OBJECTIVE It is vital to detect the full safety profile of a drug throughout its market life. Current pharmacovigilance systems still have substantial limitations, however. The objective of our work is to demonstrate the feasibility of using natural language processing (NLP), the comprehensive Electronic Health Record (EHR), and association statistics for pharmacovigilance purposes. DESIGN Narrative discharge summaries were collected from the Clinical Information System at New York Presbyterian Hospital (NYPH). MedLEE, an NLP system, was applied to the collection to identify medication events and entities which could be potential adverse drug events (ADEs). Co-occurrence statistics with adjusted volume tests were used to detect associations between the two types of entities, to calculate the strengths of the associations, and to determine their cutoff thresholds. Seven drugs/drug classes (ibuprofen, morphine, warfarin, bupropion, paroxetine, rosiglitazone, ACE inhibitors) with known ADEs were selected to evaluate the system. RESULTS One hundred thirty-two potential ADEs were found to be associated with the 7 drugs. Overall recall and precision were 0.75 and 0.31 for known ADEs respectively. Importantly, qualitative evaluation using historic roll back design suggested that novel ADEs could be detected using our system. CONCLUSIONS This study provides a framework for the development of active, high-throughput and prospective systems which could potentially unveil drug safety profiles throughout their entire market life. Our results demonstrate that the framework is feasible although there are some challenging issues. To the best of our knowledge, this is the first study using comprehensive unstructured data from the EHR for pharmacovigilance.

Chemokines, chemotactic cytokines, may promote hepatic inflammation in chronic hepatitis C virus (HCV) infection through the recruitment of lymphocytes to the liver parenchyma. We evaluated the association between inflammation and fibrosis and CXCR3-associated chemokines, interferon-␥ (IFN-␥)-inducible protein 10 (IP-10/CXCL10), monokine induced by IFN-␥ (Mig/CXCL9), and interferon-inducible T cell ␣ chemoattractant (I-TAC/ CXCL11), in HCV infection. Intrahepatic mRNA expression of these chemokines was analyzed in 106 chronic HCV-infected patients by real-time PCR. The intrahepatic localization of chemokine producer cells and CXCR3 ؉ lymphocytes was determined in selected patients by immunohistochemistry. We found elevated intrahepatic mRNA expression of all three chemokines, most markedly CXCL10, in chronic HCV-infected patients with higher necroinflammation and fibrosis. By multivariable multivariate analysis, intrahepatic CXCL10 mRNA expression levels were significantly associated with lobular necroinflammatory grade and HCV genotype 1. In the lobular region, CXCL10-expressing and CXCL9-expressing hepatocytes predominated in areas with necroinflammation. Strong CXCL11 expression was observed in almost all portal tracts, whereas CXCL9 expression varied considerably among portal tracts in the same individual. Most intrahepatic lymphocytes express the CXCR3 receptor, and the number of CXCR3 ؉ lymphocytes was increased in patients with advanced necroinflammation. Conclusion: These findings suggest that the CXCR3-associated chemokines, particularly CXCL10, may play an important role in the development of necroinflammation and fibrosis in the liver parenchyma in chronic HCV infection.

Pegylated interferon (PEG-IFN) has become standard therapy for hepatitis C virus (HCV) infection. We evaluated whether PEG-IFN pharmacodynamics and pharmacokinetics account for differences in treatment outcome and whether these parameters might be predictors of therapeutic outcome. Twenty-four IFN-naïve, HCV/human immunodeficiency virus-coinfected patients received PEG-IFN ␣-2b (1.5 g/kg) once weekly plus daily ribavirin (1,000 H epatitis C virus (HCV) and human immunodeficiency virus (HIV) are both parenterally transmitted viruses that coinfect more than 250,000 individuals in the United States. 1 In HCV monoinfected patients, viral eradication occurs in approximately 55% of those treated with current standard therapy: weekly pegylated interferon (PEG-IFN) in combination with daily ribavirin (RBV). 2,3 In HIV/HCV-coinfected patients, successful therapeutic outcomes are substantially lower, ranging from 27% to 40% overall and from 14% to 29% for difficult-to-treat genotype 1 patients. [4][5][6] Consequently, understanding why HIV/HCV-coinfected patients respond to current treatment and how to improve responses is of clinical importance. A fundamental question is whether increased serum IFN ␣ concentrations result in improved therapeutic outcomes.One method of assessing the effectiveness of anti-HCV treatment is the analysis of HCV RNA decay using mathematical models. 7-11 These models have generally not incorporated the effects of time-varying IFN concentrations. PEG-IFN ␣-2b concentration, however, wanes to a Abbreviations: PEG-IFN, pegylated interferon; HCV, hepatitis C virus; SVR, sustained virological responder; NR, nonresponder; HIV, human immunodeficiency virus; RBV, ribavirin; ETR, end-of-treatment responder. From the

Linear models with error components are widely used to analyze panel data. Some applications of these models require knowledge of the probability densities of the error components. Existing methods handle this requirement by assuming that the densities belong to known parametric families of distributions (typically the normal distribution). This paper shows how to carry out nonparametric estimation of the densities of the error components, thereby avoiding the assumption that the densities belong to known parametric families. The nonparametric estimators are applied to an earnings model using data from the Current Population Survey. The model's transitory error component is not normally distributed. Use of the nonparametric density estimators yields estimates of the probability that individuals with low earnings will become high earners in the future that are much lower than the estimates obtained under the assumption of normally distributed error components.

No abstract