---
title: "Avoiding Cardiac Toxicity with Immune Checkpoint Inhibitors: Safety Review (Title Only Available)"
id: "frontiers-in-immunology-15-avoidance-of-cardiac-toxicity-during-the-application-of-immune-checkpoint"
canonical_url: "https://medichelpline.com/clinical-feed/frontiers-in-immunology-15-avoidance-of-cardiac-toxicity-during-the-application-of-immune-checkpoint"
content_type: "clinical_feed_article"
specialty: "Infectious Disease"
source_name: "Frontiers in Immunology"
source_url: "https://www.frontiersin.org/articles/10.3389/fimmu.2026.1919841"
published_at: "2026-08-31T00:00:00.000Z"
evidence_level: "Journal Feed"
license: "CC-BY-NC-4.0 / Informational Use"
---
# Avoiding Cardiac Toxicity with Immune Checkpoint Inhibitors: Safety Review (Title Only Available)
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/frontiers-in-immunology-15-avoidance-of-cardiac-toxicity-during-the-application-of-immune-checkpoint
- **Specialty:** [Infectious Disease](https://medichelpline.com/clinical-feed/infectious-disease.md)
- **Primary Source:** Frontiers in Immunology
- **Source URL:** [Original Journal Publication](https://www.frontiersin.org/articles/10.3389/fimmu.2026.1919841)
- **Published At:** 2026-08-31T00:00:00.000Z
- **Evidence Rating:** Journal Feed
## Executive GIST (TL;DR)
- The source page identifies an article titled regarding avoidance of **cardiac toxicity** during use of **immune checkpoint inhibitors** and describes it as a safety **systematic review** combining **network meta-analysis** and **pharmacovigilance**. - The publicly accessible content provided here is limited to site navigation, journal sections, and repeated header material; the main article text, methods, results, and conclusions were not present in the provided source. - Key methodological details — including search strategy, inclusion criteria, data extraction, statistical models for the **network meta-analysis**, pharmacovigilance databases queried, case definitions for cardiac events, and duration of follow-up — were not reported in the supplied content. - No data, event rates, comparative safety rankings, signal detection metrics, or specific recommendations for clinicians were available on the captured page. - Because outcome data and study conclusions are missing from the supplied source, readers cannot determine which agents or combinations were associated with higher or lower cardiac risk, nor can they ascertain suggested monitoring strategies or management approaches from this source alone. - The article is hosted in Frontiers in Immunology; the site-level material visible covers journal sections and author-submission links but does not reproduce article-level findings in the provided excerpt. - For complete, actionable clinical information or guideline-relevant recommendations, access to the full article text or the journal page containing the full article content is required; those details were not included in the provided source material.
## Clinical Analysis & Structured Key Points
Frontiers | Avoidance of cardiac toxicity during the application of immune checkpoint inhibitors: a safety systematic review combining network meta-analysis and pharmacovigilance study ORIGINAL RESEARCH article Front. Immunol. , 31 August 2026 Sec. Cancer Immunity and Immunotherapy Volume 17 - 2026 | https://doi.org/10.3389/fimmu.2026.1919841 Published in Frontiers in Immunology Cancer Immunity and Immunotherapy 7 impact factor 11.3 citescore Part of a Research Topic Biomarkers in cancer immunotherapy: Mechanisms, efficacy prediction, and irAE management Submission open 1876 views 3 articles Editor & Reviewers Edited by T H Tao Han Reviewed by P L Pengyun Li Z S Zhen Sun Outline Figures and Tables Figure 1 View in article Figure 2 View in article Figure 3 View in article Figure 4 View in article Figure 5 View in article Table 1 Summary of baseline characteristics. View in article Table 2 Pairwise meta-analysis outcomes of Cardiotoxicity induced by immune checkpoint inhibitor according by different comparison. View in article Table 3 Positive PTs with ≥0.1% adverse event reports in SOCs of cardiac disorders of ICIs. View in article ORIGINAL RESEARCH article Front. Immunol. , 31 August 2026 Sec. Cancer Immunity and Immunotherapy Volume 17 - 2026 | https://doi.org/10.3389/fimmu.2026.1919841 Avoidance of cardiac toxicity during the application of immune checkpoint inhibitors: a safety systematic review combining network meta-analysis and pharmacovigilance study H C Han Chen 1 † Y S Yongqi Shan 2 † H X Hanfang Xu 3 K X Keer Xuan 3 T R Tianshu Ren 1 * Q Z Qingchun Zhao 1 * 1. Department of Clinical Pharmacy, General Hospital of Northern Theater Command, Shenyang, Liaoning, China 2. Department of General Surgery, General Hospital of Northern Theater Command, Shenyang, Liaoning, China 3. Department of Clinical Pharmacy, Shenyang Pharmaceutical University, Shenyang, Liaoning, China See more Article metrics View details Abstract Background: Although immune checkpoint inhibitor (ICI) therapy has revolutionized cancer treatment, the incidence of cardiac toxicity associated with ICIs remains unclear. This study aimed to evaluate the cardiac toxicity risks associated with ICI and identify the most common types of cardiac toxicity caused by ICI. A further objective was to determine the types of ICI that require the most monitoring of cardiac toxicity through systematic review and network meta-analysis, assisted by pharmacovigilance studies. Design: Systematic review and network meta-analysis, complemented by a pharmacovigilance study of the FAERS database. Data source and methods: A systematic search of electronic databases up to November 2025 was conducted. Randomized controlled trials (RCTs) were eligible if they compared ICIs with appropriate controls without restrictions. Data were analyzed using random-effects pairwise and network meta-analyses to evaluate cardiac toxicity. Disproportionality analysis was performed using FAERS data from 2011 to 2026 (Q1), employing PRR, ROR, IC, and EBGM to quantify the risk and incidence of cardiac toxicity associated with ICIs. Results: In total, 135 RCTs were included in the network meta-analysis. Pairwise meta-analysis demonstrated that ICIs can induce cardiac toxicity in four key manifestations, which were selected through pairwise meta-analysis: acute myocardial infarction, cardiac arrest, myocarditis, and ventricular tachycardia. Network meta-analysis indicated that both PD-1/PD-L1 monotherapy and combination therapies present a higher risk of cardiac toxicity. The risks associated with PD-1 and PD-L1 agents were relatively elevated when used as monotherapies. Furthermore, combinations of PD-1 (nivolumab, pembrolizumab) and PD-L1 (avelumab) with CTLA-4 or other small molecule targeted inhibitors were associated with increased risk of the four aforementioned cardiac toxicities. Disproportionality analysis revealed that both PD-1 and PD-L1 inhibitors carry risks of myocarditis, and the combination of bevacizumab, relatlimab, and ipilimumab may cause increased cardiac toxicity. Conclusions: ICIs, particularly PD-1/PD-L1 inhibitors, increase the risk of cardiac toxicity. PD-1 (nivolumab, pembrolizumab) and PD-L1 (atezolizumab, avelumab) agents present a higher risk of myocarditis and acute myocardial infarction. Moreover, combination regimens involving PD-1/PD-L1 further elevate the risk of cardiac toxicity, underscoring the necessity for vigilant monitoring in patients with underlying heart disease. Clinical Trial Registration: https://www.crd.york.ac.uk/PROSPERO/ , identifier CRD420261357478. 1 Introduction Immune checkpoint inhibitors (ICIs), which enhance the human immune system to target tumors, have become one of the most effective therapeutic strategies in advanced cancer and have emerged as a cornerstone of standard cancer care ( 1 ). Therapeutic agents include PD-1 inhibitors (nivolumab, pembrolizumab, cemiplimab), PD-L1 inhibitors (atezolizumab, durvalumab, avelumab), CTLA-4 inhibitors (ipilimumab, tremelimumab), and the LAG-3 inhibitor (relatlimab), while numerous studies have investigated the efficacy and safety of ICIs, alone or combined with other agents, particularly in advanced or recurrent cancer ( 2 ). Not all patients achieve equivalent benefits from ICIs, and many experience immune-related adverse events (irAEs). Because ICIs modulate autoreactivity, they have been associated with disinhibited cytotoxic T cells that may damage healthy tissues across multiple organs, leading to irAEs. Indeed, over 60%–80% of patients experience irAEs such as endocrinopathies, hepatitis, colitis, inflammatory arthritis, and pneumonitis ( 3 , 4 ). Increasingly, irAEs related to cardiac toxicity have been reported during ICI treatments, including myocarditis, pericarditis, pericardial effusion, atherosclerotic deterioration, acute myocardial infarction, and heart failure. Different ICI types and agents cause varying types and incidences of cardiac toxicity ( 5 , 6 ). Clinical manifestations range from asymptomatic or mild to severe symptoms such as chest pain and cardiogenic shock ( 7 , 8 ). Severe presentations indicate poor prognosis, with mortality rates of 25% to 50% ( 9 ), often associated with combination therapies involving ICIs ( 10 ). Accordingly, our study aims to identify ICI types and specific agents prone to cardiac toxicity, as well as combination therapy regimens most likely to induce cardiotoxic events. Although prior studies included numerous original articles ( 11 – 15 ), none applied network meta-analysis to rank ICI types and agents by cardiac toxicity risk nor utilized network analysis combined with pharmacovigilance of the FDA Adverse Event Reporting System (FAERS) database. 2 Methods This research was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, with the prospective protocol (CRD42020261357478) registered in an open repository ( 16 , 17 ). Ethical approval was waived as no individual-level data were collected. 2.1 Study design and selection criteria To comprehensively identify the ICIs most associated with cardiac toxicity, we conducted a systematic review and network meta-analysis. Eligible studies were randomized controlled trials (RCTs) involving adults (≥18 years) with any primary cancer diagnosis at any stage. Interventions included at least one ICI agent—PD-1, PD-L1, CTLA-4, or LAG-3—administered alone or in combination. Non-tumor research, only conference abstracts, studies without cardiac toxicity data (double-zero studies), and secondary analysis of previous studies were excluded. We searched PubMed, Embase, Cochrane Library, and the Clinical Trials Registry Platform (ClinicalTrials.gov) from inception to 30 September 2025, using the search terms detailed in Supplementary Table 1 , without language or other restrictions. Literature update search was completed on 10 November 2025, with literature de-duplication and screening conducted in EndNote X8 software. Titles were screened independently in duplicate by two authors (CH and SYQ), followed by abstract and full-text review. Discrepancies were resolved via consensus or adjudicated by a third reviewer (ZQC). 2.2 Data extraction and outcomes Two authors (CH and SYQ) independently extracted data, as presented in Supplementary Table 2 , including the first author, publication year, cancer stage, clinical trial number, sample size, ICI type, agent, and dosage. Risk of bias was assessed independently by two authors (CH and SYQ) using the Cochrane Risk of Bias tool version 2 (RoB 2) ( 18 ), with discrepancies resolved by a third reviewer (ZQC). The predefined primary outcomes were cardiac toxicity events attributed to ICIs, including incident rates of acute myocardial infarction, acute coronary syndrome, angina pectoris, heart failure, cardiac arrest, sudden cardiac death, atrial fibrillation, sinus tachycardia, ventricular tachycardia, conduction block, bradyarrhythmia, myocarditis, and pericardial effusion. These outcomes represent key manifestations of cardiac toxicity and were preplanned for subgroup analyses among different interventions. The Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach was employed to assess the certainty of evidence for all pairwise comparisons ( 19 ). 2.3 Data analysis of network meta-analysis The incidence of cardiac toxicity associated with ICIs, treated as a dichotomous variable, was expressed as odds ratios (ORs) with 95% confidence intervals (CIs). We first performed random-effects pairwise meta-analyses, followed by Bayesian network meta-analyses using a random-effects model with a common heterogeneity parameter across comparisons ( 20 , 21 ). Statistical heterogeneity was quantified by the I 2 statistic, with I 2 greater than 50% indicating substantial heterogeneity. Meta-regression evaluated the impact of stratification on pairwise outcomes. Publication bias was assessed using Begg’s and Egger’s tests for different interventions to comprehensively evaluate pairwise meta-analysis results ( 22 ). Network meta-analysis utilized a Bayesian multivariate framework. The Bayesian model follows a prior distribution, with 4 chains, 50,000 iterations, and 20,000 burn-in periods. The convergence diagnosis is visualized using a trace plot, and the fit index is determined by the deviation information criterion (DIC) value. The smaller the DIC value, the better. Surface under the cumulative ranking curve (SUCRA) scores (ranging from 0 to 1) ranked interventions from least to most likely to induce cardiac toxicity ( 23 ). Pairwise meta-analysis supplemented network meta-analysis, directly and indirectly comparing cardiac toxicity proportions among interventions via ORs and 95% CIs to evaluate significance. We evaluated incoherence between direct and indirect evidence globally by using the method design-by-treatment interaction and locally by using the node-splitting approach ( 24 ). For global and local inconsistencies, a p -value greater than 0.05 indicates the absence of inconsistency. For comparisons with more than 10 studies, contour-enhanced network funnel plots assessed publication bias. Statistical analyses were performed using R (version 4.4.1) with meta, netmeta, and gemtc packages and STATA (SE 15.0). 2.4 Methods for pharmacovigilance study We extracted adverse drug event (ADE) reports associated with cardiac toxicity related to ICIs from the FAERS database, covering the period from 2011 (Q1) to 2026 (Q1), which is based on the marketing timeline of ICI agents. Only the latest reports submitted to the FDA were selected, and CASEID and PRIMARYID were used as key filters to remove duplicate records. All ADEs were coded using preferred terms (PT) from the Medical Dictionary for Regulatory Activities (MedDRA, Version 28.0), within system organ classes (SOC) of cardiac disorders. The common and product names of cemiplimab, nivolumab, durvalumab, avelumab, pembrolizumab, and atezolizumab were applied for data extraction (see details in Supplementary Table 2 ), with only reports in which an ICI was listed as the primary suspect (PS) drug included. Our study used disproportionality analysis as the primary signal detection approach, supported by four statistical algorithms: reporting odds ratio (ROR), proportional reporting ratio (PRR), information component (IC), and empirical Bayes geometric mean (EBGM). For the determination of positive signals, for ROR, a signal required at least three reports and a lower 95% confidence interval (CI) greater than 1; for PRR, it required at least three reports, a PRR value of 2 or higher, and a chi-squared statistic of at least 4; for IC, the lower bound of the 95% CI (denoted IC025) had to exceed 0; and for EBGM, the lower 95% CI (EBGM05) needed to be greater than 2. A drug–ADE combination was considered a positive signal only if it simultaneously met the predefined positive criteria for all four algorithms ( 25 , 26 ). The incidence of rare adverse reactions is typically defined as ≥0.01% and <0.1%, while the incidence of common adverse reactions ranges from ≥1% to <10%. Therefore, we conducted statistics based on PT with an incidence exceeding 0.01% of the population for each ICI ( 27 ). The interval between adverse event occurrence and ICI use was used to estimate time-to-onset (TTO) analysis. We used cumulative distribution curves to present the time-to-onset characteristics of the most significant ADR following treatment with different ICIs, based on data from FAERS at the PT level to assess whether the reporting pattern varied over time ( 28 ). Moreover, we used the Ω shrinkage to measure drug–drug interactions, which is the most conservative method among multiple algorithms ( 29 ). When at least one type of drug was recorded in the report, the patient was defined as having a co-medication history. Statistical analyses were performed using R version 4.4.1. 3 Results Following our rigorous selection, 135 unique RCTs qualified for inclusion in the network meta-analysis and systematic review. Five RCTs were retrieved from ClinicalTrials.gov; the remaining 130 originated from published articles. The selection process is depicted in Figure 1 , with reasons for exclusion and included study details provided. The RCTs spanned publication years 2010–2025. Baseline characteristics are summarized in Table 1 and detailed in Supplementary Table 3 . Studies with multiple comparisons were subdivided as substudies a, b, etc., and crossover studies were also divided into substudies, or only the outcomes of the first trial were taken. Quality assessment confirmed high methodological standards suitable for meta-analysis ( Supplementary Figure 1 ). Figure 1 Diagram of the literature search and screening process. Table 1 Intervention type Intervention agent Control type Control agent Cancer type No. of clinical trial PD-1 + CTLA-4 Nivolumab + ipilimumab PD-1 Nivolumab CRC NCT04008030[S1] NSCLC NCT02785952[S19] Melanoma NCT01844505[S23] Malignant pleural mesothelioma NCT02716272[S58] Head and neck squamous cell carcinoma NCT02823574[S109] Pembrolizumab + ipilimumab Pembrolizumab NSCLC NCT03302234[S121] CTLA-4 Ipilimumab Melanoma NCT01927419[S22] SMTI Sunitinib RCC NCT02231749[S40] Sunitinib/pazopanib RCC NCT02960906[S51] Chemotherapy Pemetrexed + cisplatin Malignant pleural mesothelioma NCT02899299[S44] Placebo – SCLC NCT02538666[S101] PD-L1 + CTLA-4 Durvalumab + tremelimumab PD-L1 Durvalumab Endometrial cancer NCT03015129[S59] PD-L1 + CTLA-4 + chemotherapy Durvalumab + tremelimumab + platinum-etoposide PD-L1 + chemotherapy Durvalumab + platinum-etoposide SCLC NCT03043872[S20] Durvalumab + tremelimumab + platinum-based Durvalumab + platinum-based NSCLC NCT03164616[S26] Chemotherapy Platinum-based NSCLC NCT03164616[S26] PD-L1 + chemotherapy Atezolizumab + bevacizumab + FOLFOXIRI SMTI+chemotherapy Bevacizumab + FOLFOXIRI CRC NCT03721653[S3] Atezolizumab + carboplatin + paclitaxel Chemotherapy Carboplatin + paclitaxel Endometrial cancer NCT03603184[S10] Atezolizumab + platinum-based Platinum-based NSCLC NCT02486718[S16] Atezolizumab + carboplatin + nab-paclitaxel Carboplatin + nab-paclitaxel NSCLC NCT02367781[S50] Atezolizumab + carboplatin/cisplatin + pemetrexed Carboplatin/cisplatin + pemetrexed NSCLC NCT02657434[S66] Atezolizumab + lurbinectedin Lurbinectedin SCLC NCT05091567[S70] Atezolizumab + trastuzumab Trastuzumab BC NCT02924883[S81] Atezolizumab + nab-paclitaxel Nab-paclitaxel BC NCT02425891[S98] Atezolizumab + carboplatin + etoposide Carboplatin + etoposide SCLC NCT02763579[S107] Atezolizumab + paclitaxel Paclitaxel BC NCT03125902[S119] Avelumab + carboplatin Carboplatin Epithelial ovarian cancer, fallopian tube cancer, or peritoneal cancer NCT02718417[S38] Avelumab + carboplatin + paclitaxel Carboplatin + paclitaxel Endometrial cancer NCT03503786[S69] Epithelial ovarian cancer, fallopian tube cancer, or peritoneal cancer NCT02718417[S87] Durvalumab + azacitidine Azacitidine AML NCT02775903[S43] Sugemalimab + platinum Platinum NSCLC NCT03789604[S124] PD-L1 + SMTI Benmelstobart + anlotinib SMTI Sunitinib RCC NCT04523272[S42] Avelumab + axitinib RCC NCT02684006[S60] Atezolizumab + cobimetinib Regorafenib CRC NCT02788279[S89] Atezolizumab + cobimetinib + vemurafenib Cobimetinib + vemurafenib Melanoma NCT02908672[S112] Spartalizumab + dabrafenib + trametinib Dabrafenib + trametinib Melanoma NCT02967692[S114] PD-L1 + SMTI + chemotherapy Atezolizumab + bevacizumab + carboplatin + pemetrexed SMTI + chemotherapy Bevacizumab + carboplatin + pemetrexed Mesothelioma of pleura NCT03762018[S17] Atezolizumab + bevacizumab + paclitaxel + carboplatin Bevacizumab + paclitaxel + carboplatin Epithelial ovarian cancer, fallopian tube cancer, or peritoneal cancer NCT03038100[S39] Atezolizumab + bevacizumab + capecitabine Bevacizumab + capecitabine CRC NCT02873195[S110] Atezolizumab + bevacizumab + platinum-based Bevacizumab + platinum-based Ovarian cancer NCT02891824[S111] Avelumab + palbociclib + fulvestrant Palbociclib + fulvestrant BC NCT03147287[S72] Chemotherapy Fulvestrant BC NCT03147287[S72] PD-1 + LAG-3 + chemotherapy Nivolumab + BMS-986213 + XELOX PD-1 + chemotherapy Nivolumab + XELOX Adenocarcinoma of the gastroesophageal junction NCT03662659[S82] PD-1 + radiotherapy Durvalumab + radiotherapy SMTI + radiotherapy Cetuximab + radiotherapy Head and neck squamous cell carcinoma NCT03258554[S36] PD-1 + chemotherapy Nivolumab + platinum-based Chemotherapy Platinum-based NSCLC NCT02477826[S6] Nivolumab + carboplatin/paclitaxel/bevacizumab Carboplatin/paclitaxel/bevacizumab NSCLC NCT03117049[S118] Pembrolizumab + paclitaxel + carboplatin Paclitaxel + carboplatin Endometrial cancer NCT03914612[S14] BC NCT03036488[S54] Pembrolizumab + carboplatin + paclitaxel/nab-paclitaxel Carboplatin + paclitaxel/nab-paclitaxel NSCLC NCT02775435[S108] Pembrolizumab + pemetrexed + platinum Pemetrexed + platinum NSCLC NCT02578680[S18] NSCLC NCT03515837[S45] Pembrolizumab + cisplatin/carboplatin Paclitaxel + cisplatin/carboplatin Cervical cancer NCT03635567[S27] Pembrolizumab + cisplatin Cisplatin Head and neck squamous cell carcinoma NCT03040999[S33] Pembrolizumab + lenalidomide + dexamethasone Lenalidomide + dexamethasone Multiple myeloma NCT02579863[S52] Pembrolizumab + cisplatin-based Cisplatin-based NSCLC NCT03425643[S56] Pembrolizumab + fluorouracil/cisplatin/capecitabine/oxaliplatin Fluorouracil/cisplatin/capecitabine/oxaliplatin Adenocarcinoma of the stomach or gastroesophageal junction NCT03675737[S62] Pembrolizumab + nab-paclitaxel/paclitaxel/gemcitabine + carboplatin Nab-paclitaxel/paclitaxel/gemcitabine + carboplatin BC NCT02819518[S80] Pembrolizumab + cisplatin + fluoroura/capecitabine Cisplatin + fluoroura/capecitabine Gastric cancer NCT02494583[S99] Pembrolizumab + etoposide + cisplatin/carboplatin Placebo + etoposide + cisplatin/carboplatin SCLC NCT03066778[S117] Pembrolizumab
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