Assessment of the Frequency, Phenotypes, and Outcomes of Acute Liver Injury Associated with Amoxicillin/Clavulanate in 1.4 Million Patients in the Veterans Health Administration.

INTRODUCTION: Drug-induced liver injury is a significant health issue, yet the exposure-based incidence remains to be characterized. OBJECTIVE: We aimed to assess the frequency, phenotypes, and outcomes of acute liver injury associated with amoxicillin/clavulanate using a large electronic health record system. METHODS: Using the Veterans Health Administration electronic health record system, we developed the framework to identify unexplained acute liver injury, defined by alanine aminotransferase and/or alkaline phosphatase elevation temporally linked to prescription records of amoxicillin/clavulanate, a major culprit of clinically significant drug-induced liver injury, excluding other competing causes. The population was subcategorized by pre-existing liver conditions and inpatient status at the time of exposure for the analysis. RESULTS: Among 1,445,171 amoxicillin/clavulanate first exposures in unique individuals [92% men; mean age (standard deviation): 59 (15) years], 6476 (incidence: 0.448%) acute liver injuries were identified. Of these, 4427 (65%) had alternative causes, yielding 2249 (incidence: 0.156%) with unexplained acute liver injuries. The incidence of unexplained acute liver injury was lowest in outpatients without underlying liver disease (0.067%) and highest in inpatients with pre-existing liver conditions (0.719%). Older age, male sex, and American Indian or Alaska Native (vs White) were associated with a higher incidence of unexplained acute liver injury. Cholestatic injury affected 74%, exhibiting a higher frequency with advanced age, inpatient exposure, and pre-existing liver conditions. Hepatocellular injury with bilirubin elevation affected 0.003%, with a higher risk at age >45 years. During a 12-month follow-up, patients with unexplained acute liver injury had a higher adjusted overall mortality risk than those without evident acute liver injury. CONCLUSIONS: This framework identifies unexplained acute liver injury following drug exposure in large electronic health record datasets. After validating in other systems, this framework can aid in deducing drug-induced liver injury in the general patient population and regulatory decision making to promote drug safety and public health.

Gibbs distribution for statistical analysis of graphical data with a sample application to fcMRI brain images.

This paper develops object-oriented data analysis (OODA) statistical methods that are novel and complementary to existing methods of analysis of human brain scan connectomes, defined as graphs representing brain anatomical or functional connectivity. OODA is an emerging field where classical statistical approaches (e.g., hypothesis testing, regression, estimation, and confidence intervals) are applied to data objects such as graphs or functions. By analyzing data objects directly we avoid loss of information that occurs when data objects are transformed into numerical summary statistics. By providing statistical tools that analyze sets of connectomes without loss of information, new insights into neurology and medicine may be achieved. In this paper we derive the formula for statistical model fitting, regression, and mixture models; test their performance in simulation experiments; and apply them to connectomes from fMRI brain scans collected during a serial reaction time task study. Software for fitting graphical object-oriented data analysis is provided.

Potential impact of HITECH security regulations on medical imaging.

Title XIII of Division A and Title IV of Division B of the American Recovery and Reinvestment Act (ARRA) of 2009 [1] include a provision commonly referred to as the "Health Information Technology for Economic and Clinical Health Act" or "HITECH Act" that is intended to promote the electronic exchange of health information to improve the quality of health care. Subtitle D of the HITECH Act includes key amendments to strengthen the privacy and security regulations issued under the Health Insurance Portability and Accountability Act (HIPAA). The HITECH act also states that "the National Coordinator" must consult with the National Institute of Standards and Technology (NIST) in determining what standards are to be applied and enforced for compliance with HIPAA. This has led to speculation that NIST will recommend that the government impose the Federal Information Security Management Act (FISMA) [2], which was created by NIST for application within the federal government, as requirements to the public Electronic Health Records (EHR) community in the USA. In this paper we will describe potential impacts of FISMA on medical image sharing strategies such as teleradiology and outline how a strict application of FISMA or FISMA-based regulations could have significant negative impacts on information sharing between care providers.