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Adverse Cardiovascular Complications following prescription of programmed cell death 1 (PD-1) and programmed cell death ligand 1 (PD-L1) inhibitors: a propensity-score matched Cohort Study with competing risk analysis

Abstract

Background

Programmed death-1 (PD-1) and programmed death- ligand 1 (PD-L1) inhibitors, such as pembrolizumab, nivolumab and atezolizumab, are major classes of immune checkpoint inhibitors that are increasingly used for cancer treatment. However, their use is associated with adverse cardiovascular events. We examined the incidence of new-onset cardiac complications in patients receiving PD-1 or PD-L1 inhibitors.

Methods

Patients receiving PD-1 or PD-L1 inhibitors since their launch up to 31st December 2019 at publicly funded hospitals of Hong Kong, China, without pre-existing cardiac complications were included. The primary outcome was a composite of incident heart failure, acute myocardial infarction, atrial fibrillation, or atrial flutter with the last follow-up date of 31st December 2020. Propensity score matching between PD-L1 inhibitor use and PD-1 inhibitor use with a 1:2 ratio for patient demographics, past comorbidities and non-PD-1/PD-L1 medications was performed with nearest neighbour search strategy (0.1 caliper). Univariable and multivariable Cox regression analysis models were conducted. Competing risks models and multiple propensity matching approaches were considered for sensitivity analysis.

Results

A total of 1959 patients were included. Over a median follow-up of 247 days (interquartile range [IQR]: 72-506), 320 (incidence rate [IR]: 16.31%) patients met the primary outcome after PD-1/PD-L1 treatment: 244 (IR: 12.57%) with heart failure, 38 (IR: 1.93%) with acute myocardial infarction, 54 (IR: 2.75%) with atrial fibrillation, 6 (IR: 0.31%) with atrial flutter. Compared with PD-1 inhibitor treatment, PD-L1 inhibitor treatment was significantly associated with lower risks of the composite outcome both before (hazard ratio [HR]: 0.32, 95% CI: [0.18-0.59], P value=0.0002) and after matching (HR: 0.34, 95% CI: [0.18-0.65], P value=0.001), and lower all-cause mortality risks before matching (HR: 0.77, 95% CI: [0.64-0.93], P value=0.0078) and after matching (HR: 0.80, 95% CI: [0.65-1.00], P value=0.0463). Patients who developed cardiac complications had shorter average readmission intervals and a higher number of hospitalizations after treatment with PD-1/PD-L1 inhibitors in both the unmatched and matched cohorts (P value<0.0001). Multivariable Cox regression models, competing risk analysis with cause-specific and subdistribution hazard models, and multiple propensity approaches confirmed these observations.

Conclusions

Compared with PD-1 treatment, PD-L1 treatment was significantly associated with lower risk of new onset cardiac complications and all-cause mortality both before and after propensity score matching.

Introduction

The programmed death-1 (PD-1)/programmed death-ligand 1 (PD-L1) pathway is one of the major immune checkpoints for mitigating the immune response to prevent autoimmunity. The T-cell mediated pathway for cancer detection requires coinhibitory signal, in addition to the binding of T cell receptor on T cells. PD-1 and PD-L1, one of the best characterized co-inhibitory signaling pathway, control the magnitude and duration of response against autoimmunity [1]. However, cancer cells often devise strategies to hijack these mechanisms to evade anti-tumor immunity. In this regard, inhibitors of PD-1 (e.g., pembrolizumab, nivolumab, cemiplimab) and PD-L1 (e.g., atezolizumab, avelumab, durvalumab) have shown clinical efficacies against different types of solid tumors, including melanoma, non-small cell lung cancer (NSCLC), urothelial carcinoma and bladder cancer. Pembrolizumab is also the first agent to receive a “pan-cancer” approval by the United States Food and Drug Administration (FDA) for the treatment of unresectable or metastatic solid tumors that have high microsatellite instability or mismatch repair deficiency.

Despite their treatment efficacy in clinical oncology, immune-related adverse events associated with the use of immune checkpoint inhibitors (ICIs) are now increasingly recognized [2,3,4,5]. Adverse events include atherosclerosis, colitis, hepatitis, adrenocorticotropic hormone insufficiency, hypothyroidism, type 1 diabetes mellitus, and acute kidney injury [6,7,8]. To this end, cardiovascular complications are estimated to constitute approximately 2% of ICI-related adverse drug reactions [9]. The commonest is myocarditis, but other cardiovascular abnormalities reported are left ventricular dysfunction, acute myocardial infarction (AMI), cardiac arrhythmias and heart failure [10]. These cardiovascular complications typically present with clinical heterogeneity, and in turn account for the high morbidity and mortality rates observed in such patient cohorts. Whilst cardiotoxicity is being documented with an increasing frequency, their cumulative incidence rates remain largely unexplored. In this territory-wide study, we examined the incidence of cardiovascular events of incident heart failure, acute myocardial infarction, atrial fibrillation, or atrial flutter in cancer patients receiving PD-1 or PD-L1 inhibitors.

Methods

Study Population

This study was approved by The Joint Chinese University of Hong Kong - New Territories East Cluster Clinical Research Ethics Committee. Patients receiving PD-1 or PD-L1 inhibitors since their launch up to 31st December 2020 at publicly funded hospitals or their associated outpatient/ambulatory care facilities, without pre-existing cardiac complications (including heart failure, myocardial infarction, atrial fibrillation, and atrial flutter) were included. Patient data were obtained using the electronic health record database, which is connected to the territory-wide Clinical Data Analysis and Reporting System (CDARS). The system is an integrative centralized platform that permits the extraction of clinical data for analysis and reporting. The system attributes each patient a unique reference identification number, allowing for the retrieval of comprehensive medical records, including disease diagnoses, clinical comorbidities, laboratory parameters and operative procedures. Patients or the public were not involved in any aspect of this study. The system has been previously used by both our team and other teams in Hong Kong [11,12,13].

Patient Data

The following clinical data were extracted: patient characteristics, including demographic details (baseline age and gender), specific pre-existing comorbidities before drug prescriptions, laboratory examinations (including complete blood counts, biochemical tests, lipid/glycemic profiles) were extracted. Past comorbidities from January 1st, 2013, to December 31st, 2020, were extracted, and categorized into hypertension, liver diseases, hip fractures/accident falls, renal diseases, diabetes mellitus, malignant dysrhythmia, chronic obstructive pulmonary disease, ischemic heart disease, peripheral vascular disease, endocrine diseases, gastrointestinal diseases, and stroke/transient ischemic attack. The International Classification of Disease, Ninth Edition (ICD-9) codes that were used to extract the specific comorbidities and outcomes are included in Supplementary Table 2. The overall dosage and the duration of exposure to PD-L1 and PD-1 inhibitors for patients with new-onset cardiac complications are reported.

Primary outcomes on follow-up

The primary outcome was a composite of incident heart failure, acute myocardial infarction, atrial fibrillation, and atrial flutter. The follow-up period was defined as the first PD-1/PD-L1 prescription date until the primary endpoint or death occurred, or until the end date of August 31st, 2020, whichever was earlier.

Statistical analysis

Continuous variables were presented as median (95% confidence interval [CI] or interquartile range [IQR]) and categorical variables were presented as count (%). The Mann- Whitney U test was used to compare continuous variables. The χ2 test with Yates’ correction was used for 2 × 2 contingency data, and Pearson’s χ2 test was used for contingency data for variables with more than two categories. The patients with PD-L1 were matched with PD1 controls through propensity score matching of 1:2 ratio, based on patient demographics, Charlson’s standard comorbidity index, past comorbidities, and non-PD-1/PD-L1 medications. Negligible post-weighting intergroup standardized mean difference (SMD) was defined as SMD < 0.2. To identify the important predictors associated with new-onset cardiac complications of patients after PD-1/PD-L1 treatment, univariable Cox regression was used to calculate hazard ratios (HRs) and 95% CIs. In addition to propensity score matching, the following approaches based on the propensity scores were employed: propensity score stratification [14], inverse probability weighting [15], and high-dimensional propensity score adjustment [16]. Paired hospitalization characteristics of patients before and after treatment were compared both in the unmatched and matched cohorts. A two-sided α of < 0.05 was considered statistically significant. Statistical analyses were performed using RStudio software (Version: 1.1.456) and Python (Version: 3.6).

Results

Baseline characteristics

Initially, 2426 cancer patients receiving PD-1/PD-L1 inhibitors were identified (Fig. 1). In total 1959 patients remained in the study cohort after excluding 433 patients with prior cardiac complications and 34 patients who received both PD1 and PDL1 treatments. Propensity score matching with 1:2 ratio between PD-1 and PD-L1 inhibitor use based on demographics, Charlson’s standard comorbidity index, prior comorbidities, and non-PD-1/PD-L1 medications was performed. This yielded a matched cohort of 663 patients (Table 1).

Fig. 1
figure 1

Study flow diagram describing derivation of the study cohort

Table 1 Clinical characteristics of patients with PD-1 use and PD-L1 use before and after 1:2 propensity score matching

Propensity score matching in 1:2 ratio between PD-1 users and PD-L1 users using the nearest neighbor search strategy (caliper as 0.1) was used. The results of logistics regression for potential confounders used in propensity score calculations, balance between groups, and estimations of bootstrapped standard error are shown in Supplementary Tables 3, 4 and 5, respectively. Distributions of propensity scores before and after matching are shown in Supplementary Fig. 1. These results indicate that the covariables between the groups are balanced after matching.

Adverse cardiovascular outcomes on follow-up and their significant predictors

In the matched cohort, 425 (IR: 64.10%) patients died and 91 (IR: 13.72%) developed new onset cardiac composite outcome. Amongst the latter, 75 (IR: 11.31%) developed heart failure, 8 (IR: 1.20%) developed acute myocardial infarction, 8 (IR: 1.20%) developed atrial fibrillation and 2 (IR: 0.30%) developed atrial flutter. The incidence rate of the composite outcome was lower in the PD-L1 cohort than in the PD-1 cohort (7.0% vs. 20.7%; P < 0.001).

In the 221 PD-L1 users, there were 11 patients (IR: 4.97%) who developed the composite outcome, in which 10 (IR: 4.52%) with heart failure, 1 (0.45%) with atrial fibrillation, but none with acute myocardial infarction or atrial flutter. In the 442 patients PD-1 users, 80 patients (IR: 18.09%) developed the composite outcome. Of the latter group, 65 (IR: 14.70%) developed heart failure, 8 (IR: 1.80%) developed acute myocardial infarction, 7 (IR: 1.58%) developed atrial fibrillation, and 2 (IR: 0.45%) developed atrial flutter.

The breakdown on the individual adverse events is shown in Fig. 1 and the patient characteristics stratified by adverse cardiovascular outcomes are shown in Tables 2 and 3. The cumulative incidence curves of new onset cardiac complications and all-cause mortality in cancer patients stratified by PD-1 or PD-L1 inhibitor use before and after 1:2 propensity score matching were presented in Figs. 2 and 3, respectively. The baseline characteristics of the cohort stratified by mortality status are shown in Supplementary Table 6.

Table 2 Clinical characteristics of patients who developed the composite outcome before and after 1:2 propensity score matching
Table 3 Significant univariable predictors of new onset cardiac complication outcome and all-cause mortality before and after 1:2 propensity score matching
Fig. 2
figure 2

Cumulative incidence curves of new onset cardiac complications and all-cause mortality in cancer patients stratified by PD-1 or PD-L1 inhibitor use before 1:2 propensity score matching

Fig. 3
figure 3

Cumulative incidence curves of new onset cardiac complications and all-cause mortality in cancer patients stratified by PD-1 or PD-L1 inhibitor use after 1:2 propensity score matching

Univariable Cox regression identified significant predictors of the primary composite outcome and all-cause mortality before and after propensity score matching (Table 3). Compared with PD-1 inhibitor treatment, PD-L1 inhibitor treatment was significantly associated with a lower risk of composite outcome both before (hazard ratio [HR]: 0.32, 95% CI: [0.18-0.59], P value=0.0002***) and after matching (HR: 0.34, 95% CI: [0.18-0.65], P value=0.001**), and lower all-cause mortality risk before matching (HR: 0.77, 95% CI: [0.64-0.93], P value=0.0078**) and after matching (HR: 0.80, 95% CI: [0.65-1.00], P value=0.0463).

More PD-L1 expenditure (HR:1.000; 95% CI: 1.000-1.000; P value=0.0528), shorter PD-L1 inhibitors duration (HR:0.99; 95% CI: 0.99-1.00; P value=0.0263*), more PD-1 expenditure, HKD (HR:1.000; 95% CI: 1.000-1.001; P value=0.0018**), and shorter PD-1 inhibitors duration (HR:0.998; 95% CI: 0.997-0.999; P value=0.0007***) were associated with new onset cardiac composite outcome in the matched cohort. Significant laboratory examinations significantly associated with new onset cardiac composite outcome include higher levels of mean corpuscular volume (HR:1.03; 95% CI: 1.00-1.07; P value=0.0470*), hematocrit (HR:566.25; 95% CI: 4.88-65751.46; P value=0.0090**), HbA1c (HR:1.19; 95% CI: 1.05-1.34; P value=0.0072**), total cholesterol (HR:1.42; 95% CI: 1.00-2.00; P value=0.0490*), and low-density lipoprotein (HR:1.67; 95% CI: 1.14-2.45; P value=0.0091**).

In addition, greater variability in laboratory tests, including the standard deviations (SD) of alkaline phosphatase (HR:1.002; 95% CI: 1.000-1.004; P value=0.0359*), HbA1c (HR:1.94; 95% CI: 1.36-2.76; P value=0.0003***), lactate dehydrogenase (HR:1.002; 95% CI: 1.000-1.003; P value=0.0056**), and low-density lipoprotein (HR:7.64; 95% CI: 1.84-31.77; P value=0.0051**) were significantly associated with the composite outcome. The boxplots of significant measures of variability stratified by PD-1/PD-L1 inhibitor treatment in the matched cohort are shown in Fig. 4.

Fig. 4
figure 4

Boxplots of significant laboratory examination variability stratified by PD-1/PD-L1 inhibitor use and development of the composite outcome in the matched cohort

In addition, multivariable Cox regression models (Table 4) with multiple adjustments with significant demographics, past comorbidities, and non-PD-L1/PD-1 drugs confirmed the protection effects of PD-L1 than PD-1 for adverse study outcomes (HR<1, P < 0.05).

Table 4 Comparisons of hospitalization characteristics before and after PD1/PD-L1 treatment in patients with new onset heart failure, acute myocardial infarction, atrial fibrillation, and atrial flutter

Healthcare utilization before and after treatment with PD-1/PD-L1 inhibitors

Longer overall cumulative hospital stay (HR:1.01; 95% CI: 1.00-1.01; P value=0.0040**) and longer hospital stay after PD-1/PD-L1 drug use (HR:1.01; 95% CI: 1.00-1.01; P value=0.0040**) were significantly associated with the composite outcome. Furthermore, hospitalization characteristics before and after PD-1/PD-L1 treatment were compared in the subset of patients who developed the adverse outcomes, in both the unmatched and matched cohorts (Table 4). Patients who developed cardiovascular complications had a shorter average readmission interval, a higher number of hospitalizations and a longer duration of hospital stay after PD-1/PD-L1 treatment (P < 0.0001).

Sensitivity analysis

Supplementary Table 7 presented the adjusted hazard ratios (and 95% CIs) of PD-L1 vs. PD-1 with cause-specific and subdistribution hazard competing risk analysis models for new onset cardiac composite and mortality outcomes after 1:2 propensity score matching. Supplementary Table 8 presented the hazard ratios for associations of PD-L1 vs. PD-1 using Cox proportional hazard model for adverse new onset cardiac composite and mortality outcome in the 1:2 matched cohort, with half-year lag time. Supplementary Table 9 presented the risk of incident new onset cardiac composite and mortality outcomes associated with treatment of PD-L1 vs. PD-1 with multiple matching adjustment approaches including propensity score stratification, high-dimensional propensity score matching, and propensity score matching with inverse probability of treatment weighting. The above analysis confirmed the protective effects of PD-L1 treatment over PD-1 treatment on new onset cardiac complications and mortality risks (HR<1, P value<0.05).

Prediction strength of subclinical inflammatory biomarkers

In the matched cohort, higher aspartate transaminase-to-alanine transaminase ratio (HR: 1.09, 95% CI: [1.04-1.14], P value=0.0004) and higher monocyte-to-lymphocyte ratio (HR: 1.2, 95% CI: [1.02-1.43], P value=0.0305) were significantly associated with all-cause mortality. A higher urea-to-creatinine ratio (HR: 1.01, 95% CI: [1.00-1.01], P value=0.0007) was significantly associated with new onset cardiac composite outcome (Table 5). The boxplots of inflammatory biomarkers stratified by PD-1/PD-L1 inhibitor use and development of adverse outcomes are shown in Fig. 5.

Table 5 Multivariable univariable Cox regression models for new onset cardiac complication outcome and all-cause mortality in the matched cohort
Fig. 5
figure 5

Boxplots of subclinical inflammatory biomarkers stratified by PD-1/PD-L1 inhibitor use and development of the composite outcome in the matched cohort

Discussion

The main findings are that: (i) the incidence of cardiovascular complications after PD-1 or PD-L1 inhibitor use was 16% in this territory-wide cohort of Chinese patients from Hong Kong, (ii) multivariable Cox regression showed older age, a shorter average readmission interval and a higher number of hospital admissions were significant predictors of cardiovascular complications and (iii) patients who developed cardiovascular complications had shorter average readmission interval and higher number of hospitalizations after treatment with PD-1/PD-L1 inhibitors.

Cardiac involvement in PD1 or PD-L1 inhibitors is variable, and can potentially affect the conduction system, myocardium or pericardium [17]. Thus, heart block [18], Takotsubo cardiomyopathy [19], myocarditis [20, 21] and pericarditis [22] have been reported. A meta-analysis performed in 2018 found that anti-PD-1/PD-L1-related fatalities were often from pneumonitis (333 [35%]), hepatitis (115 [22%]), and neurotoxic effects (50 [15%]). Combination PD-1/ cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) deaths were frequently from colitis (32 [37%]) and myocarditis (22 [25%]) [23]. In an analysis of the World Health Organization global database of adverse drug reactions in 2019, 2.1% of 106,025 patients receiving PD-1 or PD-L1 inhibitors had cardiovascular complications [9]. However, previous studies have largely been limited to case reports [18, 24], case series [25], single-center studies [26] or small registries [27, 28]. In this territory-wide study from Hong Kong, we found that cardiovascular complications occurred in 16% of all patients receiving PD1 or PD-L1 inhibitors. Of these, the commonest is heart failure. Previously, acute heart failure has been described in the context of myocarditis [29], but heart failure without myocarditis has also been reported [30]. Our study also identified cases of acute myocardial infarction following the initiation of PD-1/PD-L1 inhibitor therapy. Such findings would be in keeping with coronary toxicity that has been reported in the context of PD-1 inhibitor therapy [27].

It is worth noting that the present study is the first to compare the cardiotoxicity between PD-1 and PD-L1 inhibitor treatment. The reason for the protective effect of PD-L1 inhibitor to be stronger than PD-1 inhibitor may be the stronger immune-mediated effects of PD-1 inhibitor. Contrary to PD-1 inhibitor, which blocks the interaction between PD-1 and both PD-L1 and PD-L2, PD-L1 inhibitor only blocks the interaction between PD-1 and PD-L1. [31]Although cancer may escape from immune-mediated detection via the PD-1/ PD-L2 axis under PD-L1 inhibitor treatment, it also implies that PD-L1 inhibitor may have weaker autoimmunity, thus resulting in less immune-mediated cardiotoxicity. [32] Furthermore, patients who developed cardiovascular complications had a shorter average readmission interval and more hospitalizations, which is due to the deterioration in patients’ general health and treatment needed for the complications. For example, acute exacerbation of heart failure is known to be common reasons for frequent admissions. The presence of adverse cardiovascular events is detrimental to the overall health of cancer patients who are already frail, which may lead to the decline in health and related frequent admissions, which demonstrates the critical effects of cardiovascular complications on patients’ physical health and quality of life.

Interestingly, our study did not identify any patients with myocarditis after treatment with PD-1 or PD-L1 inhibitors. Moreover, within the excluded patients with prior cardiovascular complications, none developed subsequent myocarditis. In 964 patients attending Massachusetts General Hospital, the incidence of myocarditis was 1.1% (n = 35) [28]. In this cohort, myocarditis was more frequently observed in patients with pre-existing cardiovascular comorbidities. Nevertheless, another study using the VigiBase database found 101 cases of severe myocarditis, of which 75% of the myocarditis cases did not have pre-existing cardiovascular disease [33]. A single-center study of 283 patients from China found only 3 cases (1.1%) of myocarditis, with variable presentations such as palpitations, dyspnea, and fatigue, or asymptomatic with incidental finding of grade 3 atrioventricular block and premature ventricular complexes on the electrocardiogram [26]. In a pooled, retrospective review of three trials including 448 patients with advanced melanoma receiving PD-1/PD-L1 inhibitor therapy, no cases of myocarditis were identified [34]. In association with myocarditis, different investigators have reported the presence of conduction abnormalities in the form of atrioventricular block [18, 25, 26, 35].

Limitations

There are some limitations of this study that should be acknowledged. Firstly, this was an administrative database study, and therefore cancer staging details could not be extracted. Secondly, under-coding or miscoding remains a possibility as with studies of a similar nature. Thirdly, although no cases of myocarditis were found. As our study relies on ICD-9 coding, this might be due to under-coding. Alternatively, missed cases by the clinicians and subclinical myocarditis leading to heart failure are possible. Therefore, further studies accounting for parameters that may uncover undiagnosed myocarditis, such as creatine kinase, is needed to ensure that the diagnosis of myocarditis is not missed.

Conclusions

Compared with PD-1 inhibitor use, PD-L1 inhibitor use was significantly associated with lower risks of cardiac complications and all-cause mortality both before and after propensity score matching. Patients who developed cardiovascular complications had shorter average readmission intervals and a higher number of hospitalizations after treatment with PD-1/PD-L1 inhibitors.

Availability of data and materials

Data availability upon request to the corresponding author.

References

  1. Boussiotis VA. Molecular and Biochemical Aspects of the PD-1 Checkpoint Pathway. N Engl J Med. 2016;375:1767–78.

    Article  CAS  Google Scholar 

  2. Kalinich M, Murphy W, Wongvibulsin S, et al. Prediction of severe immune-related adverse events requiring hospital admission in patients on immune checkpoint inhibitors: study of a population level insurance claims database from the USA. J Immunother Cancer 2021;9.

  3. Griewing LM, Schweizer C, Schubert P, et al. Questionnaire-based detection of immune-related adverse events in cancer patients treated with PD-1/PD-L1 immune checkpoint inhibitors. BMC Cancer. 2021;21:314.

    Article  CAS  Google Scholar 

  4. Wu C, Zhong L, Wu Q, Lin S, Xie X. The safety and efficacy of immune-checkpoint inhibitors in patients with cancer and pre-existing autoimmune diseases. Immunotherapy. 2021;13:527–39.

    Article  Google Scholar 

  5. Kang JH, Bluestone JA, Young A. Predicting and Preventing Immune Checkpoint Inhibitor Toxicity: Targeting Cytokines. Trends Immunol. 2021;42:293–311.

    Article  CAS  Google Scholar 

  6. Lee S, Tse G. A Patient with Atezolizumab-Induced Autoimmune Diabetes Mellitus Presenting with Diabetic Ketoacidosis. Cardiovascular Innovations and Applications; 2021.

  7. Zhang N, Tse G, Liu T. Neutrophil-lymphocyte ratio in the immune checkpoint inhibitors-related atherosclerosis. Eur Heart J 2021.

  8. Ball S, Ghosh RK, Wongsaengsak S, et al. Cardiovascular Toxicities of Immune Checkpoint Inhibitors: JACC Review Topic of the Week. J Am Coll Cardiol. 2019;74:1714–27.

    Article  CAS  Google Scholar 

  9. Upadhrasta S, Elias H, Patel K, Zheng L. Managing cardiotoxicity associated with immune checkpoint inhibitors. Chronic Dis Transl Med. 2019;5:6–14.

    PubMed  PubMed Central  Google Scholar 

  10. Muller OJ, Spehlmann ME, Frey N. Cardio-toxicity of checkpoint inhibitors. J Thorac Dis. 2018;10:4400–4.

    Article  Google Scholar 

  11. Lee S, Zhou J, Guo CL, et al. Predictive scores for identifying patients with type 2 diabetes mellitus at risk of acute myocardial infarction and sudden cardiac death. Endocrinol Diabetes Metab. 2021;4:e00240.

    Article  CAS  Google Scholar 

  12. Tse G, Zhou J, Lee S, et al. Relationship between angiotensin-converting enzyme inhibitors or angiotensin receptor blockers and COVID-19 incidence or severe disease. J Hypertens. 2021;39:1717–24.

    Article  CAS  Google Scholar 

  13. Lee S, Zhou J, Leung KSK, et al. Development of a predictive risk model for all-cause mortality in patients with diabetes in Hong Kong. BMJ Open Diabetes Res Care 2021;9.

  14. Austin PC. An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies. Multivariable Behav Res. 2011;46:399–424.

    Article  Google Scholar 

  15. Austin PC, Stuart EA. Moving towards best practice when using inverse probability of treatment weighting (IPTW) using the propensity score to estimate causal treatment effects in observational studies. Stat Med. 2015;34:3661–79.

    Article  Google Scholar 

  16. Schneeweiss S, Rassen JA, Glynn RJ, Avorn J, Mogun H, Brookhart MA. High-dimensional propensity score adjustment in studies of treatment effects using health care claims data. Epidemiology. 2009;20:512–22.

    Article  Google Scholar 

  17. Lyon AR, Yousaf N, Battisti NML, Moslehi J, Larkin J. Immune checkpoint inhibitors and cardiovascular toxicity. Lancet Oncol. 2018;19:e447–58.

    Article  CAS  Google Scholar 

  18. Khan A, Riaz S, Carhart R. Jr. Pembrolizumab-Induced Mobitz Type 2 Second-Degree Atrioventricular Block. Case Rep Cardiol. 2020;2020:8428210.

    PubMed  PubMed Central  Google Scholar 

  19. Geisler BP, Raad RA, Esaian D, Sharon E, Schwartz DR. Apical ballooning and cardiomyopathy in a melanoma patient treated with ipilimumab: a case of takotsubo-like syndrome. J Immunother Cancer. 2015;3:4.

    Article  Google Scholar 

  20. Norwood TG, Westbrook BC, Johnson DB, et al. Smoldering myocarditis following immune checkpoint blockade. J Immunother Cancer. 2017;5:91.

    Article  Google Scholar 

  21. Semper H, Muehlberg F, Schulz-Menger J, Allewelt M, Grohe C. Drug-induced myocarditis after nivolumab treatment in a patient with PDL1- negative squamous cell carcinoma of the lung. Lung Cancer. 2016;99:117–9.

    Article  CAS  Google Scholar 

  22. de Almeida DVP, Gomes JR, Haddad FJ, Buzaid AC. Immune-mediated Pericarditis With Pericardial Tamponade During Nivolumab Therapy. J Immunother. 2018;41:329–31.

    Article  Google Scholar 

  23. Wang DY, Salem JE, Cohen JV, et al. Fatal Toxic Effects Associated With Immune Checkpoint Inhibitors: A Systematic Review and Meta-analysis. JAMA Oncol. 2018;4:1721–8.

    Article  Google Scholar 

  24. Johnson DB, Balko JM, Compton ML, et al. Fulminant Myocarditis with Combination Immune Checkpoint Blockade. N Engl J Med. 2016;375:1749–55.

    Article  Google Scholar 

  25. Heinzerling L, Ott PA, Hodi FS, et al. Cardiotoxicity associated with CTLA4 and PD1 blocking immunotherapy. J Immunother Cancer. 2016;4:50.

    Article  Google Scholar 

  26. Wang F, Sun X, Qin S, et al. A retrospective study of immune checkpoint inhibitor-associated myocarditis in a single center in China. Chin Clin Oncol. 2020;9:16.

    Article  Google Scholar 

  27. Ferreira M, Pichon E, Carmier D, et al. Coronary Toxicities of Anti-PD-1 and Anti-PD-L1 Immunotherapies: a Case Report and Review of the Literature and International Registries. Target Oncol. 2018;13:509–15.

    Article  Google Scholar 

  28. Mahmood SS, Fradley MG, Cohen JV, et al. Myocarditis in Patients Treated With Immune Checkpoint Inhibitors. J Am Coll Cardiol. 2018;71:1755–64.

    Article  CAS  Google Scholar 

  29. Laubli H, Balmelli C, Bossard M, Pfister O, Glatz K, Zippelius A. Acute heart failure due to autoimmune myocarditis under pembrolizumab treatment for metastatic melanoma. J Immunother Cancer. 2015;3:11.

    Article  Google Scholar 

  30. Chauhan A, Burkeen G, Houranieh J, Arnold S, Anthony L. Immune checkpoint-associated cardiotoxicity: case report with systematic review of literature. Ann Oncol. 2017;28:2034–8.

    Article  CAS  Google Scholar 

  31. Duan J, Cui L, Zhao X, et al. Use of Immunotherapy With Programmed Cell Death 1 vs Programmed Cell Death Ligand 1 Inhibitors in Patients With Cancer: A Systematic Review and Meta-analysis. JAMA Oncol. 2020;6:375–84.

    Article  Google Scholar 

  32. Chen L, Han X. Anti-PD-1/PD-L1 therapy of human cancer: past, present, and future. J Clin Invest. 2015;125:3384–91.

    Article  Google Scholar 

  33. Moslehi JJ, Salem JE, Sosman JA, Lebrun-Vignes B, Johnson DB. Increased reporting of fatal immune checkpoint inhibitor-associated myocarditis. Lancet. 2018;391:933.

    Article  Google Scholar 

  34. Sznol M, Ferrucci PF, Hogg D, et al. Pooled Analysis Safety Profile of Nivolumab and Ipilimumab Combination Therapy in Patients With Advanced Melanoma. J Clin Oncol. 2017;35:3815–22.

    Article  CAS  Google Scholar 

  35. Lopez EM, Dunn S, Mazimba S. MALIGNANT ARRHYTHMIAS IN, AUTOIMMUNE MYOCARDITIS SECONDARY TO IMMUNE CHECKPOINT BLOCKADE TREATMENT. J Am Coll Cardiol. 2018;71:A2375.

    Article  Google Scholar 

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Contributions

JZ, SL, GT: data analysis, data interpretation, statistical analysis, manuscript drafting, critical revision of manuscript. IL, SL, LY, TL, YX, YZ: data interpretation, manuscript drafting. EWC, ICKW: project planning, data acquisition, data interpretation, critical revision of manuscript. QZ: study conception, study supervision, project planning, data interpretation, statistical analysis, manuscript drafting, critical revision of manuscript. The author(s) read and approved the final manuscript.

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Correspondence to Gary Tse or Qingpeng Zhang.

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This study was approved by The Joint Chinese University of Hong Kong - New Territories East Cluster Clinical Research Ethics Committee.

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Zhou, J., Lee, S., Lakhani, I. et al. Adverse Cardiovascular Complications following prescription of programmed cell death 1 (PD-1) and programmed cell death ligand 1 (PD-L1) inhibitors: a propensity-score matched Cohort Study with competing risk analysis. Cardio-Oncology 8, 5 (2022). https://doi.org/10.1186/s40959-021-00128-5

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