Background: Cancer patients often receive multiple drugs to maximize their therapeutic benefit, treat co-morbidities and counter the adverse effects of chemotherapy. Concomitant administration of multiple drugs increases the risk of drug interactions leading to compromised therapeutic efficacy or safety of therapy. The purpose of this study was to identify the prevalence, levels and predictors of potential drug-drug interactions (pDDIs) among cancer patients. Methods: Six hundred and 78 patients receiving chemotherapy from two tertiary care hospitals were included in this cross-sectional study. Patient medication profiles were screened for pDDIs using the Micromedex® database. Logistic regression analysis was performed to identify the predictors of pDDIs. Results: The overall prevalence of pDDIs was 78%, majority of patients had 1-2 pDDIs (39.2%). A total of 1843 pDDIs were detected. Major-pDDIs were most frequent (67.3%) whereas, a significant association of pDDIs was found between > 7 all prescribed drugs (p < 0.001) and ≥ 3 anti-cancer drugs (p < 0.001). Potential adverse outcomes of these interactions include reduced therapeutic effectiveness, QT interval prolongation, tendon rupture, bone marrow suppression and neurotoxicity. Conclusions: Major finding of this study is the high prevalence of pDDIs signifying the need of strict patient monitoring for pDDIs among cancer patients. Patients at higher risk to pDDIs include those prescribed with > 7 any types of drugs or ≥ 3 anticancer drugs. Moreover, list of most frequently identified major and moderate interactions will aid health care professional in timely identification and prevention of pDDIs.

BackgroundCancer patients may receive a high number of medications with the potential to prolong QT interval and subsequent TdP (torsades de pointes). This study aimed to identify the prevalence of QT prolonging drugs, their TdP risk, QT prolonging drug-drug interactions (QT-DDIs), levels, predictors, and TdP risk of drugs involved in QT-DDIs.MethodsThis multicenter study included cancer patients from three major tertiary care hospitals of Khyber-Pakhtunkhwa, Pakistan. Micromedex DrugReax® was used for identification of QT-DDIs. TdP risks were identified by AZCERT (Arizona Center for Education and Research on Therapeutics) classification. Logistic regression analysis was performed to identify predictors of QT-DDIs.ResultsOf 555 patients, 51% were females. Mean age was 46.9 ± 15.7 years. Total 28 distinct QT prolonging drugs were identified in 92.6% of the patients. Overall 21.8% patients were presented with QT-DDIs. Of total 288 identified QT-DDIs, all were of major-severity and fair-documentation. According to AZCERT classification, 59.9% of the interacting drugs were included in list-1 (known risk of TdP), 4.7% in list-2 (possible risk of TdP) and 6.8% in list-3 (conditional risk of TdP). Univariate logistic regression analysis showed significant results for various predictors such as, 8–9 prescribed medications (p < 0.001) and ≥10 medications (p < 0.001), 2 QT drugs (p < 0.001) and ≥3 QT drugs (p < 0.001), breast cancer (p = 0.03), gastrointestinal cancer (p = 0.03), 4–5 supportive care drugs (p < 0.001), 6–8 supportive care drugs (p < 0.001) and >8 supportive care drugs (p < 0.001).ConclusionsA high prevalence of QT prolonging drugs and QT-DDIs was reported in oncology. Appropriate precautions are needed to prevent harmful consequences of these interactions.Electronic supplementary materialThe online version of this article (10.1186/s40360-017-0181-2) contains supplementary material, which is available to authorized users.

Background: Cancer patients often receive multiple drugs to maximize their therapeutic benefit, treat co-morbidities and counter the adverse effects of chemotherapy. Concomitant administration of multiple drugs increases the risk of drug interactions leading to compromised therapeutic efficacy or safety of therapy. The purpose of this study was to identify the prevalence, levels and predictors of potential drug-drug interactions (pDDIs) among cancer patients. Methods: Six hundred and seventy-eight patients receiving chemotherapy from two tertiary care hospitals were included in this study. Patient medication profiles were screened for pDDIs using the Micromedex® database. Logistic regression analysis was performed to identify the predictors of pDDIs. Results: The overall prevalence of pDDIs was 78%, majority of patients had 1-2 pDDIs (39.2%). A total of 1843 pDDIs were detected. Major-pDDIs were most frequent (67.3%) whereas, a significant association of pDDIs was found between >7 all prescribed drugs (p <0.001) and ≥3 anti-cancer drugs (p <0.001). Potential adverse outcomes of these interactions include reduced therapeutic effectiveness, QT interval prolongation, tendon rupture, bone marrow suppression and neurotoxicity. Conclusions: Major finding of this study is the high prevalence of pDDIs signifying the need of strict patient monitoring for pDDIs among cancer patients. Patients at higher risk to pDDIs include those prescribed with >7 any types of drugs or ≥3 anticancer drugs. Moreover, list of most frequently identified major and moderate interactions will aid health care professional in timely identification and prevention of pDDIs.

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Background: Cancer patients often receive multiple drugs to maximize their therapeutic benefit, treat co-morbidities and counter the adverse effects of chemotherapy. Concomitant administration of multiple drugs increases the risk of drug interactions leading to compromised therapeutic efficacy or safety of therapy. The purpose of this study was to identify the prevalence, levels and predictors of potential drug-drug interactions (pDDIs) among cancer patients. Methods: Six hundred and seventy-eight patients receiving chemotherapy from two tertiary care hospitals were included in this cross-sectional study. Patient medication profiles were screened for pDDIs using the Micromedex® database. Logistic regression analysis was performed to identify the predictors of pDDIs. Results: The overall prevalence of pDDIs was 78%, majority of patients had 1-2 pDDIs (39.2%). A total of 1843 pDDIs were detected. Major-pDDIs were most frequent (67.3%) whereas, a significant association of pDDIs was found between >7 all prescribed drugs (p <0.001) and ≥3 anti-cancer drugs (p <0.001). Potential adverse outcomes of these interactions include reduced therapeutic effectiveness, QT interval prolongation, tendon rupture, bone marrow suppression and neurotoxicity. Conclusions: Major finding of this study is the high prevalence of pDDIs signifying the need of strict patient monitoring for pDDIs among cancer patients. Patients at higher risk to pDDIs include those prescribed with >7 any types of drugs or ≥3 anticancer drugs. Moreover, list of most frequently identified major and moderate interactions will aid health care professional in timely identification and prevention of pDDIs.