Low Heart Rate Variability Predicts Poor Overall Survival of Lung Cancer Patients With Brain Metastases

Abstract

BackgroundThe aim of this prospective study was to evaluate the association between heart rate variability (HRV) and overall survival of lung cancer patients with brain metastases (LCBM).MethodsFifty-six LCBM patients were enrolled in this study. Five-minute electrocardiograms were collected before the time to first brain radiotherapy. HRV was analyzed quantitatively by using the time domain parameters, i.e., the standard deviation of all normal-normal intervals (SDNN) and the root mean square of successive differences (RMSSD). Survival time for LCBM patients was defined as from the date of HRV testing to the date of death or the last follow-up.ResultsIn the univariate analysis, SDNN ≤ 13 ms (P = 0.003) and RMSSD ≤ 4.8 ms (P = 0.014) significantly predicted poor survival. Multivariate analysis confirmed that RMSSD ≤ 4.8 ms (P = 0.013, hazard ratio = 3.457, 95% confidence interval = 1.303–9.171) was also an independent negative prognostic factor after adjusting for mean heart rate, Karnofsky performance status, and number of brain metastases in LCBM patients.ConclusionDecreased RMSSD is independently associated with shorter survival time in LCBM patients. HRV might be a novel predictive biomarker for LCBM prognosis.

Association of heartbeat complexity with survival in advanced non-small cell lung cancer patients

“… De Couck et al (2016) found that SDNN can significantly predict the overall survival in patients with advanced pancreatic cancer. In addition, our previous study showed that there is an independent association between RMSSD and overall survival in LC patients with brain metastasis ( Wu et al, 2022 ). Although most previous studies showed that vagal activity based on linear HRV has a prognostic effect on cancer patients, a few studies found that the change in linear HRV is not an appropriate marker for predicting overall survival in advanced NSCLC patients.…”

“…Time domain analysis is the simplest and easiest method to study HRV and can provide relatively clear physiological information, which is easily accepted by clinicians. The published literature suggests that SDNN and RMSSD are significantly associated with the overall survival of patients with advanced malignant tumors ( Wang Y. M. et al, 2013 ; De Couck et al, 2016 ; Wu et al, 2022 ). For example, Wang Y. M. et al (2013) showed that SDNN is an independent prognostic factor for the overall survival of patients with brain metastasis.…”

“…In a recent study, vagally mediated HRV parameters were inversely associated with levels of inflammatory markers such as TNF-α, IL-6, and CRP, possibly because vagal nerve stimulation reduces inflammatory cytokines through the cholinergic anti-inflammatory pathway ( Williams et al, 2019 ). Published studies have suggested that linear (time domain or frequency domain) HRV is associated with the prognosis of patients with malignant tumors, including LC with brain metastasis ( Wu et al, 2022 ), brain metastasis ( Wang et al, 2021 ), gastric cancer ( Hu et al, 2018 ), colorectal cancer ( Mouton et al, 2012 ), liver cancer ( Chiang et al, 2010 ), pancreatic cancer ( De Couck et al, 2016 ), breast cancer ( Giese-Davis et al, 2015 ), and so on. Although accumulating evidence suggests that linear HRV may be a prognostic marker for patients with malignant tumors, its predictive value in the survival of NSCLC patients remains controversial.…”

Non-invasive prognostic biomarker of lung cancer patients with brain metastases: Recurrence quantification analysis of heart rate variability

“… Wang et al (2013 , 2021) showed a significant correlation between the time-domain index SDNN and the survival time of BM patients, and SDNN was an independent prognostic factor for patients with BM. Wu et al (2022) first explored the association between HRV time domain indexes (SDNN and RMSSD) and the survival of LCBM patients. The results showed that RMSSD, the time domain index, can be used as an independent prognostic factor for the survival of patients with LCBM.…”

“…The methods for quantitative analysis of HRV are mainly based on linear methods in the time and frequency domains and nonlinear methods. Existing studies on the relationship between the time- and frequency-domain parameters of HRV and the prognosis of many kinds of malignant tumors, such as liver cancer ( Chiang et al, 2010 ), pancreatic cancer ( De Couck et al, 2016 ), BM ( Wang et al, 2013 ; Wang et al, 2021 ), colorectal cancer ( Strous et al, 2020 ) and LCBM ( Wu et al, 2022 ), have shown that a low level of linear HRV is associated with poor prognosis of cancer patients, which is of great significance for the formulation of treatment strategies for cancer patients.…”