Colitis following the use of immune checkpoint inhibitors: A real-world analysis of spontaneous reports submitted to the FDA adverse event reporting system |
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Affiliation: | 1. Department of Pharmacy, Peking Union Medical College Hospital, Beijing 100730, PR China;2. Department of Pharmacy, The First Affiliated Hospital of Harbin Medical University, Harbin 150001, PR China;3. Research Group of Pharmcoepidemiology and Clinical Drug Evaluation, Shenyang Pharmaceutical University, Shenyang 110016, PR China;4. Department of Procurement Office, The Fourth Procurement Service Bureau of Joint Logistics Support Army, Shenyang 110000, PR China;5. State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, PR China |
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Abstract: | BackgroundAlthough colitis has been reported in patients treated with immune checkpoint inhibitors (ICIs), associations between colitis and ICIs had not been thoroughly assessed in real-world studies. Here, we identified and characterized significant colitis-associated with ICIs.MethodsBased on the Food and Drug Administration Adverse Event Reporting System (FAERS) from January 2004 to December 2019, the disproportionality analysis and Bayesian analysis, including the reporting odds ratio (ROR), the proportional reporting ratio (PRR), the Bayesian confidence propagation neural network (BCPNN) and the multi-item gamma Poisson shrinker (MGPS) algorithms were adopted to data mining of the suspected adverse events of colitis after ICIs administrating. Clinical characteristics of patients with ICIs-associated colitis and the time to onset of colitis following different ICI regimens were collected.ResultsA total of 3786 reports of colitis adverse events were identified with ICIs. Seven ICI monotherapies were associated with the reporting of colitis. Statistically significant ROR, PRR, information component (IC), and empirical Bayesian geometric mean (EBGM) emerged for all ICI monotherapies and combination therapies. ICIs-associated colitis affected mostly male (53.51%), with a wide mean age range (60.65 to 72 years). Colitis adverse events were commonly reported in patients with melanoma and lung cancer. Adverse outcomes of colitis concerning ICI were mainly outcomes of hospitalization-initiated or prolonged and other serious. Among colitis cases, 17.43% cases of colitis concerning ICI lead to death. The adverse event of colitis occurred earliest in ipilimumab monotherapy with a median time to onset of 64.21 days (IQR: 27–69 days) among all monotherapies.ConclusionsICI may lead to severe and disabling ICIs-associated colitis during therapy. Analysis of FAERS data identified signals for adverse events of colitis with ICI regimens. Practitioners should consider the factors that may increase the likelihood of colitis. The findings support a continued surveillance and risk factor identification studies. |
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Keywords: | Immune checkpoint inhibitors Colitis Immune-related adverse events Spontaneous reporting system Data mining ICIs" },{" #name" :" keyword" ," $" :{" id" :" k0035" }," $$" :[{" #name" :" text" ," _" :" immune checkpoint inhibitors FAERS" },{" #name" :" keyword" ," $" :{" id" :" k0045" }," $$" :[{" #name" :" text" ," _" :" Food and Drug Administration Adverse Event Reporting System CTLA-4" },{" #name" :" keyword" ," $" :{" id" :" k0055" }," $$" :[{" #name" :" text" ," _" :" cytotoxic T-lymphocyte antigen 4 PD-1" },{" #name" :" keyword" ," $" :{" id" :" k0065" }," $$" :[{" #name" :" text" ," _" :" programmed cell death 1 PD-L1" },{" #name" :" keyword" ," $" :{" id" :" k0075" }," $$" :[{" #name" :" text" ," _" :" programmed cell death ligand 1 FDA" },{" #name" :" keyword" ," $" :{" id" :" k0085" }," $$" :[{" #name" :" text" ," _" :" Food and Drug Administration irAEs" },{" #name" :" keyword" ," $" :{" id" :" k0095" }," $$" :[{" #name" :" text" ," _" :" immune-related adverse events ROR" },{" #name" :" keyword" ," $" :{" id" :" k0105" }," $$" :[{" #name" :" text" ," _" :" reporting odds ratio PRR" },{" #name" :" keyword" ," $" :{" id" :" k0115" }," $$" :[{" #name" :" text" ," _" :" proportional reporting ratio BCPNN" },{" #name" :" keyword" ," $" :{" id" :" k0125" }," $$" :[{" #name" :" text" ," _" :" Bayesian confidence propagation neural network MGPS" },{" #name" :" keyword" ," $" :{" id" :" k0135" }," $$" :[{" #name" :" text" ," _" :" multi-item gamma Poisson shrinker |
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