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To evaluate medication-related risks in older patients with cancer and their association with severe toxicity during antineoplastic therapy.This is a secondary analysis of two prospective, single-center observational studies which included patients ≥ 70 years with cancer. The patients' medication lists were investigated regarding possible risks: polymedication (defined as the use of ≥ 5 drugs), potentially inappropriate medication (PIM), and relevant potential drug-drug interactions (rPDDI). The risks were analyzed before and after start of cancer therapy. Severe toxicity during antineoplastic therapy was captured from medical records according to the Common Terminology Criteria for Adverse Events (CTCAE). The association between grade ≥ 3 toxicity and medication risks was evaluated by univariate as well as multivariate regression adjusted by ECOG and age.The study cohort comprised 136 patients (50% female, mean age 77 years, 42% hematological malignancies). Before the start of cancer therapy, patients took on average 5 drugs as long-term medication and 52% of patients were exposed to polymedication. More than half of patients used at least one PIM. Approximately one third of patients exhibited rPDDI. The prevalence of medication risks increased after start of cancer therapy. rPDDI were significantly associated with severe overall toxicity (OR, 5.07; p = 0.036; 95% Confidence Interval (CI) 1.11-23.14; toxicity in patients with rPDDI 94.1% (32/34) vs 75.9% (60/79) in patients without rPDDI) and hematological toxicity (OR, 3.95; p = 0.010; 95% CI 1.38-11.29; hematological toxicity in patients with rPDDI 85.3% (29/34) vs 59.5% (47/79) in patients without rPDDI). In the multivariate analysis adjusted by ECOG and age, only the association for rPDDI with hematological toxicity remained statistically significant (OR, 4.51; p = 0.007; 95% CI 1.52-13.38). These findings should be further investigated in larger studies.Medication risks are common in older patients with cancer and might be associated with toxicity. This raises the need for tailored interventions to ensure medication safety in this patient cohort.

AIMS OF THE STUDY Our aim was to explore drug-induced liver injury (DILI) in Switzerland using the real-world data of the global pharmacovigilance database VigiBase™, with a special focus on the new drug class of checkpoint inhibitors. This is the first study investigating drug-related hepatic disorders in Switzerland in a global pharmacovigilance database. METHODS This was a retrospective study analysing the ICSRs (individual case safety reports) of the global pharmacovigilance database VigiBase™. We explored all ICSRs submitted in Switzerland within the last 10 years (1 July 2010 to 30 June 2020). For data extraction, the standardised MedDRA query (SMQ) “narrow drug-related hepatic disorders – severe events only” was applied. The ICSRs, drug-reaction pairs and adverse drug reactions were analysed descriptively, including a special focus on checkpoint inhibitors. For comparing the hepatic adverse drug reactions of pembrolizumab, nivolumab and ipilimumab, the reporting odds ratios (RORs) were calculated in a disproportionality analysis. RESULTS In total, 2042 ICSRs could be investigated, comprising 10,646 drugs and 6436 adverse drug reactions. Gender was equally distributed between male and female. Patients were on average 57 years old. The mortality rate was high, with fatal adverse reactions in over 10% of cases. On average, patients used five drugs including two suspected drugs. Paracetamol, amoxicillin/clavulanic acid, esomeprazole and atorvastatin ranked among the most frequently suspected drugs for severe drug-related hepatic disorders. However, Vigibase™ data are not appropriate for judging causality and these results should be interpreted with caution owing to the possible influences of comedication or comorbidity. An average of three adverse drug reactions per ICSR were reported, most frequently including hepatocellular injury, cholestatic liver injury, and liver injury. For checkpoint inhibitors, hepatitis was the most frequently reported hepatic adverse drug reaction. In comparison with nivolumab and ipilimumab, pembrolizumab had a significantly higher ROR for hepatitis (2.41, p = 0.016), but also a lower ROR for autoimmune hepatitis (0.11, p = 0.009). CONCLUSION Our findings highlight the importance for healthcare providers in Switzerland to pay special attention to possible drug-induced liver injuries because of their high mortality rate. The analysis of real-world data confirms the previous assumption that hepatitis is the most frequent hepatic adverse event for checkpoint inhibitors. Further clinical studies are warranted to directly compare hepatic adverse drug reactions to different checkpoint inhibitors.

Objectives To compare the CARG (Cancer and Aging Research Group) and CRASH (Chemotherapy Risk Assessment Scale for High-Age Patients) score regarding the predictive performance for severe toxicity in older patients with cancer. Methods We recruited patients ≥70 years and applied the CARG and CRASH score before the start of systemic cancer treatment. The CARG predicts severe overall toxicity; the CRASH additionally predicts hematologic and nonhematologic toxicity. We captured ≥ grade 3 toxicity according to Common Terminology Criteria for Adverse Events (CTCAE) from medical records. Predictive performance was assessed using logistic regression and the area under the receiver operating characteristic curve (ROC-AUC). Results The study cohort comprised 120 patients (50% female, mean age 77.2 years, 57% solid tumors). The median of the CARG (range 0–23) and the combined CRASH (range 0–12) were 9 and 8, respectively. 81% of patients experienced toxicity; 67% showed hematologic toxicity. The predictive performance of the CARG and the combined CRASH was similar for overall toxicity (CARG: Odds ratio per unit increase (OR) 1.266, P = .015; ROC-AUC 0.681, P = .010; combined CRASH: OR 1.337, P = .029; ROC-AUC 0.650, P = .032). For hematologic toxicity, the hematologic CRASH was a significant predictor and showed numerically a higher ROC-AUC than the CARG which was not statistically different (CARG: OR 1.048, P = .462; ROC-AUC 0.564, P = .271; hematologic CRASH: OR 1.602, P = .007; ROC-AUC 0.665, P = .005). Conclusion Both scores exhibited similar predictive performance for toxicity in older patients with cancer.