Trust but Verify: Lessons Learned for the Application of AI to Case-Based Clinical Decision-Making From Postmarketing Drug Safety Assessment at the US Food and Drug Administration.

Adverse drug reactions are a common cause of morbidity in health care. The US Food and Drug Administration (FDA) evaluates individual case safety reports of adverse events (AEs) after submission to the FDA Adverse Event Reporting System as part of its surveillance activities. Over the past decade, the FDA has explored the application of artificial intelligence (AI) to evaluate these reports to improve the efficiency and scientific rigor of the process. However, a gap remains between AI algorithm development and deployment. This viewpoint aims to describe the lessons learned from our experience and research needed to address both general issues in case-based reasoning using AI and specific needs for individual case safety report assessment. Beginning with the recognition that the trustworthiness of the AI algorithm is the main determinant of its acceptance by human experts, we apply the Diffusion of Innovations theory to help explain why certain algorithms for evaluating AEs at the FDA were accepted by safety reviewers and others were not. This analysis reveals that the process by which clinicians decide from case reports whether a drug is likely to cause an AE is not well defined beyond general principles. This makes the development of high performing, transparent, and explainable AI algorithms challenging, leading to a lack of trust by the safety reviewers. Even accounting for the introduction of large language models, the pharmacovigilance community needs an improved understanding of causal inference and of the cognitive framework for determining the causal relationship between a drug and an AE. We describe specific future research directions that underpin facilitating implementation and trust in AI for drug safety applications, including improved methods for measuring and controlling of algorithmic uncertainty, computational reproducibility, and clear articulation of a cognitive framework for causal inference in case-based reasoning.

Integrated Hepatitis C-Opioid Use Disorder Care Through Facilitated Telemedicine: A Randomized Trial.

Importance: Facilitated telemedicine may promote hepatitis C virus elimination by mitigating geographic and temporal barriers. Objective: To compare sustained virologic responses for hepatitis C virus among persons with opioid use disorder treated through facilitated telemedicine integrated into opioid treatment programs compared with off-site hepatitis specialist referral. Design, Setting, and Participants: Prospective, cluster randomized clinical trial using a stepped wedge design. Twelve programs throughout New York State included hepatitis C-infected participants (n = 602) enrolled between March 1, 2017, and February 29, 2020. Data were analyzed from December 1, 2022, through September 1, 2023. Intervention: Hepatitis C treatment with direct-acting antivirals through comanagement with a hepatitis specialist either through facilitated telemedicine integrated into opioid treatment programs (n = 290) or standard-of-care off-site referral (n = 312). Main Outcomes and Measures: The primary outcome was hepatitis C virus cure. Twelve programs began with off-site referral, and every 9 months, 4 randomly selected sites transitioned to facilitated telemedicine during 3 steps without participant crossover. Participants completed 2-year follow-up for reinfection assessment. Inclusion criteria required 6-month enrollment in opioid treatment and insurance coverage of hepatitis C medications. Generalized linear mixed-effects models were used to test for the intervention effect, adjusted for time, clustering, and effect modification in individual-based intention-to-treat analysis. Results: Among 602 participants, 369 were male (61.3%); 296 (49.2%) were American Indian or Alaska Native, Asian, Black or African American, multiracial, or other (ie, no race category was selected, with race data collected according to the 5 standard National Institutes of Health categories); and 306 (50.8%) were White. The mean (SD) age of the enrolled participants in the telemedicine group was 47.1 (13.1) years; that of the referral group was 48.9 (12.8) years. In telemedicine, 268 of 290 participants (92.4%) initiated treatment compared with 126 of 312 participants (40.4%) in referral. Intention-to-treat cure percentages were 90.3% (262 of 290) in telemedicine and 39.4% (123 of 312) in referral, with an estimated logarithmic odds ratio of the study group effect of 2.9 (95% CI, 2.0-3.5; P < .001) with no effect modification. Observed cure percentages were 246 of 290 participants (84.8%) in telemedicine vs 106 of 312 participants (34.0%) in referral. Subgroup effects were not significant, including fibrosis stage, urban or rural participant residence location, or mental health (anxiety or depression) comorbid conditions. Illicit drug use decreased significantly (referral: 95% CI, 1.2-4.8; P = .001; telemedicine: 95% CI, 0.3-1.0; P < .001) among cured participants. Minimal reinfections (n = 13) occurred, with hepatitis C virus reinfection incidence of 2.5 per 100 person-years. Participants in both groups rated health care delivery satisfaction as high or very high. Conclusions and Relevance: Opioid treatment program-integrated facilitated telemedicine resulted in significantly higher hepatitis C virus cure rates compared with off-site referral, with high participant satisfaction. Illicit drug use declined significantly among cured participants with minimal reinfections. Trial Registration: ClinicalTrials.gov Identifier: NCT02933970.

High Satisfaction with Patient-Centered Telemedicine for Hepatitis C Virus Delivered to Substance Users: A Mixed-Methods Study.

Background: While telemedicine may increase health care access for vulnerable populations, data are limited on whether people with opioid use disorder (PWOUD) are satisfied with telemedicine. We assessed PWOUD satisfaction with telemedicine and identified factors that increase telemedicine satisfaction. Methods: We conducted a mixed-methods study among hepatitis C virus (HCV)-infected persons enrolled at 12 opioid treatment programs (OTPs) throughout New York State. Participants successfully completed HCV treatment either through telemedicine integrated into OTPs (N = 238) or through offsite referral (N = 106). We evaluated Patient Satisfaction Questionnaire (PSQ) response scores at the initial and final health care encounters and subsequently interviewed telemedicine study participants (N = 25) to assess their experiences with telemedicine. Results: All participants (N = 344) successfully completed HCV treatment. We observed no differences in PSQ scores between telemedicine and in-person encounters (98.3% and 98.7% of telemedicine participants provided PSQ scores of satisfied or highly satisfied at each timepoint, respectively). Study participants indicated that attributes associated with high telemedicine encounter satisfaction included: (1) communicating study information, (2) gaining trust, and (3) delivering patient-centered care. Participants weighted "General Satisfaction" and "Time Spent with Doctor" higher than "Accessibility and Convenience," and female participants were significantly more satisfied than males. Satisfaction with health care delivery among all participants increased significantly comparing timepoints. Conclusions: Participants were highly satisfied with HCV telemedicine encounters equivalent to in-person encounters. Communication augments trust facilitating delivery of patient-centered care through telemedicine. Participants value empathy and trust with providers over accessibility and convenience. In summary, PWOUD are highly satisfied with the facilitated telemedicine model and value empathetic and trusting providers. ClinicalTrials.gov Identifier: NCT02933970.

On the use of the likelihood ratio test methodology in pharmacovigilance.

The safety of medical products due to adverse events (AE) from drugs, therapeutic biologics, and medical devices is a major public health concern worldwide. Likelihood ratio test (LRT) approaches to pharmacovigilance constitute a class of rigorous statistical tools that permit objective identification of AEs of a specific drug and/or a class of drugs cataloged in spontaneous reporting system databases. However, the existing LRT approaches encounter certain theoretical and computational challenges when an underlying Poisson model assumption is violated, including in cases of zero-inflated data. We briefly review existing LRT approaches and propose a novel class of (pseudo-) LRT methods to address these challenges. Our approach uses an alternative parametrization to formulate a unified framework with a common test statistic that can handle both Poisson and zero-inflated Poisson (ZIP) models. The proposed framework is computationally efficient, and it reveals deeper insights into the comparative behaviors of the Poisson and the ZIP models for handling AE data. Our extensive simulation studies document notably superior performances of the proposed methods over existing approaches particularly under zero-inflation, both in terms of statistical (eg, much better control of the nominal level and false discovery rate with substantially enhanced power) and computational ( ∼ $$ \sim $$ 100-500-fold gains in average running times) performance metrics. An application of our method on the statin drug class from the FDA FAERS database reveals interesting insights on potential AEs. An R package, pvLRT, implementing our methods has been released in the public domain.

Distance-Based Estimation Methods for Models for Discrete and Mixed-Scale Data.

Pearson residuals aid the task of identifying model misspecification because they compare the estimated, using data, model with the model assumed under the null hypothesis. We present different formulations of the Pearson residual system that account for the measurement scale of the data and study their properties. We further concentrate on the case of mixed-scale data, that is, data measured in both categorical and interval scale. We study the asymptotic properties and the robustness of minimum disparity estimators obtained in the case of mixed-scale data and exemplify the performance of the methods via simulation.

An evaluation of statistical approaches to postmarketing surveillance.

Safety of medical products presents a serious concern worldwide. Surveillance systems of postmarket medical products have been established for continual monitoring of adverse events (AEs) in many countries, and the proliferation of electronic health record systems further facilitates continual monitoring for AEs. We review existing statistical methods for signal detection that are mostly in use in postmarketing safety surveillance of spontaneously reported AEs and we study their performance characteristics by simulation. We compare those with the likelihood ratio test (LRT) method (appropriately modified for use in pharmacovigilance) and use three different methods to generate data (AE based, drug based, and a modification of the method of Ahmed et al). Performance metrics include type I error, power, sensitivity, and false discovery rate, among others. The results show superior performance of the LRT method in almost all simulation experiments. An application to the FDA Adverse Event Reporting System database is illustrated using rhabdomyolysis-related preferred terms reported to FDA during the third-quarter of 2014 to the first-quarter of 2017 for statin drugs. We present a critical discussion and recommendations for use of these methods.

A framework for patient-centered telemedicine: Application and lessons learned from vulnerable populations.

Virtual technologies can facilitate clinical monitoring, clinician-patient interactions, and enhance patient-centered approaches to healthcare delivery. Telemedicine, two-way communication between a healthcare provider and a patient not in the same physical location, emphasizes patient preference and convenience by substituting the transportation of patients with information transfer. We present a framework for implementation of a comprehensive, dynamic, patient-centered telemedicine network deployed in 12 opioid treatment programs (OTP) located throughout New York State (NYS). The program aims to effectively manage hepatitis C virus (HCV) infection via telemedicine with co-administration of HCV and substance use medications. We have found that the Sociotechnical System model with emphasis on patient-centered factors provides a framework for telemedicine deployment and implementation to a vulnerable population. The issue of interoperability between the telemedicine platform and the electronic health record (EHR) system as well as clinical information retrieval for medical decision-making are challenges with implementation of a comprehensive, dynamic telemedicine system. Targeting telemedicine to a vulnerable population requires additional consideration of trust in the security and confidentiality of the telemedicine system. Our contribution is the valuable lessons learned from implementing a comprehensive, dynamic, patient-centered telemedicine system among an OTP network throughout NYS as applied to a vulnerable population that can be generalized to other difficult-to-reach populations.

Integrated, Co-located, Telemedicine-based Treatment Approaches for Hepatitis C Virus Management in Opioid Use Disorder Patients on Methadone.

BACKGROUND: Despite high hepatitis C virus (HCV) prevalence, opioid use disorder (OUD) patients on methadone rarely engage in HCV treatment. We investigated the effectiveness of HCV management via telemedicine in an opioid substitution therapy (OST) program. METHODS: OUD patients on methadone underwent biweekly telemedicine sessions between a hepatologist and physician assistant during the entire HCV treatment course. All pretreatment labs (HCV RNA, genotype, and noninvasive fibrosis assessments) were obtained onsite and direct-acting antivirals were coadministered with methadone using modified directly observed therapy. We used multiple correspondence analysis, least absolute shrinkage and selection operator, and logistic regression to identify variables associated with pursuit of HCV care. RESULTS: Sixty-two HCV RNA-positive patients (24% human immunodeficiency virus [HIV] infected, 61% male, 61% African American, 25.8% Hispanic) were evaluated. All patients were stabilized on methadone and all except 4 were HCV genotype 1 infected. Advanced fibrosis/cirrhosis was present in 34.5% of patients. Of the 45 treated patients, 42 (93.3%) achieved viral eradication. Of 17 evaluated patients who were not treated, 5 were discontinued from the drug treatment program or did not follow up after the evaluation, 2 had HIV adherence issues, and 10 had insurance authorization issues. Marriage and a mental health diagnosis other than depression were the strongest positive predictors of treatment pursuit, whereas being divorced, separated, or widowed was the strongest negative predictor. CONCLUSIONS: HCV management via telemedicine integrated into an OST program is a feasible model with excellent virologic effectiveness. Psychosocial and demographic variables can assist in identification of subgroups with a propensity or aversion to pursue HCV treatment.

Patient Reaction to Telemedicine for Clinical Management of Hepatitis C Virus Integrated into an Opioid Treatment Program.

Background and Introduction: Virtual integration of hepatitis C virus (HCV) infection management within the opioid treatment program (OTP) through telemedicine may overcome limited treatment uptake encountered when patients are referred offsite. To evaluate the diffusion of telemedicine within the OTP, we conducted a pilot study to assess acceptance of and satisfaction with telemedicine among 45 HCV-infected opioid use disorder (OUD) patients on methadone.Materials and Methods: We administered a modified 11-item telemedicine satisfaction questionnaire after the initial HCV telemedicine evaluation, when initiating HCV treatment, and 3 months post-HCV treatment completion. Among a patient subset, a semistructured interview further assessed issues of participant referral to the telemedicine program as well as convenience and confidentiality with the telemedicine encounters.Results: Patients demonstrated their acceptance of telemedicine-based encounters by referral of additional participants. They highlighted the convenience of on-site treatment with a liver specialist through recognition of the benefit of "one-stop shopping." They also expressed confidence in the privacy and confidentiality of telemedicine encounters.Discussion: In this pilot study, telemedicine appears to be well accepted as a modality for HCV management among OUD patients on methadone. Virtual integration of medical and behavioral therapy through telemedicine warrants further investigation for its use in this population.Conclusions: In this pilot study, we found that a largely racial minority population of substance users grew to accept telemedicine over time with diminished privacy and confidentiality concerns. Telemedicine was well accepted within the OTP community as reflected by participant referral to the program.

Non-Quadratic Distances in Model Assessment.

One natural way to measure model adequacy is by using statistical distances as loss functions. A related fundamental question is how to construct loss functions that are scientifically and statistically meaningful. In this paper, we investigate non-quadratic distances and their role in assessing the adequacy of a model and/or ability to perform model selection. We first present the definition of a statistical distance and its associated properties. Three popular distances, total variation, the mixture index of fit and the Kullback-Leibler distance, are studied in detail, with the aim of understanding their properties and potential interpretations that can offer insight into their performance as measures of model misspecification. A small simulation study exemplifies the performance of these measures and their application to different scientific fields is briefly discussed.

Paritaprevir and Ritonavir Liver Concentrations in Rats as Assessed by Different Liver Sampling Techniques.

The liver is crucial to pharmacology, yet substantial knowledge gaps exist in the understanding of its basic pharmacologic processes. An improved understanding for humans requires reliable and reproducible liver sampling methods. We compared liver concentrations of paritaprevir and ritonavir in rats by using samples collected by fine-needle aspiration (FNA), core needle biopsy (CNB), and surgical resection. Thirteen Sprague-Dawley rats were evaluated, nine of which received paritaprevir/ritonavir at 30/20 mg/kg of body weight by oral gavage daily for 4 or 5 days. Drug concentrations were measured using liquid chromatography-tandem mass spectrometry on samples collected via FNA (21G needle) with 1, 3, or 5 passes (FNA1, FNA3, and FNA5); via CNB (16G needle); and via surgical resection. Drug concentrations in plasma were also assessed. Analyses included noncompartmental pharmacokinetic analysis and use of Bland-Altman techniques. All liver tissue samples had higher paritaprevir and ritonavir concentrations than those in plasma. Resected samples, considered the benchmark measure, resulted in estimations of the highest values for the pharmacokinetic parameters of exposure (maximum concentration of drug in serum [Cmax] and area under the concentration-time curve from 0 to 24 h [AUC0-24]) for paritaprevir and ritonavir. Bland-Altman analyses showed that the best agreement occurred between tissue resection and CNB, with 15% bias, followed by FNA3 and FNA5, with 18% bias, and FNA1 and FNA3, with a 22% bias for paritaprevir. Paritaprevir and ritonavir are highly concentrated in rat liver. Further research is needed to validate FNA sampling for humans, with the possible derivation and application of correction factors for drug concentration measurements.

Multivariate semiparametric control charts for mixed-type data.

A useful tool that has gained popularity in the Quality Control area is the control chart which monitors a process over time, identifies potential changes, understands variations, and eventually improves the quality and performance of the process. This article introduces a new class of multivariate semiparametric control charts for monitoring multivariate mixed-type data, which comprise both continuous and discrete random variables (rvs). Our methodology leverages ideas from clustering and Statistical Process Control to develop control charts for MIxed-type data. We propose four control chart schemes based on modified versions of the KAy-means for MIxed LArge KAMILA data clustering algorithm, where we assume that the two existing clusters represent the reference and the test sample. The charts are semiparametric, the continuous rvs follow a distribution that belongs in the class of elliptical distributions. Categorical scale rvs follow a multinomial distribution. We present the algorithmic procedures and study the characteristics of the new control charts. The performance of the proposed schemes is evaluated on the basis of the False Alarm Rate and in-control Average Run Length. Finally, we demonstrate the effectiveness and applicability of our proposed methods utilizing real-world data.

Social determinants of health derived from people with opioid use disorder: Improving data collection, integration and use with cross-domain collaboration and reproducible, data-centric, notebook-style workflows.

Deriving social determinants of health from underserved populations is an important step in the process of improving the well-being of these populations and in driving policy improvements to facilitate positive change in health outcomes. Collection, integration, and effective use of clinical data for this purpose presents a variety of specific challenges. We assert that combining expertise from three distinct domains, specifically, medical, statistical, and computer and data science can be applied along with provenance-aware, self-documenting workflow tools. This combination permits data integration and facilitates the creation of reproducible workflows and usable (reproducible) results from the sensitive and disparate sources of clinical data that exist for underserved populations.

Surveillance for Guillain-Barré syndrome after influenza vaccination among the Medicare population, 2009-2010.

OBJECTIVES: We implemented active surveillance for Guillain-Barré syndrome (GBS) following seasonal or H1N1 influenza vaccination among the Medicare population during the 2009-2010 influenza season. METHODS: We used weekly Medicare claims data to monitor vaccinations and subsequent hospitalizations with principal diagnosis code for GBS within 42 days. Group sequential testing assessed whether the observed GBS rate exceeded a critical limit based on the expected rate from 5 previous years adjusted for claims delay. We evaluated the lag between date of service and date of claims availability and used it for adjustment. RESULTS: By July 30, 2010 (after 26 interim surveillance tests), 14.0 million seasonal and 3.3 million H1N1 vaccinations had accrued. Taking into account claims delay appropriately lowered the critical limit during early monitoring. The observed GBS rate was below the critical limit throughout the surveillance. CONCLUSIONS: Medicare data contributed rapid safety monitoring among millions of 2009-2010 influenza vaccine recipients. Adjustment for claims delay facilitates early detection of potential safety issues. Although limited by lack of medical record review to confirm cases, this claims-based surveillance did not indicate a statistically significant elevated GBS rate following seasonal or H1N1 influenza vaccination.

Innovations in education: A prospective study of storytelling narratives to enhance hepatitis C virus knowledge among substance users.

BACKGROUND: Even though substance users have the highest hepatitis C virus (HCV) burden, many lack knowledge about the infection. Lack of knowledge is an important obstacle to pursuing HCV care. Although printed materials are conventionally utilized to disseminate HCV-related knowledge, narrative story-telling videos may be an alternative. Data are extremely limited, however, in the ability of storytelling videos to increase HCV knowledge among substance users. In this study, we hypothesized that a story-telling narrative video would increase substance user's immediate and 1-month HCV-related knowledge compared to a printed format. AIM: To assess immediate and 1-month HCV-related knowledge retention among substance users comparing education delivered via a storytelling narrative video compared to a printed format. METHODS: We conducted a prospective matched, case-control study among substance users actively prescribed buprenorphine enrolled from two sites. The intervention site received the video and the control site, the brochure. Participants (n = 176) were matched on age, gender, and race. We obtained extensive patient and stakeholder input on the video's design, validated the video's content, and developed a recruitment plan to guide participant enrollment. Knowledge was assessed by administration of a 25-item instrument immediately before, immediately after, or one month after the intervention. Data were analyzed using nonparametric and generalized linear mixed-effects models. RESULTS: We recruited a total of 176 substance users, 90 and 86 individuals, from each site, respectively. One-month follow up occurred in 92% and 94% of enrollees in the control and intervention groups, respectively. In comparison with the pre-intervention scores, immediate knowledge recall increased significantly for both the intervention (P < 0.0001) and control (P < 0.0001) groups. Multivariate modeling revealed a significant improvement in HCV-related knowledge and retention (P = 0.033) among participants who viewed the storytelling video. CONCLUSION: Storytelling narratives emphasizing HCV education appear to be an effective method to increase HCV-related knowledge among substance users. They should become an educational cornerstone to promote HCV management among this population.

Patient-centered HCV care via telemedicine for individuals on medication for opioid use disorder: Telemedicine for Evaluation, Adherence and Medication for Hepatitis C (TEAM-C).

BACKGROUND: Telemedicine has the potential to increase healthcare access especially for vulnerable populations. Telemedicine for Evaluation, Adherence, and Medication for Hepatitis C (TEAM-C) is comparing telemedicine access to specialty medical care to usual care for management of hepatitis C virus (HCV) infection among persons with opioid use disorder (PWOUD). PWOUD have the highest hepatitis C virus (HCV) prevalence and incidence, yet they infrequently receive HCV care. The study objectives are to compare access to specialty care via telemedicine to offsite specialty referral (usual care) on 1) treatment initiation, completion, and sustained virological response, 2) patient satisfaction with health care delivery, and 3) HCV reinfection after successful HCV cure. METHODS: TEAM-C is a multi-site, non-blinded, randomized pragmatic clinical trial conducted at 12 opioid treatment programs (OTP) throughout New York State that utilizes the stepped-wedge design. The unit of randomization is the OTP with a total sample size of 624 participants. HCV-infected PWOUD were treated via telemedicine or referral. Telemedicine encounters are conducted onsite in the OTP with co-administration of direct acting antivirals for HCV with medications for opioid use disorder. The primary outcome is undetectable HCV RNA obtained 12 weeks post-treatment cessation. We also follow participants for two years to assess for reinfection. CONCLUSIONS: The study utilizes a rigorous study design to evaluate the effectiveness and implementation of virtual treatment for HCV integrated into behavioral treatment. We demonstrate the feasibility, engagement principles and lessons learned from the initial prospective randomized trial of telemedicine targeted to a vulnerable population.

A platform for processing expression of short time series (PESTS).

BACKGROUND: Time course microarray profiles examine the expression of genes over a time domain. They are necessary in order to determine the complete set of genes that are dynamically expressed under given conditions, and to determine the interaction between these genes. Because of cost and resource issues, most time series datasets contain less than 9 points and there are few tools available geared towards the analysis of this type of data. RESULTS: To this end, we introduce a platform for Processing Expression of Short Time Series (PESTS). It was designed with a focus on usability and interpretability of analyses for the researcher. As such, it implements several standard techniques for comparability as well as visualization functions. However, it is designed specifically for the unique methods we have developed for significance analysis, multiple test correction and clustering of short time series data. The central tenet of these methods is the use of biologically relevant features for analysis. Features summarize short gene expression profiles, inherently incorporate dependence across time, and allow for both full description of the examined curve and missing data points. CONCLUSIONS: PESTS is fully generalizable to other types of time series analyses. PESTS implements novel methods as well as several standard techniques for comparability and visualization functions. These features and functionality make PESTS a valuable resource for a researcher's toolkit. PESTS is available to download for free to academic and non-profit users at http://www.mailman.columbia.edu/academic-departments/biostatistics/research-service/software-development.

Time-series clustering of gene expression in irradiated and bystander fibroblasts: an application of FBPA clustering.

BACKGROUND: The radiation bystander effect is an important component of the overall biological response of tissues and organisms to ionizing radiation, but the signaling mechanisms between irradiated and non-irradiated bystander cells are not fully understood. In this study, we measured a time-series of gene expression after α-particle irradiation and applied the Feature Based Partitioning around medoids Algorithm (FBPA), a new clustering method suitable for sparse time series, to identify signaling modules that act in concert in the response to direct irradiation and bystander signaling. We compared our results with those of an alternate clustering method, Short Time series Expression Miner (STEM). RESULTS: While computational evaluations of both clustering results were similar, FBPA provided more biological insight. After irradiation, gene clusters were enriched for signal transduction, cell cycle/cell death and inflammation/immunity processes; but only FBPA separated clusters by function. In bystanders, gene clusters were enriched for cell communication/motility, signal transduction and inflammation processes; but biological functions did not separate as clearly with either clustering method as they did in irradiated samples. Network analysis confirmed p53 and NF-κB transcription factor-regulated gene clusters in irradiated and bystander cells and suggested novel regulators, such as KDM5B/JARID1B (lysine (K)-specific demethylase 5B) and HDACs (histone deacetylases), which could epigenetically coordinate gene expression after irradiation. CONCLUSIONS: In this study, we have shown that a new time series clustering method, FBPA, can provide new leads to the mechanisms regulating the dynamic cellular response to radiation. The findings implicate epigenetic control of gene expression in addition to transcription factor networks.

Selecting information in electronic health records for knowledge acquisition.

Knowledge acquisition of relations between biomedical entities is critical for many automated biomedical applications, including pharmacovigilance and decision support. Automated acquisition of statistical associations from biomedical and clinical documents has shown some promise. However, acquisition of clinically meaningful relations (i.e. specific associations) remains challenging because textual information is noisy and co-occurrence does not typically determine specific relations. In this work, we focus on acquisition of two types of relations from clinical reports: disease-manifestation related symptom (MRS) and drug-adverse drug event (ADE), and explore the use of filtering by sections of the reports to improve performance. Evaluation indicated that applying the filters improved recall (disease-MRS: from 0.85 to 0.90; drug-ADE: from 0.43 to 0.75) and precision (disease-MRS: from 0.82 to 0.92; drug-ADE: from 0.16 to 0.31). This preliminary study demonstrates that selecting information in narrative electronic reports based on the sections improves the detection of disease-MRS and drug-ADE types of relations. Further investigation of complementary methods, such as more sophisticated statistical methods, more complex temporal models and use of information from other knowledge sources, is needed.

Peripheral CXCR3-associated chemokines as biomarkers of fibrosis in chronic hepatitis C virus infection.

BACKGROUND: CXCR3-associated chemokines CXCL9-CXCL11 promote histologic progression in chronic hepatitis C virus (HCV) infection, as indicated by elevated intrahepatic levels of messenger RNA in patients with advanced inflammation and fibrosis. We evaluated the potential of peripheral chemokine levels to discriminate among patients with chronic HCV infection who had different stages of fibrosis. METHODS: Peripheral levels of CXCR3-associated chemokines were measured by enzyme-linked immunosorbent assay of plasma samples obtained from 93 patients with chronic HCV infection. Of the subjects, 79 (85%) were white, and 68 (73%) were infected with HCV genotype 1. RESULTS: Expression of all 3 chemokines, when analyzed as a group, was significantly associated with intrahepatic inflammation and fibrosis. Plasma levels of CXCL10 were significantly elevated in patients with advanced fibrosis, whereas CXCL9 levels were significantly elevated in patients with advanced inflammation. By proportional odds multivariate modeling, we observed an association between fibrosis and CXCL10 (P< .002) as well as between fibrosis and inflammation (P<.001). Of the individual parameters, the CXCL10 level was most useful in identifying patients with more-severe (stage 3-4) fibrosis. Discriminatory ability was improved by the combination of CXCL10 and CXCL9. CONCLUSIONS: The strong association between CXCR3-associated chemokines and fibrosis suggests that they may have promise as noninvasive markers of hepatic fibrosis in a predominantly white HCV genotype 1-infected population.