The Predictive Value of ePWV for All-Cause and Cardiovascular-Related Mortality in Cancer Patients

Qiu Yanli, Gao Yongyin, Bian Xueyan, Wang Xue, Li Yue*

Department of Cardiopulmonary Function, Tianjin Medical University Cancer Institute & Hospital/National Clinical Research Center for Cancer/Tianjin's Clinical Research Center for Cancer/Tianjin Key Laboratory of Cancer Prevention and Therapy, Tianjin 300060, China

Corresponding author: Li Yue, Attending physician; E-mail: liyue@163.com

Abstract

Background: The landscape of cancer treatment has undergone significant transformations, with numerous cancer patients now surviving for extended periods in a chronic disease paradigm. Research indicates that a substantial proportion of cancer survivors die from non-tumor causes, with cardiovascular disease (CVD) being a prominent contributor. Nevertheless, the potential cardiovascular toxicity associated with cancer treatment is frequently overlooked, resulting in inadequate early intervention and protective measures. Estimated pulse wave velocity (ePWV) can reflect the degree of arterial stiffness and serves as an independent predictor of cardiovascular events. Its simple calculation method provides feasibility for cardiovascular risk stratification in cancer patients.

Objective: To analyze the relationship between ePWV and all-cause mortality as well as cardiovascular disease mortality in cancer patients through a cohort study.

Methods: This retrospective cohort study included 4,632 cancer patients from the National Health and Nutrition Examination Survey (NHANES) database (1999–2018). Baseline data were collected including age, gender, race, body mass index (BMI), chest circumference, resting heart rate, total cholesterol, high-density lipoprotein cholesterol, systolic blood pressure, diastolic blood pressure, diabetes history, cardiovascular disease history, smoking status, and alcohol consumption status. Follow-up continued until July 2023, with all-cause mortality and cardiovascular disease mortality recorded. ePWV values were calculated using the formula derived by the Arterial Stiffness Collaboration Group, and patients were divided into quartile groups Q1–Q4 based on ePWV values. Baseline characteristics were compared across the four groups. Kaplan-Meier survival curves for all-cause and cardiovascular mortality were constructed. Multivariate Cox proportional hazards models were used to analyze influencing factors for all-cause and cardiovascular mortality. Receiver operating characteristic (ROC) curves were plotted to evaluate ePWV's predictive value for cardiovascular death in cancer patients, and the area under the curve (AUC) was calculated.

Results: A total of 4,632 patients were enrolled with a mean age of (60.7 ± 1.01) years, including 2,426 females (52.37%) and 2,206 males (47.63%). Each quartile group (Q1–Q4) contained 1,158 patients (25.0%). Significant differences were observed among the four groups in age, gender, race, BMI, chest circumference, resting heart rate, total cholesterol, HDL-C, systolic blood pressure, diastolic blood pressure, diabetes history, cardiovascular disease history, smoking status, and alcohol consumption status (P < 0.05). Over a median follow-up of 11.8 years, 830 all-cause deaths occurred (17.9%) and 376 cardiovascular-related deaths occurred (8.1%) among the 4,632 cancer patients. Both all-cause mortality and cardiovascular disease mortality differed significantly across the four groups (P < 0.001). Kaplan-Meier survival analysis revealed statistically significant differences in survival curves related to all-cause mortality and cardiovascular disease mortality among the four groups (χ² = 587.11, P < 0.001; χ² = 322.97, P < 0.001). Multivariate Cox regression analysis showed that compared with the Q1 group, patients in Q2, Q3, and Q4 had increased risks of all-cause mortality (Q2: HR = 1.30, 95% CI = 1.23–1.38, P = 0.045; Q3: HR = 1.46, 95% CI = 1.01–2.13, P = 0.047; Q4: HR = 1.24, 95% CI = 1.04–1.49, P = 0.017). Patients in Q3 and Q4 also showed elevated risks of cardiovascular disease mortality (Q3: HR = 1.28, 95% CI = 1.05–1.56, P = 0.013; Q4: HR = 2.73, 95% CI = 1.67–4.48, P = 0.026). ROC curve analysis yielded AUC values of 0.514, 0.624, 0.598, and 0.772 for Q1, Q2, Q3, and Q4 groups, respectively.

Conclusion: For the first time, this study verified that elevated ePWV is positively correlated with increased risks of all-cause mortality and cardiovascular disease mortality in cancer patients. ePWV may serve as a predictor of mortality risk in this population.

Keywords: Cancer; Arterial stiffness; Cardiovascular disease; Mortality rate; Cohort studies; Retrospective study; Root cause analysis

Introduction

Over recent decades, cancer incidence has increased annually. However, advancements in medical technology have significantly improved long-term survival rates among cancer patients, with over 80% now achieving extended survival. Studies demonstrate that many cancer survivors face a higher risk of death from cardiovascular disease (CVD) compared with the general population. Epidemiological evidence indicates that more than 10% of cancer survivors die from cardiovascular causes. This chronic condition reduces quality of life and represents a major contributor to mortality risk in cancer survivors, underscoring the critical need to refine cardiovascular disease prediction factors for this population.

Current cardio-oncology guidelines primarily focus on cardiac function indicators (left ventricular ejection fraction, diastolic function parameters) and myocardial injury markers (troponin, brain natriuretic peptide) for detecting cardiovascular toxicity. Research shows that cancer survivors experience vascular changes manifesting as endothelial dysfunction, coronary artery spasm, and increased arterial stiffness—changes that often precede alterations in myocardial structure. Arterial stiffness increases cardiovascular disease risk and serves as a predictor of major cardiovascular events and all-cause mortality in non-cancer patients and other healthy populations. Therefore, arterial stiffness may provide valuable prognostic information regarding risk severity in cancer patients.

Carotid-femoral pulse wave velocity (cfPWV) is the gold standard for reflecting arterial stiffness, but its complex measurement process and high cost have limited widespread clinical adoption. Estimated pulse wave velocity (ePWV), calculated from age and blood pressure, offers greater clinical feasibility and shows good consistency with cfPWV results, serving as a potential alternative indicator. Early identification of patients with elevated ePWV levels and timely control of ePWV progression can reduce further deterioration of arterial stiffness and improve patient outcomes. Consequently, this cohort study investigates whether ePWV can serve as a predictor of CVD death risk in cancer patients, providing a theoretical basis for individualized stratified management of cancer survivors.

Methods

Study Subjects

This retrospective study included 4,632 cancer patients from the National Health and Nutrition Examination Survey (NHANES) database (1999–2018) who met eligibility criteria. The database adheres to strict ethical guidelines, was approved by the National Institutes of Health (NIH) Institutional Review Board (IRB), and all participants provided informed consent.

Inclusion criteria: (1) Age ≥ 18 years at baseline interview; (2) Ability to complete questionnaires, laboratory tests, and physical examinations; (3) History of cancer diagnosed by at least one physician; (4) Survival for at least one year following cancer diagnosis; (5) Cancer types including skin cancer, breast cancer, prostate cancer, colorectal cancer, melanoma, lung cancer, bladder cancer, endometrial cancer, ovarian cancer, cervical cancer, and other malignant tumors.

Exclusion criteria: Incomplete information or physical/biochemical examination data at baseline interview.

ePWV Calculation and Grouping

ePWV was calculated using the formula proposed by Greve et al. and derived by the Arterial Stiffness Collaboration Group:

ePWV = 9.587 - 0.402×age + 4.560×10⁻³×age² - 2.621×10⁻⁵×age²×MBP + 3.176×10⁻³×age×MBP - 1.832×10⁻²×MBP

MBP = DBP + 0.4(SBP - DBP)

Where MBP is mean arterial pressure, DBP is diastolic blood pressure, and SBP is systolic blood pressure.

Based on ePWV values, patients were divided into four groups: Q1 (ePWV = 5.129–8.758), Q2 (ePWV = 8.759–10.639), Q3 (ePWV = 10.640–12.286), and Q4 (ePWV = 12.287–19.159), with 1,158 patients in each group.

Statistical Analysis

Statistical analysis was performed using R software version 4.4.1. Normally distributed measurement data were expressed as mean ± standard deviation (x̄ ± s), while non-normally distributed data were presented as median (P25, P75). Intergroup comparisons were performed using independent samples t-tests for normally distributed data and non-parametric rank-sum tests for non-normally distributed data. Kaplan-Meier survival curves were constructed for all-cause and cardiovascular mortality, with intergroup comparisons using the log-rank test. Multivariate Cox proportional hazards regression models were used to analyze factors influencing all-cause and cardiovascular mortality. ROC curves were plotted to evaluate ePWV's predictive value for cardiovascular death in cancer patients, and the area under the curve (AUC) was calculated. A P-value < 0.05 was considered statistically significant.

Results

Baseline Characteristics Comparison Among Q1–Q4 Groups

A total of 4,632 patients were included with a mean age of (60.7 ± 1.0) years, comprising 2,426 females (52.37%) and 2,206 males (47.63%). The Q1–Q4 groups each contained 1,158 patients (25.0%). Significant differences were observed among the four groups in age, gender, race, BMI, chest circumference, resting heart rate, total cholesterol, HDL-C, systolic blood pressure, diastolic blood pressure, diabetes history, cardiovascular disease history, smoking status, and alcohol consumption status (P < 0.05) [TABLE:1].

Comparison of All-Cause and Cardiovascular Mortality Rates Among Q1–Q4 Groups

With all-cause death and cardiovascular disease death as follow-up outcomes, the results showed that among 4,632 cancer patients, 1,778 deaths occurred (38.38%) during the follow-up period, including 572 cancer-related deaths (32.17%) and 376 cardiovascular-related deaths (21.15%). All-cause mortality occurred in 830 patients (17.9%). Both all-cause mortality and cardiovascular disease mortality differed significantly across the four groups (P < 0.001) [TABLE:2].

Kaplan-Meier Survival Analysis of Q1–Q4 Groups

Kaplan-Meier survival curves were constructed for all-cause and cardiovascular-related mortality in cancer patients. Log-rank test results demonstrated statistically significant differences in survival curves related to all-cause mortality and cardiovascular disease mortality among the four groups (χ² = 587.11, P < 0.001; χ² = 322.97, P < 0.001) [FIGURE:1].

Cox Proportional Hazards Regression Analysis of ePWV Levels and Mortality

Using all-cause mortality as the dependent variable (yes = 1, no = 0) and ePWV level as the independent variable (with Q1 as reference), multivariate Cox proportional hazards regression analysis was performed. Model 1 was unadjusted. Model 2 adjusted for age, gender, BMI, chest circumference, resting heart rate, systolic blood pressure, diastolic blood pressure, smoking status, alcohol consumption, and baseline ePWV value. Model 3 additionally adjusted for diabetes history, total cholesterol, and HDL-C. Results showed that compared with Q1, patients in Q2, Q3, and Q4 had increased risks of all-cause mortality (Q2: HR = 1.30, 95% CI = 1.23–1.38, P = 0.045; Q3: HR = 1.46, 95% CI = 1.01–2.13, P = 0.047; Q4: HR = 1.24, 95% CI = 1.04–1.49, P = 0.017) [TABLE:3].

Using cardiovascular disease mortality as the dependent variable and ePWV level as the independent variable (with Q1 as reference), multivariate Cox regression analysis showed that Q3 and Q4 patients had increased risks of cardiovascular disease mortality compared with Q1 (Q3: HR = 1.28, 95% CI = 1.05–1.56, P = 0.013; Q4: HR = 2.73, 95% CI = 1.67–4.48, P = 0.026) [TABLE:4].

Multivariate Cox Proportional Hazards Regression Analysis of All-Cause Mortality Risk

Using all-cause mortality in cancer patients as the dependent variable and gender, age, race, cardiovascular disease history, and diabetes history as independent variables (with assignments as described above), and BMI, chest circumference, resting heart rate, systolic blood pressure, diastolic blood pressure, total cholesterol, and HDL-C as covariates, multivariate Cox regression analysis revealed that male gender, age > 65 years, diabetes, cardiovascular disease, and smoking were risk factors for all-cause mortality (P < 0.05) [TABLE:5].

ROC Curve of ePWV for Predicting Cardiovascular Death in Cancer Patients

The ROC curve for ePWV predicting cardiovascular disease death in cancer patients showed: Q1 group AUC = 0.514 (95% CI = 0.479–0.545), P = 0.416, sensitivity = 0.628, specificity = 0.595, optimal cutoff = 7.674; Q2 group AUC = 0.624 (95% CI = 0.587–0.661), P < 0.001, sensitivity = 0.812, specificity = 0.272, optimal cutoff = 9.272; Q3 group AUC = 0.598 (95% CI = 0.566–0.631), P < 0.001, sensitivity = 0.658, specificity = 0.419, optimal cutoff = 11.318; Q4 group AUC = 0.772 (95% CI = 0.729–0.815), P < 0.001, sensitivity = 0.445, specificity = 0.818, optimal cutoff = 13.577. Comparison of AUC values among the four groups showed no statistically significant differences (χ² = 3, P = 0.392) [FIGURE:2].

Discussion

Advances in medical technology have led to increasingly sophisticated cancer treatments and continuously improving long-term survival rates. Currently, over 80% of cancer patients achieve extended survival, but they simultaneously face additional health challenges. Previous studies have shown that cardiovascular disease is a leading cause of death among cancer patients. However, the potential cardiovascular toxicity of cancer treatment is often overlooked, leading to insufficient early intervention and protection. More proactive cardiovascular disease prevention strategies are needed to reduce morbidity and mortality risk.

Vascular aging is a progressive pathological process that contributes to cardiovascular disease and is closely associated with its development and progression. The primary manifestation of early vascular aging is increased arterial stiffness, which has independent predictive value for cardiovascular events and all-cause mortality. The gold standard indicator for reflecting arterial stiffness is carotid-femoral pulse wave velocity (cfPWV), but its complex measurement process has limited widespread clinical application. Estimated pulse wave velocity (ePWV), calculated from age and blood pressure, is more convenient to obtain and shows good consistency with cfPWV results, yet research on ePWV in cancer patients remains limited. This study analyzes whether ePWV is a contributing factor to all-cause and cardiovascular mortality in cancer patients.

A cross-sectional survey specifically examining 781 cancer patients demonstrated that arterial stiffness can predict cardiovascular mortality in this population. Another cohort study of 13,223 rural Chinese residents showed that higher arterial stiffness increases all-cause and cardiovascular mortality risk in men and individuals under 65 years. ePWV reflects the degree of arterial stiffness. Our findings indicate that cancer patients in higher ePWV quartiles had relatively greater risks of all-cause and cardiovascular mortality, suggesting that elevated ePWV may be associated with increased mortality risk in cancer patients.

As a non-invasive indicator, ePWV assesses arterial elasticity and hardness. ePWV is an estimated formula for PWV that positively correlates with arterial stiffness. Chronic exposure to risk factors such as hypertension, diabetes, smoking, and alcohol accelerates the vascular aging process, reducing wall elasticity and compliance while increasing stiffness, leading to elevated ePWV values. This indicator reflects the degree of arterial aging and hardening; both increasing age and arterial stiffening contribute to rising ePWV values and increased risk of adverse cardiovascular events. A study evaluating 14,044 adults found that each 1 m/s increase in ePWV increased all-cause mortality risk by 56% and cardiovascular and cancer mortality risks by 60% and 73%, respectively. Our results show that each 1 m/s increase in ePWV increased cardiovascular and cancer mortality risks by 50% and 33%, respectively, in cancer patients. ePWV can serve as a predictor of mortality risk in cancer patients and is easily obtained in clinical practice, offering significant clinical advantages.

Dividing ePWV into quartiles, our ROC curve analysis indicated that the Q4 group had the highest AUC value of 0.639, with an optimal cutoff of 13.577 m/s. Therefore, cancer patients with ePWV > 13.577 m/s require greater attention and proactive prevention of complications to reduce mortality risk.

Unhealthy lifestyle factors such as smoking and alcohol consumption have multifaceted negative effects on cancer patients. Smoking increases mortality and reduces treatment efficacy by inducing gene mutations, suppressing immune function, promoting tumor angiogenesis, and causing oxidative stress damage. An analysis of 128,423 cancer patients showed that continued smoking after diagnosis significantly reduced overall survival compared with never-smokers. Alcohol consumption increases cancer-related mortality risk by producing toxic metabolites, interfering with hormone levels, causing nutritional deficiencies, and weakening immune function. Another analysis of 37,095 cancer patients demonstrated that adherence to a healthy lifestyle (including smoking cessation, limited alcohol consumption, healthy diet, and regular exercise) reduced all-cause mortality risk by 48% and cancer-related mortality risk by 43%. Our findings confirm that smoking is a significant risk factor for all-cause mortality in cancer patients.

This study has several strengths. First, the long follow-up duration and large sample size, combined with multivariate adjustment for multiple risk factors, yield reliable conclusions. Second, using participants from the NHANES survey minimizes selection bias by including subjects beyond specific hospitals and insurance systems. Finally, the National Death Index (NDI) database provides high-quality national registry data with direct follow-up and mortality information.

Several limitations should be acknowledged. First, this study did not differentiate the specific effects of different cancer types and treatments on ePWV. Second, as an estimated indicator, ePWV may have some deviation from actual measured values. Additionally, self-reported covariates from the NHANES database may introduce subjective bias. Finally, the generalizability of our findings to populations outside the United States requires further exploration in future studies.

In conclusion, this large study of cancer patients from the NHANES database demonstrates that ePWV provides valuable clinical information for cardiovascular risk stratification in cancer patients. Elevated ePWV is positively correlated with all-cause and cardiovascular-specific mortality in cancer patients, independent of traditional risk factors. Therefore, ePWV represents a valuable indicator for assessing mortality risk in the cancer population.

Acknowledgments: We thank Dr. Zhang Jing, Attending Physician in the Department of Infectious Diseases II, Shanghai Fifth People's Hospital, Fudan University, for guidance on data processing in this study.

Author Contributions: Qiu Yanli conceived and designed the study, implemented the research, collected data, analyzed and interpreted data, drafted the manuscript, and obtained research funding. Gao Yongyin, Bian Xueyan, and Wang Xue were responsible for statistical analysis. Li Yue provided administrative and technical support and critically reviewed the intellectual content of the article.

Conflict of Interest: The authors declare no conflict of interest.

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Received: March 21, 2025; Revised: April 11, 2025

Edited by: Li Weixia