ArticleImmunoTargets and therapy2026
Identification of Prognostic Clinical Features in Grade 4 Immune-Related Adverse Events: A Triangulation Study.
Article in ImmunoTargets and therapy, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Grade 4 immune-related adverse events (irAEs) is life-threatening complications of immune checkpoint inhibitor therapy. Due to its rarity and scarcity of data, there is a lack of systematic research on key factors influencing poor prognosis. This exploratory study aimed to identify clinical features robustly associated with mortality in patients with grade 4 irAEs. Given the extremely small sample size and high‑dimensional data, a triangulation approach integrating traditional univariate statistics and machine learning to maximize the reliability of feature selection. Methods: This study included 26 cancer patients admitted to the ICU for grade 4 irAEs. To maximize robustness from limited data, a "triangulation" approach was employed. Prognostic features were independently identified through two parallel approaches: (1) traditional univariate statistical analysis, and (2) multiple machine learning algorithms evaluated by Leave-One-out Cross-Validation. Features consistently highlighted as significant by both independent methodologies were integrated to form a final high-confidence feature set. Results: Univariate analysis identified 21 features significantly associated with mortality. Machine learning analysis refined this to 11 important features. Through "triangulation", 8 features were consistently validated: body mass index and VEGF-inhibitors were inversely associated with mortality, while vasopressor therapy, oxygen therapy, lactate levels at day 1 and 2, pneumonia and percentage of neutrophils, exhibited a positive correlation with the mortality. Conclusion: This small-sample exploratory study identified 8 routinely available early ICU clinical features robustly associated with mortality in grade 4 irAEs patients using a "triangulation" framework. These characteristics highlight the pivotal roles of shock, respiratory failure, and inflammation. While not directly constructing a clinical prediction model, they may facilitate early risk stratification and provide hypotheses for prioritized validation in future large-sample studies.
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