Inflammatory Burden Index as a predictor of mortality in septic patients: A retrospective study using the MIMIC-IV Database

doi:10.21203/rs.3.rs-5292326/v1

Purpose

Previous studies have identified the Inflammatory Burden Index (IBI) as a potential predictor of mortality risk in inflammatory diseases. However, its relationship with mortality rates specifically in septic patients has not been thoroughly investigated. This study aimed to explore the association between IBI and mortality risk in patients with sepsis.

Patients and methods:

We sourced clinical records of 1,828 septic patients from the MIMIC-IV (3.0) dataset. The primary endpoint was mortality within 28 days, with secondary endpoints including mortality during ICU stays and throughout hospitalization. Patients were categorized into quartiles based on their LnIBI levels. Binary logistic regression was utilized to examine the independent influence of IBI on mortality outcomes, adjusting for confounders. Additionally, the association between IBI and these outcomes was explored using restricted cubic splines and Kaplan-Meier analysis.

Results

The study involved 1,828 septic patients, including 1,047 males. The all-cause mortality rates were 17.78% (325/1828) within 28 days, 17.34% (317/1828) during ICU stays, and 18.22% (333/1828) over the course of hospitalization. In the adjusted model, a positive correlation was found between Ln IBI and mortality at 28 days (OR 1.093[1.014, 1.179], P = 0.021), during ICU stay (OR 1.106[1.025, 1.195], P = 0.01), and throughout hospitalization (OR 1.1[1.022, 1.187], P = 0.012). The analysis using restricted cubic splines showed a linear correlation between LnIBI and mortality risks. Kaplan-Meier plots revealed significantly lower survival rates for patients in the highest quartile of Ln IBI (P < 0.001).

Conclusion

Elevated IBI values are significantly linked with higher mortality risks within 28 days, during ICU, and throughout the hospitalization period in septic patients.

inflammatory burden index

sepsis

mortality risk

MIMIC-IV database

According to Sepsis 3.0, sepsis is defined as a life-threatening organ dysfunction caused by a dysregulated immune response. ¹ In 2017, it was estimated that approximately 48.9 million new sepsis cases were reported globally, along with 11 million sepsis-related deaths, accounting for 19.7% of all-cause mortality worldwide.² Sepsis has remained a major global health challenge for many years. ³ Early identification of septic patients at high risk for mortality can assist clinicians in making optimal clinical decisions and improving patient outcomes. ^4–6 Common markers of inflammation such as C-reactive protein (CRP), neutrophil, and lymphocyte levels were known to be associated with adverse outcomes in sepsis. ^7–9 However, the relationship between multiple inflammatory markers and mortality remained insufficiently explored.

Recent research has established that the Inflammatory Burden Index (IBI) is closely related to the outcomes of diseases associated with acute and chronic inflammation, such as stroke, cancer, and chronic inflammatory airway diseases (CIAD). Research conducted across multiple centers, which included 295 patients treated with endovascular thrombectomy for acute ischemic stroke, demonstrated a significant correlation between elevated IBI levels and an increased risk of unfavorable outcomes within 90 days. ¹⁰ Additionally, a comprehensive study of 6,359 cancer patients across multiple centers revealed that high IBI levels were associated with significantly reduced survival rates compared to those with low IBI levels (45.7% vs 69.1%, P < 0.001). ¹¹ Furthermore, data analysis from the National Health and Nutrition Examination Survey (NHANES) showed that in patients with CIAD, higher IBI levels were linked to increased mortality from all causes and respiratory diseases. ¹²

Despite this knowledge, it was still uncertain if the IBI directly relates to negative outcomes in septic patients. Thus, this investigation sought to assess the relationship between the IBI and adverse outcomes in septic patients.

Data source

We utilized data from the Multiparameter Intelligent Monitoring in Intensive Care IV (MIMIC-IV 3.0) database for analysis. The electronic health record dataset was collected from the intensive care units at Beth Israel Deaconess Medical Center (BIDMC), spanning the years 2008 to 2022. The Institutional Review Board at BIDMC approved the usage of data for research purposes without informed consent and authorized principal investigator ZTZ to access the database (certificate number: 47608458).

Participant selection

Inclusion Criteria: (1) Aged 18 years and older, (2) Patients identified with sepsis according to the Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3).¹ Exclusion Criteria: (1) Patients with ICU stays of less than 24 hours, (2) For individuals with more than one ICU admission, only the first admission was considered, (3) Any absence of initial laboratory tests for CRP, neutrophil, and lymphocyte counts, (4) Pregnancy.

Clinical outcomes

The primary outcome was all-cause mortality within 28 days, while secondary outcomes included all-cause mortality during ICU stays and hospitalization periods.

Data extraction

Data extractions were performed using the PostgreSQL software. Patients' demographics included both age and gender. Vital signs [heart rate, systolic blood pressure (SBP), diastolic blood pressure (DBP), temperature, respiratory rate, pulse oxygen saturation (SpO2)] and laboratory tests [hematocrit, hemoglobin, platelet (PLT), CRP, neutrophil counts, lymphocyte counts, white blood cell (WBC) count, anion gap (AG), bicarbonate, blood urea nitrogen (BUN), serum creatinine, serum glucose, serum calcium, serum chloride, serum sodium, serum potassium, international normalized ratio (INR), prothrombin time (PT), and partial thromboplastin time (PTT)] were extracted. Additionally, illness severity was assessed with the sequential organ failure assessment (SOFA) score. The inflammatory burden index (IBI) was calculated using the formula: IBI = CRP * neutrophil / lymphocyte.¹¹

Statistical analysis

All continuous variables with skewed distribution were expressed as median (Q1, Q3), and categorical variables were expressed as counts or percentages. The IBI variable was used directly for grouping if normally distributed; otherwise, it was converted to LnIBI through logarithmic transformation before grouping.^12,13 Patients were categorized into four groups based on IBI/LnIBI quartiles. We utilized the non-parametric test and chi-square tests to evaluate differences in medians and proportions across these groups. Univariate binary logistic regression identified variables with a P < 0.1 for inclusion in subsequent adjusted models.¹⁴ Multicollinearity among variables was evaluated using the variance inflation factor (VIF), with a VIF value greater than 5 indicating the presence of multicollinearity.¹⁵ In the data analysis, we displayed non-adjusted, minimally-adjusted by demographics (Model Ⅰ), moderately-adjusted by demographics and vital signs (Model Ⅱ), and fully-adjusted demographics, vital signs, and laboratory tests (Model Ⅲ) models, along with odds ratios (OR) and 95% confidence intervals (CI). The dose-response relationship between IBI/LnIBI and mortality was explored using restricted cubic spline (RCS) analysis. Kaplan–Meier survival curves evaluated by the log-rank test, compared the 28-day survival rates among the four groups. Stratified analyses and interactions were conducted based on age, gender, and SOFA score. Variables with over 20% missing data were excluded, while those with under 20% missing data were subjected to m2ultiple imputation.^16,17 Statistical analyses were conducted using R software (version 4.4.1).

Baseline characteristics

Data concerning 1,828 sepsis patients were retrieved from the MIMIC-IV database. The process of patient selection is depicted in Fig. 1. Table S1 provided details on the percentage of missing data for various variables included in the study, noting that some variables (e.g., PLT, AG) were missing, with the percentage of missing data ranging from 0.05–4.16%. Data that underwent multiple imputation could be found in Table 1, which consolidated baseline demographics, vital statistics, laboratory results, and patient outcomes. Among these 1,828 individuals, 1,047 (57.28%) were male and 781 (42.72%) female, with a median age of 64.6 years (IQR 53.2–75.0). The all-cause mortality rates recorded were 17.78% (325/1828) within 28 days, 17.34% (317/1828) during ICU stays, and 18.22% (333/1828) throughout the hospital stay. The median SOFA score across all patients was 7.0 (IQR 4.0–10.0). Patients were categorized into four quartiles (Q1 to Q4) based on LnIBI values. Compared to the highest quartile (Q4) of LnIBI, the other three quartiles (Q1-Q3) had higher hematocrit, lymphocyte counts, bicarbonate, serum chloride, sodium levels, DBP, temperature, and SpO2. Conversely, the Q4 group showed higher CRP, neutrophil counts, WBC, AG, serum creatinine, glucose, INR, PT, PTT, heart rate, respiratory rate, and SOFA scores than the other three quartiles (Q1-Q3). Additionally, the Q4 group demonstrated significantly higher 28-day, in-ICU, and in-hospital mortality rates compared to the other three groups (Q1-Q3) (P < 0.001).

Table 1

Baseline characteristics of the sepsis patients included in the study
	Overall n = 1828	Q1[0.31–4.99) n = 457	Q2[4.99–6.33) n = 457	Q3[6.33–7.49) n = 457	Q4[7.49–10.44] n = 457	P
Baseline characteristics
Gender (%)
Male	1047(57.28)	236(51.64)	260(56.89)	271(59.30)	280(61.27)	0.021
Female	781(42.72)	221(48.36)	197(43.11)	186(40.70)	177(38.73)
Age at admission(years)	64.6(53.2,75.0)	64.5(53.5,75.5)	62.9(50.8,73.6)	64.0(53.0,75.5)	66.3(55.5,75.8)	0.056
Vital signs
Heart rate (bpm)	87.2(75.7,99.8)	85.4(73.7,97.5)	86.0(73.5,99.5)	87.9(75.5,100.4)	89.8(79.5,100.8)	< 0.001
SBP (mmHg)	113.6(104.9,125.8)	116.8(106.9,130.6)	113.8(105.1,125.7)	112.5(104.4,123.6)	112.2(103.1,121.7)	< 0.001
DBP (mmHg)	63.3(57.5,70.2)	64.4(58.2,71.9)	64.1(58.0,71.4)	63.0(57.5,69.8)	61.8(56.3,67.6)	< 0.001
Respiratory rate (bpm)	20.9(18.0,24.2)	20.1(17.6,23.2)	20.3(17.6,23.1)	21.2(18.5,24.6)	22.3(19.1,25.5)	< 0.001
Temperature (℃)	36.9(36.7,37.3)	36.9(36.7,37.2)	36.9(36.7,37.3)	37.0(36.7,37.4)	36.9(36.7,37.4)	0.258
SpO2 (%)	96.8(95.3,98.2)	96.9(95.6,98.4)	97.1(95.3,98.4)	96.7(95.2,98.1)	96.5(94.9,97.8)	< 0.001
laboratory tests
Hematocrit (%)	32.5(27.7,37.3)	33.6(28.4,38.2)	32.1(27.1,37.7)	32.7(27.7,37.5)	32.1(27.7,36.2)	0.038
Hemoglobin (g/L)	10.6(8.9,12.3)	10.8(9.0,12.6)	10.3(8.8,12.3)	10.6(9.0,12.5)	10.4(8.8,11.9)	0.028
PLT (K/µL)	165.0(63.8,259.5)	164.5(69.5,252.5)	178.5(77.5,265.5)	159.0(46.0,258.5)	161.5(59.0,265.0)	0.179
CRP (mg/L)	64.1(17.0,154.4)	7.4(3.7,15.6)	38.7(19.7,68.6)	99.8(63.3,162.0)	184.5(136.6,238.4)	< 0.001
Neutrophil counts (K/µL)	9.6(6.3,14.6)	7.3(4.9,11.1)	8.9(5.8,13.2)	9.8(6.5,14.7)	13.2(9.2,17.9)	< 0.001
lymphocyte counts (K/µL)	0.960(0.584,1.457)	1.265(0.830,1.900)	1.080(0.690,1.570)	0.950(0.590,1.394)	0.635(0.380,0.950)	< 0.001
WBC (K/µL)	12.0(8.4,16.8)	10.1(7.4,14.1)	11.7(7.9,15.6)	12.1(8.3,16.4)	14.8(10.6,19.3)	< 0.001
AG (mmol/L)	14.5(12.5,17.0)	14.5(12.5,17.0)	14.5(12.0,17.0)	14.5(12.0,17.5)	15.0(13.0,17.5)	0.022
Bicarbonate (mg/dL)	22.0(19.0,25.0)	22.0(19.5,24.5)	22.0(19.5,25.0)	22.0(19.0,25.0)	21.0(18.5,24.0)	0.005
BUN (mg/dL)	24.0(14.5,42.3)	20.5(13.5,33.5)	23.5(14.0,40.0)	24.5(14.5,42.5)	30.5(19.0,51.0)	< 0.001
Serum calcium (mg/dL)	8.25(7.75,8.70)	8.35(7.85,8.90)	8.25(7.80,8.70)	8.25(7.85,8.70)	8.10(7.60,8.50)	< 0.001
Serum chloride (mg/dL)	102.5(98.5,106.5)	102.5(98.5,107.0)	103.0(99.5,107.0)	102.0(97.5,106.5)	102.0(97.5,106.0)	0.026
Serum sodium (mg/dL)	138.0(134.5,141.0)	138.0(135.5,141.0)	138.0(135.0,141.0)	138.0(134.5,141.0)	137.0(134.0,140.0)	0.001
Serum potassium (mg/dL)	4.20(3.80,4.65)	4.20(3.80,4.55)	4.20(3.80,4.65)	4.20(3.80,4.55)	4.20(3.85,4.75)	0.603
Serum creatinine (mg/dL)	1.15(0.80,2.00)	1.00(0.75,1.70)	1.10(0.80,2.00)	1.10(0.80,1.95)	1.40(0.90,2.50)	< 0.001
Serum glucose (mg/dL)	134.5(111.5,178.3)	131.0(106.5,178.5)	131.0(112.0,170.5)	139.0(114.0,179.5)	139.5(113.0,183.5)	0.040
INR	1.30(1.15,1.55)	1.25(1.10,1.45)	1.30(1.20,1.60)	1.30(1.20,1.55)	1.35(1.20,1.65)	< 0.001
PT	14.4(12.8,16.9)	13.7(12.1,15.9)	14.4(13.0,17.3)	14.4(12.9,17.0)	14.8(13.5,17.9)	< 0.001
PTT	31.6(27.8,39.6)	31.1(27.0,37.1)	31.5(27.9,39.1)	31.9(28.1,41.6)	32.1(28.5,41.1)	0.001
SOFA score	7.00(4.00,10.00)	6.00(3.00,9.00)	6.00(4.00,10.00)	7.00(4.00,10.00)	7.00(5.00,11.00)	< 0.001
28-day mortality
Yes	325(17.78)	58(12.69)	80(17.51)	76(16.63)	111(24.29)	< 0.001
No	1503(82.22)	399(87.31)	377(82.49)	381(83.37)	346(75.71)
In-ICU mortality
Yes	317(17.34)	55(12.04)	74(16.19)	81(17.72)	107(23.41)	< 0.001
No	1511(82.66)	402(87.96)	383(83.81)	376(82.28)	350(76.59)
In-hospital mortality
Yes	333(18.22)	59(12.91)	78(17.07)	83(18.16)	113(24.73)	< 0.001
No	1495(81.78)	398(87.09)	379(82.93)	374(81.84)	344(75.27)

Notes: Data were presented as median (Q1, Q3), or n (%).

Abbreviations: Q, Quartile; SBP, systolic blood pressure; DBP, diastolic blood pressure; SpO2, pulse oxygen saturation; PLT, platelet; CRP, c-reactive protein; WBC, white blood cell; AG, anion gap; BUN, blood urea nitrogen; INR, international normalized ratio; PT, prothrombin time; PTT, partial thromboplastin time; SOFA, sequential organ failure assessment; ICU, Intensive Care Unit.

Table 2

Relationship between LnIBI and 28-day mortality, in-ICU mortality, and in-hospital mortality in septic patients
Exposure	Non-adjusted		Model Ⅰ		Model Ⅱ		Model Ⅲ
Exposure	OR (95% CI)	P	OR (95% CI)	P	OR (95% CI)	P	OR (95% CI)	P
28-day mortality
Ln IBI	1.177(1.097,1.266)	<0.001	1.168(1.087,1.256)	<0.001	1.115(1.036,1.201)	0.004	1.093(1.014,1.179)	0.021
Q1	Ref		Ref		Ref		Ref
Q2	1.46(1.014,2.112)	0.043	1.515(1.047,2.204)	0.029	1.487(1.021,2.177)	0.039	1.424(0.972,2.095)	0.071
Q3	1.372(0.95,1.991)	0.093	1.391(0.958,2.029)	0.084	1.237(0.845,1.816)	0.276	1.136(0.771,1.679)	0.520
Q4	2.207(1.563,3.143)	<0.001	2.155(1.518,3.084)	<0.001	1.778(1.24,2.57)	0.002	1.638(1.135,2.382)	0.009
P for trend		<0.001		<0.001		0.008		0.032
in-ICU mortality
Ln IBI	1.194(1.111,1.285)	<0.001	1.185(1.102,1.275)	<0.001	1.129(1.049,1.218)	0.001	1.106(1.025,1.195)	0.010
Q1	Ref		Ref		Ref		Ref
Q2	1.412(0.971,2.064)	0.072	1.447(0.992,2.122)	0.056	1.422(0.969,2.097)	0.073	1.358(0.918,2.02)	0.127
Q3	1.575(1.09,2.289)	0.016	1.591(1.098,2.32)	0.015	1.416(0.97,2.078)	0.073	1.299(0.881,1.925)	0.188
Q4	2.234(1.573,3.205)	<0.001	2.18(1.53,3.137)	<0.001	1.79(1.243,2.6)	0.002	1.635(1.126,2.393)	0.010
P for trend		<0.001		<0.001		0.003		0.018
in-hospital mortality
Ln IBI	1.189(1.108,1.277)	<0.001	1.18(1.1,1.268)	<0.001	1.124(1.046,1.21)	0.002	1.1(1.022,1.187)	0.012
Q1	Ref		Ref		Ref		Ref
Q2	1.388(0.964,2.008)	0.079	1.419(0.983,2.059)	0.063	1.399(0.962,2.043)	0.080	1.344(0.917,1.978)	0.131
Q3	1.497(1.044,2.158)	0.029	1.51(1.05,2.182)	0.027	1.339(0.924,1.95)	0.125	1.234(0.844,1.813)	0.280
Q4	2.216(1.573,3.148)	<0.001	2.164(1.532,3.082)	<0.001	1.775(1.243,2.553)	0.002	1.621(1.126,2.35)	0.010
P for trend		<0.001		<0.001		0.004		0.020
Notes: Non-adjusted models: None; Model Ⅰ adjusted for: age; Model Ⅱ adjusted for: confounders in the minimally adjusted (Model Ⅰ) + temperature, respiratory rate, DBP, and SpO2; Model Ⅲ adjusted for: confounders in the moderately-adjusted (Model Ⅱ) + WBC, AG, BUN, serum creatinine, serum glucose, and PTT.

Abbreviations: Q, Quartile; OR, odds ratios; CI, confidence intervals; IBI, inflammatory burden index; ICU, intensive care unit; DBP, diastolic blood pressure; SpO2, pulse oxygen saturation; WBC, white blood cell; AG, anion gap; BUN, blood urea nitrogen; PTT, partial thromboplastin time;

Dose-response relationship between LnIBI and mortality in septic patients

The restricted cubic spline regression model, adjusting for age, temperature, respiratory rate, SpO₂, DBP, WBC, AG, BUN, serum creatinine, serum glucose, and PTT, demonstrated that the risks of 28-day, in-ICU, and in-hospital mortality increased linearly with rising LnIBI levels, as depicted in Figure 2 (D-F).

Subgroup analysis

To verify the association between LnIBI and mortality within 28 days, during ICU stays, and throughout hospitalization further, stratified analyses considering age, gender, and SOFA scores were performed, as depicted in Figure 3 and Table S4. The outcomes of these subgroup analyses aligned closely with those obtained from the logistic regression analysis. In every sub-population examined, these analyses consistently revealed a similar relationship between LnIBI values and mortality rates within 28 days, during ICU stays, and throughout hospitalization, independent of the outcome variable assessed. Furthermore, the interaction analysis revealed no significant interactions across the subgroups.

Kaplan–Meier survival curve

Patients in the lowest quartile of LnIBI (Q1) exhibited a significantly higher 28-day survival probability compared to those in the highest quartile (Q4) (P < 0.0001), as illustrated in Figure 4.

To our knowledge, the current research was the first demonstration of a significant correlation between the Inflammatory Burden Index (IBI) and mortality rates in septic patients. In multivariable-adjusted models, IBI was found to independently correlate with mortality within 28 days, during ICU stays, and throughout hospitalization in septic patients. Our results revealed that higher IBI levels were positively correlated with 28-day, in-ICU, and in-hospital mortality risks, indicating that elevated IBI levels were associated with increased mortality risks.

In alignment with our observations, earlier studies have shown that IBI is associated with negative outcomes in various health conditions. A multicenter prospective study involving 1,296 colorectal cancer (CRC) patients identified high IBI as an independent risk factor for 90-day survival following anticancer therapy (OR 1.537[1.258, 1.878], P<0.001).¹⁸ In a related finding, a study of 408 patients with aneurysmal subarachnoid hemorrhage (aSAH) showed that high IBI levels at admission correlated with an unfavorable functional prognosis (OR 1.362[1.148, 1.615], P<0.001).¹⁹ Further, an analysis of 22,343 osteoarthritis (OA) patients from NHANES indicated that elevated IBI serves as an independent predictor of all-cause mortality (OR 1.23[1.05, 1.45], P=0.012).²⁰ Our study corroborates similar findings, establishing IBI as an independent risk factor for mortality in septic patients.

A prominent manifestation of sepsis is the excessive inflammatory response to infection. CRP is frequently used as an inflammation biomarker in septic conditions.²¹ A retrospective analysis of 349 septic patients demonstrated that CRP is an independent predictor of 28-day mortality in sepsis (HR 1.004[1.002, 1.006], P<0.001),²² which was consistent with our study. Moreover, immunocyte functions are dramatically modified during the initiation and progression of sepsis.²³The advancement of sepsis is linked to neutrophil dysregulation. A retrospective study including 663 sepsis patients revealed that the percentage of neutrophils was associated with hospital mortality risk (OR 1.014[1.003, 1.025], P = 0.009),²⁴ similar to our study findings. Sepsis-induced lymphopenia is increasingly recognized as a critical factor in sepsis prognosis.²⁵ A large-scale prospective study with 98,344 patients confirmed that lymphocyte levels below 1.1 × 10^3/µL were linked to a heightened risk of sepsis onset (OR 1.51[1.21, 1.89], P < 0.05).²⁶ However, we found that lymphocyte count was not a significant independent predictor of 28-day mortality. The likely reason for this may be that, as this was a retrospective study, only 1,828 out of 32,117 sepsis patients were included in the study, potentially introducing selection bias. Ultimately, our results revealed that IBI correlated with 28-day mortality. The advantage of IBI in assessing mortality risk in septic patients might stem from its ability to integrate measures of systemic inflammation and immune responses by incorporating CRP, neutrophils, and lymphocytes, thereby offering a comprehensive assessment of the patient's health status.

The subgroup analyses revealed a more pronounced correlation between IBI and mortality rates among elderly patients (age ≥65),²⁷females, and those with elevated SOFA scores (score ≥8). ²⁸Previous research showed that being aged ≥65 was an independent risk factor for mortality in sepsis patients (OR 2.26[2.17, 2.36], P < 0.001).²⁹ Numerous investigations have assessed the influence of gender on sepsis outcomes. A meta-analysis found that females exhibited higher 28-day mortality rates (OR 1.18 [1.05-1.32], P = 0.0003).³⁰ Another nationwide prospective multicenter cohort study found that in respiratory infections, males aged 19-50 exhibited significantly lower in-hospital mortality rates compared to females in the same age group (OR 0.29 [0.12, 0.69], P ＜ 0.01). ³¹ The SOFA score served as a crucial measure for assessing organ functionality and severity among septic patients.³² A retrospective study included 329 septic patients determined that elevated SOFA scores were strong indicators of worse prognoses in patients with sepsis (P < 0.05).³³ Therefore, clinicians are recommended to closely monitor elderly and female patients, particularly those with elevated SOFA scores. These subgroup analyses advocated for using IBI not as an isolated diagnostic measure but in conjunction with other clinical and laboratory indicators.

Our research was pioneering in assessing the importance of the IBI in septic patients. however, it possessed several limitations. First, the focus on septic patients from America restricted its broader applicability. Second, due to its retrospective nature, inherent biases were inescapable. Third, although we adjusted for numerous potential confounders, we could not completely rule out the risk of residual confounding. Moreover, additional studies involving larger and more varied populations are necessary to corroborate our results.

Elevated IBI correlates with higher mortality rates within 28 days, during ICU stays, and throughout hospitalization in septic patients. As an emerging inflammatory biomarker, IBI has the potential to forecast mortality risks. Relying on retrospective data, further investigation involving larger scale, multi-center studies across different populations is required.

IBI: Inflammatory Burden Index

CRP: C-reactive protein

CIAD: chronic inflammatory airway diseases

NHANES: National Health and Nutrition Examination Survey

MIMIC-IV: Multiparameter Intelligent Monitoring in Intensive Care IV

BIDMC: Beth Israel Deaconess Medical Center

SBP: systolic blood pressure

DBP: diastolic blood pressure

SpO2: pulse oxygen saturation

PLT: platelet

WBC: white blood cell

AG: anion gap

BUN: blood urea nitrogen

INR: international normalized ratio

PT: prothrombin time

PTT: partial thromboplastin time

SOFA: sequential organ failure assessment

RCS: restricted cubic spline

CRC: colorectal cancer

aSAH: aneurysmal subarachnoid hemorrhage

ICU: Intensive Care Unit

Availability of data and materials

The data were available on the MIMIC-IV website at https://mimic.physionet.org/.

Ethics approval and consent to participate

MIMIC-IV was an anonymized public database. The Institutional Review Board at BIDMC approved the usage of data for research purposes without informed consent and authorized principal investigator ZTZ to access the database (certificate number: 47608458).

Competing interests

The authors declare that they have no competing interests" in this section.

Author Contributions

Ping Xu and Zhitao Zhong contributed to the conceptualization, data analysis, funding acquisition, and writing. Zhitao Zhong also handled the methodology and wrote the original draft. Lukai Lv and Minyan Fan were responsible for data curation , data analysis and funding acquisition. Qiong Long and Kefeng Li wrote the original draft. All authors participated in editing and reviewing the final manuscript.

Acknowledgment

We acknowledge the contributions of all staff who participated in the construction and maintenance of the MIMIC-IV database.

Funding

This study was funded by the Project of Science & Technology Department of Sichuan Province (2024JDKP0021), Special Project for Scientific and Technological Research of Sichuan Provincial Administration of Traditional Chinese Medicine (2023MS494), Sichuan Medical Association Scientific Research Project (S21019), Research project of Zigong City Science & Technology and Intellectual Property Right Bureau (2022ZCYGY05 and 2023-YGY-3-04), Research project of Health Commission of Zigong City (22zd004 and 22yb024).

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No competing interests reported.

Additionalfile.docx

Inflammatory Burden Index as a predictor of mortality in septic patients: A retrospective study using the MIMIC-IV Database

Status:

Version 1

Abstract

Purpose

Patients and methods:

Results

Conclusion

Figures

Introduction

Methods

Data source

Participant selection

Clinical outcomes

Data extraction

Statistical analysis

Results

Baseline characteristics

Dose-response relationship between LnIBI and mortality in septic patients

Subgroup analysis

Kaplan–Meier survival curve

Discussion

Conclusion

Abbreviations

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1