Background: Diabetes mellitus and cancer are amongst the leading causes of deaths worldwide; hyperglycemia plays a major contributory role in neoplastic transformation risk. From reported adverse events of PD-1 or PD-L1 (programmed death 1 or ligand 1) inhibitors in post-marketing monitoring, we aimed to construct an effective machine learning algorithm to predict the probability of hyperglycemic adverse reaction from PD-1/PD-L1 inhibitors treated patients efficiently and rapidly. Methods: Raw data was downloaded from US Food and Drug Administration Adverse Event Reporting System (FDA FAERS). Signal of relationship between drug and adverse reaction based on disproportionality analysis and Bayesian analysis. A multivariate pattern classification of Support Vector Machine (SVM) was used to construct classifier to separate adverse hyperglycemic reaction patients. A 10-fold-3-time cross validation for model setup within training data (80% data) output best parameter values in SVM within R software. The model was validated in each testing data (20% data) and two total drug data, with exactly predictor parameter variables: gamma and nu. Results: Total 95918 case files were downloaded from 7 relevant drugs (cemiplimab, avelumab, durvalumab, atezolizumab, pembrolizumab, ipilimumab, nivolumab). The number-type/number-optimization method was selected to optimize model. Both gamma and nu values correlated with case number showed high adjusted r2 in curve regressions (both r2 >0.95). Indexes of accuracy, F1 score, kappa and sensitivity were greatly improved from the prediction model in training data and two total drug data. Conclusions: The SVM prediction model established here can non-invasively and precisely predict occurrence of hyperglycemic adverse drug reaction (ADR) in PD-1/PD-L1 inhibitors treated patients. Such information is vital to overcome ADR and to improve outcomes by distinguish high hyperglycemia-risk patients, and this machine learning algorithm can eventually add value onto clinical decision making.

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Objective. Immune checkpoint inhibitors, such as programmed death-1/ligand-1 (PD-1/L1), exhibited autoimmune-like disorders, and hyperglycemia was on the top of grade 3 or higher immune-related adverse events. Machine learning is a model from past data for future data prediction. From post-marketing monitoring, we aimed to construct a machine learning algorithm to efficiently and rapidly predict hyperglycemic adverse reaction in patients using PD-1/L1 inhibitors. Methods. In original data downloaded from Food and Drug Administration Adverse Event Reporting System (US FAERS), a multivariate pattern classification of support vector machine (SVM) was used to construct a classifier to separate adverse hyperglycemic reaction patients. With correct core SVM function, a 10-fold 3-time cross validation optimized parameter value composition in model setup with R language software. Results. The SVM prediction model was set up from the number type/number optimization method, as well as the kernel and type of “rbf” and “nu-regression” composition. Two key values (nu and gamma) and case number displayed high adjusted r2 in curve regressions ( n u = 0.5649 × e −   case / 6984 , gamma = 9.005 × 10 − 4 × case − 4.877 × 10 − 8 × case 2 ). This SVM model with computable parameters greatly improved the assessing indexes (accuracy, F1 score, and kappa) as well as coequal sensitivity and the area under the curve (AUC). Conclusion. We constructed an effective machine learning model based on compositions of exact kernels and computable parameters; the SVM prediction model can noninvasively and precisely predict hyperglycemic adverse drug reaction (ADR) in patients treated with PD-1/L1 inhibitors, which could greatly help clinical practitioners to identify high-risk patients and perform preventive measurements in time. Besides, this model setup process provided an analytic conception for promotion to other ADR prediction, such ADR information is vital for outcome improvement by identifying high-risk patients, and this machine learning algorithm can eventually add value to clinical decision making.