Introduction

Congestive heart failure (CHF) affects over 60 million people globally and ranks among the leading causes of mortality in both developed and developing countries. In recent decades, CHF has emerged as a significant epidemic worldwide, particularly in Western nations where it is a primary contributor to death. CHF represents a late-stage complication of numerous cardiovascular diseases, characterized by a five-year mortality rate reaching 50% post-diagnosis. According to the American Heart Association (AHA), the prevalence of CHF is projected to increase by 46% from 2012 to 2030, with an annual addition of 960,000 new cases of CHF1.

The Aggregate Index of Systemic Inflammation (AISI) is a novel metric that integrates established inflammatory markers including neutrophils, monocytes, lymphocytes, and platelets. This index is widely employed to assess the systemic inflammatory status across various diseases such as osteoporosis, obesity, and sarcopenia. Concurrently, alongside emerging indices like the Systemic Immune-Inflammation Index (SII) and Systemic Inflammation Response Index (SIRI), AISI has demonstrated significant potential in medical research2. Calculated as the ratio of neutrophils, monocytes, and platelets to lymphocytes, AISI comprehensively evaluates the severity of inflammation, providing a robust tool for prognostic assessment in diseases.

The Systemic Inflammation Aggregate Index (AISI: Neutrophils (NEU) * Platelets (PLT) * Monocytes (MONO) / Lymphocytes (LYM)) is a composite marker that comprehensively assesses the systemic inflammatory state based on whole blood cell counts, characterized by its ease of accessibility. AISI, as a novel prognostic biomarker, has garnered attention in patients with idiopathic pulmonary fibrosis (IPF). Studies have demonstrated its ability to significantly differentiate IPF patients from healthy subjects, with AISI levels independently associated with adverse prognosis. Additionally, AISI has been found to correlate significantly with poor outcomes in patients with viral pneumonia. However, research on the predictive value of AISI in congestive heart failure (CHF) prognosis remains limited. Therefore, this study aims to investigate whether AISI can independently predict the prognosis of CHF. Furthermore, we aim to assess the clinical significance of AISI in predicting all-cause mortality, cardiovascular mortality, and cerebrovascular mortality in adult patients with congestive heart failure. Moreover, we plan to evaluate AISI as a cost-effective and readily accessible indicator for cardiovascular disease risk.

Methods

Study population

The study participants were derived from the National Health and Nutrition Examination Survey (NHANES). Informed consent was obtained from all participants, and the study protocol was approved by the NCHS Ethics Review Board. NHANES is a nationally representative cross-sectional survey conducted by the National Center for Health Statistics (NCHS) of non-institutionalized civilian households in the United States. This large-scale ongoing probability survey is conducted biennially, forming a cycle every year. The study utilized data from 10 cycles of the NHANES dataset spanning from 1999 to 2018, involving a total of 101,317 participants. Exclusion criteria were as follows: (a) individuals aged under 18 years (n = 42,113); (b) individuals without congestive heart failure (n = 57,298); (c) individuals lacking data on peripheral lymphocyte, neutrophil, monocyte, and platelet counts (n = 281); (d) participants lost to follow-up (n = 1). Therefore, the final analysis included 1,624 participants. Figure 1 illustrates the flowchart of participant selection for the study population.

Fig. 1
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Flow chart of the participants.

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Congestive Heart Failure (CHF) was confirmed based on the MCQ questionnaire, which has been validated in previous studies for the effectiveness of self-reported heart failure. Participants were asked, “Has a doctor or other healthcare professional ever told you that you have heart failure?”

Exposure

The NHANES Laboratory/Medical Technologists Procedures Manual (LPM) outlines the procedures for collection and processing of blood specimens. Complete blood count (CBC) parameters were determined using the Coulter® method for counting and sizing, with automated mixing and dilution employed for sample processing. VCS technology was utilized for differential analysis of white blood cells (WBCs), employing simultaneous measurement of individual cell volume (V), high-frequency conductivity (C), and laser light scattering (S) to differentiate neutrophils, lymphocytes, and monocytes. Platelet counts were determined by multiplying the Plt histogram by a calibration constant and expressed as n × 103 cells/µL. The Atherogenic Index of Serum (AISI) was calculated as (Neutrophils (NEU) * Platelets (PLT) * Monocytes (MONO)) / Lymphocytes (LYM).

Participants self-reported age, gender, race, smoking status, marital status, education level, and medical history (hypertension, diabetes, hyperlipidemia, stroke, coronary heart disease, angina). Laboratory measurements such as creatinine (Cr), uric acid, triglycerides, and cholesterol were collected using automated blood analysis equipment. Detailed procedures for obtaining laboratory measurements are available on the NCHS website. Comorbidities were determined based on participants’ self-reported health diagnoses within the past 12 months.

Primary outcome

Detailed mortality status information derived from the NHANES-linked National Death Index records is available on the NCHS data linkage website. Primary outcomes include all-cause mortality, cardiovascular mortality, and cardio-cerebrovascular mortality. Causes of death were classified according to the 10th Revision of the International Classification of Diseases (ICD-10). Cardiovascular mortality was classified using ICD-10 codes I00-I078. Mortality follow-up data were available through December 31, 2019, for participants in NHANES from 1999 to 2018.

Statistical analysis

We computed group-specific weights using the weights recommended by NHANES. Participants were stratified into four groups based on AISI quartiles: Q1 group 127.261 ± 41.889, Q2 group 252.119 ± 35.878, Q3 group 398.036 ± 54.999, and Q4 group 890.686 ± 459.379. Continuous variables were presented as means ± standard deviations, while categorical variables were expressed as frequencies and percentages. Baseline characteristics were analyzed using one-way analysis of variance (ANOVA) to assess differences among AISI quartiles in categorical or continuous variables. Kaplan–Meier plots and log-rank tests were used to determine differential survival rates for all-cause mortality, cardiovascular mortality, and cardio-cerebrovascular mortality according to AISI quartiles.

Multivariate Cox regression models were employed to examine the relationship between AISI and all-cause mortality, cardiovascular mortality, and cardio-cerebrovascular mortality, calculating hazard ratios (HRs) and 95% confidence intervals (CIs). Model 1 was unadjusted. Model 2 was adjusted for age, gender, race, education, and marital status. Model 3 further adjusted for BMI, white blood cell count, lymphocyte count, monocyte count, segmented neutrophils, hemoglobin, platelet count, cholesterol level, triglyceride level, uric acid level, creatinine level, smoking status, coronary heart disease, angina pectoris, stroke, hypertension, high cholesterol level, and diabetes mellitus.

Restricted cubic spline (RCS) regression models were utilized to explore the nonlinear relationship between AISI (per two-fold change) and all-cause mortality, cardiovascular mortality, and cardio-cerebrovascular mortality. Threshold points for nonlinear relationships were determined using a two-piece Cox regression model. Gender-specific models were employed to further investigate the impact of AISI on mortality risk among men and women.

Statistical analyses were conducted using Empower(R) (X&Y Solutions, Inc., MA, USA) and Stata (version 14.0). Statistical significance was defined as a two-sided P-value < 0.05.

Results

Characteristics of participants at baseline

Table 1 compares the baseline characteristics of 1624 participants with congestive heart failure included in this study. The participants had a mean age of 66.140 ± 13.471 years, with 52.557% being male. The mean follow-up period was 76.4 ± 56.6 months, during which a total of 828 participants (51.042%) died. Among these, 314 deaths (19.389%) were attributed to cardiovascular diseases, and 344 deaths (21.226%) were related to cardio-cerebrovascular causes. Most baseline variables showed statistical significance, except for gender, BMI, education level, cholesterol, triglycerides, history of coronary heart disease, stroke history, and hyperlipidemia (P < 0.05). Kaplan-Meier analysis demonstrated significant differences in AISI quartiles for all-cause mortality, cardiovascular mortality, and cardio-cerebrovascular mortality (log-rank test: all P < 0.001). Multivariable adjusted models (Table 2) indicated that participants in the highest AISI quartile had increased risks of all-cause mortality (hazard ratio [HR] = 1.599, 95% confidence interval [CI] 1.595–1.602), cardiovascular mortality (HR = 1.070, 95% CI 1.066–1.074), and cardio-cerebrovascular mortality (HR = 1.173, 95% CI 1.168–1.177) compared to those in the lowest quartile. Additionally, segmented Cox regression analysis results are presented in Table 3. AISI was log2-transformed to better fit the model due to its skewed distribution. The likelihood ratio test showed significant results (< 0.001) applicable to both the linear Cox regression model and the segmented regression model. Restricted cubic spline analysis revealed a nonlinear association between AISI and all-cause mortality (P = 0.0064), with a inflection point at AISI 8.66. On the left side of the inflection point, each twofold increase in AISI was associated with a 19.6% higher risk of all-cause mortality (HR = 1.196, 95% CI 0.930–1.538), while on the right side, there was a 126.2% increase (HR = 2.262, 95% CI 1.506–3.395). Furthermore, each twofold change in AISI was nonlinearly associated with a 60.2% higher risk of cardiovascular mortality (HR = 1.602, 95% CI 1.075–2.388) and a 56.6% higher risk of cardio-cerebrovascular mortality (HR = 1.566, 95% CI 1.072–2.286).

Table 1 Baseline characteristics of the study population stratified by quartiles of AISI value.
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Table 2 Multivariate Cox regression models between AISI with all-cause, cardiovascular and cardio-cerebrovascular mortality.
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Table 3 Threshold effect analysis of AISI on mortality using the two-piecewise regression model.
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The association between AISI and cardiovascular mortality

The Kaplan-Meier curve (Fig. 2B) demonstrated a significant inverse relationship between AISI values and survival rates among cardiovascular disease patients (log-rank P < 0.0001). Upon adjustment for all covariates (Table 2), the highest quartile of AISI showed a hazard ratio (HR) of 1.070 (95% confidence interval [CI] 1.066–1.074) for cardiovascular mortality. Restricted cubic spline analysis indicated a nonlinear association between AISI and cardiovascular mortality (non-linearity P = 0.1561; Fig. 3B). Table 3 illustrated that each twofold increase in AISI was associated with a 29% increase in the risk of cardiovascular mortality (HR = 1.29, 95% CI 0.93–1.79).

Fig. 2
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Kaplan–Meier curves for all-cause (A), cardiovascular mortality (B) and cardio-cerebrovascular mortality(C) according to AISI quartiles. AISI: Aggregate index of systemic inflammation (AISI: neutrophils (NEU) * platelets (PLT) * monocytes (MONO)/lymphocytes (LYM)).

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Fig. 3
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Restricted cubic spline curves of relations between AISI with all-cause (A), cardiovascular mortality (B) and cardio-cerebrovascular mortality (C). Analysis was adjusted for gender, age, race, education level, marital status, body mass index, white blood cell, lymphocyte, monocyte, segmented neutrophils, hemoglobin, platelet, cholesterol, triglycerides, uric acid, creatinine, smokers, coronary heart disease, angina pectoris, stroke, hypertension, high cholesterol level, diabetes mellitus. The solid and dashed lines symbolize the hazard ratios and corresponding 95% confidence intervals, respectively.

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The association between AISI and cardio-cerebrovascular mortality

In the Kaplan-Meier curve for cardio-cerebrovascular disease (Fig. 2C), higher AISI values were significantly associated with decreased survival rates (log-rank P < 0.0001). After adjustment for all covariates (Table 2), the highest quartile of AISI demonstrated a hazard ratio (HR) of 1.18 (95% confidence interval [CI] 1.168–1.177) for cardio-cerebrovascular mortality. Figure 3C depicted a nonlinear association between AISI and cardio-cerebrovascular mortality (non-linearity P = 0.0941). Table 3 indicated that each twofold increase in AISI was linked to a 30% higher risk of cardio-cerebrovascular mortality (HR = 1.30, 95% CI 0.96–1.76).

Subgroup analysis

Table 4 displays the hazard ratios (HRs) and 95% confidence intervals (CIs) for cardiovascular mortality among women in the medium and highest tertiles, which were 0.430 (0.428, 0.431) and 0.629 (0.625, 0.633), respectively. For men in the same tertiles, the HRs and 95% CIs for cardiovascular mortality were 1.351 (1.346, 1.356) and 1.401 (1.394, 1.408), respectively.

Table 4 Stratified association between AISI with all-cause, cardiovascular and cardio-cerebrovascular mortality by sex.
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Furthermore, Fig. 4A illustrates a nonlinear relationship between AISI and all-cause mortality in women (non-linearity P = 0.0055) and men (non-linearity P = 0.0058). Figure 4B demonstrates a nonlinear association between AISI and cardiovascular mortality in women (non-linearity P = 0.1783) and men (non-linearity P = 0.1315). Finally, Fig. 4C indicates that higher AISI values are nonlinearly associated with increased risk of cardio-cerebrovascular mortality in both women (non-linearity P = 0.1312) and men (P = 0.09).

Fig. 4
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Restricted cubic spline curves of relations between AISI and mortality in different sex groups. (A) all-cause mortality; (B) cardiovascular mortality; (C) cardio-cerebrovascular mortality. Analysis was adjusted for gender, age, race, education level, marital status, body mass index, white blood cell, lymphocyte, monocyte, segmented neutrophils, hemoglobin, platelet, cholesterol, triglycerides, uric acid, creatinine, smokers, coronary heart disease, angina pectoris, stroke, hypertension, high cholesterol level, diabetes mellitus.

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Sensitivity analysis

We explored the potential for unmeasured confounding between preoperative prescriptions and mortality by calculating E-value3.

Discussion

This study aimed to investigate whether the systemic immune-inflammation index (AISI) could predict long-term outcomes in patients with congestive heart failure. Also this study explored the association between the aggregate index of systemic inflammation (AISI) and the risk of all-cause, cardiovascular, and cardio-cerebrovascular mortality in patients with congestive heart failure (CHF), using data from NHANES 1999–2018. Our findings reveal several significant implications for clinical practice and future research. The findings indicate that increasing AISI levels are significantly associated with all-cause mortality, cardiovascular mortality, and cardio-cerebrovascular mortality. There was a pronounced increase in mortality risk as AISI reached 8.66. Similar association patterns were observed in both women and men. Despite comparable associations with all-cause mortality between genders, men exhibited a stronger correlation with cardiovascular and cardio-cerebrovascular mortality. Previous research has confirmed that gender differences in cardiovascular disease (CVD) outcomes are attributed to comprehensive genetic and hormonal variations between males and females.

Multiple studies have indicated that the Aggregate Index of Systemic Inflammation (AISI) is considered a promising predictive biomarker capable of assessing and predicting clinical outcomes across various health conditions. These investigations have demonstrated associations between AISI and conditions such as hypertension, idiopathic pulmonary fibrosis, thyroid nodules, severity and mortality of COVID-19, dry age-related macular degeneration, esophageal cancer, prostate cancer, acute coronary syndrome, and albuminuria. These findings underscore the potential role of AISI in diverse disease processes, offering a novel biomarker perspective for future clinical and epidemiological research4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23.

AISI and mortality risk

Our results demonstrate a clear correlation between increasing AISI levels and heightened mortality risk across all categories studied. Specifically, as AISI surpassed 8.66, the risk of all-cause mortality notably escalated. This underscores the potential utility of AISI as a prognostic biomarker in CHF, offering clinicians a quantitative tool to assess systemic inflammation and its implications for patient outcomes.

Gender-specific differences

Interestingly, while both male and female patients exhibited similar associations with all-cause mortality concerning AISI levels, males displayed a more pronounced correlation with cardiovascular and cardio-cerebrovascular mortality. This disparity suggests potential gender-specific biological factors influencing inflammation-mediated cardiovascular outcomes in CHF, which merits further investigation into genetic and hormonal influences.

Clinical implications

The identification of AISI as a predictor for adverse cardiovascular events in CHF patients has significant clinical implications. Monitoring AISI levels could aid in risk stratification, guiding personalized treatment strategies aimed at mitigating systemic inflammation and improving long-term outcomes. Interventions targeting inflammation, such as anti-inflammatory therapies or lifestyle modifications, may prove beneficial in reducing mortality rates among CHF patients with elevated AISI.

Strengths and limitations of the study

This study benefited from the inclusion of 1624 patients with congestive heart failure (CHF) and extended longitudinal follow-up, allowing for an in-depth exploration of the aggregate index of systemic inflammation (AISI) and its relationship with all-cause mortality, cardiovascular mortality, and cerebrovascular mortality in this specific population. The study population in this article is not applicable to hospitalized individuals, outpatient or emergency care populations, children, or other countries. We utilized the RCS model to analyze the complex nonlinear relationships between AISI and these mortality outcomes. However, the study has several limitations. These include potential recall bias associated with self-reported complications and lifestyle data, as well as the limitation of only capturing baseline AISI values without reflecting changes during follow-up. Despite adjustments for multiple confounding factors, unaccounted variables may still impact the study outcomes. Future prospective research is needed to validate our findings and elucidate the mechanistic pathways linking systemic inflammation, AISI, and mortality in CHF. Additionally, investigating the impact of anti-inflammatory interventions on AISI dynamics and clinical endpoints could further enhance therapeutic strategies and understanding in this field. We used the E-value sensitivity analysis to quantify the potential implications of unmeasured confounders and found that an unmeasured comfounder was unlikely to explain the entirety of the treatment effect.

Conclusion

In conclusion, our study provides compelling evidence supporting the role of AISI as a predictive marker for mortality risk in CHF patients. By integrating AISI assessment into clinical practice, healthcare providers can potentially enhance risk assessment and tailor management strategies to improve outcomes in this high-risk population.

This discussion synthesizes the implications of our findings, highlighting both the clinical relevance and avenues for future research, thereby contributing to the growing body of knowledge on inflammation-mediated outcomes in congestive heart failure.