Adverse drug reactions caused by drug–drug interactions in cardiovascular disease patients: introduction of a simple prediction tool using electronic screening database items
Authors
Kovačević, Milena
Vezmar-Kovačević, Sandra

Radovanović, Slavica
Stevanović, Predrag
Miljković, Branislava

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Objective: Cardiovascular disease (CVD) drugs have been frequently implicated in adverse drug reaction (ADR)-related hospitalizations. Drug-drug interactions (DDIs) are common preventable cause of ADRs, but the impact of DDIs in the CVD population has not been investigated. Hence, the primary aim of the study was to identify DDIs associated with ADRs in CVD patients at hospital admission. The second aim was to develop a simple tool to identify high-risk patients for DDI-related adverse events. Methods: An observational study was conducted on the Cardiology Ward of University Clinical Hospital Center. Data were obtained from medical charts. A clinical panel identified DDIs implicated in ADRs, using LexiInteract database and Drug Interaction Probability Scale. Statistics were performed using PASW 22 (SPSS Inc.). Results: DDIs contributed to hospital admission with a total prevalence of 9.69%. DDI-related ADRs affected mainly cardiac function (heart rate or rhythm, 41.07%); bleeding and e...ffect on blood pressure were equally distributed (17.86%). Non-cardiovascular ADRs were found in 23.21% of DDIs. After admission, 73% of the identified DDIs led to changes in prescription. Prediction ability of calculated DDI adverse event probability scores was rated as good (AUC = 0.80, p < .001). Conclusions: CVD patients are highly exposed to adverse DDIs; about one in ten patients hospitalized with CVD might have a DDI contributing to the hospitalization. Given the high prevalence of CVD, DDI-related harm might be a significant burden worldwide. Identification of patients with high DDI adverse event risk might ease the recognition of DDI-related harm and improve the use of electronic databases in clinical practice.
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Current Medical Research and Opinion, 2019, 35, 11, 1873-1883Publisher:
- Taylor and Francis Ltd
Funding / projects:
Note:
- This is peer-rewiewed version of the following article: Kovačević, M.; Vezmar Kovačević, S.; Radovanović, S.; Stevanović, P.; Miljković, B. Adverse Drug Reactions Caused by Drug–Drug Interactions in Cardiovascular Disease Patients: Introduction of a Simple Prediction Tool Using Electronic Screening Database Items. Curr. Med. Res. Opin. 2019, 35 (11), 1873–1883. https://doi.org/10.1080/03007995.2019.1647021
DOI: 10.1080/03007995.2019.1647021
ISSN: 0300-7995
WoS: 000483629500001
Scopus: 2-s2.0-85071442800
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PharmacyTY - JOUR AU - Kovačević, Milena AU - Vezmar-Kovačević, Sandra AU - Radovanović, Slavica AU - Stevanović, Predrag AU - Miljković, Branislava PY - 2019 UR - https://farfar.pharmacy.bg.ac.rs/handle/123456789/3733 AB - Objective: Cardiovascular disease (CVD) drugs have been frequently implicated in adverse drug reaction (ADR)-related hospitalizations. Drug-drug interactions (DDIs) are common preventable cause of ADRs, but the impact of DDIs in the CVD population has not been investigated. Hence, the primary aim of the study was to identify DDIs associated with ADRs in CVD patients at hospital admission. The second aim was to develop a simple tool to identify high-risk patients for DDI-related adverse events. Methods: An observational study was conducted on the Cardiology Ward of University Clinical Hospital Center. Data were obtained from medical charts. A clinical panel identified DDIs implicated in ADRs, using LexiInteract database and Drug Interaction Probability Scale. Statistics were performed using PASW 22 (SPSS Inc.). Results: DDIs contributed to hospital admission with a total prevalence of 9.69%. DDI-related ADRs affected mainly cardiac function (heart rate or rhythm, 41.07%); bleeding and effect on blood pressure were equally distributed (17.86%). Non-cardiovascular ADRs were found in 23.21% of DDIs. After admission, 73% of the identified DDIs led to changes in prescription. Prediction ability of calculated DDI adverse event probability scores was rated as good (AUC = 0.80, p < .001). Conclusions: CVD patients are highly exposed to adverse DDIs; about one in ten patients hospitalized with CVD might have a DDI contributing to the hospitalization. Given the high prevalence of CVD, DDI-related harm might be a significant burden worldwide. Identification of patients with high DDI adverse event risk might ease the recognition of DDI-related harm and improve the use of electronic databases in clinical practice. PB - Taylor and Francis Ltd T2 - Current Medical Research and Opinion T1 - Adverse drug reactions caused by drug–drug interactions in cardiovascular disease patients: introduction of a simple prediction tool using electronic screening database items VL - 35 IS - 11 SP - 1873 EP - 1883 DO - 10.1080/03007995.2019.1647021 ER -
@article{ author = "Kovačević, Milena and Vezmar-Kovačević, Sandra and Radovanović, Slavica and Stevanović, Predrag and Miljković, Branislava", year = "2019", abstract = "Objective: Cardiovascular disease (CVD) drugs have been frequently implicated in adverse drug reaction (ADR)-related hospitalizations. Drug-drug interactions (DDIs) are common preventable cause of ADRs, but the impact of DDIs in the CVD population has not been investigated. Hence, the primary aim of the study was to identify DDIs associated with ADRs in CVD patients at hospital admission. The second aim was to develop a simple tool to identify high-risk patients for DDI-related adverse events. Methods: An observational study was conducted on the Cardiology Ward of University Clinical Hospital Center. Data were obtained from medical charts. A clinical panel identified DDIs implicated in ADRs, using LexiInteract database and Drug Interaction Probability Scale. Statistics were performed using PASW 22 (SPSS Inc.). Results: DDIs contributed to hospital admission with a total prevalence of 9.69%. DDI-related ADRs affected mainly cardiac function (heart rate or rhythm, 41.07%); bleeding and effect on blood pressure were equally distributed (17.86%). Non-cardiovascular ADRs were found in 23.21% of DDIs. After admission, 73% of the identified DDIs led to changes in prescription. Prediction ability of calculated DDI adverse event probability scores was rated as good (AUC = 0.80, p < .001). Conclusions: CVD patients are highly exposed to adverse DDIs; about one in ten patients hospitalized with CVD might have a DDI contributing to the hospitalization. Given the high prevalence of CVD, DDI-related harm might be a significant burden worldwide. Identification of patients with high DDI adverse event risk might ease the recognition of DDI-related harm and improve the use of electronic databases in clinical practice.", publisher = "Taylor and Francis Ltd", journal = "Current Medical Research and Opinion", title = "Adverse drug reactions caused by drug–drug interactions in cardiovascular disease patients: introduction of a simple prediction tool using electronic screening database items", volume = "35", number = "11", pages = "1873-1883", doi = "10.1080/03007995.2019.1647021" }
Kovačević, M., Vezmar-Kovačević, S., Radovanović, S., Stevanović, P.,& Miljković, B.. (2019). Adverse drug reactions caused by drug–drug interactions in cardiovascular disease patients: introduction of a simple prediction tool using electronic screening database items. in Current Medical Research and Opinion Taylor and Francis Ltd., 35(11), 1873-1883. https://doi.org/10.1080/03007995.2019.1647021
Kovačević M, Vezmar-Kovačević S, Radovanović S, Stevanović P, Miljković B. Adverse drug reactions caused by drug–drug interactions in cardiovascular disease patients: introduction of a simple prediction tool using electronic screening database items. in Current Medical Research and Opinion. 2019;35(11):1873-1883. doi:10.1080/03007995.2019.1647021 .
Kovačević, Milena, Vezmar-Kovačević, Sandra, Radovanović, Slavica, Stevanović, Predrag, Miljković, Branislava, "Adverse drug reactions caused by drug–drug interactions in cardiovascular disease patients: introduction of a simple prediction tool using electronic screening database items" in Current Medical Research and Opinion, 35, no. 11 (2019):1873-1883, https://doi.org/10.1080/03007995.2019.1647021 . .