Venlafaxine-induced interstitial lung disease with COVID-19 pandemic-related depression.

Venlafaxine-associated pulmonary toxicity is rare, with only a few reports of pneumonitis, eosinophilic pneumonia, and asthma. We report a case of venlafaxine-induced interstitial lung disease in a patient with coronavirus disease 2019 pandemic-related depression. Chest imaging findings improved after discontinuation of venlafaxine and treatment with corticosteroids.

Rapid Improvement Following Receipt of Infliximab in Steroid-refractory Durvalumab-Associated Grade 3 Pneumonitis.

Immune checkpoint inhibitors (ICIs) are important novel agents used in advanced non-small cell lung cancer (NSCLC) standard regimens; however, their use increases the risk of immune-related adverse effects (IRAEs). The incidence of IRAE pneumonitis is well documented in ICI use. Corticosteroids continue to be the mainstay of treatment for IRAEs. Here we report one of the first cases of using infliximab to treat durvalumab-associated pneumonitis.

Deep Learning Algorithm Trained with COVID-19 Pneumonia Also Identifies Immune Checkpoint Inhibitor Therapy-Related Pneumonitis.

BACKGROUND: Coronavirus disease 2019 (COVID-19) pneumonia and immune checkpoint inhibitor (ICI) therapy-related pneumonitis share common features. The aim of this study was to determine on chest computed tomography (CT) images whether a deep convolutional neural network algorithm is able to solve the challenge of differential diagnosis between COVID-19 pneumonia and ICI therapy-related pneumonitis. METHODS: We enrolled three groups: a pneumonia-free group (n = 30), a COVID-19 group (n = 34), and a group of patients with ICI therapy-related pneumonitis (n = 21). Computed tomography images were analyzed with an artificial intelligence (AI) algorithm based on a deep convolutional neural network structure. Statistical analysis included the Mann-Whitney U test (significance threshold at p < 0.05) and the receiver operating characteristic curve (ROC curve). RESULTS: The algorithm showed low specificity in distinguishing COVID-19 from ICI therapy-related pneumonitis (sensitivity 97.1%, specificity 14.3%, area under the curve (AUC) = 0.62). ICI therapy-related pneumonitis was identified by the AI when compared to pneumonia-free controls (sensitivity = 85.7%, specificity 100%, AUC = 0.97). CONCLUSIONS: The deep learning algorithm is not able to distinguish between COVID-19 pneumonia and ICI therapy-related pneumonitis. Awareness must be increased among clinicians about imaging similarities between COVID-19 and ICI therapy-related pneumonitis. ICI therapy-related pneumonitis can be applied as a challenge population for cross-validation to test the robustness of AI models used to analyze interstitial pneumonias of variable etiology.