Comparative Performance of Two Drug Interaction Screening Programmes Analysing a Cross-Sectional Prescription Dataset of 84,625 Psychiatric Inpatients

Abstract

BACKGROUND: Clinical decision support software (CDSS) solutions can automatically identify drug interactions and thereby aim to improve drug safety. However, data on the comparative performance of different CDSS to detect and appropriately classify interactions in real-life prescription datasets is limited. OBJECTIVE: The aim of this study was to compare the results from two different CDSS analysing the pharmacotherapy of a large population of psychiatric inpatients for drug interactions. METHODS: We performed mass analyses of cross-sectional patient-level prescriptions from 84,625 psychiatric inpatients using two CDSS -MediQ and ID PHARMA CHECK(®). Interactions with the highest risk ratings and the most frequent ratings were reclassified according to the Zurich Interaction System (ZHIAS), a multidimensional classification that incorporates the OpeRational ClassificAtion of Drug Interactions (ORCA) and served as a reference standard. RESULTS: MediQ reported 6,133 unique interacting combinations responsible for 270,617 alerts affecting 63,454 patients. ID PHARMA CHECK(®) issued 5,400 interactions and 157,489 alerts in 48,302 patients. Only 2,154 unique interactions were identified by both programmes, but overlap increased with higher risk rating. MediQ reported high-risk interactions in 2.5 % of all patients, compared with 5 % according to ID PHARMA CHECK(®). The positive predictive value for unique major alerts to be (provisionally) contraindicated according to ORCA was higher for MediQ (0.63) than for either of the two ID PHARMA CHECK(®) components (0.42 for hospINDEX and 0.30 for ID MACS). MediQ reported more interactions, and ID PHARMA CHECK(®) tended to classify interactions into a higher risk class, but overall both programmes identified a similar number of (provisionally) contraindicated interactions according to ORCA criteria. Both programmes identified arrhythmia as the most frequent specific risk associated with interactions in psychiatric patients. CONCLUSIONS: CDSS can be used for mass-analysis of prescription data and thereby support quality management. However, in clinical practice CDSS impose an overwhelming alert burden on the prescriber, and prediction of clinical relevance remains a major challenge. Only a small subset of yet to be determined alerts appears suitable for automated display in clinical routine. DOI: https://doi.org/10.1007/s40264-013-0027-9Posted at the Zurich Open Repository and Archive, University of Zurich ZORA URL: https://doi.org/10.5167/uzh-76756 Accepted Version Originally published at: Zorina, Olesya I; Haueis, Patrick; Greil, Waldemar; Grohmann, Renate; Kullak-Ublick, Gerd A; Russmann, Stefan (2013). Comparative performance of two drug interaction screening programmes analysing a cross-sectional prescription dataset of 84,625 psychiatric inpatients. Drug Safety, 36(4):247-258.

Clinical decision support systems differ in their ability to identify clinically relevant drug interactions of immunosuppressants in kidney transplant patients

“…found sensitivities of 0·71 and 0·72 using pDDIs classified as OpeRational ClassificAtion of drug interactions (ORCA) levels 1–3 as part of the Zürich Interaction System as a silver standard. When changing the silver standard to ORCA 1–2 as part of Zürich Interaction System, sensitivity was 0·52 for ID PHARMA CHECK ® and 0·94 for mediQ . Data indicate the dependance of sensitivity on the definition of clinical relevance as discussed.…”

“…Clinical decision support systems (CDSSs) can be successfully used to reduce serious potential DDIs (pDDIs) . However, a series of studies have shown that CDSSs differ widely in their ability to identify pDDIs . Vitry et al .…”

“…11 However, a series of studies have shown that CDSSs differ widely in their ability to identify pDDIs. [12][13][14][15][16][17][18][19] Vitry et al 13 found that out of a set of DDIs classified as major in any one of four compendia, between 14% and 44% were not listed in the other compendia. According to Vonbach et al, 16 only 11% of 157 DDIs were identified by all four CDSSs included in the study.…”

Potential Drug interactions with Drugs used for Bipolar Disorder: A Comparison of 6 Drug Interaction Database Programs

“…Multiple resources were used to select the 125 drug interaction pairs. These include studies of potential DDI detected in various healthcare settings [11,[39][40][41][42][43][44], reviews of potential DDI involving psychiatric drugs [23,27,28,[45][46][47], lists of serious drug interactions used in prior testing of drug interaction database programs [48][49][50], and lists of frequently prescribed drugs [51,52]. All 125 drug interaction pairs had at least 1 category rating of major from at least one of the 6 drug interaction database programs.…”

The American Psychiatric Association Practice Guidelines for the Psychiatric Evaluation of Adults

“…Medications used to treat medical conditions can interact with psychotropic medications (Ferrando et al 2010;Sinclair et al 2010;Zorina et al 2013). Many individuals receiving psychiatric treatment are taking multiple medications, and this magnifies the likelihood of drug-drug interactions (Haueis et al 2011;Mojtabai and Olfson 2010;Sandson et al 2005;Thomas et al 2010).…”