Development of statistical methodologies and risk models to perform real-time safety monitoring in interventional cardiology

Thesis (S.M.)--Harvard-MIT Division of Health Sciences and Technology, 2006.Vita.Includes bibliographical references (p. 52-56).Post-marketing surveillance of medical pharmaceuticals and devices has received a great deal of media, legislative, and academic attention in the last decade. Among medical devices, these have largely been due to a small number of highly publicized adverse events, some of them in the domain of cardiac surgery and interventional cardiology. Phase three clinical trials for these devices are generally underpowered to detect rare adverse event rates, are performed in near-optimal environments, and regulators face significant pressure to deliver important medical devices to the public in a timely fashion. All of these factors emphasize the importance of systematic monitoring of these devices after being released to the public, and the FDA and other regulatory agencies continue to struggle to perform this duty using a variety of voluntary and mandatory adverse event rate reporting policies. Data quality and comprehensiveness have generally suffered in this environment, and delayed awareness of potential problems. However, a number of mandatory reporting policies combined with improved standardization of data collection and definitions in the field of interventional cardiology and other clinical domains have provided recent opportunities for nearly "real-time" safety monitoring of medical device data.(cont.) Existing safety monitoring methodologies are non-medical in nature, and not well adapted to the relatively heterogeneous and noisy data common in medical applications. A web-based database-driven computer application was designed, and a number of experimental statistical methodologies were adapted from non-medical monitoring techniques as a proof of concept for the utility of an automated safety monitoring application. This application was successfully evaluated by comparing a local institution's drug-eluting stent in-hospital mortality rates to University of Michigan's bare-metal stent event rates. Sensitivity analyses of the experimental methodologies were performed, and a number of notable performance parameters were discovered. In addition, an evaluation of a number of well-validated external logistic regression models, and found that while population level estimation was well-preserved, individual estimation was compromised by application to external data. Subsequently, exploration of an alternative modeling technique, support vector machines, was performed in an effort to find a method with superior calibration performance for use in the safety monitoring application.by Michael E. Matheny.S.M

Large-scale prediction of adverse drug reactions using chemical, biological, and phenotypic properties of drugs

Abstract Objective Adverse drug reaction (ADR) is one of the major causes of failure in drug development. Severe ADRs that go undetected until the post-marketing phase of a drug often lead to patient morbidity. Accurate prediction of potential ADRs is required in the entire life cycle of a drug, including early stages of drug design, different phases of clinical trials, and post-marketing surveillance. Methods Many studies have utilized either chemical structures or molecular pathways of the drugs to predict ADRs. Here, the authors propose a machine-learning-based approach for ADR prediction by integrating the phenotypic characteristics of a drug, including indications and other known ADRs, with the drug's chemical structures and biological properties, including protein targets and pathway information. A large-scale study was conducted to predict 1385 known ADRs of 832 approved drugs, and five machine-learning algorithms for this task were compared. Results This evaluation, based on a fivefold cross-validation, showed that the support vector machine algorithm outperformed the others. Of the three types of information, phenotypic data were the most informative for ADR prediction. When biological and phenotypic features were added to the baseline chemical information, the ADR prediction model achieved significant improvements in area under the curve (from 0.9054 to 0.9524), precision (from 43.37% to 66.17%), and recall (from 49.25% to 63.06%). Most importantly, the proposed model successfully predicted the ADRs associated with withdrawal of rofecoxib and cerivastatin. Conclusion The results suggest that phenotypic information on drugs is valuable for ADR prediction. Moreover, they demonstrate that different models that combine chemical, biological, or phenotypic information can be built from approved drugs, and they have the potential to detect clinically important ADRs in both preclinical and post-marketing phases.This study was supported in part by grants from the NHLBI 5U19HL065962 and the NCI R01CA141307. ML is supported by the NLM training grant 3T15LM007450-08S1. JS is partially supported by the 2010 NARSAD Young Investigator Award. ZZ is partially supported by the 2009 NARSAD Maltz Investigator Award. MM is supported by a Veterans Administration HSR&D Career Development Award (CDA-08-020)