Dataset Information

Major adverse cardiovascular events in advanced-stage lung cancer: a multicenter cohort study.

ABSTRACT:

Background

Lung cancers are common worldwide. First-line targeted therapy and chemotherapy are both standard treatments in the current guidelines. With the development of new anticancer therapy, the lifespan of patients with late-stage lung cancer has increased. Cardiovascular events can occur during cancer treatment. This observational study aimed to report the incidence of major adverse cardiovascular events (MACE) after cancer treatment using real-world data.

Objectives

Patients diagnosed with advanced-stage lung cancer between January 2011 and December 2017 were enrolled. Data were collected from the Chang Gung Research Database (CGRD).

Design

Retrospective cohort study.

Methods

Baseline characteristics, clinical stages, pathologies, and outcomes were retrieved from the CGRD.

Results

We identified 4406 patients with advanced lung cancer, of whom 2197 received first-line epidermal growth factor receptor tyrosine kinase inhibitor (EGFR-TKI) therapy and 2209 received first-line platinum-based chemotherapy. Most patients in the first-line EGFR-TKI group were never-smokers (74.9%), whereas those in the first-line chemotherapy group were ever-smokers (66.0%). The incidence of MACE was not significantly different between the two groups (12.0% versus 11.9%, p = 0.910). However, the incidence of ischemic stroke was higher in the first-line EGFR-TKI group than in the first-line chemotherapy group (3.9% versus 1.9%, p < 0.001).

Conclusion

MACEs are common in patients with advanced-stage lung cancer during treatment. The incidence of MACE was similar between the first-line EGFR-TKI therapy and first-line chemotherapy groups. Although more patients in the EGFR-TKI group were female and never-smokers, the risk of ischemic stroke was higher in patients who received first-line EGFR-TKI therapy than in those who received first-line chemotherapy.

SUBMITTER: Chang CH

PROVIDER: S-EPMC10798069 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature

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Publications

Major adverse cardiovascular events in advanced-stage lung cancer: a multicenter cohort study.

Chang Chih-Hao CH Huang Shih-Hao SH Huang Hung-Yu HY Lin Meng-Hung MH Lee Chung-Shu CS Lee Hsin-Fu HF Hsieh Jason Chia-Hsun JC Cheng Chun-Yu CY

Therapeutic advances in medical oncology 20240118

<h4>Background</h4>Lung cancers are common worldwide. First-line targeted therapy and chemotherapy are both standard treatments in the current guidelines. With the development of new anticancer therapy, the lifespan of patients with late-stage lung cancer has increased. Cardiovascular events can occur during cancer treatment. This observational study aimed to report the incidence of major adverse cardiovascular events (MACE) after cancer treatment using real-world data.<h4>Objectives</h4>Patient ...[more]

PMID: 38249337

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Project description:ObjectivesThe purpose of this study was to evaluate whether immune checkpoint inhibitors (ICIs) are associated with an increased risk of major adverse cardiovascular events (MACE) compared with non-ICI therapies in patients with lung cancer.BackgroundICIs activate the host immune system to target cancer cells. Though uncommon, cardiovascular immune-related adverse events can be life-threatening.MethodsA retrospective single-institution cohort study of 252 patients with pathologically confirmed lung cancer who received ICI or non-ICI therapy was analyzed. The primary endpoint was MACE, defined as a composite of cardiovascular death, nonfatal myocardial infarction, nonfatal stroke, and hospitalization for heart failure.ResultsDuring a median follow-up of 6 months, MACE occurred in 13.3% of ICI-treated patients, with a median time to event of 51 days, compared with 10.3% and 64 days in non-ICI patients. ICIs were not associated with MACE (hazard ratio [HR]: 1.18; 95% confidence interval [CI]: 0.57 to 2.43; p = 0.66) in a univariable Fine-Gray regression analysis incorporating noncardiovascular death as a competing risk. Multivariable regression analyses determined that patients treated with ICIs with elevated serum troponin I >0.01 ng/ml (HR: 7.27; 95% CI: 2.72 to 19.43; p < 0.001) and B-type natriuretic peptide (BNP) >100 pg/ml (HR: 2.65; 95% CI: 1.01 to 6.92; p = 0.047) had an increased risk of MACE. Patients pre-treated or receiving combined immunotherapy with ICIs and vascular endothelial growth factor inhibitors (VEGFIs) or tyrosine kinase inhibitors (TKIs) had an increased risk of MACE (HR: 2.15; 95% CI: 1.05 to 4.37; p = 0.04).ConclusionsICIs were not independently associated with an increased risk of MACE in patients with lung cancer, although power is an important limitation in these analyses. ICI-associated cardiotoxicity was associated with elevations in serum troponin and BNP, and combined immunotherapy with VEGFIs or TKIs. Future studies are needed to further define the role of cardiac biomarkers as a monitoring strategy with ICI therapy.

Project description:BackgroundPostoperative major adverse cardiovascular events (MACEs) account for more than one-third of perioperative deaths. Geriatric patients are more vulnerable to postoperative MACEs than younger patients. Identifying high-risk patients in advance can help with clinical decision making and improve prognosis. This study aimed to develop a machine learning model for the preoperative prediction of postoperative MACEs in geriatric patients.MethodsWe collected patients' clinical data and laboratory tests prospectively. All patients over 65 years who underwent surgeries in West China Hospital of Sichuan University from June 25, 2019 to June 29, 2020 were included. Models based on extreme gradient boosting (XGB), gradient boosting machine, random forest, support vector machine, and Elastic Net logistic regression were trained. The models' performance was compared according to area under the precision-recall curve (AUPRC), area under the receiver operating characteristic curve (AUROC) and Brier score. To minimize the influence of clinical intervention, we trained the model based on undersampling set. Variables with little contribution were excluded to simplify the model for ensuring the ease of use in clinical settings.ResultsWe enrolled 5705 geriatric patients into the final dataset. Of those patients, 171 (3.0%) developed postoperative MACEs within 30 days after surgery. The XGB model outperformed other machine learning models with AUPRC of 0.404(95% confidence interval [CI]: 0.219-0.589), AUROC of 0.870(95%CI: 0.786-0.938) and Brier score of 0.024(95% CI: 0.016-0.032). Model trained on undersampling set showed improved performance with AUPRC of 0.511(95% CI: 0.344-0.667, p < 0.001), AUROC of 0.912(95% CI: 0.847-0.962, p < 0.001) and Brier score of 0.020 (95% CI: 0.013-0.028, p < 0.001). After removing variables with little contribution, the undersampling model showed comparable predictive accuracy with AUPRC of 0.507(95% CI: 0.338-0.669, p = 0.36), AUROC of 0.896(95%CI: 0.826-0.953, p < 0.001) and Brier score of 0.020(95% CI: 0.013-0.028, p = 0.20).ConclusionsIn this prospective study, we developed machine learning models for preoperative prediction of postoperative MACEs in geriatric patients. The XGB model showed the best performance. Undersampling method achieved further improvement of model performance.Trial registrationThe protocol of this study was registered at www.chictr.org.cn (15/08/2019, ChiCTR1900025160).

Dataset Information

Major adverse cardiovascular events in advanced-stage lung cancer: a multicenter cohort study.

Background

Objectives

Design

Methods

Results

Conclusion

Publications

Major adverse cardiovascular events in advanced-stage lung cancer: a multicenter cohort study.

Similar Datasets

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