Predictive value of early measurement of cytokine levels for persistent inflammation-immunosuppression-catabolism syndrome in ICU patients: A retrospective study

Abstract

Investigating cytokine levels in ICU patients reveals their potential in predicting the occurrence of Persistent Inflammatory Response-Immunosuppression-Catabolic Syndrome (PICS). Our study encompassed clinical data from ICU patients admitted between December 2020 and January 2022. The cohort was divided based on the incidence of PICS, and a comparative analysis was conducted on their clinical data. Using logistic regression, we identified independent factors influencing PICS. Among 132 patients meeting our inclusion criteria, 39 (31.70%) developed PICS. Significant differences were observed between the PICS and non-PICS groups in terms of average age, APACHE II scores, hospital stay duration, mortality, and infection rates. Notably, laboratory parameters indicated lower pre-albumin and IL-4 levels, alongside higher IL-6, IL-10, IL-17, and IFN-y levels in the PICS group. Multivariate analysis pinpointed pre-albumin, IL-4, IL-6, and IL-10 as independent risk factors for PICS in ICU settings. Our findings underscore the importance of IL-4, IL-6, and IL-10 as key cytokines in the early detection and management of PICS, offering significant insights for clinical practice.

Keywords

inflammation cytokines intensive care units logistic regression analysis

Introduction

Adequate nutrition and including natural compounds with antioxidant and anti-inflammatory properties, such as flavonoids and polyphenols, are fundamental in promoting human health and preventing diseases.^1,2 However, despite the best nutritional and medical care, not all critically ill patients witness a favorable outcome after intensive care. Some patients may develop a state of persistent low-level inflammation, severe immunosuppression, catabolic abnormalities, and organ damage, known as Persistent Inflammation, Immunosuppression, and Catabolism Syndrome (PICS).^3–5 A study in two level 1 trauma centers in the United States suggested that 19% of severe trauma patients had PICS, suggesting that PICS is a common clinical complication.⁶ PICS patients often have prolonged conditions, long-term retention in ICU, need long-term close medical support, consume enormous medical resources, and have a high mortality rate and poor prognosis.^7,8 A recent study explored the impact of PICS on the long-term prognosis of trauma patients, emphasizing the importance of early identification of trauma patients at risk of PICS-related complications in the ICU. Therefore, early screening of such high-risk patients and pre-intervention are the key to treatment.⁹

Persistent Inflammation, Immunosuppression, and Catabolism Syndrome is mainly driven by a dysregulated immune response that leads to tissue damage and organ failure.⁸ Cytokines are small proteins that mediate the communication between immune cells and regulate the inflammatory response. They include pro-inflammatory factors (IL-6, IL-17, TNF-α, IFN-γ) that promote inflammation and anti-inflammatory factors (IL-4, IL-10) that inhibit inflammation. Alongside cytokines, high-sensitivity C-reactive protein (HS-CRP) is a marker traditionally linked with systemic inflammation.¹⁰ The balance between pro- and anti-inflammatory cytokines is crucial for maintaining homeostasis and resolving inflammation. However, this balance is disrupted in PICS patients, leading to chronic inflammation and immunosuppression.¹¹ Therefore, measuring the levels of cytokines in the early stage of critical illness may provide valuable information about the risk of developing PICS.

This study attempts to retrospectively collect the relevant information on critically ill patients in ICU. We aim to understand the potential role of cytokines in the early identification of PICS in critically ill patients.

Materials and methods

Study design, setting, and ethics approval

This study was conducted in the ICU ward of a tertiary grade A hospital in eastern China. It was part of a single-center retrospective study that adhered to the principles of the Declaration of Helsinki. All patients provided written informed consent for the study. The Dongyang People's Hospital Ethics Committee approved the study design and possible ethical issues (Approval number: 2021-YX-072).

Participants

Patients admitted to the ICU between December 2020 and January 2022 were enrolled in the study. The primary objective of this study was to evaluate the value of early measurement of cytokine levels in predicting the occurrence of PICS in ICU patients. Participants aged 18 years and older were included in the study. The exclusion criteria were: (1) age <18 years; (2) patients with a severe history of malignant tumor, autoimmune disease, or immunosuppression upon ICU admission; (3) length of hospital stay less than 14 days, including patients who died within this period; and (4) incomplete clinical data for main variables such as age, gender, and diagnosis.

All patients received standard treatment after admission, and the observation point was 14 days after admission to ICU. We assessed the presence of PICS by measuring clinical data on day 14; the specific diagnostic criteria are as follows¹²: (1) The patient’s body due to the ongoing inflammatory response. C-reactive protein (CRP) > 50 μg dL⁻¹ (2) The patient was in a state of immunosuppression. Total peripheral blood lymphocyte count <0.80 × 10⁹ L⁻¹, (3) The patient was in a state of abnormal catabolism. Plasma albumin <3 g DL⁻¹, pre-albumin <10 mg DL⁻¹. In this study, patients who met the above diagnostic criteria were included in the PICS Group, and the rest in the non-PICS group.

Data collection

General clinical data and demographic characteristics of Critically ill patients admitted to the ICU were collected and recorded using our hospital’s Le Jiu data platform. The data encompasses various parameters: gender, age, BMI, and history of underlying diseases such as hypertension and diabetes. Additionally, nutritional scores were evaluated using the NRS2002¹³ and the NUTRIC¹⁴ scores at the time of ICU admission. Other details documented include the diagnosis leading to ICU admission, whether surgery was conducted, and the duration of the ICU stay. Laboratory indicators were collected on Day 1 and 14 of ICU admission. The indicators include blood routine (measured by Beckman-coulter LH 750 automated blood cell analyzer), coagulation function (measured by Sysmex CA-1500 coagulation analyzer), liver function and renal function, along with high-sensitivity C-reactive protein and procalcitonin (measured by Beckman Coulter AU5800 automatic biochemical analyzer). The APACHE II score on the first day of ICU admission was calculated based on the collected information and indicators.¹⁵

For detecting cytokine levels (including IL-4, IL-6, IL-10, IL-17, TNF-α, and IFN-γ), all cases were measured by collecting venous blood in the morning on the 1st day of ICU admission, and serum was separated by centrifugation at 3000 r/min for 10 min, and stored at −80°C for detection. The cytokine levels were measured using enzyme-linked immunosorbent assay (ELISA) methods. The ELISA kits for these measurements were procured from J & L Biological, China. The specific batch numbers for the kits are as follows: IL-4 (JL19287), IL-6 (JL14113), IL-10 (JL19246), IL-17 (JL19255), TNF-α(JL10208), and IFN-γ(JL12191). For outlier data, we use the box diagram's interquartile distance (IQR) to detect outliers, and IQR is the difference between the upper and lower quartiles. We adopted 1.5 times IQR as the standard. We stipulated that points exceeding (upper quartile +1.5 times IQR distance or lower quartile −1.5 times IQR distance) were outliers, and the outliers screened out were treated as missing values.

Statistical analysis methods

This paper’s statistical data analysis was performed by applying SPSS 25.0 software. The measurement data were expressed as mean ± SD or median (upper and lower quartiles), and the two independent samples, the t test or Mann-Whitney U test, were used for comparative analysis. Counting data were expressed as adoption rates and percentages, and differences between groups were compared using Chi-square tests or Fisher’s exact tests. A multivariate logistic model was used to analyze and screen the independent influencing factors of PICS. Taking p < .05 was considered statistically significant.

For the outlier value, we use the box diagram’s interquartile distance (IQR) to detect outliers, and IQR is the D-value between the first and third quantile of the variable. One and a half times the IQR was considered the standard. We stipulated that values exceeding (upper quartile add one and a half times IQR distance or lower quartile minus 1.5 times IQR distance) were outliers, and the outliers screened out were treated as missing values.

For missing value data, we treat missing values according to the percentage of missing values if the incidence of missing values. If the incidence of missing values was less than 2%, we used the mean or median of the variable for continuous quantitative data and the mode attribute of the variable (i.e., the value with the highest frequency) for qualitative data to impute the missing values. If the missing ratio is greater than 2% and less than 15%, we excluded patients who had more than 15% of their data missing or incomplete, and we used multiple interpolation methods¹⁶ to impute the missing values for the remaining patients who had less than 15% of their data missing or incomplete. Multiple interpolation methods estimate the missing values based on the available data and some assumptions, such as the distribution and correlation of the variables. All variables had a missing percentage of less than 15%.

Results

Initially, 132 patients admitted to the ICU in this study were included in our study, in which 9 patients died within 14 days of admission to the ICU. A total of 123 patients met the inclusion criteria for inclusion in this study (see Figure 1). After 14 days of hospitalization, a total of 39 patients met the diagnosis of PICS, and 84 patients did not meet the diagnosis of PICS. The incidence of PICS was 31.70%.

Figure 1.

The diagram showing the patient screening process.

The general clinical data of the two groups are shown in Table 1. The average age of patients in the PICS group was significantly higher than that in the Non-PICS control group (p < .05). APACHE II scores on admission in the PICS group were significantly higher than those in the control group (p < .05). The length of hospital stay in the PICS group was significantly higher than in the control group (p < .05). Regarding mortality, the PICS group had a significantly higher death rate, with 10 deaths (25.64%), compared to 8 (9.52%) in the Non-PICS group (p = .033). Additionally, the infection rate was notably higher in the PICS group at 58.97% (23 cases) versus 25.00% (21 cases) in the Non-PICS group, which was statistically significant (p < .001). There were no significant differences in gender, BMI, underlying disease, occurrence of ARDS, presence of infection, NRS2002 score, NUTRIC score, ICU diagnosis, and whether or not surgery was performed between the two groups (p > .05).

Table 1.

Comparison of general clinical data between the two groups on the first day of hospitalization.

Variables	Non-PICS (n = 84)	PICS (n = 39)	p Value
Age (years)	64.25 ± 12.35	70.12 ± 13.14	0.020
Male gender (n, %)	49 (58.33)	26 (66.67)	0.378
Body mass index (kg/m²)	20.02 ± 2.46	19.44 ± 2.64	0.237
Hypertension (n, %)	42 (50.00)	19 (48.72)	0.895
Diabetes mellitus (n, %)	38 (45.23)	22 (56.41)	0.249
APACHE II score	19.41 ± 7.13	23.22 ± 7.21	0.007
NRS2002 score	3.43 ± 1.44	3.82 ± 1.38	0.159
NUTRIC score	5.31 ± 2.43	5.87 ± 2.32	0.230
Primary diagnosis (N, %)			0.316
Sepsis	6 (7.14)	7 (17.94)
Diseases of the respiratory system	16 (19.05)	8 (20.51)
Cardiovascular disease	13 (15.47)	7 (17.94)
Cerebrovascular disease	44 (52.38)	14 (35.90)
Other	5 (5.95)	3 (7.69)
ARDS	71 (84.5)	30 (76.9)	0.306
Infection	20 (51.28)	52 (61.90)	0.266
Surgical treatment (n, %)	38 (45.23)	20 (51.28)	0.426
ICU length of stay (d)	23.02 ± 9.91	26.95 ± 10.02	0.044
Deaths (n, %)	8 (9.52)	10 (25.64)	0.033
Infection rate (n, %)	21 (25.00)	23 (58.97)	<0.001

APACHE II, Acute Physiology And Chronic Health Evaluation II; NRS2002, Nutritional Risk Screening 2002; ARDS:Acute Respiratory Distress Syndrome; NUTRIC, Nutrition Risk in the Critically ill.

The laboratory indicators of patients in the two groups are shown in Table 2. Compared with the test indicators of patients on the first day of ICU admission, there was no statistically significant difference in the main blood routine, coagulation routine, and biochemical blood indicators between the two groups (p > .05). The pre-albumin and IL-4 in the PICS group were significantly lower than in the non-PICS group (p < .05). The levels of IL-6, IL-10, IL-17 and IFN-γ were significantly higher than those of the non-PICS group (p < .05), as shown in Table 2. However, there was no significant difference in high-sensitivity CRP levels between the two groups (p > .05), suggesting that this marker may not be sensitive enough to distinguish PICS from non-PICS patients.

Table 2.

Comparison of laboratory indexes between the two groups on the first day of hospitalization.

Variables	Non-PICS (n = 84)	PICS (n = 39)	p Value
Lymphocyte (10⁹/L)	1.28 ± 0.69	1.07 ± 0.64	0.111
PLT (10⁹/L)	231.46 ± 80.45	208.66 ± 70.83	0.132
PLR	169.39 ± 62.34	187.82 ± 50.23	0.108
Neutrophil (10⁹ L)	5.57 ± 2.14	6.24 ± 2.16	0.110
NLR	4.32 ± 2.15	4.94 ± 2.06	0.134
D-dimer (mg/L)	5.29 ± 3.11	6.43 ± 3.56	0.099
PT (s)	14.11 ± 4.46	16.11 ± 7.57	0.132
Creatinine (μmol/L)	122.43 ± 82.44	136.18 ± 80.36	0.387
ALT (U/L)	65.35 ± 42.54	58.36 ± 44.27	0.404
AST (U/L)	121.45 ± 56.64	107.38 ± 52.35	0.192
Albumin (g/L)	29.02 ± 3.85	27.86 ± 3.77	0.120
Pre-albumin (mg/L)	142.23 ± 74.46	67.32 ± 41.16	<0.001
PCT (mg/L)	10.27 ± 7.34	12.71 ± 8.45	0.105
HS-CRP (mg/L)	99.09 ± 45.45	114.63 ± 55.73	0.104
Lac (mmol/L)	3.08 ± 1.12	2.77 ± 1.24	0.170
IL-4 (pg/ml)	2.58 ± 0.81	2.02 ± 0.69	<0.001
IL-6 (pg/ml)	252.12 ± 110.25	330.78 ± 115.52	<0.001
Il-10 (pg/ml)	42.15 ± 11.14	49.82 ± 10.01	<0.001
Il-17 (pg/ml)	3.03 ± 1.17	3.95 ± 1.18	<0.001
TNF-α (pg/ml)	46.29 ± 10.58	50.02 ± 11.74	0.081
IFN-γ (pg/ml)	31.38 ± 11.67	37.26 ± 10.32	0.008

PLT, platelet; PLR, platelet-to-lymphocyte ratio; NLR, neutrophil-to-lymphocyte ratio; PT, prothrombin time; ALT, alanine aminotransferase; AST, aspartate aminotransferase; PCT, procalcitonin; HS-CRP, high-sensitivity C-reactive protein; Lac, lactate; IL, interleukin; TNF-α, tumor necrosis factor alpha; IFN-γ, interferon gamma.

To further demonstrate the factors that influence the occurrence of PICS, we first compared the difference indexes between PICS and non-PICS groups, setting the significance level at p < .05. The above difference indexes, including age, APACHE II score, pre-albumin, IL-4, IL-6, IL-10, IL-17, IFN-γ. Additionally, sepsis was included as a variable not due to a demonstrated statistical difference, but rather based on clinical expertise and its known influence on inflammatory responses and patient outcomes. This inclusion ensures a comprehensive evaluation of factors that may be predictive of PICS. Our logistic regression analysis, which compared two distinct patient groups within our study cohort, identified pre-albumin, IL-4, IL-6, IL-10 as independent risk factors for PICS in ICU patients, as shown in Table 3. These factors are indicative of their significant roles in the pathogenesis and progression of PICS. Concurrently with the logistic regression analysis, we evaluated each variable’s variance inflation factor (VIF) to address potential multicollinearity among the cytokines. The VIF values ranged from 0.746 to 2.235, indicating that multicollinearity was not a significant concern in our analysis.

Table 3.

Multivariate logistic regression analysis of predictive risk factor of PICS in the ICU.

Variables	B value	Wald’s value	p Value	OR	95% CI
Age	0.466	4.216	0.094	1.503	0.812∼2.548
Sepsis	0.327	5.423	0.088	2.435	0.895∼5.312
APACHE II score	0.423	4.217	0.062	1.281	0.929∼1.398
Pre-albumin (mg/L)	−0.344	12.131	<0.001	0.632	0.456∼0.903
IL-4 (pg/ml)	−0.253	9.832	0.009	0.415	0.160∼0.634
IL-6 (pg/ml)	2.43	8.476	0.016	1.213	1.042∼1.524
Il-10 (pg/ml)	0.095	9.242	0.023	1.234	1.046∼1.627
Il-17 (pg/ml)	0.244	6.025	0.062	1.434	0.984∼2.145
IFN-γ (pg/ml)	0.105	3.965	0.102	1.234	0.965∼1.423

Discussion

Persistent Inflammation, Immunosuppression, and Catabolism Syndrome often arises as a complication of severe trauma or sepsis in ICU patients. It begins with an innate immune response to infection or trauma, characterized by the release of inflammatory mediators and an acute inflammatory response. Progression of the disease is marked by T lymphocyte depletion, apoptosis, and subsequent immunosuppression. This state can lead to nutrient synthesis decline, increased metabolic consumption, and malnutrition, exacerbating patient outcomes by impairing wound healing and increasing infection risk. In advanced stages, this can escalate to malignancy or mortality.¹⁷

In our study, the incidence of PICS during ICU hospitalization was 31.70%, lower than the 43.1% reported by Hu¹⁸ and about 60% reported by Nakamura.¹⁹ This difference may be attributed to the study population’s inclusion and diagnostic criteria. Hu’s cohort included patients with a minimum 10-days hospital stay and conditions like intestinal fistula and acute ileus, which may increase the risk for PICS. Meanwhile, our study used a 14-days benchmark. Moreover, diagnostic criteria for PICS remain inconsistent across studies, with Nakamura redefining the CRP threshold at 3.0 mg/dl, diverging from our adherence to the 2012 standards.¹² Differences in healthcare environments and treatment protocols between studies could also significantly influence the observed variations in PICS incidence.

Our study observed a higher average age in the PICS group compared to the non-PICS group, suggesting that the former’s increased susceptibility may be linked to age-related immune decline and chronic inflammation.²⁰ Older adults’ multiple comorbidities and nutritional issues also contribute to this heightened risk.²¹ Therefore, vigilant immune and metabolic monitoring, along with timely intervention, is particularly critical for elderly patients with severe diseases.

The APACHE II score, a well-established metric for assessing illness severity, emerged as an independent risk factor for PICS, corroborating the score’s predictive validity as reported in other critical conditions, including sepsis and COVID-19 pneumonia.^22–26 Regarding the severity of their conditions, our data revealed that the PICS group possessed significantly higher APACHE II scores than the non-PICS group, indicating that they had more severe disease and organ dysfunction at admission. This group also experienced significantly greater mortality (25.64% vs 9.52%, p < .05) and hospital infection rates (58.97% vs 25.00%, p < .001) after 14 days compared to their non-PICS counterparts.

The presence of catabolic imbalance is a defining feature of PICS, with serum pre-albumin levels serving as a key indicator. Pre-albumin plays a dual role in metabolism and immune defense, which is particularly crucial during infections to eliminate toxic metabolites.²⁷ Despite robust nutritional support, severe cases, often exacerbated by sepsis, can result in marked malnutrition. Our data revealed significantly lower pre-albumin levels in PICS patients, establishing it as an independent risk factor and underscoring the urgency of addressing nutritional deficits to prevent PICS.

Persistent Inflammation, Immunosuppression, and Catabolism Syndrome’s pathology involves persistent inflammation and immunosuppression, leading to protracted tissue damage. A notable strength of our study is assessing several cytokines not previously extensively studied in PICS. In this study, the levels of IL-4, IL-6, IL-10, IL-17, and IFN-γ were statistically different between the two groups. Multivariate logistic regression analysis showed the levels of IL-4, IL-6, IL-10 were independent influencing factors for PICS in ICU patients. Delving deeper into the role of specific cytokines, IL-6 stands out in numerous inflammatory events, and its transient spike can also spur the release of counter-cytokines such as IL-10. The simultaneous increase in both pro-inflammatory (specifically IL-6) and anti-inflammatory markers (like IL-10) in the PICS group can be likened to the phenomenon of a “cytokine storm”. Broadly, this “storm” represents a hyperactive immune response characterized by the excessive release of interleukins, interferons, tumor necrosis factors, chemokines, and other mediators.^28,29 Consistent with Mankowski’s findings, IL-10 in our study mirrored disease severity, particularly in the elderly with chronic conditions.³⁰ Reduced IL-4 implicates immunosuppression, while increased IFN-γ and IL-17 levels reflect their roles in inflammation and immune activation.^31–33 These insights into cytokine dynamics in PICS patients underline the need for further research to fully elucidate their roles.

Acknowledging the limitations of our retrospective study design and the single-center, modest sample size, we recognize that these factors may impact the generalizability of our findings. One notable limitation was the incomplete collection of serum cytokine data on the 14th day of ICU admission, which may have provided additional insights into the progression of PICS. This gap underscores the need for more systematic data collection protocols in future research. Future studies could benefit from a prospective or cohort methodology and a larger, multi-center sample to validate and extend our results. Moreover, monitoring older patients with serious illnesses for immune and metabolic changes remains paramount for early intervention, a notion that is central to the management of PICS.

Conclusions

Our study found that the combination of cytokines, including IL-4, IL-6 and other indicators, is associated with the occurrence of PICS in ICU patients. This has implications for improving early diagnosis and intervention strategies for PICS in ICU patients.

Footnotes

Author contributions

This study was designed and conducted by YH, YW, and SG. YH and YW collected and analyzed the data. SG performed the ELISA assays. YH wrote the manuscript. WZ supervised the study and revised the manuscript. All authors read and approved the final version of the paper.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Jinhua Science and Technology Bureau Project (2022-3-014).

ORCID iD

Yongxia Hu

Data availability statement

All data included in this study are available on request from relevant authors.

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