Transdisciplinary approach for potentially harmful drug–drug interaction detection as a part of a comprehensive medication review and geriatric assessment

2019 ◽  
Vol 19 (5) ◽  
pp. 462-463
Author(s):  
Naoki Tomita ◽  
Fumihiro Mizokami ◽  
Shigeki Kisara ◽  
Hiroyuki Arai
Keyword(s):  
Drug Interaction ◽  
Geriatric Assessment ◽  
Medication Review ◽  
Transdisciplinary Approach ◽  
Interaction Detection ◽  
Drug Drug Interaction

Comparison of eight screening tools to detect interactions between herbal supplements and oncology agents

2020 ◽  
Vol 26 (8) ◽  
pp. 1843-1849
Author(s):  
Faisal Shakeel ◽  
Fang Fang ◽  
Kelley M Kidwell ◽  
Lauren A Marcath ◽  
Daniel L Hertz
Keyword(s):  
Drug Interaction ◽  
Positive Predictive Value ◽  
Drug Interactions ◽  
Predictive Value ◽  
Screening Tools ◽  
Herbal Supplements ◽  
Interaction Detection ◽  
Natural Medicine ◽  
Interaction Screening ◽  
Drug Drug Interaction

Introduction Patients with cancer are increasingly using herbal supplements, unaware that supplements can interact with oncology treatment. Herb–drug interaction management is critical to ensure optimal treatment outcomes. Several screening tools exist to detect drug–drug interactions, but their performance to detect herb–drug interactions is not known. This study compared the performance of eight drug–drug interaction screening tools to detect herb–drug interaction with anti-cancer agents. Methods The herb–drug interaction detection performance of four subscription (Micromedex, Lexicomp, PEPID, Facts & Comparisons) and free (Drugs.com, Medscape, WebMD, RxList) drug–drug interaction tools was assessed. Clinical relevance of each herb–drug interaction was determined using Natural Medicine and each drug–drug interaction tool. Descriptive statistics were used to calculate sensitivity, specificity, positive predictive value, and negative predictive value. Linear regression was used to compare performance between subscription and free tools. Results All tools had poor sensitivity (<0.20) for detecting herb–drug interaction. Lexicomp had the highest positive predictive value (0.98) and best overall performance score (0.54), while Medscape was the best performing free tool (0.52). The worst subscription tools were as good as or better than the best free tools, and as a group subscription tools outperformed free tools on all metrics. Using an average subscription tool would detect one additional herb–drug interaction for every 10 herb–drug interactions screened by a free tool. Conclusion Lexicomp is the best available tool for screening herb–drug interaction, and Medscape is the best free alternative; however, the sensitivity and performance for detecting herb–drug interaction was far lower than for drug–drug interactions, and overall quite poor. Further research is needed to improve herb–drug interaction screening performance.

scholarly journals Analysis of overridden alerts in a drug-drug interaction detection system

2008 ◽  
Vol 20 (6) ◽  
pp. 400-405 ◽  
Author(s):  
F. Mille ◽  
C. Schwartz ◽  
F. Brion ◽  
J.-E. Fontan ◽  
O. Bourdon ◽  
...  
Keyword(s):  
Drug Interaction ◽  
Detection System ◽  
Interaction Detection ◽  
Drug Drug Interaction

scholarly journals Structure-Based Drug-Drug Interaction Detection via Expressive Graph Convolutional Networks and Deep Sets (Student Abstract)

2020 ◽  
Vol 34 (10) ◽  
pp. 13927-13928
Author(s):  
Mengying Sun ◽  
Fei Wang ◽  
Olivier Elemento ◽  
Jiayu Zhou
Keyword(s):  
Drug Interaction ◽  
Detection Method ◽  
Molecular Structures ◽  
Convolutional Networks ◽  
Interaction Detection ◽  
Drug Drug Interaction

In this work, we proposed a DDI detection method based on molecular structures using graph convolutional networks and deep sets. We proposed a more discriminative convolutional layer compared to conventional GCN and achieved permutation invariant prediction without losing the capability of capturing complicated interactions.

Evaluation of Consultation in Community Pharmacies with Mystery Shoppers

2007 ◽  
Vol 41 (6) ◽  
pp. 1023-1030 ◽  
Author(s):  
Dietrich Alte ◽  
Werner Weitschies ◽  
Christoph A Ritter
Keyword(s):  
Drug Interaction ◽  
Pharmacy Practice ◽  
Community Pharmacies ◽  
Pharmacy Staff ◽  
Interaction Detection ◽  
Size Number ◽  
Interaction Variables ◽  
Drug Drug Interaction ◽  
Mystery Shoppers ◽  
Northeastern Germany

BACKGROUND: Consultation of patients in community pharmacies (CPs) must meet standards, especially in selling over-the-counter drugs; however, there has been no information as to whether northeastern German CPs meet these standards. OBJECTIVE: To estimate aspects of consultation quality in CPs in Mecklenburg-Vorpommern, located in northeastern Germany, study factors related to consultation quality, and check compliance with Pharmacy Practice Law, because not all pharmaceutical professions may legally sell drugs. METHODS: In 2005, 6 mystery shoppers (pharmacy students) presented with a headache to 146 of 398 CPs; they requested a sleeping pill plus an antihistaminic drug and completed data collection forms. Consultation scores were calculated and effects of pharmacy/staff characteristics on consultation were modeled with linear (consultation score) and logistic regression (failure to detect a drug—drug interaction). Variables used in models were staff profession, pharmacy size (number of staff), city/town size (number of pharmacies), and day of the week in which shoppers visited the pharmacy. RESULTS: Despite a high willingness of pharmacy staff to provide consultation (83% spontaneously offered advice), northeastern German CPs did not achieve their professional mission. Extreme variation was evident in their questioning of the mystery shoppers regarding use of important single items (from 1% for pregnancy/breast-feeding considerations to 56% for dosing instructions). In all cases, drugs were sold to the shoppers; most (91%) were single agents. Drug—drug interaction detection was low: 43 (30%) counselors informed mystery shoppers about the interaction. The profession of the consulting staff and the size of the pharmacy were associated with consultation quality (highest for pharmacists; lowest for small pharmacies [2–4 staff]). For interaction detection, consulting staff profession was relevant: pharmacists had OR of 3.2 for the detection compared with pharmacy engineers/assistants. In 7 pharmacies, staff illegally sold drugs to customers. CONCLUSIONS: Northeastern German CPs have much need and potential for improvement in consultation quality and drug—drug interaction detection. In-depth elicitation of symptoms and details of patients' situations must be improved. Relevant training should be provided, including use of software to identify drug interactions. Mystery shopper studies give valuable information for tailoring training schemes.

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