ArticleFrontiers in cardiovascular medicine2026
Predicting cardiovascular toxicity in anti-PD-1/PD-L1 therapy: a risk factor analysis and model development.
Article in Frontiers in cardiovascular medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed, 1 synthesis or guideline pooled it.
- Prediction models for immune checkpoint inhibitor-related cardiovascular toxicity: a systematic review.Frontiers in immunology · 2026Pooled it
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: This study aimed to investigate risk factors for cardiovascular toxicity following anti-PD-1/PD-L1 therapy and develop a predictive model. Methods: We retrospectively collected data from 2,665 patients with solid tumors treated with anti-PD-1/PD-L1 therapy at two-center between October 2018 and October 2023.We performed univariate and multivariate logistic regression to identify predictors of cardiovascular toxicity and developed a nomogram. Internal evaluation and internal validation were performed using receiver operating characteristic (ROC), decision curve analysis (DCA), calibration curve (CC) for internal evaluation and internal validation. Results: Univariate logistic regression identified the Systemic Inflammatory Response Index (SIRI;OR 2.26, 95% CI 1.19-4.27, Conclusions: SIRI, ECOG, hypertension, diabetes, tumor metastasis, tumor stage, and sex were significant predictors of cardiovascular toxicity. ECOG was an independent risk factor, while tumor metastasis was an independent protective factor, after adjusting for other covariates. The nomogram showed good accuracy and discrimination, with clinical utility for predicting cardiovascular toxicity risk in patients receiving anti-PD-1/PD-L1 therapy.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.