Evaluation of drug interactions and dosing in 484 neurological inpatients using clinical decision support software and an extended operational interaction classification system (Zurich Interaction System)

Guzek, Markus; Zorina, Olesya I.; Semmler, Alexander; Gonzenbach, Roman; Huber, Martin; Kullak‐Ublick, Gerd A.; Weller, Michael; Russmann, Stefan

doi:10.1002/pds.2197

Cited by 17 publications

(22 citation statements)

References 34 publications

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“…Subsequently, we used this system for an evaluation of the CDSS MediQ in neurological inpatients. 20 The current study extends our previous work for a comparative evaluation of another CDSS regarding the identification of clinically relevant drug interactions.…”

Section: Introductionsupporting

confidence: 62%

“…The studied patients were identical with the ones in our previous study, and the demographics, medical diagnoses, and pharmacotherapy as well as the data collection procedures of this population are described there in detail . Briefly, we included 484 patients admitted to the neurological ward of the University Hospital Zurich during the year 2007.…”

Section: Methodsmentioning

confidence: 99%

“…ZHIAS is a four‐dimensional drug interaction classification system with particular emphasis of patient management . The first dimension uses the well‐established five‐level grading according to the ORCA criteria .…”

Section: Methodsmentioning

confidence: 99%

“…Although several structured drug interaction assessment systems have aimed to particularly consider clinical relevance and management, [13][14][15] no system has been recognized as the "gold standard." [16][17][18] To address this issue, we recently developed an extended multidimensional operational classification system, the Zurich Interaction System (ZHIAS), [19][20][21] which incorporates the wellestablished Operational Classification of Drug Interactions (ORCA) 13 plus three additional dimensions that contain information on mechanisms of interaction, management options, and expected adverse outcomes. Subsequently, we used this system for an evaluation of the CDSS MediQ in neurological inpatients.…”

Section: Introductionmentioning

confidence: 99%

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Comparative evaluation of the drug interaction screening programs MediQ and ID PHARMA CHECK in neurological inpatients

Zorina

Haueis

Semmler

et al. 2012

Pharmacoepidemiology and Drug

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Section: Introductionsupporting

confidence: 62%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Comparative evaluation of the drug interaction screening programs MediQ and ID PHARMA CHECK in neurological inpatients

Zorina

Haueis

Semmler

et al. 2012

Pharmacoepidemiology and Drug

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show abstract

“…More than 75% of majorly severe interactions are considered manageable [8]; 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) [9,10]. Another management-oriented classification system is based on types of adverse drug reactions [8].…”

Section: Introductionmentioning

confidence: 99%

Validation of a transparent decision model to rate drug interactions

Far

Curkovic

Byrne

et al. 2012

BMC Pharmacol Toxicol

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BackgroundMultiple databases provide ratings of drug-drug interactions. The ratings are often based on different criteria and lack background information on the decision making process. User acceptance of rating systems could be improved by providing a transparent decision path for each category.MethodsWe rated 200 randomly selected potential drug-drug interactions by a transparent decision model developed by our team. The cases were generated from ward round observations and physicians’ queries from an outpatient setting. We compared our ratings to those assigned by a senior clinical pharmacologist and by a standard interaction database, and thus validated the model.ResultsThe decision model rated consistently with the standard database and the pharmacologist in 94 and 156 cases, respectively. In two cases the model decision required correction. Following removal of systematic model construction differences, the DM was fully consistent with other rating systems.ConclusionThe decision model reproducibly rates interactions and elucidates systematic differences. We propose to supply validated decision paths alongside the interaction rating to improve comprehensibility and to enable physicians to interpret the ratings in a clinical context.

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Comparative evaluation of three clinical decision support systems: prospective screening for medication errors in 100 medical inpatients

Fritz

Ceschi

Curkovic

et al. 2012

Eur J Clin Pharmacol

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PURPOSE: Clinical decision support systems (CDSS) are promoted as powerful screening tools to improve pharmacotherapy. The aim of our study was to evaluate the potential contribution of CDSS to patient management in clinical practice. METHODS: We prospectively analyzed the pharmacotherapy of 100 medical inpatients through the parallel use of three CDSS, namely, Pharmavista, DrugReax, and TheraOpt. After expert discussion that also considered all patient-specific clinical information, we selected apparently relevant alerts, issued suitable recommendations to physicians, and recorded subsequent prescription changes. RESULTS: For 100 patients with a median of eight concomitant drugs, Pharmavista, DrugReax, and TheraOpt generated a total of 53, 362, and 328 interaction alerts, respectively. Among those we identified and forwarded 33 clinically relevant alerts to the attending physician, resulting in 19 prescription changes. Four adverse drug events were associated with interactions. The proportion of clinically relevant alerts among all alerts (positive predictive value) was 5.7, 8.0, and 7.6%, and the sensitivity to detect all 33 relevant alerts was 9.1, 87.9, and 75.8% for Pharmavista, DrugReax and TheraOpt, respectively. TheraOpt recommended 31 dose adjustments, of which we considered 11 to be relevant; three of these were followed by dose reductions. CONCLUSIONS: CDSS are valuable screening tools for medication errors, but only a small fraction of their alerts appear relevant in individual patients. In order to avoid overalerting CDSS should use patient-specific information and management-oriented classifications. Comprehensive information should be displayed on-demand, whereas a limited number of computer-triggered alerts that have management implications in the majority of affected patients should be based on locally customized and supported algorithms. Methods We prospectively analyzed the pharmacotherapy of 100 medical inpatients through the parallel use of the CDSS Pharmavista, DrugReax and TheraOpt. After expert discussion that also considered all patient-specific clinical information we selected apparently relevant alerts, issued according recommendations to physicians and recorded subsequent prescription changes.Results For 100 patients with a median of eight concomitant drugs Pharmavista, DrugReax and TheraOpt generated a total of 53, 362 and 328 interaction alerts, respectively. Among those we identified and forwarded 33 clinically relevant alerts that were followed by 19 according prescription changes. Four adverse drug events were associated with interactions.The proportion of clinically relevant alerts among all alerts (positive predictive value) was 5.7, 8.0 and 7.6%, and the sensitivity to detect all 33 relevant alerts 9.1, 87.9 and 75.8% for Pharmavista, DrugReax and TheraOpt, respectively. TheraOpt recommended 31 dose adjustments, of which we considered 11 as relevant, and three were followed by dose reductions.Conclusions CDSS are valuable screening tools for medication errors, but on...

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Evaluation of drug interactions and dosing in 484 neurological inpatients using clinical decision support software and an extended operational interaction classification system (Zurich Interaction System)

Cited by 17 publications

References 34 publications

Comparative evaluation of the drug interaction screening programs MediQ and ID PHARMA CHECK in neurological inpatients

Comparative evaluation of the drug interaction screening programs MediQ and ID PHARMA CHECK in neurological inpatients

Validation of a transparent decision model to rate drug interactions

Comparative evaluation of three clinical decision support systems: prospective screening for medication errors in 100 medical inpatients

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