|
 
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| ORIGINAL ARTICLE |
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| Year : 2016 | Volume
: 5
| Issue : 4 | Page : 257-263 |
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A comparison of five common drug–drug interaction software programs regarding accuracy and comprehensiveness
Raziyeh Kheshti, Mohammadsadegh Aalipour, Soha Namazi
Department of Clinical Pharmacy, Shiraz University of Medical Sciences, Shiraz, Iran
| Date of Web Publication | 18-Oct-2016 |
Correspondence Address:
Soha Namazi
Department of Clinical Pharmacy, Shiraz University of Medical Sciences, Shiraz
Iran
 Source of Support: None, Conflict of Interest: None  | Check |
DOI: 10.4103/2279-042X.192461
Objective: Drug–drug interactions (DDIs) can cause failure in treatment and adverse events. DDIs screening software is an important tool to aid clinicians in the detection and management of DDIs. However, clinicians should be aware of the advantages and limitations of these programs. We compared the ability of five common DDI programs to detect clinically important DDIs.
Methods: Lexi-Interact, Micromedex Drug Interactions, iFacts, Medscape, and Epocrates were evaluated. The programs' sensitivity, specificity, and positive and negative predictive values were determined to assess their accuracy in detecting DDIs. The accuracy of each program was identified using 360 unknown pair interactions, taken randomly from prescriptions, and forty pairs of clinically important ones. The major reference was a clinical pharmacist alongside the Stockley's Drug Interaction and databases including PubMed, Scopus, and Google Scholar. Comprehensiveness of each program was determined by the number of components in the drug interaction monograph. The aggregate score for accuracy and comprehensiveness was calculated.
Findings: Scoring 250 out of possible 400 points, Lexi-Interact and Epocrates, provided the most accurate software programs. Micromedex, Medscape, and iFacts ranked third, fourth, and fifth, scoring 236, 202, and 191, respectively. In comprehensiveness test, iFacts showed the highest score, 134 out of possible 134 points, whereas Lexi-Interact rated second, with a score of 120. Scoring 370 and 330 out of possible 534 points, Lexi-Interact and Micromedex, respectively, provided the most competent, complete, and user-friendly applications.
Conclusion: Lexi-Interact and Micromedex showed the best performances. An increase in sensitivity is possible by the combination of more than one programs and expert pharmacist intervention. Keywords: Accuracy; comprehensiveness; drug interaction screening program; drug–drug interactions software
How to cite this article:
Kheshti R, Aalipour M, Namazi S. A comparison of five common drug–drug interaction software programs regarding accuracy and comprehensiveness. J Res Pharm Pract 2016;5:257-63 |
How to cite this URL:
Kheshti R, Aalipour M, Namazi S. A comparison of five common drug–drug interaction software programs regarding accuracy and comprehensiveness. J Res Pharm Pract [serial online] 2016 [cited 2023 Mar 28];5:257-63. Available from: https://www.jrpp.net/text.asp?2016/5/4/257/192461 |
| Introduction | |  |
Treatment of complex diseases often needs the simultaneous use of several drugs. Drug combination therapy can be very effective, whereas multiple drug therapy is also related to the occurrence of drug–drug interactions (DDIs).[1] DDIs can cause failure in treatment and adverse drug events (ADEs). Consequent ADEs are highly associated with increased morbidity and mortality.[2] A review study identified an incidence of up to 2.8% of hospital admissions to be caused by ADEs due to DDIs.[3]
It is likely that every physician and pharmacist cannot remember and understand all potential DDIs and therefore cannot take corrective actions accordingly. They may be more familiar with drugs used in their specialty but not with drugs used in other specialties. In one study, Glassman et al. found that clinicians can correctly recognize only 44% (range 11–64%) of all DDI pairs and 54% of disease-contraindication pairs.[4] Therefore, an improvement in the clinicians' ability to detect DDIs can reduce the chance of ADEs, preserve patients' safety, and prevent related medical and legal problems.
One of the tools that clinicians trust into review patients' medication sheet for DDIs is computerized DDI software. By manual review of drug regimens by pharmacists, without the use of utility (e.g., drug interaction reference and computer program), only 66% of DDIs in a 2-drug regimen can be correctly identified and the proportion decreases substantially as the number of drugs increases.[5] While a DDI screening program can be highly desirable, there is concern about variation between programs and about quality and effectiveness of the information. Thus, clinicians should be aware of the advantages and limitations of the DDI applications. In 2001, Hazlet et al. reported that up to 33% of relevant drug interactions were not recognized by computer software.[6] Another problem is the numerous alerts of insignificant drug interactions by software. Clinicians are likely to ignore excessive alerts of unimportant drug interactions, which may also lead to potential unfavorable consequences.[7]
Among the different computer platforms, the personal digital assistant (PDA) is frequently used for finding drug interactions. Like desktop interaction software, PDA drug interaction software often derives from familiar handbooks, textbooks, and internet sources that can be updated regularly. In addition, PDA software can be accessible at the point of patient care and because of the ease of use are expected to substitute for standard references. Only a few studies have compared some PDA drug interaction software programs such as iFacts, Micromedex, Lexi-Interact, Pharmavista, and Epocrates with each other, to select the best program regarding accuracy, comprehensiveness, and ease of use.[8],[9]
The objective of this study was to evaluate and compare five common PDA drug interaction software programs, which are known by Iranian clinicians in outpatient/inpatient setting, with regard to accuracy and comprehensiveness and to introduce the most reliable software program for clinical practice based on this findings.
| Methods | | |
The last current versions of five drug interaction PDA software programs were obtained from their publishers' website for purposes of this study. Their selection was based on their popularity among Iranian physicians and pharmacists and also reviewing the previous studies.[8],[9],[10] The following software programs were evaluated: Lexi-Interact (v. 1.12.1 (162) ed 2013), Micromedex Drug Interactions (v. 1.46 ed 2013), iFacts (Facts and Comparison's Drug Interactions Facts) (v. 2.9 ed 2013), Medscape (V. 3.2.1 ed 2013), and Epocrates (v. 5 ed 2013). Software was run on a New iPad 3 tablet of Apple™ Company.
To assess the quality of the DDI monographs, 360 unknown interaction pairs (extracted from 360 prescriptions that were randomly selected from two pharmacy affiliated with Shiraz University of Medical Sciences, Shiraz, Iran) and 40 known pairs [Table 1][11] were analyzed by each program. One experienced clinical pharmacist was employed as the major reference in identifying DDIs. When the clinical pharmacist could not make a clinical judgment or there was a great difference between her opinion and the software results, we searched the reliable databases (PubMed, Scopus, and Google Scholar) and also the hard copy of Stockley's Drug Interactions [11] to find information. Each interaction pair was stated by one of the authors, and the clinical pharmacist specified if it was a DDI or not. Moreover, if it was a DDI, the clinical pharmacist specified the severity and level of the interaction. Moreover, then each pair interaction was checked by each program. In this way, the number of true positive (TP), false positive (FP), true negative (TN), and false negative (FN) values was identified for each program. Then, the software programs' sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were determined to assess software accuracy in detecting DDI. Sensitivity was defined as the software's ability to correctly identify clinically important interaction pairs. Specificity was defined as the software's ability to ignore clinically unimportant interaction pairs. The PPV showed the probability that when the software identified a DDI, it was a clinically important interaction. The NPV showed the probability that when the software ignored a DDI, it was a clinically unimportant interaction. Adding the values of sensitivity, specificity, PPV, and NPV and multiplying the sum of them by 100 obtained the accuracy score.[9]
To assess programs ease of use, the ability of each program to find the management strategy for clinically important DDIs was timed. However, because of the recent advances in computer technology and the high speed of all DDI screening programs these days (less than a second), we ignored the ease of use in final score of the programs. To identify
the comprehensiveness, each software program was assessed for its ability to detect these elements of a drug interaction monograph: Severity of interaction, onset, mechanism, level of interaction, level of documentation, management, effect (clinical manifestations), case discussion, related drugs, and availability of references. It is of note that these parameters have an important role in detecting and making a clinical judgment about the DDIs. To obtain each program's comprehensiveness score, the number of drug interaction monograph's elements multiplied by 13.4.
The sum of the two factors (accuracy and comprehensiveness) was calculated to obtain the total score for each program. Accuracy and comprehensiveness accounted for 75% and 25% of the total score, respectively. While it is important to get more information about interacting drugs, the most important thing is the accuracy of the information.
The results were analyzed using SPSS version 18 for windows ® (IBM Corporation; Armonk, New York, United States. https://www.ibm.com/marketplace/cloud/statistical-analysis-and-reporting/us/en-us). Categorical values were reported as percentage. Chi-square test was performed for comparison between categorical variables. P < 0.05 was considered statistically significant.
| Results | | |
The performance of five common DDI screening programs was evaluated. The results of the software programs accuracy and comprehensiveness evaluation are described in [Table 2] and [Table 3], respectively. | Table 2: The comparison between 5 software programs for evaluating the 400 pairs of drug-drug interactions s regarding accuracy
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 | Table 3: The comparison between 5 drug-drug interaction software programs regarding comprehensiveness
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The most accurate programs were Lexi-Interact and Epocrates; both of them scored 250 out of 400. The least accurate program was iFacts, scoring 191 out of 400 possible accuracy score. Chi-square test showed that the differences between accuracy scores of programs were statistically significant (P < 0.001).
The highest percentage of correct answers (TP plus TN) was received by Lexi-Interact (279 out of 400, 69.8%) and the lowest by iFacts (145 out of 395, 36.7%). The highest percentage of incorrect answers (FN plus FP) was received by iFacts (250 out of 395, 63.3%) and the lowest by Lexi-Interact (121 out of 400, 30.3%). Percentages of correct answers of Epocrates, Micromedex, and Medscape were 60.3% (241 out of 400), 50.8% (203 out of 400), and 49% (196 out of 400), respectively.
Ten possible elements outlined the drug interaction monograph's comprehensiveness. The most comprehensive DDI screening program was iFacts, providing all 10 possible monographs' element (Comprehensive Score =134). Lexi-Interact provided 9 out of 10 possible elements and specified as the second program regarding comprehensiveness. The least comprehensive programs were Epocrates and Medscape, each providing only 5 out of 10 possible elements. All programs provided the drug interaction mechanism and management. Only iFacts and Lexi-Interact reviewed the evidence for the interaction, a component that is very important in the patient care setting.
Final score combined the scores of accuracy and comprehensiveness [Table 4]. Lexi-Interact and Micromedex got the highest total score, respectively. About iFacts, although it was the most comprehensive program, it was the least accurate program. Hence, the total score for iFacts was 325 out of 534 and it was ranked third among five programs.
Results showed that 78 DDI pairs (19.5%) had similar severity in all five programs. The results for 150 pairs (37.5%) were similar in three programs and 158 (39.5%) out of them showed similar results in four programs.
| Discussion | | |
Clinicians wanted to classify relevant drug interactions, know how to manage them, and differentiate them from irrelevant and unimportant interactions. DDI screening programs are widely used to identify potentially harmful drug interactions in the inpatient and outpatient setting. Halkin et al. showed that using DDI screening programs by physicians and pharmacists could decrease 67.5% of hazardous DDIs.[12] What is important is that these programs vary in accuracy and the information within interaction monographs.
An applicable DDI screening program should have both high sensitivity (to detect clinically relevant interactions) and high specificity (to ignore clinically unimportant and irrelevant interactions). If the PPV is too low, many unimportant warnings will confuse the clinician, who may, therefore, miss clinically important interactions.[13],[14],[15] In our analysis, Lexi-Interact showed the highest sensitivity (0.77) and Micromedex showed the highest specificity (0.78). Several studies which have assessed the performance of the DDI screening software programs reported that Lexi-Interact has high sensitivity (87–100%) and specificity (80–90%).[8],[16],[17] In total accuracy analysis, Lexi-Interact and Epocrates received the highest score (250 out of 400 each) in our study. In Barron's study that evaluated 9 DDI programs (including iFacts, Mobile Micromedex, Lexi-Interact, Mosby's Drug Consult, Clinical Pharmacology OnHand, Epocrates Rx, Handbook of Adverse Drug Interactions, Mobile PDR, and Tarascon Pharmacopoeia Deluxe), iFacts and Micromedex received the highest accuracy score (both of them scored 390 out of 400).[8] In that study, Lexi-Interact and Epocrates got third and sixth place, respectively (375 and 344 out of 400, respectively). The difference between his and our study was that Barron's selected three drug interaction references (Facts and Comparisons, Micromedex Drug-Reax, and Hansten and Horn's Drug Interaction and Analysis Management) to identify clinically important and unimportant drug interactions. Clinically important drug interactions were those that identified by all three references as moderate to severe interaction requiring monitoring. Hence, this could have biased the results of the study because Facts and Micromedex were among three gold standards. However, in our study, a clinical pharmacist was considered as gold standard alongside the mentioned Web site and Stockley's Drug Interactions, to identify clinically important interactions. Vonbach et al.'s study that evaluated four DDI screening programs (iFacts, Drug-Reax [Micromedex], Lexi-Interact, and Pharmavista) reported the highest precision score for Lexi-Interact.[9] In their study, the Stockley's Drug Interaction was considered as the gold standard, so they supported our results.
Ten possible elements defined the monograph's comprehensiveness. Receiving 134 out of 134 possible comprehensiveness score, iFacts was the most comprehensive program among the five programs. Our result is similar to Barron's study that reported iFacts and Lexi-Interact as the most comprehensive resources (both received all possible comprehensiveness score).[8] However, in our study, Lexi-Interact received 120 out of 134 possible comprehensiveness score. The reason was that in the version of Lexi-Interact that we evaluated, the onset of the interactions was not reported, unlike the previous versions.
On the one hand, we wanted to get more information about the interacting drug (highest comprehensiveness). On the other hand, the resource reliability is very important. Thus, it is important for DDI screening programs to contain a part that shows references related to each interaction monograph. Programs may cite evidence for interaction from a study without a control group to identify confounding factors. An interaction's evidence may also originate from hypothetical or study-based pharmacokinetic findings that do not contain outcomes assessment. Among five programs that we assessed, only two of them (Lexi-Interact and iFacts) included references to interaction evidence. There are, in addition, other deficiencies. Interaction monographs often do not include detectable patient and medication risk factors that make nonsevere drug interactions clinically important. Another problem about the reliability of the programs is a lack of standardization in assigning levels of significance to the interaction.[18],[19] Among the five drug interaction software programs in this study, disagreement on the severity of interactions was seen. Other studies supported our results.[16],[20] This discrepancy in severity rating of identified DDIs between electronic software programs can be explained with inconsistency of evidence and different criteria for the classification of severity of DDIs by various software.[16]
Many drug interactions are related to the dose of drugs that are consumed together. For example, some drugs may have interaction in high doses, but if they are used in lower doses, they will not lead to interaction. An ideal DDI screening program should be able to ignore an interaction if the drugs are given in doses that will not result in interaction.[18],[21] Among the five programs that we evaluated, none of them had this ability. Other studies reported that the software programs also do not consider dosing of the drugs in the assessment of DDIs.[17],[22] Therefore, one option should be defined in software programs, so clinicians can insert the dose of suspected drugs. Another limitation of 5 understudied drug interaction software programs is that they cannot detect the DDIs regarding duplicate prescription, for example, co-administration of two beta-blockers or two benzodiazepines. In this condition, it is expected that the software will identify the type of interaction as contraindicated. None of the mentioned software programs was able to detect such an interaction. Hence, improvement is needed to advance the DDI screening programs contribution to detection of DDIs.
Clinical relevance of DDIs evaluated with DDI software is a special concern because software does not consider patient's characteristics, dosing schedule, and precautions taken by the clinicians. Therefore, DDIs detected by the electronic databases may be over-detected compared to the clinician's assessment. Studies reported a high level of discrepancy between the number of DDIs identified by electronic software and the number of clinically relevant DDIs assessed by a clinician.[23],[24] One positive point of the present study is that an expert clinical pharmacist alongside the mentioned web site and Stockley's Drug Interactions was considered as major gold standard. Hence, clinical relevance in the assessment of DDI software was considered.
Finally, our results support previous literature in this area. Studied have indicated variation and deficiencies in DDI screening programs. Of the five drug interaction screening programs evaluated, none was considered to be ideal. However, Lexi-Interact was better than others. Improvement is needed to advance the DDI screening programs contribution to detection of DDIs. A good suggestion is to check interaction pairs by more than one program, for example, two programs and compare their results to achieve more sensitivity. Moreover, as a final point, the clinician's judgment is so important to distinguish relevant from irrelevant interactions.
Lexi-Interact received the highest total score (370 out of 534), followed by Micromedex (330), iFacts (325), Epocrates (317), and Medscape (269), respectively. Hence, Lexi-Interact seems to be the best software program among the programs tested in our study. The point is that all programs have some deficiencies, and they are not thorough alone in detecting DDIs. Thus, simultaneously, an expert specialist is needed to make a final clinical judgment.[23],[24]
| Authors' Contribution | | |
Soha Namazi designed the study, analyzed the data and edited and approved the article draft. Raziyeh Kheshti and Mohammadsadegh Aalipour collected and analyzed the data, and wrote the article draft.
Financial support and sponsorship
This study was financially supported by Pharmaceutical Sciences Research Center of Shiraz University of Medical Sciences.
Conflicts of interest
There are no conflicts of interest.
| References | | |
| 1. | Bjerrum L, Andersen M, Petersen G, Kragstrup J. Exposure to potential drug interactions in primary health care. Scand J Prim Health Care 2003;21:153-8.  |
| 2. | Yeh ML, Chang YJ, Wang PY, Li YC, Hsu CY. Physicians' responses to computerized drug-drug interaction alerts for outpatients. Comput Methods Programs Biomed 2013;111:17-25. |
| 3. | Jankel CA, Fitterman LK. Epidemiology of drug-drug interactions as a cause of hospital admissions. Drug Saf 1993;9:51-9. |
| 4. | Glassman PA, Simon B, Belperio P, Lanto A. Improving recognition of drug interactions: Benefits and barriers to using automated drug alerts. Med Care 2002;40:1161-71. |
| 5. | Weideman RA, Bernstein IH, McKinney WP. Pharmacist recognition of potential drug interactions. Am J Health Syst Pharm 1999;56:1524-9. |
| 6. | Hazlet TK, Lee TA, Hansten PD, Horn JR. Performance of community pharmacy drug interaction software. J Am Pharm Assoc (Wash) 2001;41:200-4. |
| 7. | Kuperman GJ, Bobb A, Payne TH, Avery AJ, Gandhi TK, Burns G, et al. Medication-related clinical decision support in computerized provider order entry systems: A review. J Am Med Inform Assoc 2007;14:29-40. |
| 8. | Barrons R. Evaluation of personal digital assistant software for drug interactions. Am J Health Syst Pharm 2004;61:380-5. |
| 9. | Vonbach P, Dubied A, Krähenbühl S, Beer JH. Evaluation of frequently used drug interaction screening programs. Pharm World Sci 2008;30:367-74. |
| 10. | Abarca J, Colon LR, Wang VS, Malone DC, Murphy JE, Armstrong EP. Evaluation of the performance of drug-drug interaction screening software in community and hospital pharmacies. J Manag Care Pharm 2006;12:383-9. |
| 11. | Baxter K, Stockley IH. Stockley's Drug Interactions. 9 th ed. London, UK: Pharmaceutical Press; 2010. |
| 12. | Halkin H, Katzir I, Kurman I, Jan J, Malkin BB. Preventing drug interactions by online prescription screening in community pharmacies and medical practices. Clin Pharmacol Ther 2001;69:260-5. |
| 13. | Magnus D, Rodgers S, Avery AJ. GPs' views on computerized drug interaction alerts: Questionnaire survey. J Clin Pharm Ther 2002;27:377-82. |
| 14. | Payne TH, Nichol WP, Hoey P, Savarino J. Characteristics and override rates of order checks in a practitioner order entry system. Proc AMIA Symp 2002:602-6. |
| 15. | Weingart SN, Toth M, Sands DZ, Aronson MD, Davis RB, Phillips RS. Physicians' decisions to override computerized drug alerts in primary care. Arch Intern Med 2003;163:2625-31. |
| 16. | Roblek T, Vaupotic T, Mrhar A, Lainscak M. Drug-drug interaction software in clinical practice: A systematic review. Eur J Clin Pharmacol 2015;71:131-42. |
| 17. | Hadjibabaie M, Badri S, Ataei S, Moslehi AH, Karimzadeh I, Ghavamzadeh A. Potential drug-drug interactions at a referral hematology-oncology ward in Iran: A cross-sectional study. Cancer Chemother Pharmacol 2013;71:1619-27. |
| 18. | Hansten PD, Horn JR, Hazlet TK. ORCA: Operational classification of drug interactions. J Am Pharm Assoc (Wash) 2001;41:161-5. |
| 19. | Kelly WN. Potential risks and prevention, part 4: Reports of significant adverse drug events. Am J Health Syst Pharm 2001;58:1406-12. |
| 20. | Van Leeuwen RW, Brundel DH, Neef C, van Gelder T, Mathijssen RH, Burger DM, et al. Prevalence of potential drug-drug interactions in cancer patients treated with oral anticancer drugs. Br J Cancer 2013;108:1071-8. |
| 21. | Bergk V, Gasse C, Rothenbacher D, Loew M, Brenner H, Haefeli WE. Drug interactions in primary care: Impact of a new algorithm on risk determination. Clin Pharmacol Ther 2004;76:85-96. |
| 22. | Reis AM, Cassiani SH. Evaluation of three brands of drug interaction software for use in intensive care units. Pharm World Sci 2010;32:822-8. |
| 23. | Bertoli R, Bissig M, Caronzolo D, Odorico M, Pons M, Bernasconi E. Assessment of potential drug-drug interactions at hospital discharge. Swiss Med Wkly 2010;140:w13043. |
| 24. | Vonbach P, Dubied A, Beer JH, Krähenbühl S. Recognition and management of potential drug-drug interactions in patients on internal medicine wards. Eur J Clin Pharmacol 2007;63:1075-83. |
[Table 1], [Table 2], [Table 3], [Table 4]
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|
| Nida Ashraf Choudary, Asad Khan, Abdul Wahid, Muhammad Abubakar, Muhammad Atif, Nafees Ahmad | | Journal of Oncology Pharmacy Practice. 2022; : 1078155222 | | [Pubmed] | [DOI] | | | 26 |
Frequency, severity, and factors associated with clinically significant drug-drug interactions among patients with cancer attending Mbarara Regional Referral Hospital Cancer Unit, Uganda |
|
| Bonny Luzze, Barnabas Atwiine, Henry Mark Lugobe, Tadele Mekuriya Yadesa | | BMC Cancer. 2022; 22(1) | | [Pubmed] | [DOI] | | | 27 |
Prevalence of potential drug?-?drug interactions in the cardiothoracic intensive care unit patients in a Chinese tertiary care teaching hospital |
|
| Haitao Wang, Haitao Shi, Na Wang, Yan Wang, Li Zhang, Yujie Zhao, Jiao Xie | | BMC Pharmacology and Toxicology. 2022; 23(1) | | [Pubmed] | [DOI] | | | 28 |
Polypharmacy and the occurrence of potential drug–drug interactions among geriatric patients at the outpatient pharmacy department of a regional hospital in Durban, South Africa |
|
| Adetola Olaniyi Bojuwoye, Fatima Suleman, Velisha Ann Perumal-Pillay | | Journal of Pharmaceutical Policy and Practice. 2022; 15(1) | | [Pubmed] | [DOI] | | | 29 |
Potential drug-drug interactions among patients with spontaneous intracerebral hemorrhage treated at the Neurological Intensive Care Unit: a single-center experience |
|
| Dejan Z. Aleksic, Miloš N. Milosavljevic, Slobodan M. Jankovic, Ana D. Azanjac Arsic, Srdjan M. Stefanovic | | Brazilian Journal of Pharmaceutical Sciences. 2022; 58 | | [Pubmed] | [DOI] | | | 30 |
Potentially Inappropriate Medication and Associated Factors Among Older Patients with Chronic Coronary Syndrome at Hospital Discharge in Beijing, China |
|
| Mei Zhao,Jun-Xian Song,Fang-Fang Zheng,Lin Huang,Yu-Fei Feng | | Clinical Interventions in Aging. 2021; Volume 16: 1047 | | [Pubmed] | [DOI] | | | 31 |
Polypharmacy and Potential Drug–Drug Interactions in Home-Dwelling Older People – A Cross-Sectional Study |
|
| Monica Hermann,Nina Carstens,Lars Kvinge,Astrid Fjell,Marianne Wennersberg,Kjersti Folleso,Knut Skaug,Ake Seiger,Berit Seiger Cronfalk,Anne-Marie Bostrom | | Journal of Multidisciplinary Healthcare. 2021; Volume 14: 589 | | [Pubmed] | [DOI] | | | 32 |
Polypharmacy in Chronic Neurological Diseases: Multiple Sclerosis, Dementia and Parkinson’s Disease |
|
| Niklas Frahm,Michael Hecker,Uwe Klaus Zettl | | Current Pharmaceutical Design. 2021; 27 | | [Pubmed] | [DOI] | | | 33 |
Potential drug–drug interactions in patients with cardiovascular diseases: findings from a prospective observational study |
|
| Zarka Akbar,Sundas Rehman,Asad Khan,Amjad Khan,Muhammad Atif,Nafees Ahmad | | Journal of Pharmaceutical Policy and Practice. 2021; 14(1) | | [Pubmed] | [DOI] | | | 34 |
Prevention and management of physical health conditions in adults with severe mental disorders: WHO recommendations |
|
| Petra C. Gronholm,Neerja Chowdhary,Corrado Barbui,Jayati Das-Munshi,Kavitha Kolappa,Graham Thornicroft,Maya Semrau,Tarun Dua | | International Journal of Mental Health Systems. 2021; 15(1) | | [Pubmed] | [DOI] | | | 35 |
Treatment patterns in pediatric patients with multiple sclerosis in Germany—a nationwide claim-based analysis |
|
| Niklas Frahm, Melanie Peters, Jörg Bätzing, David Ellenberger, Manas K. Akmatov, Judith Haas, Paulus S. Rommer, Alexander Stahmann, Uwe K. Zettl, Jakob Holstiege | | Therapeutic Advances in Neurological Disorders. 2021; 14: 1756286421 | | [Pubmed] | [DOI] | | | 36 |
Prediction of potential drug interactions between repurposed COVID-19 and antitubercular drugs: an integrational approach of drug information software and computational techniques data |
|
| Levin Thomas, Sumit Raosaheb Birangal, Rajdeep Ray, Sonal Sekhar Miraj, Murali Munisamy, Muralidhar Varma, Chidananda Sanju S.V., Mithu Banerjee, Gautham G. Shenoy, Mahadev Rao | | Therapeutic Advances in Drug Safety. 2021; 12: 2042098621 | | [Pubmed] | [DOI] | | | 37 |
Polypharmacy and severe potential drug-drug interactions among older adults with cardiovascular disease in the United States |
|
| Marwan Sheikh-Taha,Myriam Asmar | | BMC Geriatrics. 2021; 21(1) | | [Pubmed] | [DOI] | | | 38 |
Novel Drug Interaction index and Risk of Mortality in Older Patients With Atrial Fibrillation Receiving Non Vitamin K Oral Anticoagulants (NOEL Drug) |
|
| Ibrahim Ersoy | | Clinical and Applied Thrombosis/Hemostasis. 2021; 27: 1076029621 | | [Pubmed] | [DOI] | | | 39 |
Sensitivity and specificity of drug interaction databases to detect interactions with recently approved oral antineoplastics |
|
| John B Bossaer, Danielle Eskens, Austin Gardner | | Journal of Oncology Pharmacy Practice. 2021; : 1078155220 | | [Pubmed] | [DOI] | | | 40 |
A Cross-Sectional Study on Single-Day Use of Proton Pump Inhibitors in Tertiary Care Hospitals of South India |
|
| Zabiuddin Ahad M,Alekhya Lavu,Maria Ansari,Raviraj Acharya V,Rajesh Vilakkathala | | Hospital Pharmacy. 2021; 56(2): 109 | | [Pubmed] | [DOI] | | | 41 |
Potential drug-drug interactions with psychotropic drugs in paediatric inpatients: A cross-sectional study |
|
| Nadir Yalçin,Nesligül Özdemir,Halime Tuna Çak Esen,Sadriye Ebru Çengel Kültür,Kutay Demirkan | | International Journal of Clinical Practice. 2021; | | [Pubmed] | [DOI] | | | 42 |
The prevalence of potential drug–drug interactions in adults with intellectual disability |
|
| M. McMahon,C. Hatton,D. L. Bowring,C. Hardy,N. J. Preston | | Journal of Intellectual Disability Research. 2021; | | [Pubmed] | [DOI] | | | 43 |
Prevalence and characteristics of drug-drug interactions among hospitalised patients in the Capital Region of Denmark |
|
| Hans Kristian Råket Pedersen, Anne Mette Skov Sørensen, Astrid Blicher Schelde, Martin Erik Nyeland, Espen Jimenez-Solem | | Adverse Drug Reaction Bulletin. 2021; Publish Ah | | [Pubmed] | [DOI] | | | 44 |
Bradyarrhythmias in Cardio-Oncology |
|
| Marta Fonseca, Evaline Cheng, Duc Do, Shouvik Haldar, Shelby Kutty, Eric H. Yang, Arjun K. Ghosh, Avirup Guha | | South Asian Journal of Cancer. 2021; | | [Pubmed] | [DOI] | | | 45 |
Risk factors for potential drug–drug interactions in patients with myasthenia gravis |
|
| Dejan Z. Aleksic,Miloš N. Milosavljevic,Srdan M. Stefanovic,Andriana Bukonjic,Jovana Z. Milosavljevic,Slobodan M. Jankovic,Ivo Božovic,Stojan Peric,Dragana Lavrnic | | Neurological Research. 2021; : 1 | | [Pubmed] | [DOI] | | | 46 |
Prescription patterns of direct oral anticoagulants and concomitant use of interacting medications in the Netherlands |
|
| R. E. Harskamp,J. C. L. Himmelreich,G. W. M. Wong,M. Teichert | | Netherlands Heart Journal. 2021; | | [Pubmed] | [DOI] | | | 47 |
Prevalence and Factors Associated with Potential Drug-Drug Interactions in Older Community-Dwelling Adults: A Prospective Cohort Study |
|
| John E. Hughes,Veronica Russo,Caroline Walsh,Enrica Menditto,Kathleen Bennett,Caitriona Cahir | | Drugs & Aging. 2021; | | [Pubmed] | [DOI] | | | 48 |
Impact of Polypharmacy Prior to Allogeneic Hematopoietic Stem Cell Transplantation in Older Adults |
|
| Matthew Sugidono,Mimi Lo,Rebecca Young,Kimberly Rosario,Yoonie Jung,Chiung-Yu Huang,Ying Sheng,Li-Wen Huang,Rebecca L. Olin | | Transplantation and Cellular Therapy. 2021; | | [Pubmed] | [DOI] | | | 49 |
Prevalence of drug–drug interactions in sarcoma patients: key role of the pharmacist integration for toxicity risk management |
|
| Audrey Bellesoeur,Ithar Gataa,Anne Jouinot,Sarah El Mershati,Anne-Catherine Piketty,Camille Tlemsani,David Balakirouchenane,Anthia Monribot,Michel Vidal,Rui Batista,Sixtine de Percin,Clémentine Villeminey,Jérôme Alexandre,François Goldwasser,Benoit Blanchet,Pascaline Boudou-Rouquette,Audrey Thomas-Schoemann | | Cancer Chemotherapy and Pharmacology. 2021; | | [Pubmed] | [DOI] | | | 50 |
Die pharmakologische/pharmazeutische Visite in der Intensivmedizin |
|
| H. Hilgarth,M. Baehr,S. Kluge,C. König | | Medizinische Klinik - Intensivmedizin und Notfallmedizin. 2021; | | [Pubmed] | [DOI] | | | 51 |
Management of drug-disease interactions: a best practice from the Netherlands |
|
| Maaike M. E. Diesveld,Suzanne de Klerk,Pieter Cornu,Dorothea Strobach,Katja Taxis,Sander D. Borgsteede | | International Journal of Clinical Pharmacy. 2021; | | [Pubmed] | [DOI] | | | 52 |
Determination of potential drug–drug interactions in prescription orders dispensed in a community pharmacy setting using Micromedex® and Lexicomp®: a retrospective observational study |
|
| Afraa Abbas, Samaher Al-Shaibi, Sowndramalingam Sankaralingam, Ahmed Awaisu, Vyas S. Kattezhathu, Supakit Wongwiwatthananukit, Yaw B. Owusu | | International Journal of Clinical Pharmacy. 2021; | | [Pubmed] | [DOI] | | | 53 |
Differences in Information Accessed in a Pharmacologic Knowledge Base Using a Conversational Agent vs Traditional Search Methods |
|
| Anita M. Preininger,Bedda L. Rosario,Adam M. Buchold,Jeff Heiland,Nawshin Kutub,Bryan S. Bohanan,Brett South,Gretchen P. Jackson | | International Journal of Medical Informatics. 2021; : 104530 | | [Pubmed] | [DOI] | | | 54 |
The screening and evaluation of potential clinically significant HIV drug combinations against the SARS-CoV-2 virus |
|
| Draško Tomic,Davor Davidovic,Attila Marcel Szasz,Melinda Rezeli,Boris Pirkic,Jozsef Petrik,Vesna Bacic Vrca,Vladimir Jandel,Tomislav Lipic,Karolj Skala,Josip Mesaric,Marija Milkovic Periša,Zorislav Šojat,Branka Medved Rogina | | Informatics in Medicine Unlocked. 2021; : 100529 | | [Pubmed] | [DOI] | | | 55 |
Precision Medicine into Clinical Practice: A Web-Based Tool Enables Real-Time Pharmacogenetic Assessment of Tailored Treatments in Psychiatric Disorders |
|
| Stefania Zampatti,Carlo Fabrizio,Michele Ragazzo,Giulia Campoli,Valerio Caputo,Claudia Strafella,Clelia Pellicano,Raffaella Cascella,Gianfranco Spalletta,Laura Petrosini,Carlo Caltagirone,Andrea Termine,Emiliano Giardina | | Journal of Personalized Medicine. 2021; 11(9): 851 | | [Pubmed] | [DOI] | | | 56 |
Comparing Potential Drug–Drug Interactions in Companion Animal Medications Using Two Electronic Databases |
|
| Tussapon Boonyarattanasoonthorn,Phisit Khemawoot,Anusak Kijtawornrat | | Veterinary Sciences. 2021; 8(4): 60 | | [Pubmed] | [DOI] | | | 57 |
Drug–Drug Interactions and Pharmacogenomic Evaluation in Colorectal Cancer Patients: The New Drug-PIN® System Comprehensive Approach |
|
| Michela Roberto,Alessandro Rossi,Martina Panebianco,Leda Marina Pomes,Giulia Arrivi,Debora Ierinò,Maurizio Simmaco,Paolo Marchetti,Federica Mazzuca | | Pharmaceuticals. 2021; 14(1): 67 | | [Pubmed] | [DOI] | | | 58 |
Pediatric Cardio-Oncology Medicine: A New Approach in Cardiovascular Care |
|
| Hugo R. Martinez, Gary S. Beasley, Jason F. Goldberg, Mohammed Absi, Kaitlin A. Ryan, Karine Guerrier, Vijaya M. Joshi, Jason N. Johnson, Cara E. Morin, Caitlin Hurley, Ronald Ray Morrison, Parul Rai, Jane S. Hankins, Michael W. Bishop, Brandon M. Triplett, Matthew J. Ehrhardt, Ching-Hon Pui, Hiroto Inaba, Jeffrey A. Towbin | | Children. 2021; 8(12): 1200 | | [Pubmed] | [DOI] | | | 59 |
Assessing Consistency of Drug-Drug Interaction-Related Information Across Various Drug Information Resources |
|
| Atiqulla Shariff,Sathvik Belagodu Sridhar,Neelu Farhath Abdullah Basha,Shamma Sulaiman Hasan Bin Taleth Alshemeil,Noora Adel Ahmed Aljallaf Alzaabi | | Cureus. 2021; | | [Pubmed] | [DOI] | | | 60 |
Investigation of Potential Drug-Drug Interactions in an Intensive Care Unit Using Lexicomp Drug Interactions |
|
| Hiroki Tateishi, Daisuke Miyazu, Takao Goto, Michiko Mukae, Yoshitoyo Miyauchi, Yoichi Koda, Hirokazu Tanaka, Masaaki Hirakawa | | Iryo Yakugaku (Japanese Journal of Pharmaceutical Health Care and Sciences). 2020; 46(10): 576 | | [Pubmed] | [DOI] | | | 61 |
Prevalence of Potential Drug–Drug Interaction Risk among Chronic Kidney Disease Patients in a Spanish Hospital |
|
| Gracia Santos-Díaz,Ana María Pérez-Pico,Miguel Ángel Suárez-Santisteban,Vanesa García-Bernalt,Raquel Mayordomo,Pedro Dorado | | Pharmaceutics. 2020; 12(8): 713 | | [Pubmed] | [DOI] | | | 62 |
The pharmacoepidemiology of potential drug-drug interactions in patients with psychiatric disorders |
|
| O. O. Kirilochev,A. R. Umerova | | Kachestvennaya klinicheskaya praktika. 2020; (3): 85 | | [Pubmed] | [DOI] | | | 63 |
Performance Assessment of Software to Detect and Assist Prescribers with Antimicrobial Drug Interactions: Are all of them Created Equal? |
|
| Elena Morte-Romea,Pilar Luque-Gómez,Mercedes Arenere-Mendoza,Jose Luis Sierra-Monzón,Ana Camón Pueyo,Galadriel Pellejero Sagastizabal,Guillermo Verdejo Muñoz,David Sánchez Fabra,José Ramón Paño-Pardo | | Antibiotics. 2020; 9(1): 19 | | [Pubmed] | [DOI] | | | 64 |
Prevalence, predictors and outcomes of potential drug-drug interactions in left ventricular failure: considerable factors for quality use of medicines |
|
| Inamul Haq,Mohammad Ismail,Fahadullah Khan,Qasim Khan,Zahid Ali,Sidra Noor | | Brazilian Journal of Pharmaceutical Sciences. 2020; 56 | | [Pubmed] | [DOI] | | | 65 |
Patterns and characteristics of polypharmacy among elderly residents in Danish nursing homes |
|
| Jacob Astorp,Mimoza Gjela,Pernille Jensen,Rasmus D Bak,Parisa Gazerani | | Future Science OA. 2020; : FSO590 | | [Pubmed] | [DOI] | | | 66 |
Optimierung des Interaktionsmanagements zur Prävention potenzieller schwerwiegender Arzneimittelinteraktionen bei onkologischen Patienten |
|
| J. Rosentreter,A.-K. Engl,L. Wojnowski,M. Kühn,T. Wölfel,I. Krämer | | best practice onkologie. 2020; | | [Pubmed] | [DOI] | | | 67 |
Chronic Obstructive Pulmonary Disease: A palliative medicine review of the disease, its therapies and drug interactions |
|
| Andrea Weiss,Sandra Porter,Dmitry Rozenberg,Erin O’Connor,Tiffany Lee,Meyer Balter,Kirsten Wentlandt | | Journal of Pain and Symptom Management. 2020; | | [Pubmed] | [DOI] | | | 68 |
Potential Drug interactions with Drugs used for Bipolar Disorder: A
Comparison of 6 Drug Interaction Database Programs |
|
| Scott Monteith,Tasha Glenn,Michael Gitlin,Michael Bauer | | Pharmacopsychiatry. 2020; 53(05): 220 | | [Pubmed] | [DOI] | | | 69 |
Enzymatic deinduction phenomenon and clinical implications with a focus on direct-acting oral anticoagulants |
|
| Jennifer L. Cole | | Blood Coagulation & Fibrinolysis. 2020; 31(4): 283 | | [Pubmed] | [DOI] | | | 70 |
Drug interactions of natural supplements in dermatology: a review |
|
| Edward K. Hadeler,Andrea D. Maderal | | International Journal of Dermatology. 2020; | | [Pubmed] | [DOI] | | | 71 |
Assessing Drug-Drug Interaction Potential among Patients Admitted to Surgery Departments in Three Palestinian Hospitals |
|
| Abdullah K. Rabba,Ayeshe M. Abu Hussein,Bayan K. Abu Sbeih,Somaya I. Nasser | | BioMed Research International. 2020; 2020: 1 | | [Pubmed] | [DOI] | | | 72 |
Prevalence and significance of potential drug-drug interactions among cancer patients receiving chemotherapy |
|
| Mohammad Ismail,Sehrash Khan,Fahadullah Khan,Sidra Noor,Hira Sajid,Shazia Yar,Irum Rasheed | | BMC Cancer. 2020; 20(1) | | [Pubmed] | [DOI] | | | 73 |
Gaps and challenges: WHO treatment recommendations for tobacco cessation and management of substance use disorders in people with severe mental illness |
|
| Jayati Das-Munshi,Maya Semrau,Corrado Barbui,Neerja Chowdhary,Petra C. Gronholm,Kavitha Kolappa,Dzmitry Krupchanka,Tarun Dua,Graham Thornicroft | | BMC Psychiatry. 2020; 20(1) | | [Pubmed] | [DOI] | | | 74 |
Potential drug-drug interactions and risk of unplanned hospitalization in older patients with cancer: A survey of the prospective ELCAPA (ELderly CAncer PAtients) cohort |
|
| Guillaume Beinse,Delphine Reitter,Lauriane Segaux,Muriel Carvahlo-Verlinde,Benoit Rousseau,Christophe Tournigand,Tristan Cudennec,Marie Laurent,Pascaline Boudou-Rouquette,Elena Paillaud,Florence Canouï-Poitrine,Philippe Caillet | | Journal of Geriatric Oncology. 2019; | | [Pubmed] | [DOI] | | | 75 |
Prediction of Drug-Drug Interactions Related to Inhibition or Induction of Drug-Metabolizing Enzymes |
|
| Alexander V. Dmitriev,Alexey A. Lagunin,Dmitry ?. Karasev,Anastasia V. Rudik,Pavel V. Pogodin,Dmitry A. Filimonov,Vladimir V. Poroikov | | Current Topics in Medicinal Chemistry. 2019; 19(5): 319 | | [Pubmed] | [DOI] | | | 76 |
Safety of psychopharmacotherapy: a clinical and pharmacological approach |
|
| O. O. Kirilochev,A. R. Umerova | | Zhurnal nevrologii i psikhiatrii im. S.S. Korsakova. 2019; 119(10): 127 | | [Pubmed] | [DOI] | | | 77 |
Yogun Bakim Hastalarinda Antibiyotiklerin Diger Ilaçlarla Etkilesimlerinin Degerlendirilmesi |
|
| EMRE KARA,Kamer Tecen-Yücel,Nesligül Özdemir,Ahmet Çagkan Inkaya,Aygin Bayraktar-Ekincioglu,Kutay Demirkan,Serhat Ünal | | STED / Sürekli Tip Egitimi Dergisi. 2019; : 404 | | [Pubmed] | [DOI] | | | 78 |
Comparison of potential drug-drug interactions with metabolic syndrome medications detected by two databases |
|
| Bovornpat Suriyapakorn,Pun Chairat,Suwanan Boonyoprakarn,Pimonwan Rojanarattanangkul,Wassana Pisetcheep,Natthaphon Hunsakunachai,Pornpun Vivithanaporn,Supakit Wongwiwatthananukit,Phisit Khemawoot,Masaki Mogi | | PLOS ONE. 2019; 14(11): e0225239 | | [Pubmed] | [DOI] | | | 79 |
Prevalence and trend of potential drug–drug interaction among children with depression in U.S. outpatient settings |
|
| L. Leanne Lai,Goar Alvarez,Linh Dang,Dung Vuong,Vy Ngo,Yailin Jo,Leroy Koh | | Journal of Pharmaceutical Health Services Research. 2019; | | [Pubmed] | [DOI] | | | 80 |
The prevalence and severity of potential drug- drug interactions among polypharmacy patients in Jordan |
|
| Mohammad B. Nusair,Sayer I. Al-azzam,Rasha. M. Arabyat,Haneen A. Amawi,Karem H. Alzoubi,Asma Rabah | | Saudi Pharmaceutical Journal. 2019; | | [Pubmed] | [DOI] | | | 81 |
A comparison of potential psychiatric drug interactions from six drug interaction database programs |
|
| Scott Monteith,Tasha Glenn | | Psychiatry Research. 2019; | | [Pubmed] | [DOI] | | | 82 |
Safety and Tolerability of Pharmacotherapies for Parkinson’s Disease in Geriatric Patients |
|
| Martin Klietz,Stephan Greten,Florian Wegner,Günter U. Höglinger | | Drugs & Aging. 2019; | | [Pubmed] | [DOI] | | | 83 |
Prevalence of potential drug–drug interactions in outpatients on treatment with parenteral antineoplastic drugs |
|
| M. Castro-Manzanares,F. do Pazo-Oubiña,R. M. Rodríguez-Rincón | | International Journal of Clinical Pharmacy. 2019; | | [Pubmed] | [DOI] | | | 84 |
Risk Factors for the Occurrence of Potential Drug-drug Interactions in Surgical Patients |
|
| Miloš N. Milosavljevic,Aleksandar G. Kocovic,Slobodan M. Jankovic,Dragce M. Radovanovic,Slobodan S. Milisavljevic,Srdan M. Stefanovic | | Serbian Journal of Experimental and Clinical Research. 2019; 0(0) | | [Pubmed] | [DOI] | | | 85 |
Clozapine Patients at the Interface between Primary and Secondary Care |
|
| Marita Barrett,Anna Keating,Deirdre Lynch,Geraldine Scanlon,Mary Kigathi,Fidelma Corcoran,Laura Sahm | | Pharmacy. 2018; 6(1): 19 | | [Pubmed] | [DOI] | | | 86 |
Drug-Drug Interactions in Hepatitis Patients: Do these Interactions Matter in Clinical Perspectives? |
|
| Sidra Noor,Mohammad Ismail,Iqbal Haider,Faiza Khadim | | Annals of Hepatology. 2018; 17(6): 1001 | | [Pubmed] | [DOI] | | | 87 |
Inhibitory effect of isavuconazole, ketoconazole, and voriconazole on the pharmacokinetics of methadone in vivo and in vitro |
|
| Leibin Shen,Jun Wang,Yongdong Yi,Chenmin Ye,Rongrong Wang,Guojun Xia,Chengyang Yu,Fuyang Tu,Jingxuan Xu,Zhiqiang Zheng | | Drug Testing and Analysis. 2018; | | [Pubmed] | [DOI] | | | 88 |
E-Health und die Realität – was sehen wir heute schon in der Klinik? |
|
| H. Gehring,K. Rackebrandt,M. Imhoff | | Bundesgesundheitsblatt - Gesundheitsforschung - Gesundheitsschutz. 2018; | | [Pubmed] | [DOI] | | | 89 |
Therapeutisches Drug-Monitoring (TDM) in der Praxis |
|
| Andreas von Heydwolff | | psychopraxis. neuropraxis. 2018; | | [Pubmed] | [DOI] | | | 90 |
Risk factors for potential drug-drug interactions in intensive care unit patients |
|
| Slobodan M. Jankovic,Ana V. Pejcic,Miloš N. Milosavljevic,Valentina D. Opancina,Nikola V. Pešic,Tamara T. Nedeljkovic,Goran M. Babic | | Journal of Critical Care. 2018; 43: 1 | | [Pubmed] | [DOI] | | | 91 |
Prevalence of QT interval prolonging drug–drug interactions (QT-DDIs) in psychiatry wards of tertiary care hospitals in Pakistan: a multicenter cross-sectional study |
|
| Qasim Khan,Mohammad Ismail,Iqbal Haider,Fahadullah Khan | | International Journal of Clinical Pharmacy. 2017; | | [Pubmed] | [DOI] | | | 92 |
Potential drug-drug interactions in pediatric patients admitted to intensive care unit of Khyber Teaching Hospital, Peshawar, Pakistan: A cross-sectional study |
|
| Mohammad Ismail,Sana Aziz,Sidra Noor,Iqbal Haider,Faryal Shams,Inamul Haq,Faiza Khadim,Qasim Khan,Fahadullah Khan,Muhammad Asif | | Journal of Critical Care. 2017; 40: 243 | | [Pubmed] | [DOI] | |
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