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.

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A substantial number of patients were exposed to risk factors for QT prolongation; and QT prolonging drugs such as proton pump inhibitors, antimicrobials and diuretics which may lead to serious outcomes.

Moxifloxacin is widely used for the treatment of a number of infectious diseases because of its favorable pharmacological profile and high clinical success rate. However, it is often criticized for its higher risk of QTc interval prolongation (QTIP) and torsades de pointes (TdP). Areas covered: A review of published literature on moxifloxacin-related QTIP and TdP. Readers will be provided with a comprehensive overview of the prevalence, cellular mechanism, risk factors, and magnitude of QTIP of moxifloxacin. Expert commentary: In healthy subjects, moxifloxacin prolongs the QTc interval by 11.5-19.5 ms, it binds at the Tyr652 residue in the S6 pore domain of the human ether a-go-go gene related potassium channel. Considerable QTIP (30-60 ms) have also been reported in some patients, for instance the incidence of QTIP (30-60 ms) in elderly pneumonia patients was 15.5%. Moxifloxacin-induced QTIP may be of little clinical importance in healthy individuals. However, marked QTIP (>60 ms) and TdP have been reported in high-risk patients (patients who have multiple QT prolonging risk factors). Patients must be thoroughly assessed prior to the use of moxifloxacin and high-risk patients must be identified using risk assessment tools to ensure safe use of moxifloxacin and to safeguard patients' health.

Background Pneumonia patients are usually hospitalized due to severe nature of the disease or for the management of comorbid illnesses or associated symptoms. Such patients are prescribed with multiple medications which increase the likelihood of potential drug-drug interactions (pDDIs). Therefore, in this study the prevalence, levels (severity and documentation), predictors (risk factors), and clinical relevance of pDDIs among inpatients diagnosed with pneumonia have been investigated. Methods Clinical records of 431 hospitalized patients with pneumonia were checked for pDDIs using drug interactions screening software (Micromedex-DrugReax). Odds-ratios for predictors were calculated using logistic regression analysis. Clinical relevance of pDDIs was assessed by evaluation of patients’ clinical profiles for potential adverse outcomes of the most frequent pDDIs. Abnormal patients’ signs/symptoms and laboratory investigations indicating adverse outcomes of interactions were reported. Results Of total 431 profiles, pDDIs were reported in 73.1%. Almost half of the profiles were having major-pDDIs (53.8%). Total number of pDDIs were 1318, of which 606 were moderate- and 572 were major-pDDIs. Patient’s profiles identified with the most frequent interactions were presented with signs, symptoms, and abnormalities in labs indicating decrease therapeutic response, electrolyte abnormalities, hypoglycemia, bleeding, hepatotoxicity, and hypertension. These adverse events were more prevalent in patients taking higher doses of the interacting drugs as compared to lower doses. Logistic regression analysis revealed significant association for major-pDDIs with 6–10 prescribed medicines (OR = 26.1; p = 0.002), > 10 prescribed medicines (OR = 144; p <  0.001), and tuberculosis (OR = 8.2; p = 0.004). Conclusions PDDIs are highly prevalent in patients with pneumonia. Most frequent and clinically important pDDIs need particular attention. Polypharmacy and tuberculosis increase the risk of pDDIs. Identifying patients more at risk to pDDIs and careful monitoring of pertinent signs/symptoms and laboratory investigations are important measures to reduce pDDIs and their related adverse consequences.

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 QT prolongation and associated arrhythmias, torsades de pointes (TdP), are considerable negative outcomes of many antipsychotic and antidepressant agents frequently used by psychiatric patients. Objective To identify the prevalence, levels, and predictors of QT prolonging drug-drug interactions (QT-DDIs), and AZCERT (Arizona Center for Education and Research on Therapeutics) classification of drugs involved in QT-DDIs. Setting Psychiatry wards of three major tertiary care hospitals of Khyber-Pakhtunkhwa, Pakistan. Method This was a multicenter cross-sectional study. Micromedex DrugReax was used for identification of QT-DDIs. TdP risks were identified by the AZCERT classification. Multivariate logistic regression analysis was performed to identify predictors of QT-DDIs. Main outcome measure Prevalence of QT-DDIs (overall, age-wise and gender-wise) and their levels of severity and documentation; AZCERT classes of drugs involved in QT-DDIs; and odds ratios for predictors of QT-DDIs. Results Of 600 patients, 58.5% were female. Median age was 25 years (IQR = 20-35). Overall 51.7% patients had QT-DDIs. Of total 698 identified QT-DDIs, most were of major-severity (98.4%) and fair-documentation (93.7%). According to the AZCERT classification, 36.4% of the interacting drugs were included in list-1 (known risk of TdP), 26.9% in list-2 (possible risk of TdP) and 27.5% in list-3 (conditional risk of TdP). Drugs commonly involved in QT-DDI were olanzapine (n = 146), haloperidol (138), escitalopram (122), risperidone (91), zuclopenthixol (87), quetiapine (n80) and fluoxetine (74). In multivariate logistic regression analysis, QT-DDIs were significantly associated with 6-7 prescribed medications (p = 0.04) and >7 medications (p = 0.03). Similarly, there was significant association of occurrence of QT-DDIs with 2-3 QT drugs (p < 0.001) and >3 QT drugs (p < 0.001). Conclusion A considerable number of patients are exposed to QT-DDIs in psychiatry. There is a need to implement protocol for monitoring the outcomes of QT-DDIs.

A substantial number of patients in cardiology wards presented with QT prolongation. Proper considerations are needed in order to minimize the associated risk particularly in patients with abnormally high QT prolongation, old age, polypharmacy, one or more QT-prolonging drugs, and high pro-QTc scores.