically altered myelin triggers autoimmune demyelination.

Although immune attack against central nervous system (CNS) myelin is a central feature of multiple sclerosis (MS), its root cause is unresolved. In this report, we provide direct evidence that subtle biochemical modifications to brain myelin elicit pathological immune responses with radiological and histological properties similar to MS lesions. A subtle myelinopathy induced by abbreviated cuprizone treatment, coupled with subsequent immune stimulation, resulted in lesions of inflammatory demyelination. The degree of myelin injury dictated the resulting immune response; biochemical damage that was too limited or too extensive failed to trigger overt pathology. An inhibitor of peptidyl arginine deiminases (PADs), enzymes that alter myelin structure and correlate with MS lesion severity, mitigated pathology even when administered only during the myelin-altering phase. Moreover, cultured splenocytes were reactive against donor myelin isolates, a response that was substantially muted when splenocytes were exposed to myelin from donors treated with PAD inhibitors. By showing that a primary biochemical myelinopathy can trigger secondary pathological inflammation, "cuprizone autoimmune encephalitis" potentially reconciles conflicting theories about MS pathogenesis and provides a strong rationale for investigating myelin as a primary target for early, preventative therapy.

The effect of a secondary task on drivers' gap acceptance and situational awareness at junctions.

The current studies explored the roles of the visuospatial and phonological working memory subsystems on drivers' gap acceptance and memory for approaching vehicles at junctions. Drivers' behaviour was measured in a high-fidelity driving simulator when at a junction, with, and without a visuospatial or phonological load. When asked to judge when to advance across the junction, gap acceptance thresholds, memory for vehicles and eye movements were not different when there was a secondary task compared to control. However, drivers' secondary task performance was more impaired in the visuospatial than phonological domain. These findings suggest that drivers were able to accept impairment in the secondary task while maintaining appropriate safety margins and situational awareness. These findings can inform the development of in-car technologies, improving the safety of road users at junctions. Practitioner summary: Despite research indicating that concurrent performance on working memory tasks impairs driving, a matched visuospatial or phonological memory load did not change drivers' gap acceptance or situational awareness at junctions. Drivers displayed appropriate compensatory behaviour by prioritising the driving task over the visuospatial secondary task. Abbreviations: ROW: right of way; RIG: random time interval generation.

Gestational bisphenol-A exposure lowers the threshold for autoimmunity in a model of multiple sclerosis.

Environmental and hormonal factors are implicated in dysimmunity in multiple sclerosis. We investigated whether bisphenol-A, a prominent contaminant with endocrine-disrupting capabilities, altered susceptibility in an inflammatory model of multiple sclerosis. We found that gestational, but not adult, exposure to bisphenol-A increased the development of experimental autoimmune encephalomyelitis in adulthood in male, but not female, mice when a suboptimal disease-inducing immunization was used. Gestational bisphenol-A in male mice primed macrophages in adulthood and raised granulocyte-colony stimulating factor and neutrophil counts/activity postsuboptimal immunization. Neutralizing granulocyte-colony stimulating factor blocked susceptibility to disease in bisphenol-A mice. Early life exposure to bisphenol-A may represent an environmental consideration in multiple sclerosis.

Comparative effectiveness of generic and brand-name medication use: A database study of US health insurance claims.

BACKGROUND: To the extent that outcomes are mediated through negative perceptions of generics (the nocebo effect), observational studies comparing brand-name and generic drugs are susceptible to bias favoring the brand-name drugs. We used authorized generic (AG) products, which are identical in composition and appearance to brand-name products but are marketed as generics, as a control group to address this bias in an evaluation aiming to compare the effectiveness of generic versus brand medications. METHODS AND FINDINGS: For commercial health insurance enrollees from the US, administrative claims data were derived from 2 databases: (1) Optum Clinformatics Data Mart (years: 2004-2013) and (2) Truven MarketScan (years: 2003-2015). For a total of 8 drug products, the following groups were compared using a cohort study design: (1) patients switching from brand-name products to AGs versus generics, and patients initiating treatment with AGs versus generics, where AG use proxied brand-name use, addressing negative perception bias, and (2) patients initiating generic versus brand-name products (bias-prone direct comparison) and patients initiating AG versus brand-name products (negative control). Using Cox proportional hazards regression after 1:1 propensity-score matching, we compared a composite cardiovascular endpoint (for amlodipine, amlodipine-benazepril, and quinapril), non-vertebral fracture (for alendronate and calcitonin), psychiatric hospitalization rate (for sertraline and escitalopram), and insulin initiation (for glipizide) between the groups. Inverse variance meta-analytic methods were used to pool adjusted hazard ratios (HRs) for each comparison between the 2 databases. Across 8 products, 2,264,774 matched pairs of patients were included in the comparisons of AGs versus generics. A majority (12 out of 16) of the clinical endpoint estimates showed similar outcomes between AGs and generics. Among the other 4 estimates that did have significantly different outcomes, 3 suggested improved outcomes with generics and 1 favored AGs (patients switching from amlodipine brand-name: HR [95% CI] 0.92 [0.88-0.97]). The comparison between generic and brand-name initiators involved 1,313,161 matched pairs, and no differences in outcomes were noted for alendronate, calcitonin, glipizide, or quinapril. We observed a lower risk of the composite cardiovascular endpoint with generics versus brand-name products for amlodipine and amlodipine-benazepril (HR [95% CI]: 0.91 [0.84-0.99] and 0.84 [0.76-0.94], respectively). For escitalopram and sertraline, we observed higher rates of psychiatric hospitalizations with generics (HR [95% CI]: 1.05 [1.01-1.10] and 1.07 [1.01-1.14], respectively). The negative control comparisons also indicated potentially higher rates of similar magnitude with AG compared to brand-name initiation for escitalopram and sertraline (HR [95% CI]: 1.06 [0.98-1.13] and 1.11 [1.05-1.18], respectively), suggesting that the differences observed between brand and generic users in these outcomes are likely explained by either residual confounding or generic perception bias. Limitations of this study include potential residual confounding due to the unavailability of certain clinical parameters in administrative claims data and the inability to evaluate surrogate outcomes, such as immediate changes in blood pressure, upon switching from brand products to generics. CONCLUSIONS: In this study, we observed that use of generics was associated with comparable clinical outcomes to use of brand-name products. These results could help in promoting educational interventions aimed at increasing patient and provider confidence in the ability of generic medicines to manage chronic diseases.

The Effects of Brain Tumours upon Medical Decision-Making Capacity.

PURPOSE OF REVIEW: Informed consent is the integral part of good medical practice in patients with brain tumours. Capacity to consent may be affected by the brain disorder or its treatment. We intend to draw upon the current neuro-oncology literature to discuss the influence intracranial tumours have upon patients' capacity to consent to treatment and research. RECENT FINDINGS: We performed a systematic review of studies of capacity to consent for treatment or research in patients with intracranial tumours. The search retrieved 1597 papers of which 8 were considered eligible for review. Although there are obvious inherent limitations to solely assessing cognition, most research consistently demonstrated increased risk of incapacity in brain tumour patients with cognitive impairment. Specific items in cognitive screening batteries, for example Semantic Verbal Fluency Test (SVFT), Hopkins Verbal Learning Test (HVLT-Recall), and Trail Making Test A/B (TMT), are simple, easily applied tests that may act as significant red flags to identify patients at increased risk of incapacity and who subsequently will require additional cognitive/psychiatric evaluation or more formal tests for capacity to consent for treatment or research.

Ensembles of natural language processing systems for portable phenotyping solutions.

BACKGROUND: Manually curating standardized phenotypic concepts such as Human Phenotype Ontology (HPO) terms from narrative text in electronic health records (EHRs) is time consuming and error prone. Natural language processing (NLP) techniques can facilitate automated phenotype extraction and thus improve the efficiency of curating clinical phenotypes from clinical texts. While individual NLP systems can perform well for a single cohort, an ensemble-based method might shed light on increasing the portability of NLP pipelines across different cohorts. METHODS: We compared four NLP systems, MetaMapLite, MedLEE, ClinPhen and cTAKES, and four ensemble techniques, including intersection, union, majority-voting and machine learning, for extracting generic phenotypic concepts. We addressed two important research questions regarding automated phenotype recognition. First, we evaluated the performance of different approaches in identifying generic phenotypic concepts. Second, we compared the performance of different methods to identify patient-specific phenotypic concepts. To better quantify the effects caused by concept granularity differences on performance, we developed a novel evaluation metric that considered concept hierarchies and frequencies. Each of the approaches was evaluated on a gold standard set of clinical documents annotated by clinical experts. One dataset containing 1,609 concepts derived from 50 clinical notes from two different institutions was used in both evaluations, and an additional dataset of 608 concepts derived from 50 case report abstracts obtained from PubMed was used for evaluation of identifying generic phenotypic concepts only. RESULTS: For generic phenotypic concept recognition, the top three performers in the NYP/CUIMC dataset are union ensemble (F1, 0.634), training-based ensemble (F1, 0.632), and majority vote-based ensemble (F1, 0.622). In the Mayo dataset, the top three are majority vote-based ensemble (F1, 0.642), cTAKES (F1, 0.615), and MedLEE (F1, 0.559). In the PubMed dataset, the top three are majority vote-based ensemble (F1, 0.719), training-based (F1, 0.696) and MetaMapLite (F1, 0.694). For identifying patient specific phenotypes, the top three performers in the NYP/CUIMC dataset are majority vote-based ensemble (F1, 0.610), MedLEE (F1, 0.609), and training-based ensemble (F1, 0.585). In the Mayo dataset, the top three are majority vote-based ensemble (F1, 0.604), cTAKES (F1, 0.531) and MedLEE (F1, 0.527). CONCLUSIONS: Our study demonstrates that ensembles of natural language processing can improve both generic phenotypic concept recognition and patient specific phenotypic concept identification over individual systems. Among the individual NLP systems, each individual system performed best when they were applied in the dataset that they were primary designed for. However, combining multiple NLP systems to create an ensemble can generally improve the performance. Specifically, the ensemble can increase the results reproducibility across different cohorts and tasks, and thus provide a more portable phenotyping solution compared to individual NLP systems.

Mechanisms of lysophosphatidylcholine-induced demyelination: A primary lipid disrupting myelinopathy.

For decades lysophosphatidylcholine (LPC, lysolecithin) has been used to induce demyelination, without a clear understanding of its mechanisms. LPC is an endogenous lysophospholipid so it may cause demyelination in certain diseases. We investigated whether known receptor systems, inflammation or nonspecific lipid disruption mediates LPC-demyelination in mice. We found that LPC nonspecifically disrupted myelin lipids. LPC integrated into cellular membranes and rapidly induced cell membrane permeability; in mice, LPC injury was phenocopied by other lipid disrupting agents. Interestingly, following its injection into white matter, LPC was cleared within 24 hr but by five days there was an elevation of endogenous LPC that was not associated with damage. This elevation of LPC in the absence of injury raises the possibility that the brain has mechanisms to buffer LPC. In support, LPC injury in culture was significantly ameliorated by albumin buffering. These results shed light on the mechanisms of LPC injury and homeostasis.

The relative benefits of claims and electronic health record data for predicting medication adherence trajectory.

BACKGROUND: Healthcare providers are increasingly encouraged to improve their patients' adherence to chronic disease medications. Prediction of adherence can identify patients in need of intervention, but most prediction efforts have focused on claims data, which may be unavailable to providers. Electronic health records (EHR) are readily available and may provide richer information with which to predict adherence than is currently available through claims. METHODS: In a linked database of complete Medicare Advantage claims and comprehensive EHR from a multi-specialty outpatient practice, we identified patients who filled a prescription for a statin, antihypertensive, or oral antidiabetic during 2011 to 2012. We followed patients to identify subsequent medication filling patterns and used group-based trajectory models to assign patients to adherence trajectories. We then identified potential predictors from both claims and EHR data and fit a series of models to evaluate the accuracy of each data source in predicting medication adherence. RESULTS: Claims were highly predictive of patients in the worst adherence trajectory (C=0.78), but EHR data also provided good predictions (C=0.72). Among claims predictors, presence of a prior gap in filling of at least 6 days was by far the most influential predictor. In contrast, good predictions from EHR data required complex models with many variables. CONCLUSION: EHR data can provide good predictions of adherence trajectory and therefore may be useful for providers seeking to deploy resource-intensive interventions. However, prior adherence information derived from claims is most predictive, and can supplement EHR data when it is available.

Data Mining for Adverse Drug Events With a Propensity Score-matched Tree-based Scan Statistic.

The tree-based scan statistic is a statistical data mining tool that has been used for signal detection with a self-controlled design in vaccine safety studies. This disproportionality statistic adjusts for multiple testing in evaluation of thousands of potential adverse events. However, many drug safety questions are not well suited for self-controlled analysis. We propose a method that combines tree-based scan statistics with propensity score-matched analysis of new initiator cohorts, a robust design for investigations of drug safety. We conducted plasmode simulations to evaluate performance. In multiple realistic scenarios, tree-based scan statistics in cohorts that were propensity score matched to adjust for confounding outperformed tree-based scan statistics in unmatched cohorts. In scenarios where confounding moved point estimates away from the null, adjusted analyses recovered the prespecified type 1 error while unadjusted analyses inflated type 1 error. In scenarios where confounding moved point estimates toward the null, adjusted analyses preserved power, whereas unadjusted analyses greatly reduced power. Although complete adjustment of true confounders had the best performance, matching on a moderately mis-specified propensity score substantially improved type 1 error and power compared with no adjustment. When there was true elevation in risk of an adverse event, there were often co-occurring signals for clinically related concepts. TreeScan with propensity score matching shows promise as a method for screening and prioritization of potential adverse events. It should be followed by clinical review and safety studies specifically designed to quantify the magnitude of effect, with confounding control targeted to the outcome of interest.

"Impact of drug-reimbursement policies on prescribing: A case-study of a newly marketed long-acting injectable antipsychotic among relapsed schizophrenia patients".

OBJECTIVE: To quantify and explain variation in use of long-acting injectable antipsychotics (LAIs) in the United States, and understand the relationship between patient characteristics, drug reimbursement policies, and LAI prescribing after relapse. METHODS: A cohort of recently relapsed patients with schizophrenia ages 18 to 64, were identified immediately after discharge from a related inpatient hospitalization, partial hospitalization, or emergency room visit, drawn from 2004 to 2006 Medicaid claims, and followed for 90 days until LAI initiation. Data on state-level Medicaid prior authorization (PA) policies for LAIs were collected. Sequential longitudinal Poisson regression models were developed to understand the relationship between patient and PA policy variables and LAI prescribing, including prior adherence to oral antipsychotics, demographics, clinical variables, and presence of PA policy for LAI. RESULTS: Among 36 282 patients, 3.1% received risperidone LAI, and 3.8% received a first-generation (FGA) LAI with wide variation across states. Prior adherence ranged from 29% to 89% but was marginally associated with initiation and did not explain variation for LAI prescribing. FGA initiation was associated with geography and race/ethnicity but not PA policy. For risperidone LAI initiation, demographics and clinical factors explained, respectively, 5.0% and 3.0% of the variation; PA policy had a large negative association with initiation (RR = 0.41; 95%CI 0.20-0.87) and explained 8.4% of the variation. CONCLUSIONS: PA policies may represent a major treatment barrier for risperidone LAI among relapsed patients. Non-adherence plays a little role in predicting which patients receive LAIs. Policy makers and health insurers will need to consider these findings when guiding the use of LAIs. KEY POINTS Among a nationwide cohort of relapsed schizophrenia patients enrolled in US Medicaid, 3.1% received Risperdal Consta, a long-acting injectable antipsychotic (LAI), and 3.8% initiated a first-generation first-generation LAI within 90 days after discharge. During 2004 to 2006, there was marked variation in 90 day post-relapse initiation of Risperdal-Consta-a newly marketed medication during this period-and also marked variation in 90 day post-relapse initiation of any first-generation LAI, which appeared to be associated with race/ethnicity and geography. Prior authorization policies were associated with substantially lower initiation of Risperdal Consta in this cohort of relapsed patients even after accounting for clinical indication (non-adherence), relapse history, demographics, adjunctive medication, and mental health service use.

Inactivation of Bacillus anthracis spores to decontaminate subway railcar and related materials via the fogging of peracetic acid and hydrogen peroxide sporicidal liquids.

The inactivation of Bacillus anthracis spores on subway and used subway railcar materials was evaluated using fogged peracetic acid/hydrogen peroxide (PAA) and hydrogen peroxide (H2O2). A total of 21 separate decontamination tests were conducted using bacterial spores of both B. anthracis Ames (B.a.) and Bacillus atrophaeus (B.g.) inoculated onto several types of materials. Tests were conducted using commercial off-the-shelf fogging equipment filled with either PAA or H2O2 to fumigate a ∼15 cubic meter chamber under uncontrolled ambient relative humidity and controlled temperature (10 or 20 °C) from 8 to 168 h. For the present study, no conditions were found that resulted in complete inactivation of either B.a. Ames or B.g. on all test materials. Approximately 41% and 38% of the decontamination efficacies for B.a. and B.g., respectively, exhibited ≥6 log10 reduction (LR); efficacy depended greatly on the material. When testing at 10 °C, the mean LR was consistently lower for both B.a. and B.g. as compared to 20 °C. Based on the statistical comparison of the LR results, B.g. exhibited equivalent or greater resistance than B.a. for approximately 92% of the time across all 21 tests. The efficacy data suggest that B.g. may be a suitable surrogate for B.a. Ames when assessing the decontamination efficacy of fogged PAA or H2O2. Moreover, the results of this testing indicate that in the event of B.a. spore release into a subway system, the fogging of PAA or H2O2 represents a decontamination option for consideration.