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Abstract

Sepsis-induced coagulopathy (SIC) is a critical condition in sepsis patients, with varying outcomes depending on the type of infection. This study aims to analyze the prognosis of different infections in SIC cohort. A retrospective cohort study was conducted on 525 patients diagnosed with SIC in the intensive care unit from December 2013 to December 2022. These patients were divided into four groups: a non-pneumonia or bacteremia group, a severe pneumonia group, a bacteremia group, and a severe pneumonia concomitant with bacteremia group. The 28-day mortality was 18% (49/271) in the other infections group, 31% (33/106) in the lung infections group, 23% (29/126) in the blood infections group and 36% (8/36) in the lung and blood co-infections group, respectively. Pearson correlation analysis showed that procalcitonin (PCT) correlated strongly with all detected hemostatic markers (p < 0.001). The 28-day mortality rate in Lung infections group was significantly higher (p = 0.019), while Blood infections group had a higher incidence of disseminated intravascular coagulation (p = 0.011). By multivariable model analyses, longer duration of ventilation (p = 0.039) and severe pneumonia (p = 0.040) are risk factors associated with mortality. Different infections, including Lung and Blood infections, indicated different conditions in vivo. Longer duration of ventilation is associated with mortality, while Lung infections indicated higher 28-day mortality rate.

Keywords

sepsis-induced coagulopathy lung infections bacteremia disseminated intravascular coagulation prognosis

Introduction

Sepsis-induced coagulopathy (SIC) is a diagnostic criterion introduced by the International Society on Thrombosis and Haemostasis (ISTH) subcommittee in 2017.¹ Although comprehensive data on a large scale are lacking, SIC accounts for approximately 21%–29% of sepsis patients, with a reported 28-day mortality rate ranging from 23% to 44%.^2–6 Sepsis-induced immune responses can harm the host's tissues, leading to disruptions in coagulation and fibrinolysis, a condition known as disseminated intravascular coagulation (DIC).^7,8 Notably, according to research by Iba et al., half of the patients meeting SIC criteria also receive a diagnosis of overt DIC by ISTH standards.⁹ SIC is considered an early indicator of DIC, which is a dynamic condition associated with organ dysfunction.¹⁰

It is reported that bacterial endotoxins, particularly in Gram-negative infections, are potent triggers for activating both the coagulation and fibrinolystic systems.¹¹ Moreover, DIC can manifest in Gram-positive bacterial sepsis, systemic fungal infections, and other pathogenic microorganism.¹² In a study involving 612 patients with Gram-negative bacteremia, DIC developed in 10% of cases.¹³ The exposure of tissue factor to the bloodstream plays a critical role in the dynamic balance between coagulation and the inflammatory response. Endotoxins are identified as a significant contributor to tissue factor release, resulting in coagulation dysfunction.¹⁴ Various infections, such as lung infections and bacteremia, may suggest a more intricate in vivo pathobiology.^12,15 Previous research highlights the respiratory tract as the most common site of infection leading to sepsis and septic DIC.^16–19 Pneumonia, in particular, has been associated with a pro-thrombotic state and coagulation disorder.²⁰ Additionally, severe pneumonia often necessitates mechanical ventilation, which can further exacerbate coagulation dysfunction and lead to pulmonary embolism.^21,22 Nevertheless, the distinct inflammatory reactions triggered by severe pneumonia versus bacteremia, and their contribution to coagulopathy during sepsis, remain unexplored.

The pathophysiology of the intricate interplay between coagulopathy and inflammation is multifaceted, and ongoing research continues to unravel its underlying mechanisms. Central to these mechanisms is the systemic and overwhelming host inflammatory response to various infectious mediators released during sepsis or systemic inflammatory response syndrome.¹² These mediators serve as indicators that specific infections have taken place but often fall short in pinpointing the precise nature of the infecting agent.

The current study aims to elucidate how severe pneumonia and bacteremia impact the prognosis of SIC, shedding light on the correlations between adverse outcomes and various types of infections.

Methods

Clinical Data

From December 2013 to December 2022, a total of 525 patients with sepsis-induced coagulopathy (326 of whom were males) received treatment in the intensive care units (ICU) at Peking Union Medical College Hospital (PUMCH). This retrospective study was conducted with the approval of the Ethics Committee of PUMCH. Exclusion criteria were established as follows: 1) Insufficient essential information to meet the diagnostic criteria for SIC, 2) Diagnosis of any active malignant disease or liver cirrhosis, 3) Pregnancy, 4) Age less than 18 years, 5) Hospitalization following sepsis treatment at other facility for more than 2 days.

Clinical data, including baseline characteristics at the time of admission, as well as additional variables such as the Sequential Organ Failure Assessment (SOFA) score and etiological culture, were reviewed for this study. The initial inflammatory and infections parameters were collected from day 0 (the day of admission to the ICU) to day 7. D-dimer levels and routine blood investigations within the first 3 days of hospital admission were also recorded. Cytokine levels were assessed at least once for all patients. All laboratory tests were conducted in accordance with institutional standards within the hospital's laboratory.

Definitions

The assessment of organ involvement utilized the SOFA score. DIC was diagnosed based on the overt DIC score criteria established by the ISTH.²³ Sepsis was defined in accordance with the Sepsis-3 publication.⁷ SIC was diagnosed based on the presence of organ dysfunction, as determined by the SOFA score, and coagulopathy characterized by thrombocytopenia and prolonged prothrombin time ratio.¹ DIC and SIC scores were recalculated every 3 days following the guidelines provided above. The diagnosis of severe pneumonia adhered to the 2007 Infectious Disease Society of America/American Thoracic Society (ATS/IDSA) consensus guidelines for pneumonia management in adults.²⁴ Bacteremia was defined as a bloodstream infection, confirmed by positive blood cultures in patients displaying systemic signs of infection.²⁵

Mechanical ventilation and the weaning process strictly adhered to evidence-based guidelines. The cohort was stratified into two groups based on ventilation duration: those with the ventilation duration ≥ 96 h and those with the ventilation duration < 96 h. The various infection types were categorized into four subgroups: non-pneumonia or bacteremia group (Other infections), severe pneumonia group (Lung infections), bacteremia group (Blood infections), and severe pneumonia concomitant with bacteremia group (Lung and Blood co-infections). The number of days not spent in ICU from day 1 to day 28 (ICU-free days) was defined as the duration during which a patient did not require ICU stay during the initial 28 days following enrollment. The primary outcomes encompassed in-hospital and 28-day mortality within the cohort, while the secondary outcomes considered the rate of DIC and multi-organ dysfunction syndrome.

Statistics

Descriptive statistics were employed to summarize continuous variables, expressed as means with standards (SD), while categorical variables were presented as frequencies and percentages. Student's t-test were applied to evaluate quantitative data. Chi-squared analysis was used to calculate p-values for all categorical data, and Fisher's Exact Test was employed to compute p-values for relative risks. Pearson correlation was used to analyze the relationship between in-hospital data and the outcomes, including DIC and 28-day mortality. Survival estimates were generated using Kaplan-Meier analysis, and differences in survival were assessed with Log-rank tests. All statistical analyses were conducted using R 4.2.2 (The R Foundation for Statistical Computing, Vienna, Austria). Additionally, a Cox proportional hazard model was applied to identify the risk factors for mortality and DIC. A two-sided p-value less than 0.05 was considered statistically significant.

Results

Baseline Clinical Characteristics

Figure 1 is a study flow chart. The baseline characteristics were summarized in Table 1. The mean weight (p = 0.633), gender (p = 0.475), hospital duration (p = 0.646), SOFA score (p = 0.143), and the level of procalcitonin (PCT) (p = 0.087) and tumor necrosis factor (TNF) (p = 0.829) were similar across these groups. Due to the retrospective nature of this study, many patients were not tested for interleukin factor levels. However, the level of interleukin-10 (IL-10) was higher in Lung and Blood co-infections group compared to the other three groups (p = 0.019). The other interleukins showed not statistically different between these groups. The parameter associated with coagulopathy such as international normalized ratio (INR) was notably higher in the Blood infections group and Lung and Blood co-infections group (p = 0.012). The median ICU duration was 7.0 days [IQR 3.0 to 16.0 days] in the Other infections group, 11 days [IQR: 8 to 21 days] in the Lung infections group, 10 days [IQR: 5 to 19 days] in the Blood infections group, and 13 days [IQR: 4 to 21 days] in the Lung and Blood co-infections group (p = 0.031).

Figure 1.

The study flowchart. In total of 525 patients were analyzed about the prognosis among 6502 sepsis patients.

Table 1.

Baseline Characteristics of Sepsis-Induced Coagulopathy.

	Other Infections (n=271)	Lung Infections (n=106)	Blood Infections (n=126)	Lung and Blood Co-infections (n=22)	P value
Weight (kg)	67.4 ± 14.7	69.2 ± 18.5	66.6 ± 16.0	65.7 ± 12.1	0.633
Male (%)	118 (44)	39 (37)	59 (47)	10 (45)	0.475
Age (median [IQR])	75 (65–86)	70 (55–81)	72 (61–88)	69 (57–89)	0.401
Ventilation time (hours)	162.8 ± 274.8	230 ± 204.4	233.8 ± 302.2	298.5 ± 352.3	0.011
APACHE II score (median [IQR])	20.5 (18–29)	24 (20–31.5)	23 (16–28)	26 (21, 33)	0.021
DIC (%)	91 (34)	32 (30)	60 (48)	5 (23)	0.011
Hospital duration (days)	21.2 ± 29.9	24.5 ± 21.6	24.1 ± 23.8	22.2 ± 22.8	0.646
ICU duration (median [IQR]) (days)	7 [3, 16]	11 [8, 21]	10 [5, 19]	13 [4, 21]	0.031
Death (%)	70 (26)	49 (46)	36 (29)	12 (55)	< 0.001
28-day mortality (%)	49 (18)	33 (31)	29 (23)	8 (36)	0.019
IL-6 (ng/L) (median [IQR])	94.2 [22.9, 243.0] (NA = 237)	41.0 [31.1, 137.5] (NA = 51)	73.2 [26.1, 132.25] (NA = 31)	87.5 [19.3, 211.8] (NA = 12)	0.303
IL-8 (ng/L) (median [IQR])	115.5 [61.7, 189.7] (NA = 238)	81.5 [42.2, 220.7] (NA = 51)	151.0 [84.2, 309.2] (NA = 31)	222 [89.0, 1029.0] (NA = 12)	0.019
IL-10 (ng/L) (median [IQR])	12.1 [6.2, 20.3] (NA = 233)	16.6 [9.6, 25.2] (NA = 55)	10.4 [6.3, 18.9] (NA = 41)	45.3 [17.7, 66.0] (NA = 12)	0.578
TNF (ng/mL)	37.2 ± 76.7	34.6 ± 42.0	27.3 ± 15.6	35.1 ± 18.0	0.826
Hb (g/L)	100.8 ± 24.3	96.7 ± 24.6	92.9 ± 24.8	94.3 ± 24.8	0.021
DBIL (umol/L)	25.4 ± 39.8	17.0 ± 26.4	36.5 ± 50.4	22.5 ± 46.2	0.004
INR	1.5 ± 0.5	1.4 ± 0.5	1.6 ± 0.7	1.6 ± 0.7	0.012
APTT (s)	42.1 ± 18.2	38.2 ± 16.8	40.4 ± 15.8	42.3 ± 19.1	0.253
Fibrinogen (g/L)	3.59 ± 1.72	4.04 ± 1.61	3.44 ± 1.75	3.76 ± 1.48	0.048
PT (s)	20.7 ± 7.6	23.2 ± 14.0	21.4 ± 7.5	23.1 ± 10.9	0.117
SOFA	9.5 ± 5.5	10.0 ± 4.7	10.2 ± 5.0	12.2 ± 4.5	0.143
DIC score	3.9 ± 1.8	3.7 ± 1.8	4.5 ± 1.9	3.9 ± 1.6	0.006

DIC, disseminated intravascular coagulation; APACHE: Acute Physiology and Chronic Health Evaluation; SOFA: Sequential Organ Failure Assessment; IL: interleukin; TNF: Tumor Necrosis Factor; Hb: hemoglobin; DBIL: direct bilirubin; INR: International Normalized Ratio; PTA: prothrombin activity; PCT: Procalcitonin; ICU: intensive care unit; IQR: interquartile range.

In our study, the 28-day mortality was 18% (49/271) in the Other infections group, 31% (33/106) in the Lung infections group, 23% (29/126) in the Blood infections group, and 36% (8/36) in the Lung and Blood co-infections group, respectively (p = 0.019). The in-hospital mortality rate in Lung and Blood co-infections group was significantly higher than in the other groups (26% in the Other infections group, 46% in the Lung infections group, 29% in the Blood infections group, 55% in the Lung and Blood co-infections group, p < 0.001). The incidence of DIC was the highest in the Blood infections group, with a significant difference (p = 0.011). Of the 525 patients, 428 (81.5%) required mechanical ventilation. comparing different durations of ventilator assistance groups, the incidence of severe pneumonia was significantly higher in the group receiving ventilation for 96 h or more (17% in ventilation duration < 96 h group VS 32% in ventilation duration ≥ 96 h group, p < 0.001) (refer to supplemental table 1). The rate of MODS was similar between the groups based on ventilation duration (20% in the ventilation duration < 96 h group VS 17% in the ventilation duration ≥ 96 h group, p = 0.439).

Correlation Between Inflammatory Cytokines and Coagulation Parameters

Among all patients, the ventilation assistance duration showed a positive correlation with the SOFA score, though the difference was not statistically significant (p = 0.07, cor = 0.08, Figure 2A). Additionally, the DIC scores were also significantly positively correlated with in-hospital mortality (p = 0.018, cor = 0.11, Figure 2B). To investigate correlations among various biomarkers, including inflammatory and infection biomarkers, as well as coagulation parameters and the DIC score, we conducted a Pearson correlation analysis. The correlation matrix, highlighting the relationship between individual markers, is presented in Table 2. Significant correlations (p < 0.05) are denoted in bold.

Figure 2.

Pearson correlation analysis among A. Ventilation duration and SOFA (p = 0.07, cor = 0.08); B. Mortality and DIC score in deaths (p = 0.001, cor = 0.11); SOFA: Sequential Organ Failure Assessment; DIC: disseminated intravascular coagulation.

Table 2.

Correlations among Biomarker Levels in Sepsis-Induced Coagulopathy Patients.

	IL-8	IL-6	TNF	PCT	PLT	PTA	Fib	INR	PT	APTT	DIC score	SOFA
IL-8	1.00	0.43	0.44	0.22	–0.06	–0.22	0.05	0.22	0.21	0.32	0.17	0.11
IL-6	0.43	1.00	0.21	0.18	0.07	–0.11	0.12	0.11	0.01	0.26	0.01	0.01
TNF	0.44	0.21	1.00	0.34	0.01	–0.12	0.03	0.13	0.12	0.34	0.24	0.18
PCT	0.22	0.18	0.34	1.00	–0.17	–0.35	0.20	0.35	0.14	0.32	0.15	0.09
PLT	-0.06	0.07	0.01	-0.17	1.00	0.17	0.18	–0.18	0.03	–0.24	–0.18	–0.25
PTA	–0.22	–0.11	–0.12	–0.35	0.17	1.00	0.20	–1.00	-0.57	–0.64	–0.38	–0.17
Fib	0.05	0.12	0.03	0.20	0.18	0.20	1.00	–0.20	–0.17	–0.22	–0.31	–0.14
INR	0.22	0.11	0.13	0.35	–0.18	–1.00	–0.20	1.00	0.57	0.65	0.38	0.17
PT	0.21	0.01	0.12	0.14	0.03	–0.57	–0.17	0.57	1.00	0.29	0.59	0.23
APTT	0.32	0.26	0.34	0.32	–0.24	–0.64	–0.22	0.65	0.29	1.00	0.28	0.22
DIC score	0.17	0.01	0.24	0.15	–0.18	–0.38	–0.31	0.38	0.59	0.28	1.00	0.26
SOFA	0.11	0.01	0.18	0.09	–0.25	–0.17	–0.14	0.17	0.23	0.22	0.26	1.00

DIC: disseminated intravascular coagulation; SOFA: sequential organ failure assessment; IL: interleukin; TNF: tumor necrosis factor; PCT: procalcitonin; Fib: fibrinogen; PTA: prothrombin activity; APTT: activated partial thromboplastin time; INR: international normalized ratio; PLT: platelet; PT: prothrombin time. Significant correlations (P < 0.05) are bolded. Significant correlations with a Spearman correlation >0.40 as a cutoff are underlined.

PCT exhibited strong correlations with all the hemostatic markers used in its calculation, while IL-6 and IL-8 displayed fewer associated markers (see Table 2). Correlations with a significant level of P < .05 are bolded, and correlations with an r value exceeding 0.4 are underlined. PCT, a well-established inflammatory marker, was found to be correlated with platelets, prothrombin activity (PTA), activated partial thromboplastin time (APTT), INR, and fibrinogen. IL-6 exhibited a strong connection with APTT, while IL-8 correlated with PTA, INR, and APTT. Apart from IL-6, the other inflammatory markers were also found to be related to the DIC score.

Risk Factors for Mortality and DIC

In the current cohort, individuals with lung infections exhibited a significantly higher mortality rate (p = 0.044, Figure 3A). However, the ventilation duration did not show a significant association with mortality (p = 0.360, Figure 3B). Patients diagnosed with DIC were more likely to experience severe physiological distress and organ dysfunction, resulting in a higher mortality rate (p = 0.006, Figure 3C). On the other hand, the concomitant group (Lung and Blood co-infections group) displayed a higher mortality rate than the other groups, while the Blood infections group has a similar mortality rate to the Other infections group (p = 0.190, Figure 3D).

Figure 3.

Kaplan-Meier analysis of overall survival among A. Lung infections and non-Lung infections, B. Ventilation duration >96 h and ≤ 96 h; C. DIC and non-DIC patients; D. Different infection groups. Other: Other infections group; Lung: Lung infections group; Blood: Blood infections group; Co-infections: Lung and Blood co-infections. DIC: disseminated intravascular coagulation.

Cox proportional hazard model was used to analyze risk factors for DIC and 28-day mortality, as presented in Table 3. Laboratory indicators related to coagulation, such as platelet count (p = 0.852) and INR (p = 0.616), were not significantly associated with the incidence of DIC. Conversely, a lower level of fibrinogen was statistically linked to the occurrence of DIC (p = 0.001). Severe pneumonia (p = 0.873) did not emerge as a significant risk factor for DIC, while a longer duration of ventilator assistance (p < 0.001) indicated a higher incidence of DIC. Other laboratory parameters related to infection, such as IL-8 and PCT, were identified as independent risk factors for DIC. Notably, in contrast to other studies,^26,27 the multivariable model demonstrated that the level of fibrinogen (p = 0.850) and platelet count (p = 0.816) were not significant risk factors for 28-day mortality. Instead, patients with severe respiratory infections exhibited a significant correlation with 28-day mortality (p = 0.040). These patients might require an extended period of mechanical ventilator support, and this prolonged assistance was significantly associated with 28-day mortality (p = 0.039). Additionally, higher level of serum total bilirubin (p = 0.044) and IL-8 (p = 0.043) emerged as significant risk factors for 28-day mortality. Bacteremia, indicative of bloodstream infections, was not significantly associated with 28-day mortality (p = 0.059). However, it was identified as an independent and significant risk factor associated with DIC (p = 0.028), as per the multivariable Cox hazard model.

Table 3.

Multivariable Model of Factors Associated With DIC and Mortality.

Variables	Multivariable Model for DIC		Variables	Multivariable Model for Mortality
Variables	HR (95% CI)	P value	Variables	HR (95% CI)	P value
Gender (male)	1.87 [0.89, 3.92]	0.097	Gender	1.23 [0.55, 2.73]	0.619
Bacteremia	2.14 [1.09, 4.22]	0.028	Bacteremia	2.25 [0.97, 5.21]	0.059
TBIL (umol/L)	1.00 [0.99, 1.01]	0.706	TBIL (umol/L)	1.01 [0.90, 1.01]	0.044
INR	1.13 [0.69, 1.85]	0.616	INR	1.55 [0.98, 2.44]	0.059
Fib (g/L)	0.71 [0.58, 0.87]	0.001	Fib (g/L)	0.98 [0.76, 1.25]	0.850
IL-8 (ng/L)	1.08 [0.99, 1.10]	0.024	IL-8 (ng/L)	1.01 [0.97, 1.05]	0.043
TNF (ng/mL)	1.10 [0.92, 1.20]	0.394	TNF (ng/mL)	1.00 [0.99, 1.01]	0.943
PCT (ng/mL)	1.03 [1.01, 1.04]	<0.001	PLT (*10⁹/L)	1.04 [0.90, 1.08]	0.816
PLT (*10⁹/L)	0.91 [0.90, 1.00]	0.852	Ventilation time (h)	1.02 [0.99, 1.09]	0.039
Ventilation time (h)	1.03 [0.98, 1.09]	<0.001	Severe pneumonia	2.34 [1.04, 5.28]	0.040
Severe pneumonia	1.07 [0.46, 2.50]	0.873	DIC score	0.91 [0.74, 1.13]	0.409
SOFA	1.03 [0.96, 1.10]	0.405

DIC: disseminated intravascular coagulation; SOFA: sequential organ failure assessment; IL: interleukin; TNF: tumor necrosis factor; Fib: fibrinogen; DBIL: direct bilirubin; INR: international normalized ratio; PLT: platelet; PCT: procalcitonin; Vent.time: ventilation time; HR: hazard ratio; CI: confidence interval.

Discussions

In our present study, we aimed to explore the characteristics of SIC and its correlation with patient outcomes, particularly in cases of severe infections such as pneumonia and bacteremia. SIC activation ranges from minor abnormal levels of platelet or other coagulation parameters to the onset of fulminant DIC.²⁸ Research has highlighted the association of neutrophil extracellular traps with platelets and fibrin during infections, leading to the formation of activated inflammatory microvascular thrombi.²⁹ Our data revealed significant associations between severe pneumonia, bacteremia, extended duration of ventilator support, and the occurrence of DIC. Additionally, patients with lung infections exhibited a considerably higher 28-day mortality rate.

The extent of cytokine elevation differed between patients with lung infections and bacteremia. Previous studies have compared the levels of IL-6, IL-10, and PCT in patients with overt and non-overt DIC to those of normal control, showing a remarkable increase of these biomarkers within the DIC cohort.^30,31 It has been suggested that pro-inflammatory cytokines trigger the coagulation system, with high levels of TNF, IL-1, and IL-6 being detectable in patients experiencing severe infections accompanied by a hypercoagulable status.³² The upregulation of inflammatory markers contributed to a decrease in protein C, indicating the interplay of inflammation, coagulation activation, and impairment of fibrinolysis in sepsis and overt DIC.³¹ Coagulation abnormalities are a result of a bidirectional crosstalk with inflammatory mediators, serving as potent inducers of the coagulation cascade.¹⁴ As for the extent of elevation, it has been reported that patients with COVID-19 pneumonia, exhibited a 9.7% increase in APTT and a 2.1% increase in PT, and an 18.8% reduction in platelet levels.³³ However, limited studies have explored variations in inflammatory and coagulation parameters between patients with SIC caused by bacteremia or severe pneumonia. Our study established a significant association between bacteremia and DIC, while severe pneumonia was associated with 28-day mortality. In comparing the baseline clinical characteristics, we observed variations in inflammatory markers among the groups. PCT was notably higher in the Blood infections group, and IL-8 exhibited significant elevation in the Lung infections group. While our study lacks definitive evidence, the dynamic changes in these biomarkers may potentially reflect different clinical conditions in vivo.

In our current patient cohort, bloodstream infection was found to be significantly associated with severe coagulation dysfunction, with PCT emerging as the primary contributor to the development of DIC, as opposed to other inflammatory markers. Pearson analysis demonstrated a clear relationship between PCT levels and coagulation parameters, indicative of a propensity for coagulation dysfunction. As observed in previous studies, inflammatory markers tend to be elevated in septic patients, including those in the severe pneumonia group and bacteremia group.³⁴ PCT levels, specifically, were elevated in sepsis patients, serving as a valuable biomarker of ongoing infection, though some evidence has suggested limitations in PCT's ability to differentiate between various types of bacteremia.^31,35,36 One study, which investigated 110 patients with severe community-acquired pneumonia, found a significant increase in PCT levels among patients who developed DIC and other complications.³⁷ Contrary to a previous study,³⁸ our research indicated that a statistically significant relationship between higher PCT levels and lower PTA, reduced platelets counts, prolonged APTT, and elevated fibrinogen levels. Notably, in the Blood infections group, which exhibited a higher incidence of DIC, PCT levels were significantly higher than in the other groups. This underscores PCT's role as a reliable biomarker of overt DIC in bacteremia patients, in contrast to other less specific inflammatory markers such as IL-6, IL-8, IL-10, and TNF.

Several factors may account for the prominence of PCT over IL or TNF in septic DIC. As discussed in various studies, thrombosis is more strongly associated with inflammatory markers than with coagulation parameters.^34,39 Additionally, the mechanisms responsible for sepsis-associated hypofibrinolysis, such as the increased production of plasminogen activator inhibitor-1, can be attributed to TNF and IL.¹² Consequently, the levels of inflammatory markers may vary at different stages of DIC. Further investigations are required to elucidate the relationship between specific biomarkers levels and distinct stages of septic overt DIC.

Severe pneumonia was an independent risk factor for an unfavorable prognosis among SIC patients, notably leading to a significantly higher 28-day mortality rate. The occurrence of a cytokine storm is well-established as a pivotal factor contributing to poor outcomes in patients with severe COVID-19,⁴⁰ with interleukin and TNF being key players in these storm events.⁴¹ As indicated in Table 1, cytokines levels were elevated in patients with infection, with a notably higher elevation observed in the subgroup of patients suffering from severe pneumonia concurrent with bacteremia. Pearson correlation analysis revealed a strong connection between cytokine levels and coagulation parameters. A previous study understood that severe infections trigger an inflammatory host response, leading to activation of the endothelial dysfunction and hemostatic response.⁴² The data presented here further support the integration of inflammatory cytokine storms with hemostatic abnormality. In critically ill patients, the levels of interleukin and TNF surpassed those of PCT, affirming the predictive value of interleukin and TNF in forecasting worse prognosis. In addition to cytokine storm, patients with severe pneumonia probably required extended mechanical support, creating a reciprocal causation between pneumonia and mechanical ventilator assistance. Consequently, patients immobilized in bed were at greater risk of venous thrombus embolism, necessitating anticoagulation treatment, which, in turn, contributed to coagulation abnormalities. In the current cohort, those requiring longer duration of ventilator assistance exhibited higher mortality rates, although without significant difference.

Limitations

The present study was subjected to the common limitations of a retrospective study, including potential selection bias and the influence of other therapies. While treatment regimens could impact overall mortality rates, this study did not include patients who did not receive any anticoagulant treatment. Moreover, the inclusion of critical ill patients who succumbed within days of ICU admission may have initially skewed the mortality rate in the shorter duration of the ventilator assistance group. These critically ill patients may not have been able to undergo all the necessary laboratory tests due to the rapid progression of their disease, including the assessments of cytokine levels. Furthermore, this cohort primarily focused on the inflammatory and coagulation systems, without accounting for anti-coagulation or antibiotic treatments.

Conclusions

Sepsis-induced coagulation appears to identify a subset of critically ill sepsis patients highly susceptible to poor outcomes, including the development of DIC and elevated 28-day mortality. Bacteremia is likely associated with the onset of DIC, whereas lung infections signal a more critical status. The markedly elevated levels of interleukins and TNF in the Lung infections group suggest that these biomarkers were indicative markers that could serve as valuable indicators of poorer prognoses.

Supplemental Material

sj-docx-1-cat-10.1177_10760296231219249 - Supplemental material for Sepsis-induced Coagulopathy: The Different Prognosis in Severe Pneumonia and Bacteremia Infection Patients

Supplemental material, sj-docx-1-cat-10.1177_10760296231219249 for Sepsis-induced Coagulopathy: The Different Prognosis in Severe Pneumonia and Bacteremia Infection Patients by Rong Liufu, MD, Yan Chen, MD, Xi-Xi Wan, MD, Rui-Ting Liu, MD, Wei Jiang, MD, ChYao Wang, MD, Jin-Min Peng, MD, Li Weng, MD, and Bin Du, MD in Clinical and Applied Thrombosis/Hemostasis

Footnotes

Acknowledgements

We thank the other doctors who works hard in the ICU ward of PUMCH for every patient including in this current cohort.

Authors’ Contributions

The corresponding author, Bin Du, is responsible for funding acquisition and ensuring that the descriptions are accurate. Rong Liufu is responsible for the methodology, conceptualization and original draft. Yan Chen is required to review the methodology and edit the manuscript. Xi-Xi Wan and Rui-Ting Liu are responsible for the validation and formal analysis. Wei Jiang, Chun-Yao Wang, and Run Dong are supposed to finish the data curation, investigation, and supervision, respectively. Li Weng is responsible for the project administration.

Availability of Data and Materials

The datasets used or analysed during the current study are available from the corresponding author on reasonable request.

Declaration of Conflicting Interests

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

Funding

This study was supported by National Key R&D Program of China (No.2022YFC2304600), CAMS Innovation Fund for Medical Sciences (CIFMS) from Chinese Academy of Medical Sciences (2021-I2M-1-062), National key clinical specialty construction projects from National Health Commission, National Key R&D Program of China from Ministry of Science and Technology of the People's Republic of China (2021YFC2500801).

IRB

This retrospective study was approved by the Ethics Committee of Peking Union Medical College Hospital (I-22PJ660).

ORCID iD

Bin Du

Supplemental Material

for this article is available online.

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