Using Real-time Data Management to increase patient safety

Using Real-time Data Management to increase patient safety

Data integrity is among one of the main aspects of clinical research. Data management is an indispensable part of any clinical research that contributes to good data quality. Proper data management involves a series of activities – from case report form (CRF) and study database development, randomization, source data integration, data query generation, data cleaning and data validation to finally database locking.

Advancements in technology have improved data capture proficiency, making more prompt and authentic information available to drug developers. Especially, electronic data capture (EDC) systems electronically collect patient data directly from an investigative site and data integration software combine electronic data from number of sources in real time. With the help of these technological advancements Drug Developers have access to more complete and higher-quality data quickly. Electronic data capture and electronic data review software enable drug developers to review data right after it is captured, allowing them to improve the process and result of clinical trials and proactively managing patient safety, quality, and risk.

Identifying patient safety concerns during clinical trials is of preeminent importance, and the earlier issues related with developmental treatments are marked and addressed, the better. The continuous review of patient safety data throughout a trial plays an important role in ability to monitor patient safety events, build a safety profile, identify early safety signals and check compliance with protocol. The faster this information is made available, the quicker sponsors and regulatory authorities will be able to stop a study that is found to increase risk in patients, or to establish additional safety measures for their protection.

The utility of real-time data with regard to evaluating safety is illustrated with the application of Hy’s Rule , which is used during drug development to decide whether a drug could cause fatal damage to liver or sufficient damage to liver that require liver transplant. Most of drugs causing severe liver damage do so rarely, and the cases of damage are generally not noticed during the course of a trial. Access to accruing laboratory data enables faster detection of damage indicating markers (eg, alanine aminotransferase (ALT) and aspartate aminotransferase (AST), formerly referred to as the SGPT and SGOT) that cannot be explained by any other causes, therefore, playing an important role in assessing the toxicity of a drug. Therefore real time data capture is important to obtain data generated from patients’ medical history, concomitant medications, vital signs, laboratory and diseases, and AEs.

Early detection depends not only on access to data in real time, but on the ability to integrate information that has already been stored in separate locations so that a much informative reports can be created. Reliable reports made available shortly after data capture are critical to the flow of useful information, and are substantial for all levels of study management to take action when safety concerns arise. Graphical representation are specifically helpful in identifying patterns and to evaluate temporal relationships for individual patients. These review tools depend upon real-time data that allow drug developers to recognize possible safety concerns as they emerge, rather than months after the case. The most common problem of DMC (data monitoring committee) is to tackle with is having a very limited window of information because of which they are unable to search into specific patients and information related. These interactive tools help DMC to perform these reviews more effectively and thoroughly. As an additional protection, accumulating safety information can be linked and compared with safety data from previously completed studies, essentially forming a dynamic, integrated summary of safety. In this way, drug developers are more quickly able to identify and monitor for adverse events and safety flags, making the clinical trial more informative with respect to a drug’s relative risk/ benefit. As information are generated, researchers are aware of the totality of safety information available for the drug being studied by integrating it with previously acquired information. Adverse events in excess of previous studies might be an indicator for a serious safety issue which require to scrutinize the data more thoroughly.

References:                                                                          

  1. Shzarfman A. Use of Screening Algorithms and Computer Systems to Efficiently Signal Higher-Than-Expected Combinations of Drugs and Events in the US FDA’s Spontaneous Reports Database: 19th DIA Annual Euro meeting. Vienna, Austria, March 26–28, 2007
  2. Trontell AE. How the US food and drug administration defines and detects adverse drug events. Curr Ther Res Clin Exp 2001; 62: 641–9
  3. FDA: Guidance for Industry Drug-Induced Liver Injury: Premarketing Clinical Evaluation, http://www.fda.gov/downloads/Drugs/.../Guidances/UCM174090.pdf
  4. FDA: The Establishment and Operation of Clinical Trial Data Monitoring Committees for Clinical Trial Sponsors, http://www.fda.gov/downloads/RegulatoryInformation/Guidances/ucm127073.pdf
  5. Szarfman A. The application of bayesian data mining and graphic visualization tools to screen FDA’s spontaneous reporting system database. Proceedings of the Section on Bayesian Statistical Science, 2000. American Statistical Association, 2000: 67–71