Clinical value of NT-proBNP, MPO, and NLR combined with echocardiography in prediction of malignant arrhythmia in elderly patients with valvular heart disease

Abstract

Objective

We aimed to probe the clinical value of N-terminus pro-brain natriuretic peptide (NT-proBNP), myeloperoxidase (MPO), and neutrophil lymphocyte ratio (NLR) combined with echocardiography in the prediction of malignant arrhythmias (MA) in elderly patients with valvular heart disease (VHD).

Methods

MPO, NT-proBNP, and NLR were detected in blood samples. After 1 year of follow-up, receiver operating characteristic curves were analyzed to determine the clinical value of NT-proBNP, MPO, NLR, and echocardiography for predicting MA in patients with VHD.

Results

MPO, NT-proBNP, and NLR were higher in the VHD group. MPO, NT-proBNP, and NLR were higher with severe cardiac dysfunction. MPO, NT-proBNP, NLR, and LVESV in the MA group were higher. NT-proBNP was an independent factor influencing the occurrence of MA in elderly patients with VHD. The AUC for predicting MA in elderly patients with VHD using NT-proBNP, MPO, NLR, and echocardiography were 0.782 (sensitivity 61.50%, specificity 94.60%, 95% CI 0.630–0.934), 0.759 (sensitivity 69.20%, specificity 81.10%, 95% CI 0.579–0.938), 0.736 (sensitivity 76.90%, specificity 64.90%, 95% CI 0.562–0.910), and 0.782 (sensitivity 76.90%, specificity 75.70%, 95% CI 0.646–0.918), respectively. The AUC for the combined prediction using NT-proBNP, MPO, NLR, and echocardiography was 0.913 (sensitivity 76.90%, specificity 94.60%, 95% CI 0.820–1.000), higher than that of each parameter alone (P < 0.05).

Conclusion

The combination of NT-proBNP, MPO, NLR, and echocardiography has a predictive value in detecting MA in elderly VHD patients.

Introduction

In primary care settings, valvular heart disease (VHD) is a prevalent abnormality. Many causes can contribute to VHD, including congenital, degenerative, infectious, and traumatic factors (Kisling and Gallagher 2024). In the heart, there are four valves (tricuspid, pulmonic, mitral, and aortic) that prevent backward flow between the four chambers of the heart and ensure that the pressure gradients necessary for hemodynamic circulation are maintained (Roth et al. 2020). However, effective pharmacological therapies are limited, and treatment methods have traditionally been confined to surgical techniques such as valve repair or replacement, resulting in inadequate treatment for elderly patients and those with severe complications, frailty, or left ventricular dysfunction (Praz et al. 2024). Additionally, The cardiovascular system progressively deteriorates with age, leading to aortic and/or mitral valve insufficiency and stenosis. Dilation and calcification of the heart, along with fibrosis, can result in various arrhythmic conditions (Eleid et al. 2023). Malignant arrhythmias (MA) are a type of rapid arrhythmia that can lead to hemodynamic abnormalities within a short period, most commonly ventricular tachycardia or ventricular fibrillation, which require prompt management (Chen et al. 2019). Therefore, predicting MA in elderly patients after VHD surgery and taking timely intervention measures are of significant clinical importance for improving prognosis.

Echocardiography, as a routine non-invasive imaging technique for the diagnosis of cardiovascular diseases, can intuitively display the structural and functional status of heart valves, providing an important basis for the diagnosis and evaluation of VHD (Wang et al. 2018; O'Riordan et al. 2023). However, its ability to predict MA post-surgery in patients is often insufficient due to its reliance on real-time monitoring. Consequently, MA in elderly VHD patients has been a hot topic in clinical research. Identifying new indicators to enhance the predictive value of MA in elderly patients with VHD is of great significance.

Studies have indicated that an elevated C-reactive protein-to-albumin ratio (CAR) increases the risk of long-term all-cause mortality in patients with heart failure with reduced ejection fraction (HFrEF) and those with implantable cardiac defibrillators (ICDs) (Cinier et al. 2021a). Additionally, CAR is an inflammatory marker (Header et al. 2022). This highlights the importance of addressing inflammation in the detection and treatment of related heart diseases. Furthermore, it has been reported that a lower prognostic nutritional index (PNI) value predicts all-cause mortality during long-term follow-up in HFrEF patients with secondary ICD implantation (Cinier et al. 2021b). PNI is a simple parameter reflecting a patient's nutritional status, inflammation, and immune status (Shim et al. 2022). This also suggests the significance of considering a patient's inflammatory status and immune status in the detection and treatment of related heart diseases. Moreover, Muammer et al. report that the HALP (hemoglobin, albumin, lymphocyte, and platelet) score may be an important independent predictor of in-hospital mortality in ST-segment elevation myocardial infarction (STEMI) patients undergoing primary percutaneous coronary intervention. The HALP score is a new indicator of nutritional status and systemic inflammation, providing prognostic information (Karakayali et al. 2023). This further emphasizes the importance of monitoring nutritional status and inflammation in related heart diseases.

N-terminus pro-brain natriuretic peptide (NT-proBNP), secreted primarily by the heart, lungs, kidneys, adrenal gland, and also the brain, is a biomarker for predicting functional outcomes from cardiac events (Srisujikul et al. 2023). In the context of valve disease, NT-proBNP is considered a cornerstone of prognosis (Bergler-Klein 2020) and an effective risk stratifier for elderly patients with VHD (Zhang et al. 2020). Changes in NT-proBNP levels are closely related to the functional status of the heart and can reflect alterations in cardiac structure and impaired function at an early stage (Siebers et al. 2024). It has been reported that a member of the heme peroxidase superfamily, myeloperoxidase (MPO) plays a key role in inflammation-induced cardiovascular disease (CVD) at a molecular and cellular level (Ndrepepa 2019). The use of MPO in animal models of CVD has demonstrated favorable outcomes relating to disease progression (Ramachandra et al. 2020). As a reliable and easily accessible marker of immune response to various infectious and non-infectious stimuli, neutrophil–lymphocyte ratio (NLR) is widely used across almost all medical disciplines today. A dynamic change in NLR may indicate the onset of a pathological process several hours before the clinical state appears (Zahorec 2021; Cupido et al. 2022). In patients with acute coronary syndromes, NLR has been shown to be a predictor of cardiac arrhythmias (Afari and Bhat 2016). Furthermore, studies have shown that MPO levels increase after valve replacement surgery, possibly reflecting heightened inflammatory activity (Pan et al. 2023), and NT-proBNP and NLR levels are elevated in valve patients, potentially indicating enhanced inflammatory activity and immune imbalance (Bergler-Klein 2020; Song et al. 2019). That is, NT-proBNP, MPO, and NLR may reflect the pathophysiological changes of VHD and MA in three dimensions: cardiac function, inflammation, and immune status. These three biomarkers possess diagnostic significance for VHD complicated by MA. Therefore, our intention was to determine the predictive value of NT-proBNP, MPO, and NLR in elderly patients with VHD and MA in conjunction with echocardiography.

Materials and methods

Ethics statement

The selected patients signed the informed consent and were approved by the Ethics Committee of Quanzhou First Hospital and followed the tenets of the Declaration of Helsinki.

Participants

The present study was a cross-sectional study. A total of 50 cases of elderly VHD patients who underwent valve replacement in Quanzhou First Hospital from January 2020 to March 2021 were screened as the VHD group. In the same period, 55 cases of those who underwent health checkups were included as the healthy control group, with no obvious abnormalities in chest radiograph, electrocardiogram and ultrasound, and no chronic diseases such as heart disease, diabetes, hypertension, and so on. There were 30 males and 20 females in the VHD group, with a mean age of 62.20 ± 9.36 years; 31 males and 24 females in the healthy control group, with a mean age of 63.47 ± 9.98 years.

Criteria

Inclusion criteria: patients with typical clinical symptoms, diagnosed with VHD by echocardiography and treated with aortic or mitral valve replacement; patients aged 60 years and older.

Exclusion criteria: patients with rheumatic heart disease or congenital heart valve insufficiency; patients with severe hepatic or renal dysfunction; patients with other major cardiac diseases or malignant arrhythmias preoperatively; patients with incomplete clinical data or lost to follow-up; patients who were unable to cooperate with the treatment due to mental or language disorders; patients with major diseases of the liver, kidneys, lungs and other vital organs, as well as malignant tumors; patients with hematological diseases; patients with chronic inflammatory diseases or autoimmune diseases.

Indicator test

Fasting venous blood (5 mL) was collected early in the morning of the next day after the admission of elderly VHD patients, and 5 mL of fasting venous blood was extracted from the healthy control group on the day of physical examination. The blood was centrifuged at 3000 r/min for 10 min, and the supernatant was extracted and stored at − 80 ℃. MPO level was measured by latex-enhanced turbidimetric immunoassay using an MPO kit (Medicalsystem Biotechnology Co., Ltd.) and detected on the BECKMAN5811 instrument. NT-proBNP level was detected by chemiluminescence method using the NT-ProBNP detection kit (Huachn, Henan, China) and Beckman Coulter DXI800 automatic immunoassay analyzer. NLR was detected using an automatic hemocytometer (Myriad BC- 3600). The mean value was taken from two measurements.

Echocardiography

All procedures and measurements were performed by an experienced echocardiographer not involved in the study, using an IE-type echocardiography machine with a 3D ultrasound X4 probe at a frequency of 2–4 MHz. Patients were positioned in the left lateral decubitus position with simultaneous electrocardiogram display. The apical four-chamber and two-chamber views were obtained from the apical window, and left ventricular end-diastolic volume (LVEDV) and left ventricular end-systolic volume (LVESV) were measured using the biplane Simpson’s method. Another cardiologist with special experience in valvular disease performed the review and the two needed to reach a consensus.

Follow-up

Regular monthly outpatient and telephone follow-ups were conducted. Outpatient follow-ups involved electrocardiogram monitoring for the occurrence of MA. The incidence of MA during the 1-year follow-up period in the VHD group was recorded, and patients were divided into two subgroups: the MA group (n = 13, all experiencing MA for the first time) and the non-MA group (n = 37). All researchers involved in the follow-up were uniformly trained. MA refers to arrhythmias that cause severe hemodynamic disturbances and endanger life, potentially leading to cardiac arrest, sudden death, or other serious consequences, with a significant decrease in cardiac output and insufficient blood supply to vital organs (brain, heart, kidneys, etc.). Common types of malignant arrhythmias include ventricular tachycardia, ventricular fibrillation, high-degree atrioventricular block, and polymorphic ventricular tachycardia.

Treatment

If MA occur postoperatively, treatment was administered based on the type of arrhythmia, including removing the precipitating factors, using beta-blockers, digitalis, amiodarone, etc.

Statistical analysis

SPSS 25.0 (IBM, NY, USA) and GraphPad Prism 8.0 software (Graph Pad Inc., CA, USA) were useful for statistical analysis. Measurement data were tested for normality using the Kolmogorov–Smirnov method. All data followed a normal distribution and were expressed as ($\overline {x}$ ± sd). Independent sample t-tests were used for comparisons between groups. Numeration data were described as (n [%]), and chi-square tests were adopted for comparisons of numeration data between groups. Logistic regression was employed to analyze the influencing factors. The diagnostic performance was analyzed adopting the receiver operator characteristic (ROC) curve analysis and compared using the Delong test. P < 0.05 was statistically significant.

Results

General information

Age, gender, body mass index (BMI), and NYHA classification were not significantly different between the VHD group and the healthy control group (P > 0.05, Table 1).

Table 1 Comparison of general information between the two groups

Cardiac function indices

LVEDV and LVESV in the healthy control group were 75.41 ± 15.25 mL and 37.15 ± 8.11 mL, respectively. LVEDV and LVESV in the VHD group were 87.54 ± 18.42 mL and 51.41 ± 15.66 mL, respectively. LVEDV and LVESV in the VHD group were higher than those of the healthy control group (P < 0.05, Table 2).

Table 2 Comparison of cardiac function indices between the two groups

MPO, NT-proBNP, and NLR levels

MPO, NT-proBNP, and NLR in the healthy control group were 86.59 ± 20.62 mg/L, 49.37 ± 10.49 ng/L, and 2.16 ± 0.43), respectively; and in the VHD group, MPO, NT-proBNP, and NLR were 123.10 ± 39.36 mg/L, 684.58 ± 144.50 ng/L, and 5.73 ± 1.86, respectively. MPO, NT-proBNP, and NLR in the VHD group were higher than those in the healthy control group (P < 0.05, Table 3).

Table 3 Comparison of MPO, NT-proBNP, and NLR levels between the two groups

Changes in MPO, NT-proBNP, and NLR levels in VHD patients with different disease grade

In the VHD group, there were 35 patients in grades I–II, including 22 males and 13 females, with an average age of 62.40 ± 9.15 years and a BMI of 22.46 ± 1.38 kg/m². Six patients had hypertension, and nine had diabetes. There were 15 patients in grades III–IV, including 8 males and 7 females, with an average age of 61.73 ± 10.46 years and a BMI of 22.67 ± 1.68 kg/m². Three patients had hypertension, and four had diabetes. There were no statistically significant differences in the general characteristics between the I–II group and III–IV group (P > 0.05), allowing for comparability. MPO, NT-proBNP, and NLR were 102.37 ± 23.59 mg/L, 648.68 ± 128.09 ng/L, and 5.35 ± 1.57 in the grade I–II group, respectively; and in the grade III–IV group, MPO, NT-proBNP, and NLR were 171.47 ± 24.49 mg/L, 768.33 ± 154.89 ng/L, and 6.63 ± 1.76, respectively. MPO, NT-proBNP, and NLR were higher in the grade III–IV group than in the grade I–II group (P < 0.05, Table 4).

Table 4 Changes in MPO, NT-proBNP, and NLR level in VHD patients

MPO, NT-proBNP, and NLR levels in MA patients

After 1 year of follow-up, 13 patients who experienced malignant arrhythmias were considered as the MA group, including 8 males and 5 females, with an average age of 61.15 ± 10.62 years and a BMI of 22.92 ± 1.50 kg/m². Four patients had hypertension, and six had diabetes. Additionally, a total of 37 patients without arrhythmias were considered as the non-MA group, including 22 males and 15 females, with an average age of 62.57 ± 9.14 years and a BMI of 22.38 ± 1.44 kg/m². Five patients had hypertension, and seven had diabetes. There were no statistically significant differences in the general characteristics between the two groups (P > 0.05), allowing for comparability. MPO, NT-proBNP, and NLR in the non-MA group were 108.16 ± 28.41 mg/L, 621.49 ± 128.36 ng/L, and 5.16 ± 1.23, respectively, and LVEDV and LVESV were 80.36 ± 26.45 mL and 45.62 ± 13.16 mL, respectively. MPO, NT-proBNP, and NLR in the MA group were 137.51 ± 35.54 mg/L, 772.13 ± 138.72 ng/L, and 6.47 ± 1.47, respectively, and LVEDV and LVESV were 94.48 ± 20.32 mL and 56.94 ± 16.38 mL, respectively. MPO, NT-proBNP, NLR, and LVESV were higher in the MA group compared to the non-MA group (P < 0.05, Table 5).

Table 5 Changes in MPO, NT-proBNP, and NLR Levels in MA patients

Multifactorial logistic regression analysis and ROC curve analysis

Whether MA occurred in elderly patients with VHD was used as the dependent variable (MA = 1, no MA = 0), and MPO, NT-proBNP, and NLR were used as independent variables (all included with actual values) in a multivariate logistic regression analysis model using a stepwise regression method. The results showed that NT-proBNP was an independent factor influencing the occurrence of MA in elderly patients with VHD (P < 0.05, Table 6). ROC curve analysis showed that the area under the curve (AUC) in predicting MA in elderly VHD patients of MPO was 0.759, with a sensitivity of 0.692 and a specificity of 0.811, that of NT-proBNP was 0.782, with a sensitivity of 0.615 and a specificity of 0.946, and that of NLR was 0.736, with a sensitivity of 0.769 and a specificity of 0.649. The AUC for echocardiography was 0.782, with a sensitivity of 0.769 and a specificity of 0.757. The AUC for the combination of these four was 0.913, with a sensitivity of 0.769 and a specificity of 0.946. The Delong test showed that the AUC of the combined diagnosis was greater than that of MPO, NT-proBNP, and NLR alone (Z = 2.140, 1.986, 2.386, P < 0.05) (Table 7; Fig. 1).

Table 6 Multifactor logistic regression analysis

Table 7 Results of ROC curve analysis

Fig. 1

ROC curves for the combined prediction of MA by NT-proBNP, MPO, NLR, and echocardiography in elderly patients with VHD

Discussion

Structural and functional abnormalities in the valves can profoundly impact cardiac function and the conduction system, resulting in the emergence of severe coexisting conditions like heart failure and arrhythmias (Rees et al. 1988). This trial evaluated the clinical value of combining NT-proBNP, MPO, NLR, and echocardiography for predicting MA in elderly patients with VHD. Our study found that the combination of NT-proBNP, MPO, NLR, and echocardiography has good predictive value for detecting MA in elderly patients with VHD. This study adopts a novel perspective by combining NT-proBNP, MPO, NLR, and echocardiography to predict the risk of MA in elderly patients with VHD. By jointly detecting multiple indicators, doctors can identify high-risk patients earlier and adopt more aggressive treatment measures to improve patient outcomes. This approach is innovative.

It is known that NT-proBNP production in the ventricles increases in cardiac failure and locally as a consequence of myocardial infarction (Panagopoulou et al. 2013). Moreover, a high level of NT-proBNP is associated with abnormal left ventricular dimensions, abnormal left ventricular function, and VHD (Vaes et al. 2010). A relevant paper has determined that NT-proBNP level is higher in patients with heart failure accompanied by MA compared to those without MA (Ozmen et al. 2017). A significant association has been found between NT-proBNP and atrial fibrillation (AF), a common form of MA (Werhahn et al. 2022). Furthermore, NT-proBNP predicts recurrent AF (Staszewsky et al. 2021).

The research of Karakayalı et al. demonstrates that the white blood cell count to mean platelet volume ratio (WMR) can provide supportive data for early risk stratification and optimization methods in STEMI patients. WMR has become an indicator of inflammation in atherosclerotic diseases and plays a crucial role in identifying high-risk patients (Karakayalı et al. 2023). Similarly, as a biomarker reflecting inflammatory and oxidative stress status, chronic activation of MPO may lead to random protein alterations, causing tissue damage and being associated with sudden cardiovascular events. Chronic activation of MPO may result in random protein alterations, leading to tissue damage, and is linked to sudden cardiovascular events (Staszewsky et al. 2021). Increasing evidence has captured the notion that MPO-related atrial remodeling is a physiologic substrate for AF (Liu et al. 2023). It is interestingly observed that patients with future onset AF have increased MPO compared with non-AF patients (Meulendijks et al. 2023), and plasma MPO is independently associated with ventricular arrhythmias including AF at baseline (Mollenhauer et al. 2017; Wang et al. 2023).

Furthermore, a higher rate of recurrence of AF is associated with NLR, a marker of oxidative stress and inflammation (Silva and Borges 2023). A comprehensive meta-analysis has recognized that NLR level, whether baseline or post-surgical/procedure, is associated with the risk of recurrence/occurrence of AF (Shao et al. 2015). Furthermore, NLR is associated with a higher level of myocardial injury markers and arrhythmia events in acute myocardial infarction (Zhang et al. 2023).

The findings of this study further support the aforementioned views. Specifically, our results revealed that MPO, NT-proBNP, and NLR levels were higher in VHD patients compared to the healthy control group. Among VHD patients, levels of MPO, NT-proBNP, and NLR were higher in the NYHA grade III-IV group than in the grade I-II group. This suggests that elevated levels of MPO, NT-proBNP, and NLR may reflect the presence of inflammation, impaired cardiac function, and immune imbalance in VHD patients. As the severity of the disease increases, the levels of these biomarkers also rise accordingly. Importantly, our data indicated that MPO, NT-proBNP, NLR levels, and LVESV were higher in VHD patients who experience MA. This suggests that these indicators have certain value in predicting MA in VHD patients. Furthermore, NT-proBNP was an independent influencing factor for MA in elderly patients with VHD. The AUC of the combined diagnosis using MPO, NT-proBNP, NLR, and echocardiography was greater than that of individual diagnoses. This indicates that the combined diagnosis has higher sensitivity and specificity in predicting MA in VHD patients, allowing for more accurate identification of high-risk patients.

Although MPO, NT-proBNP, NLR, and echocardiography demonstrate certain value in predicting MA, the specific mechanisms of action and interrelationships of these different indicators still require further exploration. For example, the specific pathways of MPO and NLR in VHD, as reflectors of inflammation and immune status, are not fully understood. Additionally, although the combined diagnosis shows advantages in predicting MA, the construction and optimization of the model still need further investigation. Notably, the rapid development of artificial intelligence (AI) and machine learning (ML) technologies in recent years provides new possibilities for this. For example, a study demonstrates the powerful capabilities of ML in analyzing electrocardiogram features. This study not only improved prediction accuracy but also provided more refined evidence for clinical decision-making (Yilmaz et al. 2023). Furthermore, another study used deep learning technology to predict short-term mortality in patients with acute pulmonary embolism, further demonstrating the predictive potential of AI in complex clinical scenarios. These studies not only showcase the broad prospects of AI and ML in medical prediction but also provide ideas and methods for improving current prediction models. Therefore, future research can explore integrating AI and ML technologies into the joint analysis of biomarkers and imaging features such as NT-proBNP, MPO, NLR, and echocardiography to establish more precise and personalized prediction models. Such models can not only improve prediction accuracy but also dynamically adjust based on the specific conditions of patients, providing stronger support for clinical decision-making.

In conclusion, our study shows that the combined use of NT-proBNP, MPO, NLR, and echocardiography has good predictive value for the occurrence of MA in elderly patients with VHD. For elderly patients with VHD, the occurrence of MA is often accompanied by high rates of disability and mortality. Therefore, early and accurate prediction of MA is crucial for guiding clinical treatment and improving prognosis. By combining multiple biomarkers and echocardiographic indicators, this study aims to provide a more effective prediction tool for clinicians to better assess patient risk and develop more personalized treatment plans. However, this study has some limitations. For example, the sample size is small, and the follow-up time is limited, which may not fully reflect the complex situation of MA in elderly patients with VHD. Additionally, this study is a single-center, retrospective study without prospective validation, potentially introducing selection bias. Future research should expand the sample size to improve the generalizability of the results; consider multi-center collaborations to validate the broad applicability of the prediction model; and conduct prospective studies with longer follow-up times to more accurately assess the prognostic value of the prediction model.