World Journal of Pharmacology and Toxicology
Open Access

Algorithm; Severity; Validation; Drug; Interaction; Decision; Model; Mmx

Introduction

The management of adverse drug events (ADEs) is an important issue in healthcare. While some ADEs are unpredictable (e.g. anaphylaxis), ADEs caused by drug-drug interactions (DDI) are likely to be preventable. Nevertheless, DDIs continue to present a major problem in medical treatment [1]. One Swiss study estimated that 17% of all ADEs occurring in hospitalized patients are provoked by DDIs, while a Dutch study found that 28% of patients admitted to the hospital experienced at least one DDI. Clinical decision support software (CDSS) has been used as a supportive measure to improve medication safety [2]. The information provided by CDSS focuses on management advice rather than alerts, since more prevalent alerts may dominate less common but equally dangerous ones [3].

In the past, DDIs were classified according to their potential severity e.g. minor, moderate, or major. In 2001 a new managementoriented approach to DDI classification was advanced by Hansten and Horn [4]. More than 75% of majorly severe interactions are considered manageable; therefore this approach seems reasonable. Recently, a separate group in our department developed ZHIAS (Zurich Interaction System), an extension of the clinical management approach, which is based on Operational Classification of Drug Interactions (ORCA). Another management-oriented classification system is based on types of adverse drug reactions [5]. Even with multiple classifications being available, the assessment of DDIs depends on both the experience and the interpretation of the assessor as well as the sources of information used in the assessment. The discrepancies between different DDI ratings are well-documented [6].

Design of Decision Model

In designing the DM, we developed a listing of binary questions which we viewed would effect on the interplay rating. Similar questions had been developed iteratively, and six units of clinically applicable questions had been finally retained. The questions have been evaluated concerning their relevance to a strong and understandable DDI ranking system. The sequential order of the six binary questions used to be permuted via an assessment crew consisting of one pharmacist, two scientific pharmacologists and one physician, till consensus related to the ranking result of the DM was once achieved [7].

The interplay rankings have been standardized to make sure consistency in ranking effects via specific physicians/pharmacists. The DDI ranking used to be designed for integration into a community of extra choice guide systems, such as patient-specific chance elements (e.g. historical age, obesity, or renal insufficiency) or drug-disease nation contraindications [8], whereas the DM refers to the lowrisk regular population. A serious destructive match is described as a life-threatening or debilitating event, ensuing in death, inpatient hospitalization or prolongation of current hospitalization, or chronic or large disability/incapacity. Risk/benefit defines the stability between the effectiveness of a remedy and the chance of damage as distinctive by way of the World Health Organization Uppsala Monitoring Centre (WHO-UMC) in Sweden [9].

Validation of Decision Model

In our learn about we randomly chosen 200 conceivable drug interactions and in contrast the man or woman ranking results generated by means of three unique ranking methods [10]. Clinical relevance of the drug interactions was once assessed from queries acquired at the Department of Clinical Pharmacology and Toxicology at the University Hospital in Zurich, raised by means of pharmacists and medical doctors in principal and secondary care and from ward rounds at the University Hospital. In the first ranking method, one pharmacist utilized our DM to manually fee the 200 interactions [11]. The scores have been then reviewed and revised for plausibility via a group comprising two medical pharmacologists and one physician. The 2nd ranking was once carried out via an unbiased senior medical pharmacologist who used to be blinded with recognize to the DM and who assigned every interplay ranking based totally on her medical ride and knowledge [12]. The scientific pharmacologist used to be now not authorised use of an interplay database, however was once allowed get entry to handy scientific sources such as PubMed database, Excerpta Medica database (Embase), European Public Assessment Reports (EPARs) and précis of product characteristics [13]. The equal records sources have been on hand to the pharmacist. In the 1/3 ranking method, a health practitioner rated the 200 interactions the usage of the commercially reachable MMX database [14].

Statistical Methods

The concordance between all three rankings used to be decided the use of cross-tables, collectively with every day and weighted Cohen’s Kappa coefficients. Cohen’s Kappa measures the extent to which any two ranking structures agree by means of danger alone [15]. It stages from zero (agreement no higher than chance) to one (perfect agreement). In the tables, values adjoining to the diagonal (ratings differing through a single category) are viewed much less serious than deviations of two or greater categories. Cohen’s Kappa evaluates inter-rater settlement as follows: 0.01–0.2 moderate agreement; 0.21–0.40 truthful agreement; 0.41–0.60 reasonable agreement; 0.61–0.80 massive settlement and 0.81–1 ideal agreement [16]. To discover systematic variations between the ranking systems, Bland–Altman plots, which illustrate settlement limits, had been constructed. Identified systematic differences have been reviewed in my view by means of the aforementioned overview group and have been excluded from in addition analysis. The relative frequencies and intervals of the ultimate disagreements had been decided via the Wilson method [17].

The pharmacist, medical doctor and the medical pharmacologist independently assessed all instances of attainable drug interactions (n= 200). 62 of the interactions yielded no statistics from MMX concerning viable DDIs. The rankings evaluated with the aid of the pharmacist and the medical pharmacologist ranged from DM: B (precautionary measures) to DM: E (contraindicated) [18].

Discussion

We evaluated an obvious choice mannequin that reproducibly charges drug interactions and identifies systematic ranking discrepancies. Altman suggests that kappa is the gorgeous skill of judging settlement or reproducibility between classification classes acquired through two exceptional ranking techniques and is supported by way of the greater weighted Kappa values, which bolstered the strategy in the existing study. No systematic variations confirmed up on the Bland–Altman plot of DM versus MMX, following elimination of the systematic differences. Divergence in choice making stays a problem and assessment of sure instances is unavoidable. The evaluation time, however, decreases as an end result of the standardization. When evaluating two ratings, our visualization of the selection course allows fast comprehension of one facet of the differences, accordingly clarifying (at least partially) the ranking discrepancies. Such transparency improves the medical fee of the interpretation of the rating. To our knowledge, we put up the first visualized selection mannequin that is similar with different ratings. Previously posted ratings, although primarily based on professional crew decisions, are now not guided by means of detailed policies of an algorithm. The output of the choice model, corrected for systematic variations between ranking systems, intently resembles that of different ratings.

Conclusion

The choice mannequin reproducibly charges interactions and identifies systematic differences. Ratings are based totally on integral indications of medical significance, namely; the threat of an SAE, the extent of scientific intervention required, the medical surveillance required the existence of a safer choice and the risk-benefit ratio. The choice mannequin is constant with different ranking systems, following elimination of systematic variations between methods. We advise to furnish the choice route alongside the interplay rating, to facilitate ranking comprehensibility and to verify mortality and morbidity quotes in a medical setting. If elements such as size of medical institution continue to be or chance of problems are elevated through the usage of the model, then the mannequin represents a large enhance over present models.

Acknowledgement

I would like to acknowledge Division of General Medicine and Primary Care, Beth Israel Deaconess Medical Center, Boston, MA, USA for providing an opportunity to do research.

Conflict of Interest

The authors declare that they have no conflicts of interest.

1. Bates DW, Spell N, Cullen DJ, Burdick E, Laird N, et al. (1997) The costs of adverse drug events in hospitalized patients. Adverse drug events prevention study group. JAMA 277: 307-311.

Indexed at, Google Scholar, Crossref

2. Juurlink D, Mamdani M, Iazzetta J, Etchells E (2004) Avoiding drug interactions in hospitalized patients. Healthc Q 7: 27-28.

Indexed at, Google Scholar, Crossref

3. Schlienger R, Lampert M, Haschke M, Drewe J, Krahenbuhl S (2007) Drug-related problems in hospitals: a review of the recent literature. Drug Saf 30: 379-407.

Indexed at, Google Scholar, Crossref

4. Zwart-van Rijkom JE, Uijtendaal EV, ten Berg MJ, van Solinge WW, Egberts AC (2009) Frequency and nature of drug-drug interactions in a Dutch university hospital. Br J Clin Pharmacol 68: 187-193.

Indexed at, Google Scholar, Crossref

5. Bates DW, Leape LL, Cullen DJ, Laird N, Petersen LA, et al. (1998) Effect of computerized physician order entry and a team intervention on prevention of serious medication errors. JAMA 280: 1311-1316.

Indexed at, Google Scholar, Crossref

6. Garg AX, Adhikari NK, McDonald H, Rosas-Arellano MP, Devereaux PJ, et al. (2005) Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: a systematic review. JAMA 293: 1223-1238.

Indexed at, Google Scholar, Crossref

7. Hansten PD, Horn JR, Hazlet TK (2001) ORCA: Operational Classification of drug interactions. J Am Pharm Assoc (Wash) 41: 161-165.

Indexed at, Google Scholar, Crossref

8. Bergk V, Gasse C, Rothenbacher D, Loew M, Brenner H, et al. (2004) Drug interactions in primary care: impact of a new algorithm on risk determination. Clin Pharmacol Ther 76: 85-96.

Indexed at, Google Scholar, Crossref

9. Frolich T, Zorina O, Fontana AO, Kullak-Ublick GA, Vollenweider A, et al. (2011) Evaluation of medication safety in the discharge medication of 509 surgical inpatients using electronic prescription support software and an extended operational interaction classification. Eur J Clin Pharmacol 67: 1273-1282.

Indexed at, Google Scholar, Crossref

10. Vitry AI (2007) Comparative assessment of four drug interaction compendia. Br J Clin Pharmacol 63: 709-714.

Indexed at, Google Scholar, Crossref

11. Chan A, Tan SH, Wong CM, Yap KY, Ko Y (2009) Clinically significant drug-drug interactions between oral anticancer agents and non-anticancer agents: a Delphi survey of oncology pharmacists. Clin Ther 31: 2379-2386.

Indexed at, Google Scholar, Crossref

12. Olvey EL, Clauschee S, Malone DC (2010) Comparison of critical drug-drug interaction listings: the department of veterans affairs medical system and standard reference compendia. Clin Pharmacol Ther 87: 48-51.

Indexed at, Google Scholar, Crossref

13. Tollefson G, Lesar T, Grothe D, Garvey M (1984) Alprazolam-related digoxin toxicity. Am J Psychiatry. 141: 1612-1613.

Indexed at, Google Scholar, Crossref

14. Ochs HR, Greenblatt DJ, Verburg-Ochs B (1985) Effect of alprazolam on digoxin kinetics and creatinine clearance. Clin Pharmacol Ther 38: 595-598.

Indexed at, Google Scholar, Crossref

15. Isaac T, Weissman JS, Davis RB, Massagli M, Cyrulik A, et al. Overrides of medication alerts in ambulatory care. Arch Intern Med 169: 305-311.

Indexed at, Google Scholar, Crossref

16. Smithburger PL, Buckley MS, Bejian S, Burenheide K, Kane-Gill SL (2011) A critical evaluation of clinical decision support for the detection of drug-drug interactions. Expert Opin Drug Saf 10: 871-882.

Indexed at, Google Scholar, Crossref

17. Weingart SN, Seger AC, Feola N, Heffernan J, Schiff G, et al. (2011) Electronic drug interaction alerts in ambulatory care: the value and acceptance of high-value alerts in US medical practices as assessed by an expert clinical panel. Drug Saf 34: 587-593.

Indexed at, Google Scholar, Crossref

18. Seidling HM, Seger DL, Paterno MD, Haefeli WE, Bates DW (2011) Factors influencing alert acceptance: a novel approach for predicting the success of clinical decision support. J Am Med Inform Assoc 18: 479-484.

Indexed at, Google Scholar, Crossref