Prognostic Value of the Aggregate Index of Systemic Inflammation (AISI) in Critically Ill Patients with Heart Failure: A J-Shaped Relationship and the Mediating Role of Heart Rate

Prognostic Value of the Aggregate Index of Systemic Inflammation (AISI) in Critically Ill Patients with Heart Failure: A J-Shaped Relationship and the Mediating Role of Heart Rate | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Prognostic Value of the Aggregate Index of Systemic Inflammation (AISI) in Critically Ill Patients with Heart Failure: A J-Shaped Relationship and the Mediating Role of Heart Rate Xiaoxue Zheng, Long Tan, Yu Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9374386/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Background: Systemic inflammation plays a critical role in the progression of heart failure (HF). Aggregate Index of Systemic Inflammation (AISI) is a new composite biomarker, whose prognostic value in critically ill HF patients are still poorly characterized. Methods: 4, 936 HF patients in MIMIC-IV database were extracted in this retrospective cohort study. AISI was calculated on ICU admission [AISI = (neutrophils×monocytes×platelets)/lymphocytes]. Outcomes were 30-day and 1-year all-cause mortality. Multivariable Cox regression, restricted cubic splines (RCS), time-dependent receiver operating characteristic (ROC) curves, subgroup analysis and causal mediation analysis were used to assess independent prognostic value of AISI and mediation role of heart rate (HR). Findings: Multivariate Cox models revealed that patients who were in the highest AISI quartile (Q4) had a much higher risk of 30-day (HR: 1.47; 95% CI: 1.21-1.78) and 1-year mortality (HR: 1.27; 95% CI: 1.10-1.45). RCS analysis revealed a J-shaped dose-response relationship (P-non-linear≤0.001), with risk nadirs at AISI values of 225.97 (30-day) and 256.64 (1-year). The analysis of ROC indicated the strong acute prognostic performance of AISI. Mediation analysis showed that HR was a significant mediator in the relationship between AISI and survival with mediation of 16.7% and 22.7%, respectively, of the total effect on the 30-day mortality and 1-year mortality. Conclusion: The AISI is a strong and independent predictor of mortality in critically ill patients with HF, characterized by a non-linear, J-shaped association. HR acts as a significant partial mediator in this pathway. Figures Figure 2 Figure 3 Figure 4 Figure 5 Introduction Heart failure (HF) is a significant health issue of the world, which is estimated to impact about 64 million people across the world. The syndrome has a high burden on the healthcare systems because of its high rates of mortality and morbidity[ 1 ]. HF is no longer viewed solely as a hemodynamic disorder, but as a multifaceted clinical syndrome that is driven by chronic systemic immune activation and a condition of low grade inflammation[ 2 ]. Persistent inflammatory environment could stimulate myocardial fibrosis, ventricular remodelling, and cardiac dysfunction[ 2 ]. High levels of circulating pro-inflammatory biomarkers in patients with HF is directly linked to the severity of the disease and clinical outcome[ 3 , 4 ]. The traditional inflammatory biomarkers, including C-reactive protein (CRP) and interleukin-6 (IL-6), provide useful prognostic data in clinical practice[ 5 , 6 ]. However, they have limitations due to high costs of assays and the possibility of non-specific elevations especially in acute care. In this context, hematological indices based on regular complete blood counts (CBC) offer a cost-effective and easily available alternative. They can assess systemic inflammatory status, without implying the extra financial burden. The Aggregate Index of Systemic Inflammation (AISI) has been suggested as a composite, multidimensional inflammatory marker, which gives a more integrated assessment of inflammatory burden. It is an index that combines the data of four different hematological lineages, including neutrophils, monocytes, platelets, and lymphocytes, into one score, which provides a more comprehensive view of the systemic inflammatory environment[ 7 ]. Previous studies have reported the prognostic value of AISI in a wide range of clinical conditions, such as hypertension[ 8 ], idiopathic pulmonary fibrosis[ 9 ], severe COVID-19[ 10 ], and acute pancreatitis[ 7 ]. In the cardiology field, high levels of AISI have been reported to be associated with poor patient outcomes following acute myocardial infarction (AMI)[ 11 ]. Other emerging data through databases like NHANES also indicate a relationship between AISI and risk of mortality in general populations with HF[ 12 ]. Nevertheless, its prognostic worth in patients with HF who are in critically ill condition is still unclear. In addition, the possible mediating effect of hemodynamic parameters, such as heart rate, in the effect of systemic inflammation on clinical survival has not been studied. We utilized a large-scale clinical database in the current study to rigorously assess the relationship between AISI and 30-day (short-term) and 1-year (long-term) mortality among patients with HF. We aimed to identify the independent prognostic value of AISI and determine possible risk thresholds by using multivariable-adjusted Cox models and restricted cubic spline (RCS) analysis. We also conducted mediation analysis to examine the degree to which heart rate can explain the relationship between AISI and mortality. We aim to determine whether AISI could be a strong, cost-effective risk stratification tool that would help in identifying a risk nadir to manage clinical practices in a more personalized way. Methods 1. Data Source and Study Population. The present retrospective cohort study relied on the date of the Medical Information Mart of Intensive Care IV (MIMIC-IV, version 2.2) database. MIMIC-IV is an electronic health record (EHR) database that consists of patients admitted to the intensive care units (ICUs) of the Beth Israel Deaconess Medical Center, which is large and longitudinal, and de-identified. Access to data was provided in the PhysioNet platform (Certification ID: 58380649). The patients with HF were identified according to the International Classification of Diseases, Ninth and Tenth Revision (ICD-9 and ICD-10) codes. 2. Inclusion and Exclusion Criteria. Our study included adult patients who were diagnosed with heart failure. In the case of patients who had more than one hospitalization or ICU admission, the initial ICU admission was considered to preserve independent observations. The following were the specific exclusion criteria: Patients within the age range of below 18 or above 100 years; Patients whose values of the components needed to compute the AISI at the time of admission were missing. 3. Definition of AISI The AISI was determined based on the laboratory values acquired during the first 24 hours of ICU admission that reflect the acute inflammatory condition at the ICU admission. The index can be defined as: The study population was divided into four categories (Q1 and Q2 and Q3 and Q4) using the quartile of the baseline AISI values to make subsequent comparative analyses. 4. Covariates and Outcomes The clinical variables extracted from the database encompassed the following categories: 1) Demographic information: age and biological sex (male or female). 2) Comorbidities: hypertension, diabetes, cerebrovascular disease, hyperlipidemia, atrial fibrillation or flutter (AF), myocardial infarction (MI), and chronic kidney disease (CKD). 3) Vital signs and disease severity: Important physiological variables as assessed at the time of admission were HR, mean blood pressure (MBP), and percutaneous oxygen saturation (SpO2). The severity of the disease was measured by the Sequential Organ Failure Assessment (SOFA) score. 4)Laboratory results: A variety of admission laboratory values were obtained, such as data on renal function (serum creatinine, blood urea nitrogen [BUN]) and metabolism (blood glucose, sodium, potassium, calcium), coagulation (prothrombin time [PT], partial thromboplastin time [PTT]) and hematology (hemoglobin, platelet count, absolute counts of neutrophils, monocytes, and lymphocytes), and the calculated AISI. 5)Supportive therapies and interventions: It documented the use of special intensive care techniques such as mechanical ventilation, continuous renal replacement therapy (CRRT) and intra-aortic balloon pump (IABP) support. 6)Medications: Prescription of medications related to cardiovascular and others during hospital stay was recorded. These included guideline-based medical therapy (GDMT) (e.g., mineralocorticoid receptor antagonists, angiotensin-converting enzyme inhibitors/angiotensin receptor blockers, angiotensin receptor-neprilysin inhibitor, beta-blockers), as well as statins, antiplatelet agents, vasoactive drugs, loop diuretics, and digoxin. The main outcomes were 30 days all-cause mortality (short-term prognosis) and 1-year all-cause mortality (long-term prognosis). 5. Statistical Analysis R software (version 4.4) was used to conduct statistical analyses. Incomplete variables (missingness > 20%) were excluded, and the rest of the incomplete data were dealt with multiple imputation. The patients were stratified into AISI quartiles (Q1-Q4) to test prognostic value of 30-day and 1-year mortality. Baseline characteristics were summarized by AISI quartiles.The continuous data were reported in the form of median (interquartile range(IQR)) and compared through Kruskal-Wallis H test. Categorical data were reported in the form of n (percentage) and were analyzed via chi-square or Fisher exact test, as appropriate. Kaplan-Meier analysis was used to construct survival curves that were compared through the log-rank test. Three sequential multivariate Cox proportional hazards models were constructed to estimate Hazard Ratios (HRs) and 95% Confidence Intervals (CIs). Model I was not adjusted; Model II adjusted on age, sex, comorbidities and weight, Model III (fully adjusted) further adjusted on laboratory parameters, SOFA and treatments. Before modeling, the Multicollinearity was measured with the Variance Inflation Factor (VIF < 5). Time-dependent discriminative receiver operating characteristic (ROC) curves were used to assess the discriminative performance of AISI, which were computed by the Inverse Probability of Censoring Weighting (IPCW) approach. Since the distribution of AISI is highly skewed, we used log2 -transformed values in Restricted Cubic Spline (RCS) analysis, mediation, and subgroup analysis. In the case of RCS analysis, four knots were used to determine the non-linear dose-response relationships, according to Model III. A risk nadir (the AISI value with the lowest predicted hazard) was determined and used as the reference (HR = 1.0). The role of heart rate as a mediator was measured by a causal mediation analysis using the Accelerated Failure Time (AFT) model. Lastly, subgroup analyses were done to determine the consistency of the prognostic value of AISI across important clinical strata (age, sex, comorbidities) to determine the robustness of the results. Results 1. Baseline features Figure 1 presents the patient selection process for this study and Table 1 gives the baseline characteristics. Table 1 Baseline characteristics of the study patients by tertiles of AISI Variables Total (n = 4936) 1 (n = 1234) 2 (n = 1234) 3 (n = 1234) 4 (n = 1234) P Age(Years) 74.27 (64.29, 82.99) 73.12 (63.22,81.63) 74.36 (63.87,83.03) 74.06 (64.41,83.17) 75.37 (65.63,84.23) < .001 Gender 0.752 Female 2152 (43.60) 530 (42.95) 547 (44.33) 527 (42.71) 548 (44.41) Male 2784 (56.40) 704 (57.05) 687 (55.67) 707 (57.29) 686 (55.59) Hyperlipidemia 2525 (51.15) 676 (54.78) 662 (53.65) 613 (49.68) 574 (46.52) < .001 AF 2537 (51.40) 610 (49.43) 619 (50.16) 645 (52.27) 663 (53.73) 0.126 MI 1618 (32.78) 352 (28.53) 404 (32.74) 439 (35.58) 423 (34.28) 0.001 Cerebrovascular Disease 582 (11.79) 140 (11.35) 149 (12.07) 163 (13.21) 130 (10.53) 0.204 Diabetes 2037 (41.27) 488 (39.55) 519 (42.06) 541 (43.84) 489 (39.63) 0.087 CKD 1831 (37.09) 429 (34.76) 463 (37.52) 469 (38.01) 470 (38.09) 0.269 Weight(kg) 80.30 (66.77, 96.23) 78.95 (66.00,94.60) 81.00 (67.00,96.07) 82.15 (67.30,97.40) 80.00 (66.26,97.60) 0.045 SOFA 5.00 (3.00, 8.00) 6.00 (4.00, 8.00) 5.00 (3.00, 7.00) 5.00 (3.00, 7.00) 5.00 (3.00, 8.00) < .001 Creatinine (mg/dL) 1.23 (0.90, 1.90) 1.10 (0.85,1.70) 1.20 (0.90,1.90) 1.27 (0.90,1.97) 1.37 (0.97,2.20) < .001 BUN(mg/dL) 26.50 (17.50, 43.00) 22.50 (15.54,38.00) 24.37 (17.00,40.31) 27.00 (18.50,43.50) 31.33 (20.33,50.13) < .001 Glucose (mg/dL) 130.50 (109.00, 168.00) 123.00 (105.00,150.00) 126.33 (107.00,157.00) 135.33 (112.08,175.50) 142.00 (113.00,184.75) < .001 Sodium (mmol/L) 138.50 (135.85, 141.00) 138.50 (136.00,140.67) 139.00 (136.00,141.00) 138.50 (136.00,141.00) 137.50 (134.67,140.50) < .001 Potassium (mmol/L) 4.26 (3.90, 4.70) 4.30 (3.95,4.65) 4.25 (3.90,4.65) 4.20 (3.88,4.64) 4.30 (3.90,4.78) 0.011 Calcium (mg/dL) 8.38 (7.90, 8.80) 8.30 (7.88,8.80) 8.43 (7.95,8.87) 8.40 (8.00,8.85) 8.30 (7.85,8.75) < .001 PT (sec) 14.60 (13.02, 17.61) 14.44 (13.20,16.59) 14.40 (12.90,17.10) 14.45 (12.95,18.29) 15.17 (13.10,19.47) < .001 PTT (sec) 33.10 (28.50, 44.21) 32.46 (28.70,40.26) 32.60 (28.40,43.34) 33.10 (27.91,45.28) 34.38 (29.00,49.60) < .001 Hemoglobin (g/dL) 10.23 (8.98, 11.70) 9.79 (8.70,11.10) 10.30 (9.10,11.75) 10.60 (9.27,12.03) 10.35 (9.00,11.90) < .001 Platelet (10 9 /L) 182.75 (134.00, 244.00) 130.00 (98.50,169.45) 173.83 (135.50,216.50) 203.00 (156.45,255.38) 241.50 (181.81,323.56) < .001 Lymphocytes (10 9 /L) 1.15 (0.71, 1.76) 1.44 (0.80,2.26) 1.29 (0.85,1.95) 1.09 (0.73,1.58) 0.85 (0.55,1.30) < .001 Monocytes (10 9 /L) 0.54 (0.33, 0.86) 0.27 (0.14,0.41) 0.49 (0.34,0.67) 0.65 (0.46,0.89) 0.96 (0.66,1.36) < .001 Neutrophils (10 9 /L) 9.51 (6.43, 13.80) 5.53 (3.72,7.99) 8.13 (6.15,10.99) 10.57 (8.11,13.60) 15.53 (11.72,20.02) < .001 AISI 797.92 (293.62, 1980.35) 144.06 (66.94,214.45) 510.65 (397.59,655.32) 1213.59 (969.00,1524.44) 3671.84 (2613.77,5804.72) < .001 HR (times/min) 82.96 (73.83, 94.77) 80.91 (72.90,90.63) 81.65 (72.97,92.34) 82.91 (73.30,93.93) 88.16 (76.88,101.80) < .001 MBP (mmHg) 73.69 (67.42, 81.00) 73.16 (67.06,80.14) 74.06 (67.66,81.30) 74.33 (67.97,81.78) 73.12 (67.00,80.40) 0.018 Spo2 (%) 96.89 (95.38, 98.18) 97.30 (95.92,98.50) 97.00 (95.48,98.26) 96.72 (95.23,98.04) 96.50 (95.01,97.86) < .001 Ventilation 2438 (49.39) 684 (55.43) 581 (47.08) 568 (46.03) 605 (49.03) < .001 CRRT 392 (7.94) 93 (7.54) 81 (6.56) 92 (7.46) 126 (10.21) 0.006 IABP 201 (4.07) 27 (2.19) 46 (3.73) 61 (4.94) 67 (5.43) < .001 GDMT 4056 (82.17) 1038 (84.12) 1038 (84.12) 1019 (82.58) 961 (77.88) < .001 MRA 432 (8.75) 93 (7.54) 110 (8.91) 115 (9.32) 114 (9.24) 0.364 ACEI 1618 (32.78) 401 (32.50) 414 (33.55) 437 (35.41) 366 (29.66) 0.021 ARB 518 (10.49) 133 (10.78) 146 (11.83) 126 (10.21) 113 (9.16) 0.179 Βblocker 3602 (72.97) 929 (75.28) 908 (73.58) 893 (72.37) 872 (70.66) 0.068 ARNI 20 (0.41) 2 (0.16) 6 (0.49) 2 (0.16) 10 (0.81) 0.032 Statin drugs 3166 (64.14) 783 (63.45) 834 (67.59) 804 (65.15) 745 (60.37) 0.002 Digoxin 505 (10.23) 96 (7.78) 113 (9.16) 138 (11.18) 158 (12.80) < .001 Loop diuretic drugs 4160 (84.28) 1072 (86.87) 1040 (84.28) 1030 (83.47) 1018 (82.50) 0.020 Antiplatelet drugs 3581 (72.55) 924 (74.88) 938 (76.01) 903 (73.18) 816 (66.13) < .001 Vasoactive drugs 2650 (53.69) 697 (56.48) 649 (52.59) 609 (49.35) 695 (56.32) < .001 Abbreviations: AISI: Aggregate Index of Systemic Inflammation; AF: Atrial Fibrillation or Flutter; MI: Myocardial Infarction; CKD: Chronic Kidney Disease; SOFA: Sequential Organ Failure Assessment; BUN: Blood Urea Nitrogen; PT: Prothrombin Time; PTT: Partial Thromboplastin Time; HR: Heart Rate; MBP: Mean Blood Pressure; SpO2: Percutaneous Oxygen Saturation; CRRT: Continuous Renal Replacement Therapy; IABP: Intra-aortic Balloon Pump; GDMT: Guideline-Directed Medical Therapy; ACEI/ARB: Angiotensin-Converting Enzyme Inhibitors / Angiotensin Receptor Blockers; ARNI: Angiotensin Receptor-Neprilysin Inhibitor; MRA: Mineralocorticoid Receptor Antagonist. In this study, 4, 936 patients with heart failure were identified and grouped into four sets according to quartiles of AISI. The median AISI value for the entire cohort was 797.92 (IQR: 293.62–1980.35). Patients were categorized into four groups based on these quartiles: Q1 (≤ 293.62), Q2 (293.62–797.92), Q3 (797.92–1980.35), and Q4 (≥ 1980.35). It is important to note that the AISI was highly skewed with values ranging from near zero to a maximum of 315,567.63 in extreme cases. Major variations were identified between AISI quartiles on most of the demographic, clinical, and laboratory variables (all P < 0.05). Higher AISI quartiles, especially Q4, were mostly older and also had a higher comorbidity burden, with a higher prevalence of myocardial infarction (Q4: 34.28% vs. Q1: 28.53%). Lab results showed a characteristic pattern of deteriorating renal function and metabolic dysregulation related to high AISI levels. The levels of serum creatinine, BUN, and glucose showed a significant step-wise change between Q1 and Q4 (all P < 0.001). The hematologic picture was defined by an increasingly intense systemic inflammatory condition with neutrophils and monocytes increasing in counts, with lymphocytes reducing significantly in the upper quartile than in the lower. Hemodynamically, a positive graded relationship was observed between AISI levels and HR, which rose from 80.91 (72.90, 90.63) bpm in Q1 to 88.16 (76.88, 101.80) bpm in Q4 (P < 0.001). On the other hand, Q4 had significantly reduced MBP and SpO2. As expected, patients with higher AISI needed to use more intensive supportive therapies, such as CRRT and IABP support (both P < 0.01). It is worth noting that a difference on pharmacotherapy was observable. The rate of prescription of GDMT, statins and antiplatelet agents, was lower in Q4. Conversely, digoxin and vasoactive medications were more frequently used in the higher AISI groups, reflecting more progressive disease and hemodynamic impairment. 2. Kaplan-Meier survival analysis Kaplan-Meier survival analysis revealed that there was a graded significant association between greater AISI levels and risk of mortality (Log-rank P < 0.001), as shown in Fig. 2 . This correlation was both significant over the long and short term. There was evident mortality gradient at 30 days. The death rate grew gradually with the lowest quartile (Q1) of 13.53% to the highest (Q4) of 28.20%, which is more than two-fold. Q1 and Q2 were very similar in the survival curves, but a significant fall of the survival curve started at Q3. Such prognostic difference continued to increase in the one-year follow-up. The cumulative mortality rate increased to 46.84% in Q4, from 30.79% in Q1. As a result, patients in the highest group of AISI quartile experienced a 1.5 higher risk of death within one year than the lowest quartile. 3. Multivariate Cox Regression Analysis. Table 2 shows the findings of the Cox proportional hazards regression analysis of the relationship between AISI quartiles and mortality. In the unadjusted model (Model I), a major graded relationship was seen, with increasing AISI quartiles being linked to increasingly elevated risks of both 30-day and 1-year mortality. Patients in the highest quartile (Q4) had a substantially greater than twofold increased risk of 30-day mortality (HR, 2.28; 95% CI, 1.90–2.74; P < 0.001) and a 78% increased risk of 1-year mortality (HR, 1.78; 95% CI, 1.57–2.03; P < 0.001) compared to those in the lowest quartile (Q1). Table 2 Multivariable Cox Regression Analysis of the Association Between AISI Quartiles and Mortality Outcome & Model AISI Quartile Hazard Ratio (95% CI) P-value 30-Day Mortality Model I Q1 1.00 (Ref) - Q2 1.02 (0.83–1.26) 0.844 Q3 1.39 (1.14–1.70) 0.001 Q4 2.28 (1.90–2.74) < 0.001 Model II Q1 1.00 (Ref) - Q2 0.98 (0.79–1.22) 0.863 Q3 1.33 (1.09–1.63) 0.005 Q4 2.16 (1.80–2.60) < 0.001 Model III Q1 1.00 (Ref) - Q2 0.99 (0.79–1.22) 0.900 Q3 1.14 (0.93–1.41) 0.204 Q4 1.47 (1.21–1.78) < 0.001 1-Year Mortality Model I Q1 1.00 (Ref) - Q2 0.96 (0.83–1.11) 0.551 Q3 1.19 (1.04–1.36) 0.014 Q4 1.78 (1.57–2.03) < 0.001 Model II Q1 1.00 (Ref) - Q2 0.92 (0.80–1.07) 0.286 Q3 1.16 (1.01–1.33) 0.036 Q4 1.71 (1.51–1.95) < 0.001 Model III Q1 1.00 (Ref) - Q2 0.93 (0.81–1.08) 0.361 Q3 1.03 (0.90–1.19) 0.658 Q4 1.27 (1.10–1.45) < 0.001 Abbreviations:​ AISI, Aggregate Index of Systemic Inflammation; CI, Confidence Interval. Model descriptions:Model I: Unadjusted; Model II: Adjusted for demographics and comorbidities; Model III: Fully adjusted for demographics, comorbidities, laboratory parameters, and treatments. The strength of the association was diminished after successive adjustment of the confounding factors, but remained significantly high in case of the Q4 group. In the fully adjusted model (Model III), patients in the highest AISI quartile (Q4) had a 47% higher risk of 30-day mortality (HR: 1.47; 95% CI: 1.21–1.78; P < 0.001) and a 27% higher risk of 1-year mortality (HR: 1.27; 95% CI: 1.10–1.45; P < 0.001) compared to those in the lowest quartile (Q1). 4. Non-linear Relationship between AISI and Mortality. RCS analysis was carried out to further explain the shape of the association. The multi-variable adjusted RCS analysis showed a significant non-linear, J-shaped correlation between log2 transformed AISI with both 30-day and 1-year mortality (both P-overall < 0.001 and P-non-linear < 0.001; Fig. 3 ). For 30-day mortality, the hazard ratio (HR) was lowest at an AISI value of 225.97 (risk nadir), and the risk was greater at lower and higher values (Fig. 3 A). For 1-year mortality, the lowest risk was observed at an AISI of 256.64 (Fig. 3 B). It was found that for both time points, the mortality risk rose steeply in the higher AISI range, especially above the fourth quartile. 5. Time dependent ROC analysis. Time dependent ROC analysis showed that AISI has a strong discriminative capacity of mortality at different intervals (Fig. 4 ). The 30-day AUC reached 0.59, followed by 0.577 at 90 days and 0.556 at 365 days. The reported time-dependent decrease in AUC indicates that the prognostic value of baseline AISI is strongest at the acute stage of heart failure. 6. Heart rate mediation analysis. To determine the biological pathways between systemic inflammation and poor outcomes, we conducted a causal mediation analysis to ascertain whether HR mediated the relationship between AISI and mortality(Table 3 , Fig. 5 ). AISI was analyzed with log2 transformation. Therefore, the estimates reflect the difference in the survival time with a two-fold rise in the levels of AISI. Table 3 Mediation Analysis of Heart Rate in the Association Between AISI and Mortality Outcome Effect Estimate(95%CI) P-value 30-Day Mortality ACME (Indirect) −50.50(−120.54,−23.34) < 0.001 ADE (Direct) −252.62(−865.77,−43.54) 0.008 Total Effect −303.12(−962.27,−75.09) 0.002 Proportion Mediated 16.7%(8.2%,42%) 0.002 1-Year mortality ACME (Indirect) −468.00(−1030.0,−212.47) < 0.001 ADE (Direct) −1600.00(−5800.0,−47.80) 0.036 Total Effect −2070.0(−6650.0,−373.58) 0.010 Proportion Mediated 22.7%(10.0%, 75%) 0.010 Note:ACME: Average Causal Mediation Effect; ADE: Average Direct Effect. Estimates represent the change in survival time (AFT model). For 30-Day Mortality: The results indicated a significant total effect of AISI on 30-day survival (Total Effect = -303.12; 95% CI: -962.27 to -75.09; P = 0.002). Heart rate was identified as a significant mediator, with an Average Causal Mediation Effect (ACME) of -50.50 (95% CI: -120.54 to -23.34; P < 0.001).The Average Direct Effect (ADE) remained significant after accounting for the mediator (P = 0.008), suggesting that heart rate partially mediated the relationship, accounting for 16.7% (95% CI: 8.2%–42.0%) of the total effect. For 1-Year Mortality: The mediating effect of heart rate was stronger in long-term results. The total effect of AISI on 1-year survival was − 2070.0 (95% CI: -6650.0 to -373.58; P = 0.010). The indirect effect through heart rate (ACME) was − 468.00 (95% CI: -1030.0 to -212.47; P < 0.001), representing a proportion mediated of 22.7% (95% CI: 10.0%–75.0%; P = 0.010). 7. Subgroup analysis The analysis was conducted in subgroups to assess the stability of prognostic value of AISI in different clinical strata of 30-day and 1-year mortality (Table S1 and Table S2). In 30-day mortality, the positive relationship between log2(AISI) and higher risk was robust across all analyzed subgroups. There were no significant interaction effects (all P interaction > 0.05), indicated that sex and comorbidities (e.g. hypertension, diabetes, and CKD) did not significantly alter the effect of a doubling of AISI on short-term mortality (Overall HR: 1.05, 95% CI: 1.02–1.08, P = 0.002). In 1-year mortality, log2(AISI) prognostic value was also evident but the effect size was slightly reduced compared to short-term results (Overall HR: 1.02, 95% CI: 1.00104, P = 0.040). In the 30-day results, there were no significant interaction effects observed in all subgroups (all P for interaction > 0.05). Discussion This was a large scale retrospective cohort study, which thoroughly assessed the prognostic importance of AISI in patients with HF. The outcomes of our study proved that AISI is a powerful, independent predictor of both short-term (30-day) and long-term (1-year) mortality. Previous researches have suggested that elevated levels of inflammatory biomarkers, including individual cell counts as well as composite indices like the neutrophil-to-lymphocyte ratio(NLR) and the systemic immune-inflammation index(SII), was associated with higher risk of cardiovascular disease outcomes[ 13 , 14 ]. Some associations demonstrated non-linear patterns[ 13 ]. As a composite inflammatory marker, AISI offers a distinctive composite view of systemic inflammation and immune dysregulation in HF by combining four different cell lineages, namely neutrophils, monocytes, platelets, and lymphocytes. Its prognostic usefulness is based on its capacity to thoroughly indicate fundamental pathophysiological mechanisms. The numerator (Neutrophils × Monocytes × Platelets) represents a synergistic pro-inflammatory and pro-thrombotic axis. The recruited neutrophils and pro-inflammatory monocytes are involved in myocardial injury with uncontrolled proteolysis and oxidative stress, which may result in negative ventricular remodelling and the development of congestive heart failure[ 15 ]. Monocytes are recruited to the heart and differentiate into macrophages. The cells exhibit high heterogeneity and plasticity in phenotype and function. They are not only involved in repair, but can also exacerbate oxidative stress and fibrosis by releasing mediators, and contribute to the balance between repair and maladaptive remodeling[ 16 ]. At the same time, platelets, which were activated by the systemic inflammatory milieu, have hemostatic functions. They stimulate aggregation of leukocytes, microvascular thrombosis, and endothelial dysfunction, which establish a cycle of thrombo-inflammation that directly enhances ischemic injury and myocardial damage[ 17 , 18 ]. Lymphocytes have an important immunomodulatory role. Lymphopenia could be mediated by a variety of mechanisms such as splanchnic congestion[ 19 ], infection[ 20 ] and activation of the sympathetic nervous system[ 21 ], which might lead to lymphocyte apoptosis and depletion. Lymphopenia is a biomarker that reflects a condition of extreme immune dysregulation, and associated with high risk in HF[ 22 ]. AISI enables measurement of this culmination of inflammatory and immune markers, and higher AISI level is linked to a greater chance of having heart failure[ 23 ]. This study found an important, independent relationship between AISI and short- and long-term all-cause mortality in patients with heart failure. Subgroup analyses confirmed the robustness of AISI's prognostic value. Multivariable COX analysis showed that patients in the highest AISI quartile (Q4) faced a significantly elevated mortality risk compared to those in the lowest quartile (Q1), as previously known that inflammation, particularly systemic inflammation, plays a significant role in the progression of HF[ 12 , 24 ]. Interestingly, RCS analysis showed that there was a non-linear, J-shaped association between AISI and death. The increasing left arm of the curve indicated that very low AISI is not protective, which represented malfunctioning of the immune system or possible immunosuppression[ 25 ]. The ascending right limb reflects a state of maladaptive thrombo-inflammation. In this circumstances, platelets and innate immunity (e.g., neutrophils, monocytes) overactivate, accompanied by lymphocyte depletion, which promotes myocardial injury and adverse myocardial remodeling, resulting in a high mortality rate. To be clinically interpretable, the risk nadirs were scaled back to the original scale (approximately 226.0 at 30-day and 256.6 at 1-year mortality), which would be a possible optimal immunological window. The identified nadirs for 30-day and 1-year mortality are close to the median AISI value (~ 237.7) reported in recent large-scale cohorts[ 26 ], suggesting that the lowest mortality risk is associated with a balanced immune-inflammatory state. The time-dependent ROC analysis confirms that AISI possesses significant discriminative ability for mortality across all evaluated time points. It is worth noting that its predictive power was greatest for 30-day mortality (AUC 0.59), and smaller for 1-year mortality (AUC 0.556). It indicates that the prognostic information provided by a single baseline AISI measurement is more potent in short term of an acute decompensation. This is in line with its physiological nature as a marker of the acute systemic inflammatory and thrombotic response. The declining correlation in the long-term indicates that late outcomes are increasingly governed by non-inflammatory factors or the inflammatory condition itself is dynamic and requires sequential measurements to continuously assess the risk. In addition, causal mediation analysis indicated that heart rate mediated about 17%-23% of the connection between AISI and survival, giving mechanistic understanding of the inflammation-autonomic axis in the progression of HF. The significant mediation effects observed are consistent with a pathway in which systemic inflammation contributes to autonomic dysfunction[ 27 , 28 ], manifesting in elevated heart rate, a known driver of worse outcomes in heart failure[ 29 – 31 ]. The increasing proportion of AISI-related mortality risk mediated by heart rate over time suggests that sympathetic overactivation (tachycardia) becomes a relatively more important driver of long-term outcomes. Our findings have clear clinical meaning. For heart failure patients with high AISI levels, our results suggest that it may be helpful to control heart rate, as current guidelines recommend. Drugs like beta-blockers or ivabradine could be especially useful for this group of patients with high inflammation. Future studies are warranted to investigate whether anti-inflammatory interventions can attenuate risk by modulating autonomic tone, and whether integrated serial assessment of AISI and HR can dynamically guide personalized therapy. Limitations and Future Directions Our study has a number of limitations. To begin with, the retrospective design limits definitive causal inference, despite our application of advanced causal mediation methods. Second, baseline AISI was measured only; prospective studies should determine whether AISI change is a better prognostic. Third, even though a full range of confounders was considered, the absence of biomarkers like N-terminal pro-B-type natriuretic peptide (NT-proBNP) and interleukin-6 (IL-6) may result in residual confounding. Lastly, our results should be further generalized to non-hospitalized populations with HF, a process that requires us to conduct additional studies. Conclusion This paper confirms that AISI is a strong and independent mortality predictor of critically ill patients with HF, which has a J-shaped and non-linear association. The risk nadirs indicate the presence of a favorable immunological window in terms of survival. Moreover, heart rate is a major partial mediator in this pathway which offers a mechanistic connection between inflammation and prognosis. These results have direct clinical implications. AISI is a conveniently accessible, composite bedside measurement that can be useful in the risk stratification of HF patients, identifying those with a high inflammatory load and unfavorable prognosis. In this high-risk phenotype, our findings indicate the importance of aggressive, guideline-directed heart rate control. Declarations Acknowledgements We acknowledged the contributions of the Medical Information Mart for Intensive Care (MIMIC) Program registries for creating and updating the MIMIC-III and MIMIC-IV databases. Author contributions Methodology: XX. Z., L.T., Y.Z.; Formal analysis and investigation: XX. Z., L.T.; Validation: XX. Z., L.T., Y.Z.; Writing - original draft preparation: XX. Z.; Writing - review and editing: XX. Z., L.T., Y.Z.; Supervision: XX. Z., and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Funding There was no project specific funding for this research. Data availability The datasets presented in this study can be found in online repositories (https://physionet.org/content/mimiciv/2.2/). Ethics approval and consent to participate Institutional Review Boards (IRB) of Boston, MA, USA (Massachusetts Institute of Technology) and Beth Israel Deaconess Medical Center (Cambridge, MA, USA) approved the use of the MIMIC-IV database. The IRBs waived the need to have individual patient informed consent since the database holds de-identified data as required by the Health Insurance Portability and Accountability Act (HIPAA) Safe Harbor provision. Consent for publication Not applicable. Competing interests The authors declare no competing interests. References Shahim B, Kapelios CJ, Savarese G, Lund LH. Global Public Health Burden of Heart Failure: An Updated Review. Card Fail Rev. 2023;9:e11. Zhang Y, Bauersachs J, Langer HF. Immune mechanisms in heart failure. Eur J Heart Fail. 2017;19(11):1379–89. Boulet J, Sridhar VS, Bouabdallaoui N, Tardif JC, White M. Inflammation in heart failure: pathophysiology and therapeutic strategies. Inflamm Res. 2024;73(5):709–23. Briasoulis A, Androulakis E, Christophides T, Tousoulis D. The role of inflammation and cell death in the pathogenesis, progression and treatment of heart failure. Heart Fail Rev. 2016;21(2):169–76. Berger M, März W, Niessner A, Delgado G, Kleber M, Scharnagl H, Marx N, Schuett K. IL-6 and hsCRP predict cardiovascular mortality in patients with heart failure with preserved ejection fraction. ESC Heart Fail. 2024;11(6):3607–15. Pah AM, Serban S, Mateescu DM, Cotet IG, Muresan CO, Ilie AC, Buleu F, Craciun ML, Crisan S, Avram A. Systemic Inflammatory Biomarkers (Interleukin-6, High-Sensitivity C-Reactive Protein, and Neutrophil-to-Lymphocyte Ratio) and Prognosis in Heart Failure: A Meta-Analysis of Prospective Cohort Studies. J Clin Med 2025, 14(23). Zengin O, Göre B, Öztürk O, Cengiz AM, Güler Kadıoğlu S, Asfuroğlu Kalkan E, Ateş İ. Evaluation of Acute Pancreatitis Severity and Prognosis Using the Aggregate Systemic Inflammation Index (AISI) as a New Marker: A Comparison with Other Inflammatory Indices. J Clin Med 2025, 14(10). Xiu J, Lin X, Chen Q, Yu P, Lu J, Yang Y, Chen W, Bao K, Wang J, Zhu J, et al. The aggregate index of systemic inflammation (AISI): a novel predictor for hypertension. Front Cardiovasc Med. 2023;10:1163900. Zinellu A, Collu C, Nasser M, Paliogiannis P, Mellino S, Zinellu E, Traclet J, Ahmad K, Mangoni AA, Carru C et al. The Aggregate Index of Systemic Inflammation (AISI): A Novel Prognostic Biomarker in Idiopathic Pulmonary Fibrosis. J Clin Med 2021, 10(18). Zinellu A, Paliogiannis P, Mangoni AA. Aggregate Index of Systemic Inflammation (AISI), Disease Severity, and Mortality in COVID-19: A Systematic Review and Meta-Analysis. J Clin Med 2023, 12(14). Jiang Y, Luo B, Lu W, Chen Y, Peng Y, Chen L, Lin Y. Association Between the Aggregate Index of Systemic Inflammation and Clinical Outcomes in Patients with Acute Myocardial Infarction: A Retrospective Study. J Inflamm Res. 2024;17:7057–67. Bai X, Cheng L, Wang H, Deng Y, Tong X, Wen W, Liu X, Zhou J, Yuan Z. The aggregate index of systemic inflammation (AISI) and the risk of all-cause, cardiovascular, and cardio-cerebrovascular mortality in congestive heart failure patients: results from NHANES 1999–2018. Sci Rep. 2025;15(1):18282. Qin P, Ho FK, Celis-Morales CA, Pell JP. Association between systemic inflammation biomarkers and incident cardiovascular disease in 423,701 individuals: evidence from the UK biobank cohort. Cardiovasc Diabetol. 2025;24(1):162. Wu CC, Wu CH, Lee CH, Cheng CI. Association between neutrophil percentage-to-albumin ratio (NPAR), neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR) and long-term mortality in community-dwelling adults with heart failure: evidence from US NHANES 2005–2016. BMC Cardiovasc Disord. 2023;23(1):312. Swirski FK, Nahrendorf M. Leukocyte behavior in atherosclerosis, myocardial infarction, and heart failure. Science. 2013;339(6116):161–6. Peet C, Ivetic A, Bromage DI, Shah AM. Cardiac monocytes and macrophages after myocardial infarction. Cardiovasc Res. 2020;116(6):1101–12. von Hundelshausen P, Weber C. Platelets as immune cells: bridging inflammation and cardiovascular disease. Circ Res. 2007;100(1):27–40. Stark K, Massberg S. Interplay between inflammation and thrombosis in cardiovascular pathology. Nat Rev Cardiol. 2021;18(9):666–82. Battin DL, Ali S, Shahbaz AU, Massie JD, Munir A, Davis RC Jr., Newman KP, Weber KT. Hypoalbuminemia and lymphocytopenia in patients with decompensated biventricular failure. Am J Med Sci. 2010;339(1):31–5. Castro A, Bemer V, Nóbrega A, Coutinho A, Truffa-Bachi P. Administration to mouse of endotoxin from gram-negative bacteria leads to activation and apoptosis of T lymphocytes. Eur J Immunol. 1998;28(2):488–95. Maisel AS, Knowlton KU, Fowler P, Rearden A, Ziegler MG, Motulsky HJ, Insel PA, Michel MC. Adrenergic control of circulating lymphocyte subpopulations. Effects of congestive heart failure, dynamic exercise, and terbutaline treatment. J Clin Invest. 1990;85(2):462–7. Vaduganathan M, Ambrosy AP, Greene SJ, Mentz RJ, Subacius HP, Maggioni AP, Swedberg K, Nodari S, Zannad F, Konstam MA, et al. Predictive value of low relative lymphocyte count in patients hospitalized for heart failure with reduced ejection fraction: insights from the EVEREST trial. Circ Heart Fail. 2012;5(6):750–8. Huang L, Shen R, Yu H, Jin N, Hong J, Luo Y, Chen X, Rong J. The levels of systemic inflammatory markers exhibit a positive correlation with the occurrence of heart failure: a cross-sectional study from NHANES. Front Cardiovasc Med. 2024;11:1457534. Luo Y, Yang L, Cheng X, Bai Y, Xiao Z. The association between blood count based inflammatory markers and the risk of atrial fibrillation heart failure and cardiovascular mortality. Sci Rep. 2025;15(1):10056. Spoor J, Farajifard H, Rezaei N. Congenital neutropenia and primary immunodeficiency diseases. Crit Rev Oncol Hematol. 2019;133:149–62. Wang R, Chen R, Tao W, Cheng X. Nonlinear associations between the aggregate index of systemic inflammation and cardiovascular disease in adults: evidence from NHANES 2011–2020. BMC Public Health. 2025;25(1):3031. Rupprecht S, Finn S, Hoyer D, Guenther A, Witte OW, Schultze T, Schwab M. Association Between Systemic Inflammation, Carotid Arteriosclerosis, and Autonomic Dysfunction. Transl Stroke Res. 2020;11(1):50–9. Aeschbacher S, Schoen T, Dörig L, Kreuzmann R, Neuhauser C, Schmidt-Trucksäss A, Probst-Hensch NM, Risch M, Risch L, Conen D. Heart rate, heart rate variability and inflammatory biomarkers among young and healthy adults. Ann Med. 2017;49(1):32–41. Ancion A, Tridetti J, Nguyen Trung ML, Oury C, Lancellotti P. Serial heart rate measurement and mortality after acute heart failure. ESC Heart Fail. 2020;7(1):103–6. Vollmert T, Hellmich M, Gassanov N, Er F, Yücel S, Erdmann E, Caglayan E. Heart rate at discharge in patients with acute decompensated heart failure is a predictor of mortality. Eur J Med Res. 2020;25(1):47. Zhang D, Shen X, Qi X. Resting heart rate and all-cause and cardiovascular mortality in the general population: a meta-analysis. CMAJ. 2016;188(3):E53–63. Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterial.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 15 May, 2026 Reviewers agreed at journal 13 May, 2026 Reviewers invited by journal 05 May, 2026 Editor invited by journal 13 Apr, 2026 Editor assigned by journal 11 Apr, 2026 Submission checks completed at journal 11 Apr, 2026 First submitted to journal 10 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9374386","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":639945641,"identity":"fdb6d4ed-4f59-43a0-a71d-7e8b8f03c27b","order_by":0,"name":"Xiaoxue Zheng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAr0lEQVRIiWNgGAWjYDACCSB+UMAgx8befoAELQkGDMZ8PGcSSNOSOE/CwYA4HfKze8w+JBgcTm+TYEhg+FGxjbAWgztnjGcAteS2STceYOw5c5sILRI5xgxgLTIHEpgZ24jQIj8DoiWdTSLBgDgtDDcgWhKI12JwI60YqCXdsA0YyAeJ8ov8jOTNDB8qrOXl29sPPvhRQYzDIKAZTB4gWj0Q1JGieBSMglEwCkYaAAChAzlAxNEQ0QAAAABJRU5ErkJggg==","orcid":"","institution":"Department of Health Care, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences","correspondingAuthor":true,"prefix":"","firstName":"Xiaoxue","middleName":"","lastName":"Zheng","suffix":""},{"id":639945646,"identity":"d4bc4eb5-d61e-4875-8dfa-c6500b6a4259","order_by":1,"name":"Long Tan","email":"","orcid":"","institution":"Health Service Department, Guard Bureau of the General Office of the Central Committee of the Communist Party of China","correspondingAuthor":false,"prefix":"","firstName":"Long","middleName":"","lastName":"Tan","suffix":""},{"id":639945650,"identity":"4d2676dc-0fd0-49cb-8e43-288b88eb00c2","order_by":2,"name":"Yu Zhang","email":"","orcid":"","institution":"Department of Health Care, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Yu","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2026-04-10 04:25:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9374386/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9374386/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109278936,"identity":"568b5029-fcbe-4306-a0ec-854a4f3d7aca","added_by":"auto","created_at":"2026-05-14 16:13:47","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":738225,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier survival curves for mortality categorized by AISI quartiles. Patients were stratified into four groups according to the quartiles of baseline AISI levels: Q1 (≤ 293.62), Q2 (293.62–797.92), Q3 (797.92–1980.35), and Q4 (≥ 1980.35). (A) Survival analysis for 30-day mortality; (B) Survival analysis for 1-year mortality. The cumulative survival probability significantly declined with increasing AISI quartiles (Log-rank test, P \u0026lt; 0.001).\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-9374386/v1/2a48ff9fe99f2a9290045b59.png"},{"id":109278937,"identity":"48713678-6409-4cf6-a466-ab3de8aa3f2b","added_by":"auto","created_at":"2026-05-14 16:13:47","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":84485,"visible":true,"origin":"","legend":"\u003cp\u003eRestricted cubic spline (RCS) plots of the association between AISI and mortality.\u003c/p\u003e\n\u003cp\u003eAdjusted hazard ratios (HRs) for (A) 30-day mortality and (B) 1-year mortality across the spectrum of Aggregate Index of Systemic Inflammation (AISI) levels. Data were analyzed on a log2-transformed scale to account for the right-skewed distribution of AISI, while the horizontal axes are labeled with original values for clinical interpretability. All models were fully adjusted.\u003c/p\u003e\n\u003cp\u003eThe solid red lines represent the adjusted HRs, and the light coral-shaded areas indicate the 95% confidence intervals (CIs). The dashed horizontal lines represent the reference HR = 1.0. The vertical blue dotted lines denote the risk nadir (AISI = 225.97 for 30-day and 256.64 for 1-year mortality), representing the AISI level with the minimum predicted hazard. Rug plots along the x-axis illustrate the density of the patient distribution. The vertical segments at the top partition the population into quartiles (Q1–Q4).\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9374386/v1/c65b599686e58da9807d11ce.jpg"},{"id":109296119,"identity":"b473a300-7063-4731-b520-0ee5017f1ce1","added_by":"auto","created_at":"2026-05-15 08:45:51","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":232511,"visible":true,"origin":"","legend":"\u003cp\u003eTime-dependent ROC curves of AISI for predicting mortality based on IPCW estimation. The predictive performance of the Aggregate Index of Systemic Inflammation (AISI) was evaluated using the Inverse Probability of Censoring Weighting (IPCW) method in a cohort of 2,270 patients. (A) The area under the curve (AUC) at 30 days was 0.59 (95%CI: 0.566–0.614); (B) The AUC at 90 days was 0.577(95% CI: 0.554–0.600); and (C) The AUC at 365 days was 0.556 (95% CI: 0.529–0.582).\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-9374386/v1/6586e35665183481c7b882cb.png"},{"id":109278941,"identity":"862da8eb-88fe-49d3-a2a2-2e76fe8cda4e","added_by":"auto","created_at":"2026-05-14 16:13:49","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":180967,"visible":true,"origin":"","legend":"\u003cp\u003eMediation analysis of Heart Rate in the association between log2(AISI) and survival outcomes. (A) Mediation model for 30-day mortality. (B) Mediation model for 1-year mortality.\u003c/p\u003e\n\u003cp\u003eNote: The mediation analysis was performed using the Accelerated Failure Time (AFT) model. All estimates represent the change in expected survival time (days). All models were adjusted for baseline covariates.\u003c/p\u003e\n\u003cp\u003eAbbreviations: AISI, Aggregate Index of Systemic Inflammation; ACME, Average Causal Mediation Effect; ADE, Average Direct Effect; CI, Confidence Interval.\u003c/p\u003e","description":"","filename":"Figure5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9374386/v1/f618ea85bf93885363fa339a.jpeg"},{"id":109296614,"identity":"fc8e7d65-a3f7-48ae-87d0-cb53ea1bc1a9","added_by":"auto","created_at":"2026-05-15 08:48:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1693003,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9374386/v1/98ea72df-64b0-4bfb-b2c5-12aa84fd5faf.pdf"},{"id":109296078,"identity":"fc6bc83e-a859-4b70-b8b0-42893b1e38b5","added_by":"auto","created_at":"2026-05-15 08:45:17","extension":"docx","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":27289,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-9374386/v1/1fdd45ef24a409ac852ad5d8.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prognostic Value of the Aggregate Index of Systemic Inflammation (AISI) in Critically Ill Patients with Heart Failure: A J-Shaped Relationship and the Mediating Role of Heart Rate","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHeart failure (HF) is a significant health issue of the world, which is estimated to impact about 64\u0026nbsp;million people across the world. The syndrome has a high burden on the healthcare systems because of its high rates of mortality and morbidity[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. HF is no longer viewed solely as a hemodynamic disorder, but as a multifaceted clinical syndrome that is driven by chronic systemic immune activation and a condition of low grade inflammation[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Persistent inflammatory environment could stimulate myocardial fibrosis, ventricular remodelling, and cardiac dysfunction[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. High levels of circulating pro-inflammatory biomarkers in patients with HF is directly linked to the severity of the disease and clinical outcome[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe traditional inflammatory biomarkers, including C-reactive protein (CRP) and interleukin-6 (IL-6), provide useful prognostic data in clinical practice[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. However, they have limitations due to high costs of assays and the possibility of non-specific elevations especially in acute care. In this context, hematological indices based on regular complete blood counts (CBC) offer a cost-effective and easily available alternative. They can assess systemic inflammatory status, without implying the extra financial burden.\u003c/p\u003e \u003cp\u003eThe Aggregate Index of Systemic Inflammation (AISI) has been suggested as a composite, multidimensional inflammatory marker, which gives a more integrated assessment of inflammatory burden. It is an index that combines the data of four different hematological lineages, including neutrophils, monocytes, platelets, and lymphocytes, into one score, which provides a more comprehensive view of the systemic inflammatory environment[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Previous studies have reported the prognostic value of AISI in a wide range of clinical conditions, such as hypertension[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], idiopathic pulmonary fibrosis[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], severe COVID-19[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], and acute pancreatitis[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. In the cardiology field, high levels of AISI have been reported to be associated with poor patient outcomes following acute myocardial infarction (AMI)[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Other emerging data through databases like NHANES also indicate a relationship between AISI and risk of mortality in general populations with HF[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Nevertheless, its prognostic worth in patients with HF who are in critically ill condition is still unclear. In addition, the possible mediating effect of hemodynamic parameters, such as heart rate, in the effect of systemic inflammation on clinical survival has not been studied.\u003c/p\u003e \u003cp\u003eWe utilized a large-scale clinical database in the current study to rigorously assess the relationship between AISI and 30-day (short-term) and 1-year (long-term) mortality among patients with HF. We aimed to identify the independent prognostic value of AISI and determine possible risk thresholds by using multivariable-adjusted Cox models and restricted cubic spline (RCS) analysis. We also conducted mediation analysis to examine the degree to which heart rate can explain the relationship between AISI and mortality. We aim to determine whether AISI could be a strong, cost-effective risk stratification tool that would help in identifying a risk nadir to manage clinical practices in a more personalized way.\u003c/p\u003e "},{"header":"Methods","content":"\u003ch2\u003e1. Data Source and Study Population.\u003c/h2\u003e\n\u003cp\u003eThe present retrospective cohort study relied on the date of the Medical Information Mart of Intensive Care IV (MIMIC-IV, version 2.2) database. MIMIC-IV is an electronic health record (EHR) database that consists of patients admitted to the intensive care units (ICUs) of the Beth Israel Deaconess Medical Center, which is large and longitudinal, and de-identified. Access to data was provided in the PhysioNet platform (Certification ID: 58380649).\u003c/p\u003e\n\u003cp\u003eThe patients with HF were identified according to the International Classification of Diseases, Ninth and Tenth Revision (ICD-9 and ICD-10) codes.\u003c/p\u003e\n\u003ch3\u003e2. Inclusion and Exclusion Criteria.\u003c/h3\u003e\n\u003cp\u003eOur study included adult patients who were diagnosed with heart failure. In the case of patients who had more than one hospitalization or ICU admission, the initial ICU admission was considered to preserve independent observations.\u003c/p\u003e\n\u003cp\u003eThe following were the specific exclusion criteria: Patients within the age range of below 18 or above 100 years; Patients whose values of the components needed to compute the AISI at the time of admission were missing.\u003c/p\u003e\n\u003ch3\u003e3. Definition of AISI\u003c/h3\u003e\n\u003cp\u003eThe AISI was determined based on the laboratory values acquired during the first 24 hours of ICU admission that reflect the acute inflammatory condition at the ICU admission. The index can be defined as:\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"https://myfiles.space/user_files/58893_b39df98f09c4a4bb/58893_custom_files/img1778773178.png\" width=\"541\" height=\"136\"\u003e\u003c/p\u003e\n\u003cp\u003eThe study population was divided into four categories (Q1 and Q2 and Q3 and Q4) using the quartile of the baseline AISI values to make subsequent comparative analyses.\u003c/p\u003e\n\u003ch3\u003e4. Covariates and Outcomes\u003c/h3\u003e\n\u003cp\u003eThe clinical variables extracted from the database encompassed the following categories: 1) Demographic information: age and biological sex (male or female). 2) Comorbidities: hypertension, diabetes, cerebrovascular disease, hyperlipidemia, atrial fibrillation or flutter (AF), myocardial infarction (MI), and chronic kidney disease (CKD). 3) Vital signs and disease severity: Important physiological variables as assessed at the time of admission were HR, mean blood pressure (MBP), and percutaneous oxygen saturation (SpO2). The severity of the disease was measured by the Sequential Organ Failure Assessment (SOFA) score. 4)Laboratory results: A variety of admission laboratory values were obtained, such as data on renal function (serum creatinine, blood urea nitrogen [BUN]) and metabolism (blood glucose, sodium, potassium, calcium), coagulation (prothrombin time [PT], partial thromboplastin time [PTT]) and hematology (hemoglobin, platelet count, absolute counts of neutrophils, monocytes, and lymphocytes), and the calculated AISI. 5)Supportive therapies and interventions: It documented the use of special intensive care techniques such as mechanical ventilation, continuous renal replacement therapy (CRRT) and intra-aortic balloon pump (IABP) support. 6)Medications: Prescription of medications related to cardiovascular and others during hospital stay was recorded. These included guideline-based medical therapy (GDMT) (e.g., mineralocorticoid receptor antagonists, angiotensin-converting enzyme inhibitors/angiotensin receptor blockers, angiotensin receptor-neprilysin inhibitor, beta-blockers), as well as statins, antiplatelet agents, vasoactive drugs, loop diuretics, and digoxin.\u003c/p\u003e\n\u003cp\u003eThe main outcomes were 30 days all-cause mortality (short-term prognosis) and 1-year all-cause mortality (long-term prognosis).\u003c/p\u003e\n\u003ch3\u003e5. Statistical Analysis\u003c/h3\u003e\n\u003cp\u003eR software (version 4.4) was used to conduct statistical analyses. Incomplete variables (missingness\u0026thinsp;\u0026gt;\u0026thinsp;20%) were excluded, and the rest of the incomplete data were dealt with multiple imputation. The patients were stratified into AISI quartiles (Q1-Q4) to test prognostic value of 30-day and 1-year mortality. Baseline characteristics were summarized by AISI quartiles.The continuous data were reported in the form of median (interquartile range(IQR)) and compared through Kruskal-Wallis H test. Categorical data were reported in the form of n (percentage) and were analyzed via chi-square or Fisher exact test, as appropriate. Kaplan-Meier analysis was used to construct survival curves that were compared through the log-rank test. Three sequential multivariate Cox proportional hazards models were constructed to estimate Hazard Ratios (HRs) and 95% Confidence Intervals (CIs). Model I was not adjusted; Model II adjusted on age, sex, comorbidities and weight, Model III (fully adjusted) further adjusted on laboratory parameters, SOFA and treatments. Before modeling, the Multicollinearity was measured with the Variance Inflation Factor (VIF\u0026thinsp;\u0026lt;\u0026thinsp;5). Time-dependent discriminative receiver operating characteristic (ROC) curves were used to assess the discriminative performance of AISI, which were computed by the Inverse Probability of Censoring Weighting (IPCW) approach.\u003c/p\u003e\n\u003cp\u003eSince the distribution of AISI is highly skewed, we used log2 -transformed values in Restricted Cubic Spline (RCS) analysis, mediation, and subgroup analysis. In the case of RCS analysis, four knots were used to determine the non-linear dose-response relationships, according to Model III. A risk nadir (the AISI value with the lowest predicted hazard) was determined and used as the reference (HR\u0026thinsp;=\u0026thinsp;1.0). The role of heart rate as a mediator was measured by a causal mediation analysis using the Accelerated Failure Time (AFT) model. Lastly, subgroup analyses were done to determine the consistency of the prognostic value of AISI across important clinical strata (age, sex, comorbidities) to determine the robustness of the results.\u003c/p\u003e"},{"header":"Results","content":"\n\u003ch3\u003e1. Baseline features\u003c/h3\u003e\n\u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the patient selection process for this study and Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e gives the baseline characteristics.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of the study patients by tertiles of AISI\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal (n\u0026thinsp;=\u0026thinsp;4936)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (n\u0026thinsp;=\u0026thinsp;1234)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (n\u0026thinsp;=\u0026thinsp;1234)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3 (n\u0026thinsp;=\u0026thinsp;1234)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4 (n\u0026thinsp;=\u0026thinsp;1234)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge(Years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e74.27 (64.29, 82.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e73.12 (63.22,81.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e74.36 (63.87,83.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e74.06 (64.41,83.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e75.37 (65.63,84.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.752\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2152 (43.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e530 (42.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e547 (44.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e527 (42.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e548 (44.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2784 (56.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e704 (57.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e687 (55.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e707 (57.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e686 (55.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHyperlipidemia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2525 (51.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e676 (54.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e662 (53.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e613 (49.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e574 (46.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2537 (51.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e610 (49.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e619 (50.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e645 (52.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e663 (53.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.126\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1618 (32.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e352 (28.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e404 (32.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e439 (35.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e423 (34.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCerebrovascular Disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e582 (11.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e140 (11.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e149 (12.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e163 (13.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e130 (10.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.204\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2037 (41.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e488 (39.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e519 (42.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e541 (43.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e489 (39.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.087\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCKD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1831 (37.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e429 (34.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e463 (37.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e469 (38.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e470 (38.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.269\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight(kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e80.30 (66.77, 96.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e78.95 (66.00,94.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e81.00 (67.00,96.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e82.15 (67.30,97.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e80.00 (66.26,97.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.045\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSOFA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.00 (3.00, 8.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.00 (4.00, 8.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.00 (3.00, 7.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.00 (3.00, 7.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.00 (3.00, 8.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.23 (0.90, 1.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.10 (0.85,1.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.20 (0.90,1.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.27 (0.90,1.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.37 (0.97,2.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBUN(mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26.50 (17.50, 43.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22.50 (15.54,38.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24.37 (17.00,40.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e27.00 (18.50,43.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e31.33 (20.33,50.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlucose (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e130.50 (109.00, 168.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e123.00 (105.00,150.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e126.33 (107.00,157.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e135.33 (112.08,175.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e142.00 (113.00,184.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSodium (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e138.50 (135.85, 141.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e138.50 (136.00,140.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e139.00 (136.00,141.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e138.50 (136.00,141.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e137.50 (134.67,140.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePotassium (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.26 (3.90, 4.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.30 (3.95,4.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.25 (3.90,4.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.20 (3.88,4.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.30 (3.90,4.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.011\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCalcium (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.38 (7.90, 8.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.30 (7.88,8.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.43 (7.95,8.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.40 (8.00,8.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.30 (7.85,8.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePT (sec)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14.60 (13.02, 17.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.44 (13.20,16.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14.40 (12.90,17.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14.45 (12.95,18.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e15.17 (13.10,19.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePTT (sec)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e33.10 (28.50, 44.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32.46 (28.70,40.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e32.60 (28.40,43.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e33.10 (27.91,45.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e34.38 (29.00,49.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin (g/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.23 (8.98, 11.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.79 (8.70,11.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.30 (9.10,11.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10.60 (9.27,12.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10.35 (9.00,11.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelet (10\u003csup\u003e9\u003c/sup\u003e /L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e182.75 (134.00, 244.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e130.00 (98.50,169.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e173.83 (135.50,216.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e203.00 (156.45,255.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e241.50 (181.81,323.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymphocytes (10\u003csup\u003e9\u003c/sup\u003e /L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.15 (0.71, 1.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.44 (0.80,2.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.29 (0.85,1.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.09 (0.73,1.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.85 (0.55,1.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonocytes (10\u003csup\u003e9\u003c/sup\u003e /L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.54 (0.33, 0.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.27 (0.14,0.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.49 (0.34,0.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.65 (0.46,0.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.96 (0.66,1.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeutrophils (10\u003csup\u003e9\u003c/sup\u003e /L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.51 (6.43, 13.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.53 (3.72,7.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.13 (6.15,10.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10.57 (8.11,13.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e15.53 (11.72,20.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAISI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e797.92 (293.62, 1980.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e144.06 (66.94,214.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e510.65 (397.59,655.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1213.59 (969.00,1524.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3671.84 (2613.77,5804.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHR (times/min)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e82.96 (73.83, 94.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e80.91 (72.90,90.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e81.65 (72.97,92.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e82.91 (73.30,93.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e88.16 (76.88,101.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMBP (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e73.69 (67.42, 81.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e73.16 (67.06,80.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e74.06 (67.66,81.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e74.33 (67.97,81.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e73.12 (67.00,80.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.018\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpo2 (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e96.89 (95.38, 98.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e97.30 (95.92,98.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e97.00 (95.48,98.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e96.72 (95.23,98.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e96.50 (95.01,97.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVentilation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2438 (49.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e684 (55.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e581 (47.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e568 (46.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e605 (49.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRRT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e392 (7.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e93 (7.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e81 (6.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e92 (7.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e126 (10.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.006\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIABP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e201 (4.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27 (2.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e46 (3.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e61 (4.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e67 (5.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGDMT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4056 (82.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1038 (84.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1038 (84.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1019 (82.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e961 (77.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMRA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e432 (8.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e93 (7.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e110 (8.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e115 (9.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e114 (9.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.364\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACEI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1618 (32.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e401 (32.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e414 (33.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e437 (35.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e366 (29.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.021\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eARB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e518 (10.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e133 (10.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e146 (11.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e126 (10.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e113 (9.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.179\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eΒblocker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3602 (72.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e929 (75.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e908 (73.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e893 (72.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e872 (70.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.068\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eARNI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20 (0.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2 (0.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6 (0.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2 (0.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10 (0.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.032\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStatin drugs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3166 (64.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e783 (63.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e834 (67.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e804 (65.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e745 (60.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDigoxin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e505 (10.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e96 (7.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e113 (9.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e138 (11.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e158 (12.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLoop diuretic drugs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4160 (84.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1072 (86.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1040 (84.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1030 (83.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1018 (82.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.020\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntiplatelet drugs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3581 (72.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e924 (74.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e938 (76.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e903 (73.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e816 (66.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVasoactive drugs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2650 (53.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e697 (56.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e649 (52.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e609 (49.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e695 (56.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eAbbreviations: AISI: Aggregate Index of Systemic Inflammation; AF: Atrial Fibrillation or Flutter; MI: Myocardial Infarction; CKD: Chronic Kidney Disease; SOFA: Sequential Organ Failure Assessment; BUN: Blood Urea Nitrogen; PT: Prothrombin Time; PTT: Partial Thromboplastin Time; HR: Heart Rate; MBP: Mean Blood Pressure; SpO2: Percutaneous Oxygen Saturation; CRRT: Continuous Renal Replacement Therapy; IABP: Intra-aortic Balloon Pump; GDMT: Guideline-Directed Medical Therapy; ACEI/ARB: Angiotensin-Converting Enzyme Inhibitors / Angiotensin Receptor Blockers; ARNI: Angiotensin Receptor-Neprilysin Inhibitor; MRA: Mineralocorticoid Receptor Antagonist.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn this study, 4, 936 patients with heart failure were identified and grouped into four sets according to quartiles of AISI. The median AISI value for the entire cohort was 797.92 (IQR: 293.62\u0026ndash;1980.35). Patients were categorized into four groups based on these quartiles: Q1 (\u0026le;\u0026thinsp;293.62), Q2 (293.62\u0026ndash;797.92), Q3 (797.92\u0026ndash;1980.35), and Q4 (\u0026ge;\u0026thinsp;1980.35). It is important to note that the AISI was highly skewed with values ranging from near zero to a maximum of 315,567.63 in extreme cases.\u003c/p\u003e \u003cp\u003eMajor variations were identified between AISI quartiles on most of the demographic, clinical, and laboratory variables (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Higher AISI quartiles, especially Q4, were mostly older and also had a higher comorbidity burden, with a higher prevalence of myocardial infarction (Q4: 34.28% vs. Q1: 28.53%).\u003c/p\u003e \u003cp\u003eLab results showed a characteristic pattern of deteriorating renal function and metabolic dysregulation related to high AISI levels. The levels of serum creatinine, BUN, and glucose showed a significant step-wise change between Q1 and Q4 (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The hematologic picture was defined by an increasingly intense systemic inflammatory condition with neutrophils and monocytes increasing in counts, with lymphocytes reducing significantly in the upper quartile than in the lower.\u003c/p\u003e \u003cp\u003eHemodynamically, a positive graded relationship was observed between AISI levels and HR, which rose from 80.91 (72.90, 90.63) bpm in Q1 to 88.16 (76.88, 101.80) bpm in Q4 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). On the other hand, Q4 had significantly reduced MBP and SpO2. As expected, patients with higher AISI needed to use more intensive supportive therapies, such as CRRT and IABP support (both P\u0026thinsp;\u0026lt;\u0026thinsp;0.01).\u003c/p\u003e \u003cp\u003eIt is worth noting that a difference on pharmacotherapy was observable. The rate of prescription of GDMT, statins and antiplatelet agents, was lower in Q4. Conversely, digoxin and vasoactive medications were more frequently used in the higher AISI groups, reflecting more progressive disease and hemodynamic impairment.\u003c/p\u003e\n\u003ch3\u003e2. Kaplan-Meier survival analysis\u003c/h3\u003e\n\u003cp\u003eKaplan-Meier survival analysis revealed that there was a graded significant association between greater AISI levels and risk of mortality (Log-rank P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. This correlation was both significant over the long and short term.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThere was evident mortality gradient at 30 days. The death rate grew gradually with the lowest quartile (Q1) of 13.53% to the highest (Q4) of 28.20%, which is more than two-fold. Q1 and Q2 were very similar in the survival curves, but a significant fall of the survival curve started at Q3.\u003c/p\u003e \u003cp\u003eSuch prognostic difference continued to increase in the one-year follow-up. The cumulative mortality rate increased to 46.84% in Q4, from 30.79% in Q1. As a result, patients in the highest group of AISI quartile experienced a 1.5 higher risk of death within one year than the lowest quartile.\u003c/p\u003e\n\u003ch3\u003e3. Multivariate Cox Regression Analysis.\u003c/h3\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the findings of the Cox proportional hazards regression analysis of the relationship between AISI quartiles and mortality. In the unadjusted model (Model I), a major graded relationship was seen, with increasing AISI quartiles being linked to increasingly elevated risks of both 30-day and 1-year mortality. Patients in the highest quartile (Q4) had a substantially greater than twofold increased risk of 30-day mortality (HR, 2.28; 95% CI, 1.90\u0026ndash;2.74; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and a 78% increased risk of 1-year mortality (HR, 1.78; 95% CI, 1.57\u0026ndash;2.03; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) compared to those in the lowest quartile (Q1).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariable Cox Regression Analysis of the Association Between AISI Quartiles and Mortality\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutcome \u0026amp; Model\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAISI Quartile\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHazard Ratio (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003e30-Day Mortality\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel I\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.02 (0.83\u0026ndash;1.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.844\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.39 (1.14\u0026ndash;1.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.28 (1.90\u0026ndash;2.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.98 (0.79\u0026ndash;1.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.863\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.33 (1.09\u0026ndash;1.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.16 (1.80\u0026ndash;2.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel III\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.99 (0.79\u0026ndash;1.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.900\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.14 (0.93\u0026ndash;1.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.204\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.47 (1.21\u0026ndash;1.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003e1-Year Mortality\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel I\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.96 (0.83\u0026ndash;1.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.551\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.19 (1.04\u0026ndash;1.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.78 (1.57\u0026ndash;2.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.92 (0.80\u0026ndash;1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.286\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.16 (1.01\u0026ndash;1.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.71 (1.51\u0026ndash;1.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel III\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.93 (0.81\u0026ndash;1.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.361\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.03 (0.90\u0026ndash;1.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.658\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.27 (1.10\u0026ndash;1.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eAbbreviations:​ AISI, Aggregate Index of Systemic Inflammation; CI, Confidence Interval.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eModel descriptions:Model I: Unadjusted; Model II: Adjusted for demographics and comorbidities; Model III: Fully adjusted for demographics, comorbidities, laboratory parameters, and treatments.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe strength of the association was diminished after successive adjustment of the confounding factors, but remained significantly high in case of the Q4 group. In the fully adjusted model (Model III), patients in the highest AISI quartile (Q4) had a 47% higher risk of 30-day mortality (HR: 1.47; 95% CI: 1.21\u0026ndash;1.78; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and a 27% higher risk of 1-year mortality (HR: 1.27; 95% CI: 1.10\u0026ndash;1.45; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) compared to those in the lowest quartile (Q1).\u003c/p\u003e\n\u003ch3\u003e4. Non-linear Relationship between AISI and Mortality.\u003c/h3\u003e\n\u003cp\u003eRCS analysis was carried out to further explain the shape of the association. The multi-variable adjusted RCS analysis showed a significant non-linear, J-shaped correlation between log2 transformed AISI with both 30-day and 1-year mortality (both P-overall\u0026thinsp;\u0026lt;\u0026thinsp;0.001 and P-non-linear\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFor 30-day mortality, the hazard ratio (HR) was lowest at an AISI value of 225.97 (risk nadir), and the risk was greater at lower and higher values (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). For 1-year mortality, the lowest risk was observed at an AISI of 256.64 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). It was found that for both time points, the mortality risk rose steeply in the higher AISI range, especially above the fourth quartile.\u003c/p\u003e\n\u003ch3\u003e5. Time dependent ROC analysis.\u003c/h3\u003e\n\u003cp\u003eTime dependent ROC analysis showed that AISI has a strong discriminative capacity of mortality at different intervals (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The 30-day AUC reached 0.59, followed by 0.577 at 90 days and 0.556 at 365 days. The reported time-dependent decrease in AUC indicates that the prognostic value of baseline AISI is strongest at the acute stage of heart failure.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003e6. Heart rate mediation analysis.\u003c/h3\u003e\n\u003cp\u003eTo determine the biological pathways between systemic inflammation and poor outcomes, we conducted a causal mediation analysis to ascertain whether HR mediated the relationship between AISI and mortality(Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). AISI was analyzed with log2 transformation. Therefore, the estimates reflect the difference in the survival time with a two-fold rise in the levels of AISI.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMediation Analysis of Heart Rate in the Association Between AISI and Mortality\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutcome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEffect\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEstimate(95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30-Day Mortality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eACME (Indirect)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;50.50(\u0026minus;120.54,\u0026minus;23.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eADE (Direct)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;252.62(\u0026minus;865.77,\u0026minus;43.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal Effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;303.12(\u0026minus;962.27,\u0026minus;75.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProportion Mediated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.7%(8.2%,42%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1-Year mortality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eACME (Indirect)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;468.00(\u0026minus;1030.0,\u0026minus;212.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eADE (Direct)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;1600.00(\u0026minus;5800.0,\u0026minus;47.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal Effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;2070.0(\u0026minus;6650.0,\u0026minus;373.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProportion Mediated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.7%(10.0%, 75%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eNote:ACME: Average Causal Mediation Effect; ADE: Average Direct Effect. Estimates represent the change in survival time (AFT model).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFor 30-Day Mortality: The results indicated a significant total effect of AISI on 30-day survival (Total Effect = -303.12; 95% CI: -962.27 to -75.09; P\u0026thinsp;=\u0026thinsp;0.002). Heart rate was identified as a significant mediator, with an Average Causal Mediation Effect (ACME) of -50.50 (95% CI: -120.54 to -23.34; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).The Average Direct Effect (ADE) remained significant after accounting for the mediator (P\u0026thinsp;=\u0026thinsp;0.008), suggesting that heart rate partially mediated the relationship, accounting for 16.7% (95% CI: 8.2%\u0026ndash;42.0%) of the total effect.\u003c/p\u003e \u003cp\u003eFor 1-Year Mortality: The mediating effect of heart rate was stronger in long-term results. The total effect of AISI on 1-year survival was \u0026minus;\u0026thinsp;2070.0 (95% CI: -6650.0 to -373.58; P\u0026thinsp;=\u0026thinsp;0.010). The indirect effect through heart rate (ACME) was \u0026minus;\u0026thinsp;468.00 (95% CI: -1030.0 to -212.47; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), representing a proportion mediated of 22.7% (95% CI: 10.0%\u0026ndash;75.0%; P\u0026thinsp;=\u0026thinsp;0.010).\u003c/p\u003e\n\u003ch3\u003e7. Subgroup analysis\u003c/h3\u003e\n\u003cp\u003eThe analysis was conducted in subgroups to assess the stability of prognostic value of AISI in different clinical strata of 30-day and 1-year mortality (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e and Table S2).\u003c/p\u003e \u003cp\u003eIn 30-day mortality, the positive relationship between log2(AISI) and higher risk was robust across all analyzed subgroups. There were no significant interaction effects (all P interaction\u0026thinsp;\u0026gt;\u0026thinsp;0.05), indicated that sex and comorbidities (e.g. hypertension, diabetes, and CKD) did not significantly alter the effect of a doubling of AISI on short-term mortality (Overall HR: 1.05, 95% CI: 1.02\u0026ndash;1.08, P\u0026thinsp;=\u0026thinsp;0.002). In 1-year mortality, log2(AISI) prognostic value was also evident but the effect size was slightly reduced compared to short-term results (Overall HR: 1.02, 95% CI: 1.00104, P\u0026thinsp;=\u0026thinsp;0.040). In the 30-day results, there were no significant interaction effects observed in all subgroups (all P for interaction\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis was a large scale retrospective cohort study, which thoroughly assessed the prognostic importance of AISI in patients with HF. The outcomes of our study proved that AISI is a powerful, independent predictor of both short-term (30-day) and long-term (1-year) mortality.\u003c/p\u003e \u003cp\u003ePrevious researches have suggested that elevated levels of inflammatory biomarkers, including individual cell counts as well as composite indices like the neutrophil-to-lymphocyte ratio(NLR) and the systemic immune-inflammation index(SII), was associated with higher risk of cardiovascular disease outcomes[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Some associations demonstrated non-linear patterns[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. As a composite inflammatory marker, AISI offers a distinctive composite view of systemic inflammation and immune dysregulation in HF by combining four different cell lineages, namely neutrophils, monocytes, platelets, and lymphocytes. Its prognostic usefulness is based on its capacity to thoroughly indicate fundamental pathophysiological mechanisms. The numerator (Neutrophils \u0026times; Monocytes \u0026times; Platelets) represents a synergistic pro-inflammatory and pro-thrombotic axis. The recruited neutrophils and pro-inflammatory monocytes are involved in myocardial injury with uncontrolled proteolysis and oxidative stress, which may result in negative ventricular remodelling and the development of congestive heart failure[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Monocytes are recruited to the heart and differentiate into macrophages. The cells exhibit high heterogeneity and plasticity in phenotype and function. They are not only involved in repair, but can also exacerbate oxidative stress and fibrosis by releasing mediators, and contribute to the balance between repair and maladaptive remodeling[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. At the same time, platelets, which were activated by the systemic inflammatory milieu, have hemostatic functions. They stimulate aggregation of leukocytes, microvascular thrombosis, and endothelial dysfunction, which establish a cycle of thrombo-inflammation that directly enhances ischemic injury and myocardial damage[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Lymphocytes have an important immunomodulatory role. Lymphopenia could be mediated by a variety of mechanisms such as splanchnic congestion[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], infection[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] and activation of the sympathetic nervous system[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], which might lead to lymphocyte apoptosis and depletion. Lymphopenia is a biomarker that reflects a condition of extreme immune dysregulation, and associated with high risk in HF[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. AISI enables measurement of this culmination of inflammatory and immune markers, and higher AISI level is linked to a greater chance of having heart failure[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study found an important, independent relationship between AISI and short- and long-term all-cause mortality in patients with heart failure. Subgroup analyses confirmed the robustness of AISI's prognostic value. Multivariable COX analysis showed that patients in the highest AISI quartile (Q4) faced a significantly elevated mortality risk compared to those in the lowest quartile (Q1), as previously known that inflammation, particularly systemic inflammation, plays a significant role in the progression of HF[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Interestingly, RCS analysis showed that there was a non-linear, J-shaped association between AISI and death. The increasing left arm of the curve indicated that very low AISI is not protective, which represented malfunctioning of the immune system or possible immunosuppression[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The ascending right limb reflects a state of maladaptive thrombo-inflammation. In this circumstances, platelets and innate immunity (e.g., neutrophils, monocytes) overactivate, accompanied by lymphocyte depletion, which promotes myocardial injury and adverse myocardial remodeling, resulting in a high mortality rate. To be clinically interpretable, the risk nadirs were scaled back to the original scale (approximately 226.0 at 30-day and 256.6 at 1-year mortality), which would be a possible optimal immunological window. The identified nadirs for 30-day and 1-year mortality are close to the median AISI value (~\u0026thinsp;237.7) reported in recent large-scale cohorts[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], suggesting that the lowest mortality risk is associated with a balanced immune-inflammatory state.\u003c/p\u003e \u003cp\u003eThe time-dependent ROC analysis confirms that AISI possesses significant discriminative ability for mortality across all evaluated time points. It is worth noting that its predictive power was greatest for 30-day mortality (AUC 0.59), and smaller for 1-year mortality (AUC 0.556). It indicates that the prognostic information provided by a single baseline AISI measurement is more potent in short term of an acute decompensation. This is in line with its physiological nature as a marker of the acute systemic inflammatory and thrombotic response. The declining correlation in the long-term indicates that late outcomes are increasingly governed by non-inflammatory factors or the inflammatory condition itself is dynamic and requires sequential measurements to continuously assess the risk.\u003c/p\u003e \u003cp\u003eIn addition, causal mediation analysis indicated that heart rate mediated about 17%-23% of the connection between AISI and survival, giving mechanistic understanding of the inflammation-autonomic axis in the progression of HF. The significant mediation effects observed are consistent with a pathway in which systemic inflammation contributes to autonomic dysfunction[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], manifesting in elevated heart rate, a known driver of worse outcomes in heart failure[\u003cspan additionalcitationids=\"CR30\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. The increasing proportion of AISI-related mortality risk mediated by heart rate over time suggests that sympathetic overactivation (tachycardia) becomes a relatively more important driver of long-term outcomes. Our findings have clear clinical meaning. For heart failure patients with high AISI levels, our results suggest that it may be helpful to control heart rate, as current guidelines recommend. Drugs like beta-blockers or ivabradine could be especially useful for this group of patients with high inflammation. Future studies are warranted to investigate whether anti-inflammatory interventions can attenuate risk by modulating autonomic tone, and whether integrated serial assessment of AISI and HR can dynamically guide personalized therapy.\u003c/p\u003e\n\u003ch3\u003eLimitations and Future Directions\u003c/h3\u003e\n\u003cp\u003eOur study has a number of limitations. To begin with, the retrospective design limits definitive causal inference, despite our application of advanced causal mediation methods. Second, baseline AISI was measured only; prospective studies should determine whether AISI change is a better prognostic. Third, even though a full range of confounders was considered, the absence of biomarkers like N-terminal pro-B-type natriuretic peptide (NT-proBNP) and interleukin-6 (IL-6) may result in residual confounding. Lastly, our results should be further generalized to non-hospitalized populations with HF, a process that requires us to conduct additional studies.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis paper confirms that AISI is a strong and independent mortality predictor of critically ill patients with HF, which has a J-shaped and non-linear association. The risk nadirs indicate the presence of a favorable immunological window in terms of survival. Moreover, heart rate is a major partial mediator in this pathway which offers a mechanistic connection between inflammation and prognosis.\u003c/p\u003e \u003cp\u003eThese results have direct clinical implications. AISI is a conveniently accessible, composite bedside measurement that can be useful in the risk stratification of HF patients, identifying those with a high inflammatory load and unfavorable prognosis. In this high-risk phenotype, our findings indicate the importance of aggressive, guideline-directed heart rate control.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe acknowledged the contributions of the Medical Information Mart for Intensive Care (MIMIC) Program registries for creating and updating the MIMIC-III and MIMIC-IV databases.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMethodology: XX. Z., L.T., Y.Z.; Formal analysis and investigation: XX. Z., L.T.; Validation: XX. Z., L.T., Y.Z.; Writing - original draft preparation: XX. Z.; Writing - review and editing: XX. Z., L.T., Y.Z.; Supervision: XX. Z., and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere was no project specific funding for this research.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets presented in this study can be found in online repositories (https://physionet.org/content/mimiciv/2.2/).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInstitutional Review Boards (IRB) of Boston, MA, USA (Massachusetts Institute of Technology) and Beth Israel Deaconess Medical Center (Cambridge, MA, USA) approved the use of the MIMIC-IV database. The IRBs waived the need to have individual patient informed consent since the database holds de-identified data as required by the Health Insurance Portability and Accountability Act (HIPAA) Safe Harbor provision.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eShahim B, Kapelios CJ, Savarese G, Lund LH. Global Public Health Burden of Heart Failure: An Updated Review. Card Fail Rev. 2023;9:e11.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang Y, Bauersachs J, Langer HF. Immune mechanisms in heart failure. 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J Inflamm Res. 2024;17:7057\u0026ndash;67.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBai X, Cheng L, Wang H, Deng Y, Tong X, Wen W, Liu X, Zhou J, Yuan Z. The aggregate index of systemic inflammation (AISI) and the risk of all-cause, cardiovascular, and cardio-cerebrovascular mortality in congestive heart failure patients: results from NHANES 1999\u0026ndash;2018. Sci Rep. 2025;15(1):18282.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQin P, Ho FK, Celis-Morales CA, Pell JP. Association between systemic inflammation biomarkers and incident cardiovascular disease in 423,701 individuals: evidence from the UK biobank cohort. Cardiovasc Diabetol. 2025;24(1):162.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu CC, Wu CH, Lee CH, Cheng CI. 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Circ Res. 2007;100(1):27\u0026ndash;40.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStark K, Massberg S. Interplay between inflammation and thrombosis in cardiovascular pathology. Nat Rev Cardiol. 2021;18(9):666\u0026ndash;82.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBattin DL, Ali S, Shahbaz AU, Massie JD, Munir A, Davis RC Jr., Newman KP, Weber KT. Hypoalbuminemia and lymphocytopenia in patients with decompensated biventricular failure. Am J Med Sci. 2010;339(1):31\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCastro A, Bemer V, N\u0026oacute;brega A, Coutinho A, Truffa-Bachi P. Administration to mouse of endotoxin from gram-negative bacteria leads to activation and apoptosis of T lymphocytes. Eur J Immunol. 1998;28(2):488\u0026ndash;95.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMaisel AS, Knowlton KU, Fowler P, Rearden A, Ziegler MG, Motulsky HJ, Insel PA, Michel MC. 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The association between blood count based inflammatory markers and the risk of atrial fibrillation heart failure and cardiovascular mortality. Sci Rep. 2025;15(1):10056.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSpoor J, Farajifard H, Rezaei N. Congenital neutropenia and primary immunodeficiency diseases. Crit Rev Oncol Hematol. 2019;133:149\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang R, Chen R, Tao W, Cheng X. Nonlinear associations between the aggregate index of systemic inflammation and cardiovascular disease in adults: evidence from NHANES 2011\u0026ndash;2020. BMC Public Health. 2025;25(1):3031.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRupprecht S, Finn S, Hoyer D, Guenther A, Witte OW, Schultze T, Schwab M. Association Between Systemic Inflammation, Carotid Arteriosclerosis, and Autonomic Dysfunction. 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Resting heart rate and all-cause and cardiovascular mortality in the general population: a meta-analysis. CMAJ. 2016;188(3):E53\u0026ndash;63.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-9374386/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9374386/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Systemic inflammation plays a critical role in the progression of heart failure (HF). Aggregate Index of Systemic Inflammation (AISI) is a new composite biomarker, whose prognostic value in critically ill HF patients are still poorly characterized.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e 4, 936 HF patients in MIMIC-IV database were extracted in this retrospective cohort study. AISI was calculated on ICU admission [AISI = (neutrophils×monocytes×platelets)/lymphocytes]. Outcomes were 30-day and 1-year all-cause mortality. Multivariable Cox regression, restricted cubic splines (RCS), time-dependent receiver operating characteristic (ROC) curves, subgroup analysis and causal mediation analysis were used to assess independent prognostic value of AISI and mediation role of heart rate (HR).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFindings: \u003c/strong\u003eMultivariate Cox models revealed that patients who were in the highest AISI quartile (Q4) had a much higher risk of 30-day (HR: 1.47; 95% CI: 1.21-1.78) and 1-year mortality (HR: 1.27; 95% CI: 1.10-1.45). RCS analysis revealed a J-shaped dose-response relationship (P-non-linear≤0.001), with risk nadirs at AISI values of 225.97 (30-day) and 256.64 (1-year). The analysis of ROC indicated the strong acute prognostic performance of AISI. Mediation analysis showed that HR was a significant mediator in the relationship between AISI and survival with mediation of 16.7% and 22.7%, respectively, of the total effect on the 30-day mortality and 1-year mortality.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eThe AISI is a strong and independent predictor of mortality in critically ill patients with HF, characterized by a non-linear, J-shaped association. HR acts as a significant partial mediator in this pathway.\u003c/p\u003e","manuscriptTitle":"Prognostic Value of the Aggregate Index of Systemic Inflammation (AISI) in Critically Ill Patients with Heart Failure: A J-Shaped Relationship and the Mediating Role of Heart Rate","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-14 16:13:43","doi":"10.21203/rs.3.rs-9374386/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"266919263160868461252558247755932060813","date":"2026-05-15T20:04:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"144026884558243970498955322984660088439","date":"2026-05-13T21:08:58+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-05-06T01:53:54+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-13T10:57:42+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-11T07:26:36+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-11T07:25:44+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cardiovascular Disorders","date":"2026-04-10T04:18:09+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a352af80-81a2-4ee8-840f-0c08d57cd0db","owner":[],"postedDate":"May 14th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"266919263160868461252558247755932060813","date":"2026-05-15T20:04:03+00:00","index":55,"fulltext":""},{"type":"reviewerAgreed","content":"144026884558243970498955322984660088439","date":"2026-05-13T21:08:58+00:00","index":53,"fulltext":""},{"type":"reviewersInvited","content":"30","date":"2026-05-06T01:53:54+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-14T16:13:43+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-14 16:13:43","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9374386","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9374386","identity":"rs-9374386","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}