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BMC Medical Informatics and Decision Making
Published in: BMC Medical Informatics and Decision Making 1/2014

Open Access 01-12-2014 | Research article

Adverse drug events with hyperkalaemia during inpatient stays: evaluation of an automated method for retrospective detection in hospital databases

Authors: Grégoire Ficheur, Emmanuel Chazard, Jean-Baptiste Beuscart, Béatrice Merlin, Michel Luyckx, Régis Beuscart

Published in: BMC Medical Informatics and Decision Making | Issue 1/2014

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Abstract

Background

Adverse drug reactions and adverse drug events (ADEs) are major public health issues. Many different prospective tools for the automated detection of ADEs in hospital databases have been developed and evaluated. The objective of the present study was to evaluate an automated method for the retrospective detection of ADEs with hyperkalaemia during inpatient stays.

Methods

We used a set of complex detection rules to take account of the patient’s clinical and biological context and the chronological relationship between the causes and the expected outcome. The dataset consisted of 3,444 inpatient stays in a French general hospital. An automated review was performed for all data and the results were compared with those of an expert chart review. The complex detection rules’ analytical quality was evaluated for ADEs.

Results

In terms of recall, 89.5% of ADEs with hyperkalaemia “with or without an abnormal symptom” were automatically identified (including all three serious ADEs). In terms of precision, 63.7% of the automatically identified ADEs with hyperkalaemia were true ADEs.

Conclusions

The use of context-sensitive rules appears to improve the automated detection of ADEs with hyperkalaemia. This type of tool may have an important role in pharmacoepidemiology via the routine analysis of large inter-hospital databases.
Appendix
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Literature
1.
Kohn LT, Corrigan JM, Donaldson MS: To Err Is Human: Building a Safer Health System. Volume 627. 2000, National Academies Press
2.
Lazarou J: Incidence of adverse drug reactions in hospitalized patients: a meta-analysis of prospective studies. JAMA. 1998, 279: 1200-1205.CrossRefPubMed
3.
Juntti-Patinen L, Neuvonen P: Drug-related deaths in a university central hospital. Eur J Clin Pharmacol. 2002, 58: 479-482.CrossRefPubMed
4.
Michel P, Lathelize M, Bru-Sonnet R, Domecq S, Kret M, Quenon JL: Enquête Nationale sur les Evénements Indésirables graves liés aux Soins 2009 (ENEIS2): description des résultats 2009. Rapport final à la DREES (Ministère du travail, de l’emploi et de la Santé)–Février 2011.
5.
International Drug Monitoring: The role of the hospital. World Health Organ Tech Rep Ser. 1969, 425: 5-24.
6.
Bates DW, Cullen DJ, Laird N, Petersen LA, Small SD, Servi D, Laffel G, Sweitzer BJ, Shea BF, Hallisey R, Vander Vliet M, Nemeskal R, Leape LL: Incidence of adverse drug events and potential adverse drug events: implications for prevention. JAMA J Am Med Assoc. 1995, 274: 29-34.CrossRef
7.
8.
Ponce SP, Jennings AE, Madias NE, Harrington JT: Drug-induced hyperkalemia. Medicine (Baltimore). 1985, 64: 357-370.CrossRef
9.
Alvarez-Requejo A, Carvajal A, Bégaud B, Moride Y, Vega T, Arias LHM: Under-reporting of adverse drug reactions estimate based on a spontaneous reporting scheme and a sentinel system. Eur J Clin Pharmacol. 1998, 54: 483-488.CrossRefPubMed
10.
Hazell L, Shakir SAW: Under-reporting of adverse drug reactions: a systematic review. Drug Saf. 2006, 29: 385-396.CrossRefPubMed
11.
Harpaz R, DuMouchel W, Shah NH, Madigan D, Ryan P, Friedman C: Novel data-mining methodologies for adverse drug event discovery and analysis. Clin Pharmacol Ther. 2012, 91: 1010-1021.CrossRefPubMedPubMedCentral
12.
Chazard E, Ficheur G, Bernonville S, Luyckx M, Beuscart R: Data mining to generate adverse drug events detection rules. IEEE Trans Inf Technol Biomed. 2011, 15: 823-830.CrossRefPubMed
13.
Brown JS, Kulldorff M, Chan KA, Davis RL, Graham D, Pettus PT, Andrade SE, Raebel MA, Herrinton L, Roblin D, Boudreau D, Smith D, Gurwitz JH, Gunter MJ, Platt R: Early detection of adverse drug events within population-based health networks: application of sequential testing methods. Pharmacoepidemiol Drug Saf. 2007, 16: 1275-1284.CrossRefPubMed
14.
Berlowitz DR, Miller DR, Oliveria SA, Cunningham F, Gomez-Caminero A, Rothendler JA: Differential associations of beta-blockers with hemorrhagic events for chronic heart failure patients on warfarin. Pharmacoepidemiol Drug Saf. 2006, 15: 799-807.CrossRefPubMed
15.
Schildcrout JS, Haneuse S, Peterson JF, Denny JC, Matheny ME, Waitman LR, Miller RA: Analyses of longitudinal, hospital clinical laboratory data with application to blood glucose concentrations. Stat Med. 2011, 30: 3208-3220.CrossRefPubMedPubMedCentral
16.
17.
Chazard E, Merlin B, Ficheur G, Sarfati JC, Beuscart R: Detection of adverse drug events: proposal of a data model. Stud Health Technol Inform. 2009, 148: 63-74.PubMed
18.
Ficheur G, Chazard E, Schaffar A, Genty M, Beuscart R: Interoperability of medical databases: construction of mapping between hospitals laboratory results assisted by automated comparison of their distributions. AMIA Annu Symp Proc. 2011, 2011: 392-401.PubMedPubMedCentral
19.
Chazard E, Mouret C, Ficheur G, Schaffar A, Beuscart J-B, Beuscart R: Proposal and evaluation of FASDIM, a Fast And Simple De-Identification Method for unstructured free-text clinical records. Int J Med Inform. 2014, 83: 303-312.CrossRefPubMed
20.
Bates DW, Evans RS, Murff H, Stetson PD, Pizziferri L, Hripcsak G: Detecting adverse events using information technology. J Am Med Inform Assoc. 2003, 10: 115-128.CrossRefPubMedPubMedCentral
21.
Handler SM, Altman RL, Perera S, Hanlon JT, Studenski SA, Bost JE, Saul MI, Fridsma DB: A systematic review of the performance characteristics of clinical event monitor signals used to detect adverse drug events in the hospital setting. J Am Med Inform Assoc. 2007, 14: 451-458.CrossRefPubMedPubMedCentral
22.
Dormann H, Criegee-Rieck M, Neubert A, Egger T, Levy M, Hahn EG, Brune K: Implementation of a computer-assisted monitoring system for the detection of adverse drug reactions in gastroenterology. Aliment Pharmacol Ther. 2004, 19: 303-309.CrossRefPubMed
23.
Brown S, Black K, Mrochek S, Wood A, Bess T, Cobb J, Francis J: RADARx: recognizing, assessing, and documenting adverse Rx events. Proc AMIA Symp. 2000, 101-105.
24.
Raschke RA, Gollihare B, Wunderlich TA, Guidry JR, Leibowitz AI, Peirce JC, Lemelson L, Heisler MA, Susong C: A computer alert system to prevent injury from adverse drug events. JAMA. 1998, 280: 1317-1320.CrossRefPubMed
25.
Ryan PB, Stang PE, Overhage JM, Suchard MA, Hartzema AG, DuMouchel W, Reich CG, Schuemie MJ, Madigan D: A comparison of the empirical performance of methods for a risk identification system. Drug Saf. 2013, 36: 143-158.CrossRef
26.
Schedlbauer A, Prasad V, Mulvaney C, Phansalkar S, Stanton W, Bates DW, Avery AJ: What evidence supports the use of computerized alerts and prompts to improve clinicians’ prescribing behavior?. J Am Med Inform Assoc. 2009, 16: 531-538.CrossRefPubMedPubMedCentral
27.
Kuperman GJ, Bobb A, Payne TH, Avery AJ, Gandhi TK, Burns G, Classen DC, Bates DW: Medication-related clinical decision support in computerized provider order entry systems: a review. J Am Med Inform Assoc. 2007, 14: 29-40.CrossRefPubMedPubMedCentral
28.
Gandhi TK, Weingart SN, Seger AC, Borus J, Burdick E, Poon EG, Leape LL, Bates DW: Outpatient prescribing errors and the impact of computerized prescribing. J Gen Intern Med. 2005, 20: 837-841.CrossRefPubMedPubMedCentral
29.
Bates DW, O’Neil AC, Boyle D, Teich J, Chertow GM, Komaroff AL, Brennan TA: Potential identifiability and preventability of adverse events using information systems. J Am Med Inform Assoc. 1994, 1: 404-411.CrossRefPubMedPubMedCentral
30.
Kuperman GJ, Bates DW, Teich JM, Schneider JR, Cheiman D: A new knowledge structure for drug-drug interactions. Proc Annu Symp Comput Appl Med Care. 1994, 836-840.
31.
Jha AK, Kuperman GJ, Teich JM, Leape L, Shea B, Rittenberg E, Burdick E, Seger DL, Vander Vliet M, Bates DW: Identifying adverse drug events: development of a computer-based monitor and comparison with chart review and stimulated voluntary report. J Am Med Inform Assoc. 1998, 5: 305-314.CrossRefPubMedPubMedCentral
32.
Field TS, Gurwitz JH, Harrold LR, Rothschild JM, Debellis K, Seger AC, Fish LS, Garber L, Kelleher M, Bates DW: Strategies for detecting adverse drug events among older persons in the ambulatory setting. J Am Med Inform Assoc. 2004, 11: 492-498.CrossRefPubMedPubMedCentral
33.
Agbabiaka TB, Savović J, Ernst E: Methods for causality assessment of adverse drug reactions: a systematic review. Drug Saf. 2008, 31: 21-37.CrossRefPubMed
34.
Naranjo CA, Busto U, Sellers EM, Sandor P, Ruiz I, Roberts EA, Janecek E, Domecq C, Greenblatt DJ: A method for estimating the probability of adverse drug reactions. Clin Pharmacol Ther. 1981, 30: 239-245.CrossRefPubMed
35.
Kramer MS, Leventhal JM, Hutchinson TA, Feinstein AR: An algorithm for the operational assessment of adverse drug reactions: I. Background, description, and instructions for use. JAMA. 1979, 242: 623-632.CrossRefPubMed
36.
Bégaud B, Evreux JC, Jouglard J, Lagier G: [Imputation of the unexpected or toxic effects of drugs. Actualization of the method used in France]. Therapie. 1985, 40: 111-118.PubMed
37.
38.
39.
40.
R Core Team: R: A Language and Environment for Statistical Computing. 2013, Vienna, Austria: R Foundation for Statistical Computing
41.
Chazard E, Băceanu A, Ferret L, Ficheur G: The ADE scorecards: a tool for adverse drug event detection in electronic health records. Stud Health Technol Inform. 2011, 166: 169-179.PubMed
42.
Yoon D, Park MY, Choi NK, Park BJ, Kim JH, Park RW: Detection of adverse drug reaction signals using an electronic health records database: Comparison of the Laboratory Extreme Abnormality Ratio (CLEAR) algorithm. Clin Pharmacol Ther. 2012, 91: 467-474.CrossRefPubMed
43.
Koutkias V, Kilintzis V, Stalidis G, Lazou K, Collyda C, Chazard E, McNair P, Beuscart R, Maglaveras N: Constructing clinical decision support systems for adverse drug event prevention: a knowledge-based approach. AMIA Annu Symp Proc. 2010, 2010: 402-406.PubMedPubMedCentral
44.
Jha AK, Laguette J, Seger A, Bates DW: Can surveillance systems identify and avert adverse drug events? A prospective evaluation of a commercial application. J Am Med Inform Assoc. 2008, 15: 647-653.CrossRefPubMedPubMedCentral
Metadata
Title
Adverse drug events with hyperkalaemia during inpatient stays: evaluation of an automated method for retrospective detection in hospital databases
Authors
Grégoire Ficheur
Emmanuel Chazard
Jean-Baptiste Beuscart
Béatrice Merlin
Michel Luyckx
Régis Beuscart
Publication date
01-12-2014
Publisher
BioMed Central
Published in
BMC Medical Informatics and Decision Making / Issue 1/2014
Electronic ISSN: 1472-6947
DOI
https://doi.org/10.1186/1472-6947-14-83

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