Ipilimumab and nivolumab induced immune-related adverse events in metastatic mucosal melanoma

Diabetic ketoacidosis induced by nivolumab in invasive mucinous adenocarcinoma of the lung: a case report and review of the literature

Background

Nivolumab is the first programmed cell death receptor 1 (PD-1) inhibitor approved in China. Compared with chemotherapy, nivolumab has shown advantages of good efficacy and safety in the treatment of a variety of tumors. However, due to its short time of use in China and lack of safety experience, clinical understanding of its adverse reactions has not been sufficiently elucidated. In recent years, cases of diabetic ketoacidosis caused by nivolumab have been reported in the emergency department, which has aroused our concern.

Case Description

Here we present a serious case of diabetic ketoacidosis in a 69-year-old woman with invasive mucinous adenocarcinoma of the lung, which occurred following therapy with the PD-1 inhibitor nivolumab and dendritic cell/cytokine-induced killer cell (DC/CIK) immunotherapy. She presented with diabetic ketoacidosis 5 days after the second cycle of nivolumab administration. The patient presented with dry mouth symptoms, a maximum blood glucose of 511.2 mg/dL, hemoglobin A1c (HbA1c) level of 7.4%, urine ketone body value of 3+, and extracellular fluid residual alkali level of -3.8 mmol/L. Normal saline and insulin was initiated. The patient had no history of obesity or family history of diabetes. She received a single dose of 3.75 mg of dexamethasone treatment during this period of time which resulted in cough improvement, but did not explain the onset of the diabetes. She was treated with insulin, sitagliptin phosphate tablets and acarbose tablets. Diabetic ketoacidosis was considered an immune-related toxicity caused by nivolumab, and consequently, treatment with nivolumab was suspended. Patient was maintained under insulin treatment with a blood glucose levels normalization.

Conclusions

The incubation period of nivolumab-induced diabetic ketoacidosis is dispersive and the clinical risk is high. Patients need life-long insulin therapy. Blood glucose and HbA1c should be monitored routinely before and during nivolumab immunotherapy to avoid the occurrence of diabetic ketoacidosis. After the occurrence of diabetic ketoacidosis, insulin should be used to actively control blood glucose and do a good job in medication education to ensure long-term compliance of patients. Nivolumab should only be initiated if the patient has a clinical benefit under stable glucose control.

Factors Associated with Thyroid-Related Adverse Events in Patients Receiving PD-1 or PD-L1 Inhibitors Using Machine Learning Models

Simple Summary

Although immune checkpoint inhibitors have a potential role in thyroid-related complications, no study has investigated factors associated with such adverse events. This study aims to explore the factors associated with thyroid-related adverse events in patients with anti-PD-1/PD-L1 agents by training predictive models utilizing various machine learning approaches. The results of this study could be used to develop individually tailored intervention strategies to prevent immune checkpoint inhibitor-induced thyroid-related outcomes.

Abstract

Targets of immune checkpoint inhibitors (ICIs) regulate immune homeostasis and prevent autoimmunity by downregulating immune responses and by inhibiting T cell activation. Although ICIs are widely used in immunotherapy because of their good clinical efficacy, they can also induce autoimmune-related adverse events. Thyroid-related adverse events are frequently associated with anti-programmed cell death 1 (PD-1) or anti-programmed cell death-ligand 1 (PD-L1) agents. The present study aims to investigate the factors associated with thyroid dysfunction in patients receiving PD-1 or PD-L1 inhibitors and to develop various machine learning approaches to predict complications. A total of 187 patients were enrolled in this study. Logistic regression analysis was conducted to investigate the association between such factors and adverse events. Various machine learning methods were used to predict thyroid-related complications. After adjusting for covariates, we found that smoking history and hypertension increase the risk of thyroid dysfunction by approximately 3.7 and 4.1 times, respectively (95% confidence intervals (CIs) 1.338–10.496 and 1.478–11.332, p = 0.012 and 0.007). In contrast, patients taking opioids showed an approximately 4.0-fold lower risk of thyroid-related complications than those not taking them (95% CI 1.464–11.111, p = 0.007). Among the machine learning models, random forest showed the best prediction, with an area under the receiver operating characteristic of 0.770 (95% CI 0.648–0.883) and an area under the precision-recall of 0.510 (95%CI 0.357–0.666). Hence, this study utilized various machine learning models for prediction and showed that factors such as smoking history, hypertension, and opioids are associated with thyroid-related adverse events in cancer patients receiving PD-1/PD-L1 inhibitors.