Literature DB >> 26576325

From Pharmacovigilance to Clinical Care Optimization.

Leo Anthony Celi1, Edward Moseley2, Christopher Moses3, Padhraig Ryan4, Melek Somai5, David Stone6, Kai-Ou Tang7.   

Abstract

In order to ensure the continued, safe administration of pharmaceuticals, particularly those agents that have been recently introduced into the market, there is a need for improved surveillance after product release. This is particularly so because drugs are used by a variety of patients whose particular characteristics may not have been fully captured in the original market approval studies. Even well-conducted, randomized controlled trials are likely to have excluded a large proportion of individuals because of any number of issues. The digitization of medical care, which yields rich and accessible drug data amenable to analytic techniques, provides an opportunity to capture the required information via observational studies. We propose the development of an open, accessible database containing properly de-identified data, to provide the substrate for the required improvement in pharmacovigilance. A range of stakeholders could use this to identify delayed and low-frequency adverse events. Moreover, its power as a research tool could extend to the detection of complex interactions, potential novel uses, and subtle subpopulation effects. This far-reaching potential is demonstrated by our experience with the open Multi-parameter Intelligent Monitoring in Intensive Care (MIMIC) intensive care unit database. The new database could also inform the development of objective, robust clinical practice guidelines. Careful systematization and deliberate standardization of a fully digitized pharmacovigilance process is likely to save both time and resources for healthcare in general.

Entities:  

Year:  2014        PMID: 26576325      PMCID: PMC4630790          DOI: 10.1089/big.2014.0008

Source DB:  PubMed          Journal:  Big Data        ISSN: 2167-6461            Impact factor:   2.128


  22 in total

1.  Optimisation versus certainty: understanding the issue of heterogeneity in economic evaluation.

Authors:  Warren Stevens; Charles Normand
Journal:  Soc Sci Med       Date:  2004-01       Impact factor: 4.634

2.  "Big data" in the intensive care unit. Closing the data loop.

Authors:  Leo Anthony Celi; Roger G Mark; David J Stone; Robert A Montgomery
Journal:  Am J Respir Crit Care Med       Date:  2013-06-01       Impact factor: 21.405

3.  Why we can't trust clinical guidelines.

Authors:  Jeanne Lenzer
Journal:  BMJ       Date:  2013-06-14

4.  The promise of pharmacoepidemiology in helping clinicians assess drug risk.

Authors:  Jerry Avorn
Journal:  Circulation       Date:  2013-08-13       Impact factor: 29.690

5.  Leverage hadoop framework for large scale clinical informatics applications.

Authors:  Xiao Dong; Neil Bahroos; Eugene Sadhu; Tommie Jackson; Morris Chukhman; Robert Johnson; Andrew Boyd; Denise Hynes
Journal:  AMIA Jt Summits Transl Sci Proc       Date:  2013-03-18

Review 6.  Informatic tools and approaches in postmarketing pharmacovigilance used by FDA.

Authors:  Joyce Weaver; Mary Willy; Mark Avigan
Journal:  AAPS J       Date:  2008-01-25       Impact factor: 4.009

7.  Clinical trial evidence supporting FDA approval of novel therapeutic agents, 2005-2012.

Authors:  Nicholas S Downing; Jenerius A Aminawung; Nilay D Shah; Harlan M Krumholz; Joseph S Ross
Journal:  JAMA       Date:  2014 Jan 22-29       Impact factor: 56.272

8.  Public preferences about secondary uses of electronic health information.

Authors:  David Grande; Nandita Mitra; Anand Shah; Fei Wan; David A Asch
Journal:  JAMA Intern Med       Date:  2013-10-28       Impact factor: 21.873

9.  Leveraging a critical care database: selective serotonin reuptake inhibitor use prior to ICU admission is associated with increased hospital mortality.

Authors:  Marzyeh Ghassemi; John Marshall; Nakul Singh; David J Stone; Leo Anthony Celi
Journal:  Chest       Date:  2014-04       Impact factor: 9.410

10.  Dynamic clinical data mining: search engine-based decision support.

Authors:  Leo Anthony Celi; Andrew J Zimolzak; David J Stone
Journal:  JMIR Med Inform       Date:  2014-06-23
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  2 in total

Review 1.  State of the art review: the data revolution in critical care.

Authors:  Marzyeh Ghassemi; Leo Anthony Celi; David J Stone
Journal:  Crit Care       Date:  2015-03-16       Impact factor: 9.097

2.  Artificial intelligence, machine learning and health systems.

Authors:  Trishan Panch; Peter Szolovits; Rifat Atun
Journal:  J Glob Health       Date:  2018-12       Impact factor: 4.413

  2 in total

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