Survival of Patients Treated with Antibiotics and Immunotherapy for Cancer: A Systematic Review and Meta-Analysis.
Fausto PetrelliAlessandro IaculliDiego SignorelliAntonio GhidiniLorenzo DottoriniGianluca PeregoMichele GhidiniAlberto ZaniboniStefania GoriAlessandro InnoPublished in: Journal of clinical medicine (2020)
Antibiotics (ABs) are common medications used for treating infections. In cancer patients treated with immune checkpoint inhibitors (ICIs), concomitant exposure to ABs may impair the efficacy of ICIs and lead to a poorer outcome compared to AB non-users. We report here the results of a meta-analysis evaluating the effects of ABs on the outcome of patients with solid tumours treated with ICIs. PubMed, the Cochrane Library and Embase were searched from inception until September 2019 for observational or prospective studies reporting the prognoses of adult patients with cancer treated with ICIs and with or without ABs. Overall survival (OS) was the primary endpoint, and progression-free survival (PFS) was the secondary endpoint. The effect size was reported as hazard ratios (HRs) with a 95% confidence interval (CI) and an HR > 1 associated with a worse outcome in ABs users compared to AB non-users. Fifteen publications were retrieved for a total of 2363 patients. In the main analysis (n = 15 studies reporting data), OS was reduced in patients exposed to ABs before or during treatment with ICIs (HR = 2.07, 95%CI 1.51-2.84; p < 0.01). Similarly, PFS was inferior in AB users in n = 13 studies with data available (HR = 1.53, 95%CI 1.22-1.93; p < 0.01). In cancer patients treated with ICIs, AB use significantly reduced OS and PFS. Short duration/course of ABs may be considered in clinical situations in which they are strictly needed.
Keyphrases
- free survival
- end stage renal disease
- papillary thyroid
- newly diagnosed
- ejection fraction
- chronic kidney disease
- peritoneal dialysis
- prognostic factors
- emergency department
- childhood cancer
- machine learning
- lymph node metastasis
- squamous cell carcinoma
- big data
- electronic health record
- patient reported outcomes
- data analysis
- deep learning