Immune Checkpoint Inhibitor-related Pneumonitis Research Articles

Risk factors of immune checkpoint inhibitor-related pneumonitis after neoadjuvant immunochemotherapy for resectable NSCLC

BackgroundThe incidence of checkpoint inhibitor-associated pneumonitis (CIP) in advanced non-small cell lung cancer (NSCLC) has been substantiated through large-scale clinical trials or real-world studies. However, reports on CIP incidence within the context of neoadjuvant immunotherapy for resectable NSCLC remain scarce. This study endeavors to investigate the incidence, risk factors, and outcomes of CIP in patients with resectable NSCLC receiving neoadjuvant immunochemotherapy.MethodsA retrospective, case-control study was conducted on patients diagnosed with NSCLC stages IIA–IIIB who received neoadjuvant immunochemotherapy between January 2018 and September 2022. Patients were stratified into two groups based on the presence or absence of CIP, facilitating a comparative analysis of clinical characteristics, treatment modalities, physiological indicators, and prognostic outcomes .ResultsThe study cohort comprised 245 patients, with 11.4% (28/245) experiencing CIP. The median period of CIP onset was 70 (range, 40–221) days. The incidence of severe CIP (grade 3–4) was 3.7% (9/245). Patients with CIP showed a higher all-cause mortality rate of 21.4% (6/28) compared to that of patients without CIP. Those who developed CIP exhibited elevated body mass index (BMI) values (p = 0.028) and increased fibrinogen (FIB) levels (p < 0.001), alongside a significant decrease in both diffusing capacity for carbon monoxide (DLCO)% pred (p = 0.001) and DLCO/VA% pred (p = 0.021) after neoadjuvant therapy compared to pre-indicators. Receiver operating characteristic curve (ROC) analysis showed that the area under the ROC curve of three assessed variables (FIB levels, BMI, DLCO) reached 0.806 in predicting CIP occurrence at an early stage.ConclusionsThis cohort demonstrated that elevated BMI, increased FIB levels, and decreased pulmonary diffusion function after neoadjuvant therapy are risk factors of CIP occurrence. Early assessment and continuous monitoring of these indicators are imperative for the predictive identification of CIP, enhancing patient management and outcomes.

Autoimmunity Against Surfactant Protein B Is Associated with Pneumonitis During Checkpoint Blockade.

Immune checkpoint inhibitor-related pneumonitis is a serious autoimmune event affecting up to 20% of patients with non-small cell lung cancer, yet the factors underpinning its development in some patients and not others are poorly understood. To investigate the role of autoantibodies and autoreactive T cells against surfactant-related proteins in the development of pneumonitis. The study cohort consisted of non-small cell lung cancer patients who gave blood samples before and during immune checkpoint inhibitor treatment. Serum was used for proteomics analyses and to detect autoantibodies present during pneumonitis. T cell stimulation assays and single-cell RNA sequencing were performed to investigate the specificity and functionality of peripheral autoreactive T cells. The findings were confirmed in a validation cohort comprising patients with non-small cell lung cancer and patients with melanoma. Across both cohorts, patients who developed pneumonitis had higher pre-treatment levels of immunoglobulin G autoantibodies targeting surfactant protein-B. At the onset of pneumonitis, these patients also exhibited higher frequencies of CD4+ interferon-gamma-positive surfactant protein B-specific T cells, and expanding T cell clonotypes recognizing this protein, accompanied by a pro-inflammatory serum proteomic profile. Our data suggest that the co-occurrence of surfactant protein-B-specific immunoglobulin G autoantibodies and CD4+ T cells is associated with the development of pneumonitis during ICI therapy. Pre-treatment levels of these antibodies may represent a potential biomarker for elevated risk of developing pneumonitis and on-treatment levels may provide a diagnostic aid. This article is open access and distributed under the terms of the Creative Commons Attribution Non-Commercial No Derivatives License 4.0 (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Abstract 2594: AI-powered radiomics model predicts immune checkpoint inhibitor-related pneumonitis (CIP) in advanced NSCLC patients

Abstract Background: With the escalating integration of immunotherapy in the management of advanced non-small cell lung cancer (NSCLC), the emergence of adverse events, particularly immune checkpoint inhibitor-related pneumonitis (CIP), poses important challenges. CIP is not uncommon and can be life-threatening. It often necessitates the discontinuation of immunotherapy, even in patients with an otherwise favorable response. Prevention, early detection and early management of CIP can enhance patient outcomes, yet no such predictive models have been established. This study investigates the use of Artificial Intelligence (AI) algorithms in analyzing radiomic features for the prediction of CIP in NSCLC patients receiving immunotherapy. Methods: A cohort of 105 stage III-IV NSCLC patients receiving immunotherapy was examined. Half of the patients were randomly split to a training set, while the remaining half were reserved for algorithm testing. The manual segmentation was performed by three physicians annotating in consensus using LIFEx software v7.3.0 (IMIV/CEA, Orsay, France). The Picture Health Px platform was utilized to perform an AI-powered deep phenotyping of the tumor and its surrounding habitat. A number of interpretable feature measures were extracted from baseline CT scans, which were in turn used to train a deep learning classifier for the detection of pneumonitis. Weighting techniques were applied to compensate for the imbalance of pneumonitis cases. Results: Among the 105 patients, 63 (60.0%) received immunotherapy-only regimen and 42 (40.0%) received combination immunochemotherapy. 18 (17.1%) patients had pneumonitis events. Within this subset, 10 (55.6%) had CIP. Among the CIP group, six patients (60.0%) had grade 1 pneumonitis, three patients (30.0%) had grade 2 pneumonitis, and one patient (10.0%) had grade 3 pneumonitis, and none had grade 4 or grade 5 pneumonitis. Within the training set (n=51), the cross-validated area under the ROC curve (AUC) was 0.71 (95% CI: 0.68-0.74). When applied to the test set, the model predicted pneumonitis with AUC=0.63. Across the two datasets, the model correctly identified 4/6 (66.7%) grade 1 pneumonitis events and 2/3 (66.7%) grade 2 pneumonitis events, but misclassified the only available grade 3 event. Conclusion: The utilization of CT-based radiomic features demonstrates promise in predicting CIP in NSCLC patients undergoing immunotherapy. This approach holds potential for enhancing the identification and management of CIP, among NSCLC patients treated with immunotherapy. Citation Format: Seyoung Lee, Amogh Hiremath, Jeeyeon Lee, Peter Haseok Kim, Kai Zhang, Salie Lee, Monica Yadav, Maria J. Chuchuca, Taegyu Um, Myungwoo Nam, Liam Il-Young Chung, Hye Sung Kim, Jisang Yu, Trie Arni Djunadi, Leeseul Kim, Youjin Oh, Sungmi Yoon, Zunairah Shah, Yuchan Kim, Ilene Hong, Grace Kang, Jessica Jang, Amy Cho, Soowon Lee, Cecilia Nam, Timothy Hong, Yuri S. Velichko, Vamsidhar Velcheti, Anant Madabhushi, Nathaniel Braman, Young Kwang Chae. AI-powered radiomics model predicts immune checkpoint inhibitor-related pneumonitis (CIP) in advanced NSCLC patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2594.

Abstract 7529: Harmonization radiomics model to predict immune checkpoint inhibitor-related pneumonitis (CIP) in non small cell lung cancer (NSCLC) in patients treated with immunotherapy

Abstract Background: With the increasing use of immunotherapy in advanced non-small cell lung cancer (NSCLC), managing adverse events, particularly checkpoint inhibitor-associated pneumonitis (CIP), poses a significant challenge. CIP often necessitates discontinuation of immunotherapy, even in cases of tumor response. Currently there are no effective models in predicting CIP. This study leverages artificial intelligence (AI) algorithms and the use of harmonization models to analyze radiomic features from chest CT scans, aiming to predict the occurrence of CIP in patients with NSCLC. Methods: This retrospective study examined data from 152 stage III-IV NSCLC patients undergoing immunotherapy. Tumor segmentation was performed using LIFEx software (IMIV/CEA, Orsay, France). 3D-radiomic features were extracted from both the tumor and the surrounding 1 cm thick peritumoral regions. The Random Forest (RF) algorithm was employed to develop a classification model to differentiate between CIP and non-CIP. A harmonization model was deployed to account for the scanner-related differences by using spleen and normal lung signals. The dataset was divided into a training set (75%) and a test set (25%). Bootstrapping with 1,000 iterations was performed to estimate the model's performance by calculating the median and 95% confidence interval (CI) estimate. The accuracy of the model's predictions was evaluated by creating a confusion matrix. The model's performance was assessed by calculating the sensitivity, specificity, positive predictive values (PPV), negative predictive values (NPV), and area under the ROC curve (AUC) for CIP prediction. Results: Among 152 patients, 87 (57.23%) are female and 65 (42.76%) are male. The median age was 59 years. Histology types included were Adenocarcinoma 111 (73%), Squamous cell carcinoma 27 (17.8%), and others 14 (9.2%). CIP was seen in 30 (19.73%) patients. Among the 30 patients with CIP, 17 (56.7%) patients had grade 1 pneumonitis, 11 (36.7%) patients had grade 2 pneumonitis, 2 (6.6%) patients had grade 3 pneumonitis, and none had grade 4 or grade 5 pneumonitis. The model achieved a sensitivity of 0.57, a specificity of 0.67, a PPV of 0.48, and NPV of 0.38 for predicting CIP. The AUC of 0.59 indicates that the model may predict checkpoint inhibitor-associated pneumonitis with an accuracy of 59%. Conclusions: The study provides insights into the potential of radiomic analysis coupled with AI algorithms and the use of harmonization model in predicting CIP among NSCLC patients treated with immunotherapy. Larger studies are needed to validate our findings and the utility of harmonization models in predicting CIP. Citation Format: Monica Yadav, Jeeyeon Lee, Peter Haseok Kim, Seyoung Lee, Taegyu Um, Salie Lee, Maria Jose Chuchuca, Trie Arni Djunadi, Liam Il-Young Chung, Jisang Yu, Darren Rodrigues, Nicolo Gennaro, Leeseul Kim, Myungwoo Nam, Youjin Oh, Sungmi Yoon, Zunairah Shah, Yuchan Kim, Ilene Hong, Jessica Jang, Grace Kang, Amy Cho, Soowon Lee, Timothy Hong, Cecilia Nam, Yury S Velichko, Young Kwang Chae. Harmonization radiomics model to predict immune checkpoint inhibitor-related pneumonitis (CIP) in non small cell lung cancer (NSCLC) in patients treated with immunotherapy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7529.

Treatment patterns and outcomes of high-grade immune checkpoint inhibitor-related pneumonitis in an oncology hospitalist service.

Immune checkpoint inhibitors (ICI) have become standard of care for some types of lung cancer. Along with expanding usage comes the emergence of immune-related adverse events (irAEs), including ICI-related pneumonitis (ICI-P). Treatment guidelines for managing irAEs have been developed; however, how clinicians manage irAEs in the real-world setting is less well known. We aimed to describe the outcomes and care patterns of grade ≥ 3 ICI-P in an onco-hospitalist service. We included patients with lung cancer treated with ICI who were admitted to an oncology hospitalist service with a suspicion of ICI-P. We described the hospitalization characteristics, treatment patterns, discharge practices, and clinical outcomes of patients with confirmed ICI-P. The primary outcome was time to start treatment for ICI-P. Among 49 patients admitted with a suspicion of ICI-P, 31 patients were confirmed to have ICI-P and subsequently received ICI-P directed treatment. Pulmonology was consulted in 97% of patients. Median time to start treatment for ICI-P was 1day (IQR 0-3.5days). All 31 patients received corticosteroids. Inpatient mortality was 32%. Majority of patients discharged with steroids were prescribed prophylaxis for gastritis and opportunistic infections. Thirty-eight percent of patients were seen by pulmonology and 86% were seen by the oncology team post-discharge. Our study confirms prior findings of high mortality among patients with high-grade ICI-P. Early diagnosis and treatment are key to improving clinical outcomes. Understanding the care patterns and adherence to treatment guidelines of clinicians caring for this patient population may help identify ways to further standardize management practices and improve patient outcomes.

Immune checkpoint inhibitor-related pneumonitis: research advances in prediction and management.

The advent of immune-checkpoint inhibitors (ICIs) has revolutionized the treatment of malignant solid tumors in the last decade, producing lasting benefits in a subset of patients. However, unattended excessive immune responses may lead to immune-related adverse events (irAEs). IrAEs can manifest in different organs within the body, with pulmonary toxicity commonly referred to as immune checkpoint inhibitor-related pneumonitis (CIP). The CIP incidence remains high and is anticipated to rise further as the therapeutic indications for ICIs expand to encompass a wider range of malignancies. The diagnosis and treatment of CIP is difficult due to the large individual differences in its pathogenesis and severity, and severe CIP often leads to a poor prognosis for patients. This review summarizes the current state of clinical research on the incidence, risk factors, predictive biomarkers, diagnosis, and treatment for CIP, and we address future directions for the prevention and accurate prediction of CIP.

Development and Validation of a Machine Learning–Based Model Using CT Radiomics for Predicting Immune Checkpoint Inhibitor-related Pneumonitis in Patients With NSCLC Receiving Anti-PD1 Immunotherapy: A Multicenter Retrospective Case[sbnd]Control Study

This study aimed to develop and evaluate a radiomics-based model combined with clinical and qualitative radiological (semantic feature [SF]) features to predict immune checkpoint inhibitor-related pneumonitis (CIP) in patients with non-small cell lung cancer (NSCLC) treated with programmed cell death protein 1 inhibitors. This was a multicenter retrospective casecontrol study conducted from January 1, 2018, to December 31, 2022, at three centers. Patients with NSCLC treated with anti-PD1 were enrolled and randomly divided into two groups (7:3): training (n=95) and validation (n=39). Logistic regression (LR) and support vector machine (SVM) algorithms were used to transform features into the models. The study comprised 134 participants from three independent centers (male, 114/134, 85%; mean [±standard deviation] age, 63.92 [±7.9]years). The radiomics score (RS) models built based on the LR and SVM algorithms could accurately predict CIP (area under the receiver operating characteristics curve [AUC], 0.860 [0.780, 0.939] and 0.861 [0.781, 0.941], respectively). The AUCs for the RS-clinic-SF combined model were 0.903 (0.839, 0.967) and 0.826 (0.688, 0.964) in the training and validation cohorts, respectively. Decision curve analysis showed that the combined models achieved high clinical net benefit across the majority of the range of reasonable threshold probabilities. This study demonstrated that the combined model constructed by the identified features of RS, clinical features, and SF has the potential to precisely predict CIP. The RS-clinic-SF combined model has the potential to be used more widely as a practical tool for the noninvasive prediction of CIP to support individualized treatment planning.

1016O Autoimmunity against surfactant protein B drives immune checkpoint inhibitor-related pneumonitis in patients with NSCLC

1016O Autoimmunity against surfactant protein B drives immune checkpoint inhibitor-related pneumonitis in patients with NSCLC

The Role of CT-Based Radiomics Nomogram in Differential Diagnosis of Immune Checkpoint Inhibitor-Related Pneumonitis from Radiation Pneumonitis for Patients with ESCC

The combination of immunotherapy and chemoradiotherapy has widely used for patients with esophageal squamous cell carcinoma (ESCC) and induced treatment-related adverse effects, particularly immune checkpoint inhibitor-related pneumonitis (CIP) and radiation pneumonitis (RP). The aim of this study is to differentiate between CIP and RP by the CT radiomics and clinical or radiological parameters. A total of 76 ESCC patients with pneumonitis were enrolled in this retrospective study and divided into training dataset (n = 53) and validation dataset (n = 23). A total of 837 radiomics features were extracted from regions of interest (ROIs) based on the lung parenchyma window of CT images. A radiomics signature was constructed on the basis of the predictive features by the least absolute shrinkage and selection operator (LASSO). A logistic regression was applied to develop radiomics nomogram. Receiver operating characteristics (ROC) curve and area under the curve (AUC) were applied to evaluate the performance of pneumonitis etiology identification. No significant difference was detected between training dataset and validation dataset. The radiomics signature which was made up of four radiomics features shown a favorable performance on differentiating between CIP and RP with the α-binormal-based and empirical AUC = 0.831 and 0.843. Patients with RP had a close relationship with location (p = 0.003) and shape of lesions (p = 0.002). The nomogram that combined with radiomics signature and clinical factors improved the classifying performance on discrimination in the training dataset (AUCαbin = 0.963 and AUCemp = 0.964). The results were verified in the validation dataset with AUC = 0.967 and 0.964. CT-based radiomics features have potential values for differentiating between patients with CIP and RP. Addition of bilateral changes and sharp border produced superior model performance on classifying, which could be a useful method to improve related clinical decision-making.

Clinical applications of radiomics in non-small cell lung cancer patients with immune checkpoint inhibitor-related pneumonitis.

Immune checkpoint inhibitors (ICIs) modulate the body's immune function to treat tumors but may also induce pneumonitis. Immune checkpoint inhibitor-related pneumonitis (ICIP) is a serious immune-related adverse event (irAE). Immunotherapy is currently approved as a first-line treatment for non-small cell lung cancer (NSCLC), and the incidence of ICIP in NSCLC patients can be as high as 5%-19% in clinical practice. ICIP can be severe enough to lead to the death of NSCLC patients, but there is a lack of a gold standard for the diagnosis of ICIP. Radiomics is a method that uses computational techniques to analyze medical images (e.g., CT, MRI, PET) and extract important features from them, which can be used to solve classification and regression problems in the clinic. Radiomics has been applied to predict and identify ICIP in NSCLC patients in the hope of transforming clinical qualitative problems into quantitative ones, thus improving the diagnosis and treatment of ICIP. In this review, we summarize the pathogenesis of ICIP and the process of radiomics feature extraction, review the clinical application of radiomics in ICIP of NSCLC patients, and discuss its future application prospects.

Pre-existing Interstitial Lung Abnormalities and Immune Checkpoint Inhibitor-Related Pneumonitis in Solid Tumors: A Retrospective Analysis.

Immune checkpoint inhibitors (ICIs) have demonstrated efficacy over previous cytotoxic chemotherapies in clinical trials among various tumors. Despite their favorable outcomes, they are associated with a unique set of toxicities termed as immune-related adverse events (irAEs). Among the toxicities, ICI-related pneumonitis has poor outcomes with little understanding of its risk factors. This retrospective study aimed to investigate whether pre-existing interstitial lung abnormality (ILA) is a potential risk factor for ICI-related pneumonitis. Patients with non-small cell lung cancer, malignant melanoma, renal cell carcinoma, and gastric cancer, who was administered either nivolumab, pembrolizumab, or atezolizumab between September 2014 and January 2019 were retrospectively reviewed. Information on baseline characteristics, computed tomography findings before administration of ICIs, clinical outcomes, and irAEs were collected from their medical records. Pre-existing ILA was categorized based on previous studies. Two-hundred-nine patients with a median age of 68 years were included and 23 (11.0%) developed ICI-related pneumonitis. While smoking history and ICI agents were associated with ICI-related pneumonitis (P = .005 and .044, respectively), the categories of ILA were not associated with ICI-related pneumonitis (P = .428). None of the features of lung abnormalities were also associated with ICI-related pneumonitis. Multivariate logistic analysis indicated that smoking history was the only significant predictor of ICI-related pneumonitis (P = .028). This retrospective study did not demonstrate statistically significant association between pre-existing ILA and ICI-related pneumonitis, nor an association between radiologic features of ILA and ICI-related pneumonitis. Smoking history was independently associated with ICI-related pneumonitis. Further research is warranted for further understanding of the risk factors of ICI-related pneumonitis.

Radiological assessment of immunotherapy effects and immune checkpoint-related pneumonitis for lung cancer.

Immune checkpoint inhibitors (ICIs) therapy have revolutionized advanced lung cancer care. Interestingly, the host responses for patients received ICIs therapy are distinguishing from those with cytotoxic drugs, showing potential initial transient worsening of disease burden, pseudoprogression and delayed time to treatment response. Thus, a new imaging criterion to evaluate the response for immunotherapy should be developed. ICIs treatment is associated with unique adverse events, including potential life-threatening immune checkpoint inhibitor-related pneumonitis (ICI-pneumonitis) if treated patients are not managed promptly. Currently, the diagnosis and clinical management of ICI-pneumonitis remain challenging. As the clinical manifestation is often nonspecific, computed tomography (CT) scan and X-ray films play important roles in diagnosis and triage. This article reviews the complications of immunotherapy in lung cancer and illustrates various radiologic patterns of ICI-pneumonitis. Additionally, it is tried to differentiate ICI-pneumonitis from other pulmonary pathologies common to lung cancer such as radiation pneumonitis, bacterial pneumonia and coronavirus disease of 2019 (COVID-19) infection in recent months. Maybe it is challenging to distinguish radiologically but clinical presentation may help.

Identification and prediction of immune checkpoint inhibitors-related pneumonitis by machine learning.

Immune checkpoint inhibitor (ICI)-related pneumonitis (IRP) is a common and potentially fatal clinical adverse event. The identification and prediction of the risk of ICI-related IRP is a major clinical issue. The objective of this study was to apply a machine learning method to explore risk factors and establish a prediction model. We retrospectively analyzed 48 patients with IRP (IRP group) and 142 patients without IRP (control group) who were treated with ICIs. An Elastic Net model was constructed using a repeated k-fold cross-validation framework (repeat = 10; k = 3). The prediction models were validated internally and the final prediction model was built on the entire training set using hyperparameters with the best interval validation performance. The generalizability of the final prediction model was assessed by applying it to an independent test set. The overall performance, discrimination, and calibration of the prediction model were evaluated. Eleven predictors were included in the final predictive model: sindillizumab, number of ≥2 underlying diseases, history of lung diseases, tirelizumab, non-small cell lung cancer (NSCLC), percentage of CD4+ lymphocytes, body temperature, KPS score ≤70, hemoglobin, cancer stage IV, and history of antitumor therapy. The external validation of the risk prediction model on an independent test set of 37 patients and showed good discrimination and acceptable calibration ability: with AUC of 0.81 (95% CI 0.58-0.90), AP of 0.76, scaled Brier score of 0.31, and Spiegelhalter-z of -0.29 (P-value:0.77). We also designed an online IRP risk calculator for use in clinical practice. The prediction model of ICI-related IRP provides a tool for accurately predicting the occurrence of IRP in patients with cancer who received ICIs.

Improving the care of patients with lung cancer hospitalized with immune checkpoint inhibitor-related pneumonitis: Implementation of a clinical pathway algorithm.

e18730 Background: Immune checkpoint inhibitor related pneumonitis (ICI-P) is one of the most frequent reasons for discontinuing immunotherapy. High mortality rates have been reported among those with grades ≥3 ICI-P. Early recognition and timely management are key to better patient outcomes. We conducted a quality improvement project that involved developing and implementing a clinical pathway algorithm to standardize the care of patients admitted to our Onco-Hospital Medicine (OHM) service. Methods: We included patients with lung cancer admitted to the OHM service with suspicion of ICI-P from January 1, 2020 to December 31, 2022. We developed a multi-modal educational intervention targeted to providers of the OHM service to promote the use of the developed pathway. Interventions were rolled out in 2 phases and included educational sessions (phase 1), distribution of flashcards, informational notepads, monthly emailers, and presentation of an animation of the clinical pathway (phase 2). Our primary outcome was the time to first ICI-P-directed treatment, i.e. time to treatment (TTT). We used the Kruskal-Wallis test to compare TTT between the pre-intervention and post-intervention phases. Results: Out of 82 patients admitted with a suspicion of ICI-P, 60 were confirmed to have ICI-P based on multi-disciplinary consensus. 19 patients were included in the pre-intervention group, 30 in post-intervention phase 1, and 11 in post-intervention phase 2. 92% of patients in our cohort had NSCLC, and 95% had stage 4 disease. Pembrolizumab was the most common ICI used (55%), followed by combination Ipilimumab+Nivolumab (15%). Pulmonology was consulted in 98% of patients. 98% of patients were treated with steroids. Median TTT(range) was 1 (0-12) day in the pre-intervention group, and 2 (0-17) days and 1 (0-6) day after post-intervention phases 1 and 2, respectively (p = 0.3). Utilization of immunotoxicity order set increased from 0% during pre-intervention to 20% and 27% after phase 1 and phase 2, respectively (p = 0.03). Prescription of Pneumocystis jirovecii pneumonia (PJP) prophylaxis among patients discharged on high-dose steroids increased from baseline but was not statistically significant (pre-intervention = 71%, phase 1 = 95%, phase 2 = 100%, p = 0.09). There was no significant change in ICU admissions or inpatient mortality from pre-intervention to post-intervention phases 1 and 2 (p = 0.5 and 0.6, respectively). Conclusions: We showed a decrease in time to intervention from baseline to post-intervention phase 2 after our educational interventions. While this change was not statistically significant, our multi-level approach was able to help increase the utilization of immunotoxicity tools on the OHM service. Continued education on the OHM service is needed to promote adherence to the developed pathway and help improve patient outcomes.

Predictive factors and prognosis of immune checkpoint inhibitor-related pneumonitis in non-small cell lung cancer patients.

To investigate the influencing factors and prognosis of immune checkpoint inhibitor-related pneumonitis (CIP) in advanced non-small cell lung cancer (NSCLC) patients during or after receiving immune checkpoint inhibitors(ICIs). The clinical and laboratory indicator data of 222 advanced NSCLC patients treated with PD-1/PD-L1 inhibitors at the First Affiliated Hospital of Zhengzhou University between December 2017 and November 2021 were collected retrospectively. The patients were divided into a CIP group (n=41) and a non-CIP group (n=181) according to whether they developed CIP or not before the end of follow-up. Logistic regression was used to evaluate risk factors of CIP, and Kaplan‒Meier curves were used to describe the overall survival (OS) of different groups. The log-rank test was used to compare the survival of different groups. There were 41 patients who developed CIP, and the incidence rate of CIP was 18.5%. Univariate and multivariate logistic regression analyses showed that low pretreatment hemoglobin (HB) and albumin (ALB) levels were independent risk factors for CIP. Univariate analysis suggested that history of chest radiotherapy was related to the incidence of CIP. The median OS of the CIP group and non-CIP were 15.63 months and 30.50 months (HR:2.167; 95%CI: 1.355-3.463, P<0.05), respectively. Univariate and multivariate COX analyses suggested that a high neutrophil-to-lymphocyte ratio (NLR) level, a low ALB level and the development of CIP were independent prognostic factors for worse OS of advanced NSCLC patients treated with ICIs. Additionally, the early-onset and high-grade CIP were related to shorter OS in the subgroup. Lower pretreatment HB and ALB levels were independent risk factors for CIP. A high NLR level, a low ALB level and the development of CIP were independent risk factors for the prognosis of advanced NSCLC patients treated with ICIs.

186P Elevated CXCL10:IL-8 ratio in bronchoalveolar lavage fluid of immune checkpoint inhibitor-related pneumonitis

186P Elevated CXCL10:IL-8 ratio in bronchoalveolar lavage fluid of immune checkpoint inhibitor-related pneumonitis

Characterization of immunomodulatory factors and cells in bronchoalveolar lavage fluid for immune checkpoint inhibitor-related pneumonitis.

As immune checkpoint inhibitors (ICIs) are widely used, a series of immune-related adverse events (irAEs) have been reported, including immune checkpoint inhibitor-related pneumonitis (ICI-pneumonitis). The incidence of ICI-pneumonitis is higher in reality than in clinical trials. The diagnosis is challenging, mainly based on clinical and imaging features, and requires the exclusion of other causes. The data on the biological mechanisms of ICI-pneumonitis are scarce, resulting in little knowledge of the best treatment for ICI-pneumonitis. Bronchoalveolar lavage (BAL) may be helpful to identify the biological differences or find predictive biomarkers, and may in turn help to develop phenotype-specific targeted drugs to treat ICI-pneumonitis. Herein, we outline the characterization of immunomodulatory factors and cells in bronchoalveolar lavage fluid for ICI-pneumonitis. Through careful sorting and literature review, we find crosstalk between pathogenic Th17/Th1 cells (i.e., Th17.1) and pro-inflammatory monocytes, and activation of Th17(/Th1)/IL-17A (/IFN-γ) pathways may play a key role in the pathogenesis of ICI-pneumonitis. Disruption of the interaction between pathogenic Th17/Th1 cells and pro-inflammatory monocytes (such as, anti-IL-23) may be a potential treatment for ICI-pneumonitis. We first describe the possible pathophysiological mechanisms of ICI-pneumonitis, hoping to contribute to the optimization of diagnosis and treatment, as well as provide readers with research inspiration.

Risk Factors for Refractory Immune Checkpoint Inhibitor-related Pneumonitis in Patients With Lung Cancer: Erratum.

Risk Factors for Refractory Immune Checkpoint Inhibitor-related Pneumonitis in Patients With Lung Cancer: Erratum.

Clinical and chest CT features of immune checkpoint inhibitor-related pneumonitis

Objective: To explore the clinical and chest computed tomography (CT) features and the outcome of immune checkpoint inhibitor-related pneumonitis (CIP). Methods: Clinical and chest CT data of 38 CIP patients with malignant tumors from the Cancer Hospital, Chinese Academy of Medical Sciences between August 2017 and April 2021 were retrospectively reviewed, and the outcomes of pneumonitis were followed up. Results: The median time from the administration of immune checkpoint inhibitors (ICIs) to the onset of CIP was 72.5 days in 38 patients with CIP, and 22 patients developed CIP within 3 months after the administration of ICIs. The median occurrence time of CIP in 24 lung cancer patients was 54.5 days, earlier than 119.0 days of non-lung cancer patients (P=0.138), with no significant statistical difference. 34 patients (89.5%) were accompanied by symptoms when CIP occurred. The common clinical symptoms were cough (29 cases) and dyspnea (27 cases). The distribution of CIP on chest CT was asymmetric in 31 cases and symmetrical in 7 cases. Among the 24 lung cancer patients, inflammation was mainly distributed ipsilateral to the primary lung cancer site in 16 cases and diffusely distributed throughout the lung in 8 cases. Ground glass opacities (37 cases) and consolidation (30 cases) were the common imaging manifestations, and organizing pneumonia (OP) pattern (15 cases) was the most common pattern. In 30 CIP patients who were followed up for longer than one month, 17 cases had complete absorption (complete absorption group), and 13 cases had partial absorption or kept stable (incomplete absorption group). The median occurrence time of CIP in the complete absorption group was 55 days, shorter than 128 days of the incomplete absorption group (P=0.022). Compared with the incomplete absorption group, there were less consolidation(P=0.010) and CIP were all classified as hypersensitivity pneumonitis (HP) pattern (P=0.004) in the complete absorption group. Conclusions: CIP often occurs within 3 months after ICIs treatment, and the clinical and CT findings are lack of specificity. Radiologic features may have a profound value in predicting the outcome of CIP.

Association of Clinical and Radiological Features with Disease Severity of Symptomatic Immune Checkpoint Inhibitor-Related Pneumonitis.

To investigate the predictive ability of clinical and chest computed tomography (CT) features to predict the severity of symptomatic immune checkpoint inhibitor-related pneumonitis (CIP). This study included 34 patients diagnosed with symptomatic CIP (grades 2-5) and divided into mild (grade 2) and severe CIP (grades 3-5) groups. The groups' clinical and chest CT features were analyzed. Three manual scores (extent, image finding, and clinical symptom scores) were conducted to evaluate the diagnostic performance alone and in combination. There were 20 cases of mild CIP and 14 cases of severe CIP. More severe CIP occurred within 3 months than after 3 months (11 vs. 3 cases, p = 0.038). Severe CIP was significantly associated with fever (p < 0.001) and the acute interstitial pneumonia/acute respiratory distress syndrome pattern (p = 0.001). The diagnostic performance of chest CT scores (extent score and image finding score) was better than that of clinical symptom score. The combination of the three scores demonstrated the best diagnostic value, with an area under the receiver operating characteristic curve of 0.948. The clinical and chest CT features have important application value in assessing the disease severity of symptomatic CIP. We recommend the routine use of chest CT in a comprehensive clinical evaluation.