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. 2024 Dec 2;19(12):e0314555. doi: 10.1371/journal.pone.0314555

Adverse cardiovascular events and cardiac imaging findings in patients on immune checkpoint inhibitors

Jennifer M Kwan 1,2,#, Miles Shen 1,2,#, Narjes Akhlaghi 2,3, Jiun-Ruey Hu 1,2, Ruben Mora 4, James L Cross 2, Matthew Jiang 2,3, Michael Mankbadi 2,3, Peter Wang 2,3, Saif Zaman 2,3, Seohyuk Lee 2, Yunju Im 5,6, Attila Feher 1,2, Yi-Hwa Liu 2, Shuangge S Ma 6, Weiwei Tao 6, Wei Wei 6, Lauren A Baldassarre 1,2,*
Editor: Sai-Ching Jim Yeung7
PMCID: PMC11611253  PMID: 39621799

Abstract

Background

There is an urgent need to better understand the diverse presentations, risk factors, and outcomes of immune checkpoint inhibitor (ICI)-associated cardiovascular toxicity. There remains a lack of consensus surrounding cardiovascular screening, risk stratification, and clinical decision-making in patients receiving ICIs.

Methods

We conducted a single center retrospective cohort study including 2165 cancer patients treated with ICIs between 2013 and 2020. The primary outcome was adverse cardiovascular events (ACE): a composite of myocardial infarction, coronary artery disease, stroke, peripheral vascular disease, arrhythmias, heart failure, valvular disease, pericardial disease, and myocarditis. Secondary outcomes included all-cause mortality and the individual components of ACE. We additionally conducted an imaging substudy examining imaging characteristics from echocardiography (echo) and cardiac magnetic resonance (CMR) imaging.

Results

In our cohort, 44% (n = 962/2165) of patients experienced ACE. In a multivariable analysis, dual ICI therapy (hazard ratio [HR] 1.23, confidence interval [CI] 1.04–1.45), age (HR 1.01, CI 1.00–1.01), male sex (HR 1.18, CI 1.02–1.36), prior arrhythmia (HR 1.22, CI 1.03–1.43), lung cancer (HR 1.17, CI 1.01–1.37), and central nervous system (CNS) malignancy (HR 1.23, CI 1.02–1.47), were independently associated with increased ACE. ACE was independently associated with a 2.7-fold increased risk of mortality (P<0.001). Dual ICI therapy was also associated with a 2.0-fold increased risk of myo/pericarditis (P = 0.045), with myo/pericarditis being associated with a 2.9-fold increased risk of mortality (P<0.001). However, the cardiovascular risks of dual ICI therapy were offset by its mortality benefit, with dual ICI therapy being associated with a ~25% or 1.3-fold decrease in mortality. Of those with echo prior to ICI initiation, 26% (n = 115/442) had abnormal left ventricular ejection fraction or global longitudinal strain, and of those with echo after ICI initiation, 28% (n = 207/740) had abnormalities. Of those who had CMR imaging prior to ICI initiation, 43% (n = 9/21) already had left ventricular dysfunction, 50% (n = 10/20) had right ventricular dysfunction, 32% (n = 6/19) had left ventricular late gadolinium enhancement, and 9% (n = 1/11) had abnormal T2 imaging.

Conclusion

Dual ICI therapy, prior arrhythmia, older age, lung and CNS malignancies were independently associated with an increased risk of ACE, and dual ICI therapy was also independently associated with an increased risk of myo/pericarditis, highlighting the utmost importance of cardiovascular risk factor optimization in this particularly high-risk population. Fortunately, the occurrence of myo/pericarditis was relatively uncommon, and the overall cardiovascular risks of dual ICI therapy appeared to be offset by a significant mortality benefit. The use of multimodal cardiac imaging can be helpful in stratifying risk and guiding preventative cardiovascular management in patients receiving ICIs.

Introduction

Over the past decade, immune checkpoints inhibitors (ICIs) have revolutionized the landscape of cancer care. Currently, eleven ICIs are approved by the Food and Drug Administration for the treatment of various malignancies, including but not limited to melanoma, non-small cell lung cancer, renal cell carcinoma, urothelial cancer, head and neck squamous cell carcinoma, Hodgkin’s lymphoma, gastrointestinal cancers, breast cancer, and cancers with high microsatellite instability or defective mismatch repair [1–3]. Combination ICI therapies are also used to increase anti-tumor activity [4].

Through the inhibition of cytotoxic T lymphocyte-associated protein-4 (CTLA-4), programmed cell death protein-1 (PD-1), and programmed cell death 1 ligand-1 (PD-L1), ICIs bolster the host’s immune system to effectively recognize and target tumor cells [2]. While ICIs are effective for a growing number of cancer patients, the enhanced T-cell activity against host tissues can lead to a wide range of immune-related adverse events (irAEs). Adverse cardiovascular events (ACE) were initially underappreciated in prospective trials of ICIs, likely in part due to lack of standardized cardiac monitoring and testing, nonspecific clinical manifestations, and difficulties in diagnosis, compared to other irAEs such as pneumonitis, colitis, or hepatitis [5–11]. However, since the approval of ICIs, cardiovascular toxicities have been increasingly reported [8,12–16]. There is still much to learn about the unique presentations, risk factors, and outcomes of ICI-associated cardiovascular toxicity. The existing literature on ICI-associated cardiovascular toxicity has in large part focused on myocarditis, with these studies describing fulminant and fatal presentations; however, other studies have also reported on smoldering and asymptomatic cases [13,17–23]. Other less-commonly reported presentations of ICI-associated cardiovascular toxicities include pericardial disease, vasculitis, takotsubo-like syndrome, heart failure, myocardial infarction, coronary vasospasm, and arrhythmias [16,23–31]. ICIs have also been implicated in the acceleration of atherogenesis and, importantly, have been associated with increased risk of major adverse cardiovascular events [32–37].

Among risk factors, combination ICI therapy is most strongly correlated with incidence and severity of ICI-associated myocarditis [9,13,22]. Other possible risk factors suggested by epidemiologic and cohort studies for ICI-associated cardiovascular toxicity include concomitant use of cardiotoxic anti-neoplastic agents such as anthracyclines, prior radiation therapy, underlying cardiovascular disease, underlying autoimmune disease, tumor-related factors, concurrent irAEs such as skeletal myositis, and genetic factors [20,21,38–42].

With expanding indications for ICI therapy, an increasing number of patients are eligible to receive these agents, including an increasing number of patients with preexisting cardiovascular risk factors [3,43]. As such, further efforts to improve our understanding of ICI-associated cardiotoxicity are needed in order to guide clinical decision-making and to improve patient outcomes.

Cardiac imaging modalities such as echocardiography (echo) and cardiac magnetic resonance (CMR) imaging are integral to the evaluation of cardiotoxicity from ICI. Both echo and CMR provide valuable assessment of cardiac structure and function. CMR additionally provides the added value of tissue characterization, where the presence of late gadolinium enhancement (LGE) can be indicative of myocardial injury or fibrosis, and increased T2 signal can be indicative of myocardial edema. Abnormalities in both T1- and T2-weighted imaging can be seen in both ischemic and nonischemic processes, including from prior or current cancer therapies, such as chemotherapy and radiation therapy [44]. CMR can also aid in the diagnosis of acute myocarditis with the application of the Lake Louise Criteria, which incorporates main criteria (abnormal T1 or T2 parameters), with supportive criteria (pericarditis or left ventricular dysfunction) [45]. We conducted a large cohort study evaluating the presentations, risk factors, ACE outcomes, in patients who received ICI therapy. We additionally performed an imaging substudy to compare imaging characteristics on echo and CMR pre- and post-ICI.

Methods

Study population and covariates of interest

We performed a retrospective cohort study including cancer patients treated with ICIs between 2013 to 2020 at a large academic institution (Yale New Haven Hospital, Connecticut). The study was approved by the Yale University Institutional Review Board (IRB #2000026073) and a waiver for informed consent was obtained. Data was collected from electronic health records by our institutional Joint Data Analytics Team on July 19, 2019 and was stored in a HIPAA-compliant data repository. No members of the Joint Data Analytics Team were involved in the study design, statistical analyses, and manuscript preparation. Authors had access to information that could identify individual participants during or after data collection. Covariates of interest included patient demographics, cardiovascular risk factors including hypertension (HTN), hyperlipidemia (HLD), diabetes mellitus (DM), smoking history, chronic kidney disease (CKD), and rheumatologic disorders. Cardiovascular conditions included atherosclerotic cardiovascular disease (ASCVD), heart failure (HF), valvular disease, arrhythmias, pericardial disease, endocarditis, and myocarditis. Data pertaining to cancer included cancer types, presence of metastatic disease, specific ICI agents, and dual ICI therapy, which was defined by concurrent use of two ICI agents. For the imaging substudy, echo and CMR data were extracted from the database associated with our local picture archiving and communication system (PACS). Cardiac imaging was obtained when clinically indicated, and all pre-ICI scans were compared with post-ICI scans.

Study outcomes

The primary outcome was the occurrence of ACE, defined as a composite of ASCVD—which included coronary artery disease (CAD), myocardial infarction (MI), stroke (CVA), and peripheral arterial disease (PAD)—, heart failure (HF), arrhythmia (includes supraventricular tachycardia, atrial fibrillation, atrial flutter, ventricular tachycardia, ventricular fibrillation)), valvular disease (moderate or severe), pericardial disease, and myocarditis after initiation of ICI therapy. The secondary outcomes included the occurrence of the individual components of ACE and all-cause mortality. Time to event was recorded from the date the subject was started on ICI (Jan 1, 2013) to the first occurrence of each outcome or to the study end date after most recent ICI administration. Investigators also manually adjudicated a subset of 200 randomly-chosen patients to evaluate the accuracy of electronic health record data acquisition methodology, which demonstrated good correlation with overall similar comorbidities (S1 Table); 1% of patients developed myo/pericarditis, which was not statistically significant compared to the entire cohort (P = 0.745). For the imaging substudy, abnormal echo criteria included global longitudinal strain (GLS) >-18% and left ventricular ejection fraction (LVEF) <55%. Abnormal CMR criteria included presence of LGE, abnormal T1 or T2 mapping, LVEF <57%, or right ventricular ejection fraction (RVEF) <52%.

Statistical analysis

Continuous variables were presented as means with standard deviations or medians with interquartile ranges and were compared using Student’s t-test. Categorical variables were presented as counts and percentages and were compared using Chi square or Fisher’s exact tests. Composite ACE, as well as its individual components, were analyzed in a competing risks model using specific ICI agents, dual ICI therapy, and additional covariates, including age, sex, cancer types, cardiovascular diseases, and other medical comorbidities. All-cause mortality was assessed using a Cox regression model with time-dependent covariates using ACE, myo/pericarditis (myocarditis, pericarditis, or both), and additional covariates, including age, sex, cancer types, cardiovascular diseases, and other medical comorbidities. To investigate the effect of ICI on ACE compared to patients who did not receive ICI, we performed propensity score matching between the ICI cohort and a non-ICI cohort (S1 Fig). The non-ICI cohort was constructed from patients who received tyrosine kinase inhibitors (TKI) for three cancer types (lung, GI, and renal) that are commonly treated with ICI. We used optimal pair matching which forms matched pairs such that the average absolute distance across all the matched pairs is minimized. Cox regression was performed on the propensity-matched dataset using ICI status, age, sex, select cardiovascular diseases, and other medical comorbidities as covariates. All tests were considered statistically significant at P<0.05. All statistical analyses were performed using R software, version 4.1.0 (Vienna, Austria). Relevant imaging parameters from echo and CMR were extracted from imaging reports using natural language processing (NLP) techniques–the NLP pipeline was scripted using the NLTK toolkit in Python (v3.11.5) [46].

Results

A total of 2165 patients who received any ICI for any type of cancer between 2013 to 2020 were included in this study. Median follow-up duration was 500 days (IQR 886 days). Of these patients, 962 (44%) developed ACE. Patients who developed ACE were older compared to those who did not develop ACE (mean age 70.4±11.6 vs 68.7±12.4, P = 0.001) and more patients who developed ACE had a prior history of arrhythmia compared to those who did not develop ACE (27.3% vs 20.2%, P<0.0001). Patients who developed ACE also had a higher rate of prior HF compared to patients who did not develop ACE (12.4% vs 8.7%, P = 0.007). HTN (67.0% vs 61.4%, P = 0.007) and HLD (44.5% vs 38.8%, P = 0.008) were more prevalent in those who developed ACE compared to those who did not develop ACE (Table 1).

Table 1. Baseline demographics and comorbidities by presence of post-treatment adverse cardiovascular events.

Baseline demographics and comorbidities of patients who did not experience ACE compared to those who experienced ACE.

TOTAL (N = 2165) NO ACE (N = 1203) ACE (N = 962) P-VALUE
Demographic Characteristics
 Age ‐ Mean ± SD (years) 69.4 ± 12.1 68.7 ± 12.4 70.4 ± 11.6 0.001
 BMI ‐ Mean ± SD (kg/m2) 27.0 ± 3.98 27.0 ± 3.98 27.1 ± 3.96 0.270
 Male ‐ % (n/N) 56.3% (1218/2165) 53.0% (637/1203) 60.4% (581/962) 0.001
 Hispanic ‐ % (n/N) 3.9% (82/2121) 3.5% (41/1168) 4.3% (41/953) 0.366
 Race ‐ % (n/N) 0.400
 White 86.6% (1875/2164) 86.0% (1034/1202) 87.4% (841/962)
 Black 6.3% (136/2164) 6.1% (73/1202) 6.5% (63/962)
 Asian 1.2% (27/2164) 1.4% (17/1202) 1.0% (10/962)
 Other 5.8% (126/2164) 6.5% (78/1202) 5.0% (48/962)
Comorbidities ‐ % (n/N)
 Arrhythmia 23.4% (506/2165) 20.2% (243/1203) 27.3% (263/962) <0.001
 ASCVD 39.1% (846/2165) 37.7% (454/1203) 40.7% (392/962) 0.156
 Heart failure 10.3% (224/2165) 8.7% (105/1203) 12.4% (119/962) 0.007
 Valvular disease 8.8% (190/2165) 7.7% (93/1203) 10.1% (97/962) 0.056
 Pericardial disease 1.3% (29/2165) 1.1% (13/1203) 1.7% (16/962) 0.263
 Myocarditis 0.0% (1/2165) 0.0% (0/1203) 0.1% (1/962) 0.444
 Hypertension 63.9% (1384/2165) 61.4% (739/1203) 67.0% (645/962) 0.007
 Hyperlipidemia 41.3% (895/2165) 38.8% (467/1203) 44.3% (428/962) 0.008
 Diabetes mellitus 21.3% (461/2165) 20.9% (252/1203) 21.7% (209/962) 0.673
 Ever smoker 72.3% (1559/2156) 71.4% (856/1199) 73.5% (703/957) 0.309
 Chronic kidney disease 12.6% (273/2165) 11.7% (141/1203) 13.7% (132/962) 0.171
 Venous thromboembolism 7.4% (160/2165) 7.1% (85/1203) 7.8% (78/962) 0.563
 Endocarditis 0.1% (2/2165) 0.1% (1/1203) 0.1% (1/962) >0.999
 Pulmonary diseaseΙ 42.2% (914/2165) 41.7% (502/1203) 42.8% (412/962) 0.630
 Endocrine diseaseΙΙ 7.0% (151/2165) 6.7% (81/1203) 7.3% (70/962) 0.671
 Rheumatologic diseaseΙΙΙ 3.2% (69/2165) 3.0% (36/1203) 3.4% (33/962) 0.623
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Normally distributed continuous variables are presented as mean ± standard deviation. ASCVD: Atherosclerotic cardiovascular disease, including acute coronary syndrome (ACS), those with history of myocardial infarction (MI), stable or unstable angina or coronary or other arterial revascularization, stroke, transient ischemic attack (TIA), or peripheral artery disease (PAD).

Ι Pulmonary diseases including chronic obstructive pulmonary disease, interstitial lung disease, asthma.

II Endocrine diseases including thyroid, adrenal, pituitary gland dysfunction.

III Rheumatologic diseases including systemic lupus erythematosus, rheumatoid arthritis, scleroderma, myositis, mixed connective tissue disease, Sjögren’s syndrome.

The most frequently used ICI, either as a single agent or combined with other ICIs, was pembrolizumab (n = 773), followed by nivolumab (n = 670). Furthermore, there was a higher incidence of ACE in the cohort of patients receiving dual ICI therapy compared to those who received single ICI therapy (50.9% vs 43.2%, P = 0.011). Out of all ICI agents, ipilimumab was associated with the highest rate of ACE (52.2%). The occurrences of ACE and its individual components by ICI agent are shown in Table 2.

Table 2. Adverse cardiovascular events by immune checkpoint inhibitor.

Occurrence of ACE and individual components of ACE, by single ICI versus dual ICI therapy, as well as by specific ICI agents.

ACE ARRHYTHMIA ASCVD HEART FAILURE VALVULAR DISEASE PERICARDIAL DISEASE MYOCARDITIS
Single ICI therapy 43.2% (788/1823) 20.2% (368/1823) 27.5% (502/1823) 11.1% (202/1823) 6.4% (117/1823) 1.9% (34/1823) 0.5% (10/1823)
Dual ICI therapy 50.9% (174/342) 22.2% (76/342) 31% (106/342) 13.2% (45/342) 7% (24/342) 1.8% (6/342) 2% (7/342)
P-value 0.011 0.382 0.191 0.267 0.635 1.000 0.011

ICI Agent
 Nivolumab 41.6% (279/670) 19.1% (128/670) 28.4% (190/670) 10.6% (71/670) 6.1% (41/670) 1.2% (8/670) 0.3% (2/670)
 Pembrolizumab 43.1% (333/773) 19.9% (154/773) 26.8% (207/773) 11.4% (88/773) 7.1% (55/773) 1.9% (15/773) 0.9% (7/773)
 Ipilimumab 52.2% (96/184) 20.1% (37/184) 37% (68/184) 9.8% (18/184) 9.2% (17/184) 1.1% (2/184) 0.5% (1/184)
 Atezolizumab 44.3% (94/212) 22.6% (48/212) 24.5% (52/212) 11.3% (24/212) 5.2% (11/212) 1.9% (4/212) 0.5% (1/212)
 Durvalumab 37.9% (36/95) 18.9% (18/95) 25.3% (24/95) 8.4% (8/95) 4.2% (4/95) 2.1% (2/95) 0.0% (0/97)
 Avelumab 45.5% (5/11) 9.1% (1/11) 18.2% (2/11) 0% (0/11) 9.1% (1/11) 18.2% (2/11) 0.0% (0/12)
 Cemiplimab 40% (2/5) 20.0% (1/5) 0.0% (0/5) 40.0% (2/5) 0.0% (0/5) 20.0% (1/5) 0.0% (0/5)
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ASCVD: Atherosclerotic cardiovascular disease, including acute coronary syndrome (ACS), those with history of myocardial infarction (MI), stable or unstable angina or coronary or other arterial revascularization, stroke, transient ischemic attack (TIA), or peripheral artery disease (PAD).

The occurrence of ACE by cancer type is summarized in S2 Table, with top cancers represented including lung, melanoma, liver and gastrointestinal cancers. Kaplan Meier survival curves and median times to ACE and the individual components of ACE are shown in S2 Fig.

We additionally assessed the independent risk of select demographic characteristics, cardiovascular risk factors, medical comorbidities, and dual ICI therapy on ACE as well as the individual components of ACE. Dual ICI therapy (HR 1.23, CI 1.04–1.45), age (HR 1.01, CI 1.00–1.01), male sex (HR 1.18, CI 1.02–1.36), arrhythmia (HR 1.22, CI 1.03–1.43), lung cancer (HR 1.17, CI, 1.00–1.37), and CNS malignancy (HR 1.23, CI 1.02–1.47) were independently associated with increased occurrence of ACE (Fig 1A). Age (HR 1.02, CI 1.01–1.03), male sex (HR 1.30, CI 1.08–1.55), HLD (HR 1.25, CI 1.05–1.48), and CNS malignancy (HR 1.79, CI 1.45–2.21), were independently associated with increased occurrence of ASCVD. Smoking history was strongly associated with an increased occurrence of ASCVD (HR 1.22, CI 1.00–1.48); however, this barely did not meet significance with a P = 0.052. In contrast, a prior history of ASCVD (HR 0.72, CI 0.60–0.87) was associated with a lower risk of new or worsening ASCVD following ICI therapy (Fig 1B). History of valvular disease (HR 1.70, CI 1.15–2.51) and HF (HR 1.63, CI 1.13–2.36) were independently associated with an increased risk of new or worsening HF with ICI therapy (Fig 1C). History of arrhythmia (HR 1.48, CI 1.16–1.89), lung cancer (HR 1.40, CI 1.09–1.80), and genitourinary cancers (HR 1.29, CI 1.04–1.61) were independently associated with increased risk of new or worsening arrythmia (Fig 1D). History of valvular disease (HR 1.70, CI 1.03–2.79) and hematologic malignancies (HR 1.78, CI 1.07–2.95) were independently associated with an increased risk of new or worsening valvular disease (Fig 1E). Lastly, dual ICI therapy (HR 1.96, CI 1.01–3.74), head and neck cancer (HR 2.20, CI 1.08–4.49), and lung cancer (HR 3.45, CI 1.49–7.98) were associated with an increased risk of myo/pericarditis, whereas younger age marginally reduced the risk of myo/pericarditis (HR 0.98, CI 0.95–1.00) (Fig 1F).

Fig 1. Cox regression models for adverse cardiovascular events (ACE) and individual components of ACE.

Fig 1

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We also assessed the independent risk of individual ICI agents on ACE as well as individual components of ACE (S3 Fig). Although there were varying degrees of statistical significance in comparison to the multivariable model in Fig 1, the hazard ratios for covariates largely trended in the same direction. Although a high occurrence of ACE was previously observed in patients who received ipilimumab (Table 2), after adjusting for covariates in a multivariable Cox regression model, it was not independently associated with an increased risk of ACE, ASCVD, arrhythmia, or valvular disease (S3 Fig). Moreover, a propensity score matched analysis was performed to assess whether ICI use was independently associated with ACE in comparison to a matched cohort that did not receive ICI therapy (patients who received TKIs) (S1 Fig). There were a total of 1518 matched patients, with 759 patients in each group. The standardized mean differences for the matched variables were well below the 0.1 threshold, indicating that balance between the matched variables had been achieved (Figure A of S1 Fig). Based on the matched samples, ICI use was not an independent risk factor for ACE (Figure B of S1 Fig). Demographics and comorbidities of the propensity matched cohort are featured in S3 Table.

In a secondary analysis, we evaluated the effect of ACE and myo/pericarditis on all-cause mortality. In the extended Cox regression model with time varying covariates, ACE (Fig 2A) independently increased the risk of mortality by 2.7-fold (P<0.001). Myo/pericarditis (Fig 2B) also independently increased risk of mortality by 2.9-fold (P<0.001). Pre-existing arrhythmia, metastatic disease, and ASCVD were also associated with increased risk of mortality (HR 1.18, P = 0.024; HR 1.29, P = 0.032; and HR 1.31, P<0.001, respectively). As previously noted, dual ICI therapy was associated with a higher risk of ACE and myo/pericarditis (Fig 1A and 1F). However, dual ICI therapy was also associated with a ~25% or 1.3-fold decrease in mortality (P<0.001) (Fig 2).

Fig 2. Kaplan-Meier curves and time-dependent Cox regression for outcome of mortality.

Fig 2

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Imaging substudy

Given the high prevalence of ACE in our cohort, we sought to evaluate echo and CMR imaging characteristics, with comparison of all pre- and post-ICI scans. Forty-four percent (n = 955/2165) of patients had an echo at some point in time. Forty-four percent of those patients (n = 442/955) had an echo prior to ICI initiation, of which 26% (n = 115/442) were abnormal. Median time from echo to time of first ICI administration was 112 days. Median LVEF was 50% (IQR 9.9%) and GLS was -15% (IQR 3.0%) prior to ICI initiation. Seventy-seven percent of patients (n = 740/955) had an echo after ICI initiation, of which 28% (n = 207/740) were abnormal (S4 Fig). Median time from last ICI administration to echo was 241 days. Median LVEF and GLS were 48% (IQR 12.2%) and -16% (IQR 3.0%) after ICI initiation, respectively. Of those who had abnormal echo’s prior to ICI, 9.6% (n = 11/115) had an interval decline in LVEF after initiation of ICI. The reduction in LVEF trended toward significance (P = 0.09), but the change in GLS was not significantly different (P = 0.57) in comparing pre- and post-ICI scans.

There were 104 unique patients of the entire cohort of 2165 (5%) who had CMR imaging performed, with 21 (20%) of CMR imaging being performed prior to ICI initiation (median time from ICI to CMR was 204 days) and 83 (80%) of CMR imaging being performed after ICI initiation (median time from ICI to CMR was 511 days). CMR ordering indications are shown in S4 Table. Only 4 patients had CMR imaging both pre- and post-ICI; of these patients, 3 had non-diagnostic studies post-ICI, thus precluding evaluation of imaging changes pre- and post-ICI within the same patients. Quantitative hemodynamic measurements including LVEF, cardiac output, and cardiac index pre- and post-ICI are summarized in Table A of S5 Table and is notable for a significant decline in mean cardiac index post-ICI (P = 0.024). Among patients who had CMR imaging prior to ICI initiation, 43% (n = 9/21) already had left ventricular dysfunction, 50% (n = 10/20) had right ventricular dysfunction, 32% (n = 6/19) had left ventricular LGE and 9% (n = 1/11) had abnormal T2 imaging. However, there were no significant differences in the prevalence of abnormal LVEF or RVEF, nor abnormal CMR tissue characteristics in comparing pre- and post-ICI scans (Table B of S5 Table).

Out of the 104 patients who had CMR performed, 14 (14%) had ICI myocarditis. Among patients who developed myocarditis, there was a significantly higher burden of any left ventricular LGE (61.5% vs 24.7%, P = 0.018), non-ischemic left ventricular LGE (45.5% vs 7.7%, P = 0.004), and right ventricular insertion LGE (38.5% vs 5.2%, P = 0.003) compared to those who did not develop myocarditis (Table C of S5 Table). There was also a trend towards an increased burden of abnormal T2 in patients with myocarditis (33.3% vs 8.3%), although this did not meet statistical significance (P = 0.063).

Central illustration

Risk Factors Increasing Adverse Cardiovascular Events and Cardiac Imaging findings in Patients Pre- and Post-ICI.

Discussion

In this large single-center retrospective cohort study, we found an increased risk of ACE and myo/pericarditis with dual ICI therapy, which is consistent with what has been previously reported in the literature. A multicenter registry of 35 confirmed myocarditis cases in 8 medical centers in North America between 2013 and 2017 showed that myocarditis cases were more likely to have received combination ICI therapy at any stage in treatment compared to controls [22]. Similarly, analysis of a retrospective safety database conferred a 4.7-fold increased risk of developing myocarditis with combination of nivolumab and ipilimumab compared with treatment with nivolumab alone [9,13].

Patients in our cohort who developed ACE or myo/pericarditis had shorter survival. This was found to be independent of their comorbidities, cancer type, and age. Interestingly, we found that dual ICI therapy was protective against death—this may likely be in part due to improved cancer survival as prior studies have shown [47,48], despite the increased risk of ACE and associated mortality. Myo/pericarditis affected approximately 1% of this cohort, suggesting that the majority of patients likely still derive benefit from ICI therapy. Thus, shedding light upon additional risk factors for ICI myocarditis is crucial for risk stratification of higher risk patients [49].

It remains unclear if ICI associated cardiotoxicity is more common in specific types of cancers. In our cohort, patients with lung and CNS malignancies were at a higher risk of developing ACE. The observation of increased ACE with lung cancer may be in part related to close cardiopulmonary interactions; lung cancers can be implicated in the pathogenesis of pulmonary hypertension, pericardial effusions, and pleural effusions, which can in turn lead to heart failure-like symptoms. It is also possible that some patients with lung cancer received radiation therapy, which may have driven part of this observed effect. Neurologic complications from CNS malignancies may have promoted a proarrhythmic state, which could in turn contribute to higher rates of ACE. Although our study attempts to adjust for confounding factors as much as possible through multivariable analyses and deliberate covariate selection, it is unclear whether other unidentified shared risk factors may be playing a role in both cancer and progression of underlying cardiovascular disease. Further studies are needed to determine causality of these risk factors with the development of ACE on ICI therapy.

Although the role of CTLA-4 and PD-1 in regulating the inflammatory response underlying atherosclerosis is established [50], the effects of ICIs on atherosclerosis in cancer patients are not completely understood [51]. For example, two retrospective studies on patients with non-small cell lung cancer (NSCLC) did not show a significant increase in ACE with ICI therapy [32,52]. Similarly, our propensity score matched analysis did not suggest a higher risk of ACE with ICI therapy in our cohort. In contrast, in a pooled analysis of 59 oncological trials comprising 21,664 patients, there was a trend towards increased coronary ischemia over 6 months follow up in patients who received ICIs compared to traditional cytotoxic chemotherapies [53]. Furthermore, in a single center retrospective study, authors reported a significant increase in the risk of MI, coronary revascularization, and ischemic CVA associated with the use of an ICI in a control-matched retrospective cohort study of 2842 patients. They also conducted a case-crossover analysis showing a significant increase in ischemic cardiovascular events within two years of initiation of ICIs [34]. In our propensity score matched analysis, it is possible that similar comorbidities and data supporting that TKIs may also be associated with increased ACE could explain the lack of difference between these two groups, who may both be at higher risk of developing ACE [54].

The 2022 European Society of Cardiology cardio-oncology guidelines recommend a baseline echo for patients starting ICI as a class I level recommendation for high-risk patients (patients starting dual ICI therapy, prior ICI related non-cardiovascular events, prior cardiovascular disease) and class IIb for those who are low risk [55]. Our study includes one of the largest published cohorts of patients on ICI therapy with cardiac imaging data that offers evidence to support this recommendation, given the high burden of imaging abnormalities observed in patients prior to ICI initiation. While it may be logistically challenging to obtain baseline CMR on every patient prior to ICI initiation, screening troponin, B-type natriuretic peptides, and echo may help delineate individuals who would derive the most benefit from having baseline CMR imaging. Establishing baseline cardiac imaging helps to identify individuals with pre-existing imaging abnormalities. This is not only important in informing risk of potential cardiotoxicity with ICI initiation, but is also of particular importance especially when a patient presents for evaluation of suspected ICI-related cardiotoxicity, as the clinician is afforded important comparative information, which can impact recommendations on whether to continue ICI therapy.

Study strengths

Our study included a large cohort of over 2000 patients, providing robust statistical power and confidence in our findings. Moreover, we adjusted for confounders as much as possible using deliberate variable selection and multivariable models. Our study provides greater insight into a broad spectrum of ACE beyond myocarditis, which has been the most studied of the cardiovascular irAEs. ACE other than myocarditis were common in our cohort and were associated with increased mortality, necessitating increased vigilance toward the detection of these adverse events. Importantly, this study also highlighted risk factors that may increase ACE with ICI use, which may help to guide preventative therapies for these patients. Additionally, we performed an imaging substudy using echo and CMR data and comparing pre- and post-ICI scans, which uncovered a high burden of abnormal cardiac imaging pre-ICI as well as a higher burden of abnormalities detected on post-ICI imaging.

Study limitations

This is a retrospective cohort study at a single center using electronic health record data, which can contain biases, inaccuracies associated with electronic health records and could be less representative of the entire cancer population. The primary limitation of this study is related to its retrospective nature, as confounding factors cannot be completely adjusted for. The reliance on ICD codes for identification of variables and outcomes may have been subject to coding inaccuracies. We addressed these limitations by performing a manual chart adjudication for a randomly chosen subset of patients, and this showed good correlation without significant differences between the electronic health record data acquisition and chart review. Another study limitation is the lack of cardiac imaging data for the majority of patients. Additionally, there were essentially no patients who had both pre- and post-ICI CMR imaging, precluding assessment of CMR imaging changes within the same patients. Lastly, it is possible that patients with prior cardiac imaging were already at higher cardiovascular risk, which could account for the high burden of observed imaging abnormalities in this cohort.

Conclusion

We performed a comprehensive assessment of ACE associated with ICIs in a single center retrospective study and found that dual ICI therapy was significantly associated with increased risk of composite ACE and myo/pericarditis. Consistent with prior oncology trial data, dual ICI therapy was associated with overall longer survival, despite an increased risk of ACE. Those with prior cardiac comorbidities including arrhythmia exhibited higher rates of ACE, and those who developed ACE or myo/pericarditis had significantly reduced survival. Patients with lung and CNS malignancies were more likely to develop ACE. Cardiac imaging prior to ICI initiation detected a high burden of abnormalities, suggesting higher cardiovascular risk in this patient population. Patients receiving ICI therapy stand to benefit from optimization of cardiovascular risk factors in order to attenuate the risk of developing cardiovascular complications; moreover, baseline and surveillance cardiac imaging can be considered in this high-risk patient population to provide additional risk stratification.

As a future direction, we plan to expand our cohort with more current data with the objective of constructing a robust model to estimate cardiovascular risk with ICI use. This model would incorporate patient characteristics, cardiovascular conditions, medical comorbidities, cancer information, as well as laboratory and cardiac imaging data, if available. Our goal is to eventually implement a risk score for widespread clinical use, which can provide patients who are being considered for ICI therapy with a tangible estimation of the potential cardiovascular risks, tailored to their unique risk factor profile.

Supporting information

S1 Fig. Propensity score matching for outcome of adverse cardiovascular events.

(DOCX)

pone.0314555.s001.docx (292KB, docx)
S2 Fig. Kaplan Meier curves and median times to adverse cardiovascular events (ACE) and individual components of ACE.

(DOCX)

pone.0314555.s002.docx (253.2KB, docx)
S3 Fig. Cox regression model including single immune checkpoint inhibitor agents for adverse cardiovascular events (ACE) and individual components of ACE.

(DOCX)

pone.0314555.s003.docx (4.7MB, docx)
S4 Fig. Prevalence of abnormal echocardiograms pre- and post-immune checkpoint inhibitor.

(DOCX)

pone.0314555.s004.docx (109.1KB, docx)
S1 Table. Comparison of demographic characteristics and comorbidities between entire cohort from electronic health record data acquisition and adjudicated subset.

(DOCX)

pone.0314555.s005.docx (15KB, docx)
S2 Table. Presence of adverse cardiovascular events by cancer type.

(DOCX)

pone.0314555.s006.docx (18.2KB, docx)
S3 Table. Demographics and comorbidities of propensity matched cohort.

(DOCX)

pone.0314555.s007.docx (17.2KB, docx)
S4 Table. Ordering indications for cardiac magnetic resonance imaging pre- and post-immune checkpoint inhibitor.

(DOCX)

pone.0314555.s008.docx (15.8KB, docx)
S5 Table. Cardiac magnetic resonance imaging features.

(DOCX)

pone.0314555.s009.docx (19.5KB, docx)
S1 Data. Minimal dataset.

(CSV)

pone.0314555.s010.csv (342KB, csv)
S1 File

(DOCX)

pone.0314555.s011.docx (633.5KB, docx)

Acknowledgments

We thank Krishna Daggula and Richard Hintz from the Yale JDAT team for their assistance with data acquisition.

Data Availability

All relevant data are within the manuscript and its Supporting Information files.

Funding Statement

This work was supported by: 1) LB, grant #18CDA34110361, American Heart Association (https://www.heart.org). 2) JMK, CTSA grant #KL2 TR001862, National Center for Advancing Translational Science (NCATS) (https://ncats.nih.gov/), a component of the National Institutes of Health (NIH). Funders did not play any role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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PONE-D-24-31391Adverse Cardiovascular Events and Cardiac Imaging Findings in Patients on Immune Checkpoint InhibitorsPLOS ONE

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

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Reviewer #1: Yes

Reviewer #2: Partly

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: I Don't Know

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: Yes

Reviewer #2: Yes

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5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: This is a well written manuscript.

I wonder whether it is possible to develop a computer program based on your data and research which can calculate the pre-treatment risk and survival chance of an individual patient. This calculation could be similar to the heart score for chest pain with the calculation of a heart score which assigns a low risk, medium risk and high risk to the patient.

The risk factors could include dual versus single use of ICI, age, sex, arrhythmia , lung cancer and CNS cancer.

A score can certainly not predict the exact course of he ICI treatment but it could be helpful in the discussion with the patient searching for answers about treatment, probability of adverse effects of treatment and prognosis for treatment with ICI.

A newly developed score which can be computer generated could assist o find answers for the patient and the treating physicians when giving informed consent for ICI treatment. Additionally, table 2 A can be helpful to explain the risks of treatment and the fact that these adverse effects must be weighted towards the increased survival time with ICIs.

Maybe you can mention that based on your research you could try to develop such a computer program to calculate an ICI treatment score for risk stratification for adverse cardiac events within a certain period of time.

Supplemental figure 2 shows the Kaplan Meier curves and the times composite to ACE and individual composite to ACE.

Reviewer #2: SMOKING is a primary risk factor for ACE and represents a critical source of potential confounding in this study. Its absence from basic demographics of the cohort, as well as its absence in the propensity score matching, represents a significant flaw in this study. Any information on smoking status (previous, never, etc) is better than nothing to better understand how it fits in with pre/post ICI associative data.

Overall, this paper appears to emulate Drobni et al 2020, but does so with worse methodology (retrospective cohort rather than case-control, an even larger composite outcome, and with significant potential confounders that aren’t addressed with their propensity matching), and I think these issues should be addressed, if possible, with data re-analysis prior to publication (ex: data on previous cardiotoxic cancer therapies, and smoking status). I think this study adds a large body of data to support relevant associations such as dual ICI with increased risk ACE, mortality, and myo/pericarditis; there is thought-provoking data suggesting a not insignificant mortality benefit association from dual ICI (despite apparent associated risk of ACE/myo/pericarditis). It could still be worthy of publication but the data needs to be presented in a clearer way with less conflicting information. See below for line-by-line comments. Also, I am definitely not a statistician, so please excuse any errors I may have made and help me understand further if I have made mistakes. I wish you luck in improving your paper and hope these comments help get you closer to publication.

ABSTRACT:

Line 49: CNS malignancy is included as independently associated with ACE, however Supplemental Figure 3 seems to show “neuro” as crossing 1.0 in the top panel. Why do much of the data in that cox regression analysis in Supp. Fig. 3 conflict with the primary Figure 1 results? Is the primary difference that Supp. Fig. 3 included individual ICIs in the analysis? It would be helpful if the panels were formatted in the same orientation as Figure 1 so it makes it easier to compare differences. Overall, I’m not seeing the face validity of CNS malignancies being independently associated with ACE, and the presence of multiple conflicting points in the large amount of data graphics presented is a problem (ex: Figure 1 v. Supp. Fig. 3; “ACE” panel in Supp. Fig. 3 shows “neuro” crossing 1.0 suggesting statistical insignificance, yet Figure 1 shows “CNS malignancy” as statistically significantly associated with increased ACE; or “Arrhythmia” panel in Supp. Fig. 3. Shows “neuro” as actually protective from arrhythmias, yet Figure 1 shows “CNS malignancy” trending toward protective but ultimately crossing 1.0 – which is ironic because Line 318 in the Discussion section offers that “arrhythmias” may contribute to higher rates of ACE, despite the data not supporting that hypothesis at all, at least I’m not seeing support in Figure 1 or Supp. Fig. 3). Perhaps I’m misunderstanding something, however, so please help me understand. Also, if “neuro” in the Supp. Fig 3 is meant to be the same as “CNS malignancy” in Figure 1, then please edit the labels and reorient the panels so they appears as similar to Figure 1 as possible to make it easier to compare the two sets of Cox Regression Models.

Line 54: “19 of 90 who had CMR imaging…” Where did n=90 come from? Supplemental Table 4 shows ordering indications and suggests a total of 102 pre-ICI CMRs and 28 post-ICI CMRs… yet the supplemental Table 5 lists Pre-ICI total as n=90 and post-ICI CMRs as 14. Why is there a discrepancy between these numbers? What happened to the other 26 CMR scans?

Line 61: Again, “CNS malignancies” being included here needs to be supported by all of the data presented, and apparent conflict between Supp. Fig. 3 (“neuro”) and main Figure 1 (“CNS malignancies”) tables need to be explained.

MAIN PAPER:

Line 117: “…prior to and after ICI administration.” – this statement seems pretty misleading considering my comment below regarding Lines 265-270.

Line 124-125: Was the "Joint Data Analytics Team" (data abstractor) blinded to study outcome measures? Could a supplemental document noting specific ICD code/variable requests be made available?

Line 149-150: Why was “pericardial effusion” not a component of abnormal echo? To my knowledge, it’s part of the diagnostic criterion for pericarditis, which is one of the primary outcomes being examined in both Figure 1 and Table 1. I imagine there are a great deal of clinically insignificant pericardial effusions, however if they’re universally present (or absent) in cases of confirmed ICI-related myo/pericarditis then I think it’s work reporting. Why was “valvular disease” not a component of abnormal echo? It’s one of the primary outcome elements (“valvular disease [moderate or severe]”).

Line 171-172: I think a little more detail regarding coding techniques/keywords/instructions involved in using NLP in this way would be appreciated.

Line 192-193: In my anecdotal experience, I see ipilimumab being utilized more often as part of a dual ICI strategy (ex: ipi/nivo) than as a mono-ICI. That’s in contrast to frequently seeing nivolumab monotherapy (for whatever reason). Does the data in Table 2 isolate instances of ipilimumab as only monotherapy, or could recorded instances of its use for dual ICI be potentially driving its appearance as an outlier in that list?

Line 199: Why are the “n” values so large and varied in this list? It would appear obviously much larger than total cohort of 2165. Please help me understand where these total values are coming from.

Line 210-212: How is prior ASCVD associated with a lower risk of ASCVD following ICI therapy? This seems completely counterintuitive and potentially a data error of some kind. This requires further explanation.

Line 227: see my first comment above (Line 49) that has concerns regarding Supplemental Figure 3.

Line 228-230: “after adjusting for covariates in a multivariable Cox regression model, it was not independently associated with…” need to add a bolded reference to Supplemental Figure 3 at the end of this sentence.

Line 237-238: “However, dual ICI therapy was found to decrease mortality by ~25%.” I see in line 299-305 the authors suggest that low incidence of myocarditis suggests “the majority of patients likely still [derive] mortality benefit from ICI therapy.” – if this is correct, then perhaps this findings is worthy of inclusion in the abstract for the paper.

Line 249: Supplemental Table 3 – please edit the table so the variable presented match Table 1 (ex: either switch Table 1 to be alphabetical, or re-order Supplemental Table 3 to more closely match the order of variables presented in Table 1).

Line 258: “Supplemental Figure 4” – please add “n” information to the infographic to clearly see how many echo exams were performed.

Line 265: Why does Supplemental Table 4 indicate a total of 130 CMR exams (102 pre-ICI, 28 post-ICI) that were ordered? Were 26 CMR studies ordered but never completed? Perhaps add bolded reference to Supplemental Table 5 at the end of this sentence on line 268.

Line 265: “(5%)” perhaps add clarification of some kind such as “104 of the entire cohort of 2165 (5%) had CMR performed…”

Line 265: “21 (20%) of CMR imaging being performed prior to ICI initiation…” – Supp. Table 5 suggests 90/104 (87%) were performed prior to ICI initiation. Why the discrepancy?

Line 267: “…83 (80%) of CMR images being performed after ICI initiation…” – Supp. Table 5 suggests 14/104 (13%) were performed post-ICI initiation. Why the discrepancy?

Line 268-270: If only 4 patients had CMR imaging both pre/post ICI and only 1 had diagnostic value… perhaps the entire “substudy” portion involving CMR imaging should be re-framed to focus solely on the increased LGE post-ICI (one of the only parts of Supp. Table 5 to have proper significance). I’m not familiar with “RV Insertion LGE” but that appeared to have p<0.05 in Supp. Table 5.

Line 273: “Supplemental Table 5B” – Abnormal T1 findings are part of the “Lake Louise Criteria” referenced in Line 113, how come that variable is absent in this table?

Line 277: Supp. Table 5 indicates T2 findings did not achieve statistical significance (P=0.063), perhaps add a qualifying statement to this sentence explicitly stating lack of p-value significance.

Line 287: Central Illustration – “Outcomes” in the bottom right portion of the figure – is there a way to keep the units equivalent when comparing the relative pros/cons of dual ICI? It’s tough to compare “~2x” or “~3x” with “25%.”

Line 312-315: this is a huge limitation, and calls to question the validity of the propensity score matching in this dataset.

Line 350-351: why wasn’t troponin or BNP included in this dataset?

Line 364: the absence of “smoking” and “previous cardiotoxic treatments” suggests it wasn’t adjusted as much as possible. Surely some information on these confounders are extractable from the EHR.

Line 365: “…with echo and CMR obtained prior to and after ICI initiation” – again, somewhat misleading verbiage, please find a way to word this to clearly state cardiac imaging was obtained when clinically indicated, and all pre-ICI scans were compared with post-ICI scans, though almost none of them were in the same patient.

Line 372: consider adding that “ACE” is a composite outcome with composite elements (ex: ASCVD) above and beyond what is typically considered “MACE.” This will probably make this data very difficult to

Line 377: is 10% individual chart review good enough? Is there a way to demonstrate that ~200 charts of 2000 is sufficiently powered to make the conclusion that there are no significant differences between the EHR data pull and chart review?

Line 392: if “metastatic disease” is highlighted in the main body of the conclusion, I would recommend highlighting that in the abstract rather than “CNS malignancy.”

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Reviewer #1: Yes: Tareg Bey, MD

Reviewer #2: Yes: Jonathan Rowland

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Decision Letter 1

Sai-Ching Jim Yeung

13 Nov 2024

Adverse Cardiovascular Events and Cardiac Imaging Findings in Patients on Immune Checkpoint Inhibitors

PONE-D-24-31391R1

Dear Dr. Baldassarre,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

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Sai-Ching Jim Yeung, MD, PhD

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Acceptance letter

Sai-Ching Jim Yeung

18 Nov 2024

PONE-D-24-31391R1

PLOS ONE

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Associated Data

    This section collects any data citations, data availability statements, or supplementary materials included in this article.

    Supplementary Materials

    S1 Fig. Propensity score matching for outcome of adverse cardiovascular events.

    (DOCX)

    pone.0314555.s001.docx (292KB, docx)
    S2 Fig. Kaplan Meier curves and median times to adverse cardiovascular events (ACE) and individual components of ACE.

    (DOCX)

    pone.0314555.s002.docx (253.2KB, docx)
    S3 Fig. Cox regression model including single immune checkpoint inhibitor agents for adverse cardiovascular events (ACE) and individual components of ACE.

    (DOCX)

    pone.0314555.s003.docx (4.7MB, docx)
    S4 Fig. Prevalence of abnormal echocardiograms pre- and post-immune checkpoint inhibitor.

    (DOCX)

    pone.0314555.s004.docx (109.1KB, docx)
    S1 Table. Comparison of demographic characteristics and comorbidities between entire cohort from electronic health record data acquisition and adjudicated subset.

    (DOCX)

    pone.0314555.s005.docx (15KB, docx)
    S2 Table. Presence of adverse cardiovascular events by cancer type.

    (DOCX)

    pone.0314555.s006.docx (18.2KB, docx)
    S3 Table. Demographics and comorbidities of propensity matched cohort.

    (DOCX)

    pone.0314555.s007.docx (17.2KB, docx)
    S4 Table. Ordering indications for cardiac magnetic resonance imaging pre- and post-immune checkpoint inhibitor.

    (DOCX)

    pone.0314555.s008.docx (15.8KB, docx)
    S5 Table. Cardiac magnetic resonance imaging features.

    (DOCX)

    pone.0314555.s009.docx (19.5KB, docx)
    S1 Data. Minimal dataset.

    (CSV)

    pone.0314555.s010.csv (342KB, csv)
    S1 File

    (DOCX)

    pone.0314555.s011.docx (633.5KB, docx)
    Attachment

    Submitted filename: Response to Reviewers.docx

    pone.0314555.s012.docx (121.3KB, docx)

    Data Availability Statement

    All relevant data are within the manuscript and its Supporting Information files.

    Articles from PLOS ONE are provided here courtesy of PLOS

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