Retrospective cohort observation on psychotropic drug-drug interaction and identification utility from 3 databases: Drugs.com®, Lexicomp®, and Epocrates®

Abstract

Background Pharmacotherapy is necessary for many people with psychiatric disorders and polypharmacy is common. The psychotropic drug-drug interaction (DDI) should be concerned and efficiently monitored by a proper instrument. Objectives This study aimed to investigate the prevalence and associated factors of psychotropic DDI and to compare the identification utility from three databases: Drugs.com®, Lexicomp®, and Epocrates®. Methods This was a retrospective cohort design. We collected demographic and clinical data of all patients hospitalised in the psychiatric inpatient unit in 2020. Psychotropic DDI profiles were examined through three databases. Descriptive statistics were used to report comprehensiveness of each database and prevalence of psychotropic DDI. The Fleiss’ kappa index would be analysed to indicate agreement strength of DDI severity classification among three databases. Results From 149 total admissions, the psychotropic DDIs were found in 148 admissions (99.3%). Thorough the study, there were 182 of both psychotropic and other agents prescribed under 1,357 prescriptions. In total, 2,825 psychotropic DDIs were identified by using Drugs.com® 2,500 times, Epocrates® 2,269 times, and Lexicomp® 2,265 times. Interactions with clonazepam was the three most frequent agents when co-administrated with quetiapine (n = 56), risperidone (n = 36), and valproic acid and derivatives (n = 36). Serious DDIs were comparatively lower in incidence and there was no evidence of its association with reported clinical adverse consequences. The study revealed slight and fair agreement regarding severity classification among the three databases was found. DDI events detected by Drugs.com® were greatest in number, but Lexicomp® provided the broadest list of medications prescribed in our study. Conclusion Among three databases, interactions detected by Drugs.com® were greatest in number, whereas Lexicomp® provided the broadest list of medications. Development of such databases, based on both theoretical and clinical conceptions, should be focused to balance safety of patients and weariness of healthcare providers.

Drug–Drug Interactions in Patients with Acute Respiratory Distress Syndrome

“…However, severe and clinically relevant pDDIs are usually reliably identified by all databases. This study, besides analyzing pDDIs in a patient population for which no data were available, focused on the comparison of two different methodological approaches, rather than the comparison of different databases, especially as such comparative studies have already been conducted and published by others [ 41 , 42 , 43 ]. We did not consider organ function nor perform therapeutic drug monitoring.…”

Categorical Analysis of Database Consistency in Reporting Drug–Drug Interactions for Cardiovascular Diseases

“…An indicator of EDIC inconsistency is the number of drugs shared by their databases, which consequently affects the number of DDIs in each EDIC [ 22 , 23 , 24 , 25 , 26 , 27 , 28 ]. A straightforward method to evaluate the agreement between EDICs and their databases is the percentage comparison of DDIs classified on different levels of severity [ 12 , 23 , 24 , 25 , 26 , 27 , 29 , 30 ].…”

“…A straightforward method to evaluate the agreement between EDICs and their databases is the percentage comparison of DDIs classified on different levels of severity [ 12 , 23 , 24 , 25 , 26 , 27 , 29 , 30 ]. A more elaborated method is calculating kappa and weighted kappa Fleiss’ coefficients as quantitative measures of agreement between online drug interaction checkers [ 11 , 22 , 28 , 31 , 32 ]. Some studies have combined the two methods [ 9 , 10 , 33 ], and others have used the Jaccard similarity coefficient to assess global agreement between drug databases [ 20 ].…”

“…Some studies have combined the two methods [ 9 , 10 , 33 ], and others have used the Jaccard similarity coefficient to assess global agreement between drug databases [ 20 ]. The literature also discloses wide variations in the number of checked DDIs and EDICs: 15 drug pairs checked using 3 EDICs [ 34 ], 100 and 125 drug pairs tested using 6 EDICs [ 10 , 33 ], 1393 and 1382 DDIs checked in the 2021 and 2022 successive versions of 4 EDICs [ 11 ], and around 2500 DDIs generated by 182 drugs checked against 3 EDICs [ 28 ].…”