Skip to main content
Download PDF

Association analysis of sepsis progression to sepsis-induced coagulopathy: a study based on the MIMIC-IV database

BMC Infectious Diseases volume 25, Article number: 573 (2025) Cite this article

Abstract

Background

Sepsis-induced coagulopathy (SIC) is a severe complication of sepsis, characterized by poor prognosis and high mortality. However, the predictive factors for the development of SIC in sepsis patients remain to be determined. The aim of this study was to develop an easy-to-use and efficient nomogram for predicting the risk of sepsis patients developing SIC in the intensive care unit (ICU), based on common indicators and complications observed at admission.

Methods

A total of 12, 455 sepsis patients from the MIMIC database were screened and randomly divided into training and validation cohorts. In the training cohort, LASSO regression was used for variable selection and regularization. The selected variables were then incorporated into a multivariable logistic regression model to construct the nomogram for predicting the risk of sepsis patients developing sepsis-induced coagulopathy (SIC). The model’s predictive performance was evaluated using the area under the receiver operating characteristic curve (AUC), and its calibration was assessed through a calibration curve. Additionally, decision curve analysis (DCA) was performed to evaluate the clinical applicability of the model. External validation was conducted using data from the ICU database of Xingtai People’s Hospital.

Results

Among the 12, 455 sepsis patients, 5, 145 (41. 3%) developed SIC. The occurrence of SIC was significantly associated with the SOFA score, red blood cell count, red cell distribution width (RDW), white blood cell count, platelet count, INR, and lactate levels. Additionally, hypertension was identified as a potential protective factor. A nomogram was developed to predict the risk of SIC, which showed an AUC of 0. 81 (95% CI: 0. 79–0. 83) in the training set, 0. 83 (95% CI: 0. 82–0. 84) in the validation set, and 0. 79 (95% CI: 0. 74–0. 84) in the external validation. The calibration curve of the nomogram showed good consistency between the observed and predicted probabilities of SIC.

Conclusions

The novel nomogram demonstrates excellent predictive performance for the incidence of SIC in ICU patients with sepsis and holds promise for assisting clinicians in early identification and intervention of SIC.

Clinical trial

Not applicable.

Peer Review reports

Background

According to the 2016 Third International Consensus Definitions for Sepsis and Septic Shock, sepsis is defined as life-threatening organ dysfunction caused by a dysregulated host response to infection. As a consequence of the infection response, a cascade of pathological events disrupts the hemostatic balance of coagulation, fibrinolysis, and anticoagulation, resulting in detrimental coagulopathy [1]. In 2017, the International Society on Thrombosis and Haemostasis (ISTH) Disseminated Intravascular Coagulation (DIC) Scientific and Standardization Committee introduced the diagnostic criteria for sepsis-induced coagulopathy (SIC) as a tool for the early identification of DIC [2]. SIC represents a critical complication in the progression of sepsis, significantly impacting patient outcomes. Early detection of coagulopathy is essential for assessing the severity of sepsis and predicting its clinical outcomes [3].

The interplay between inflammation and coagulation plays a pivotal role in the pathogenesis of organ dysfunction [4]. Studies have demonstrated that the incidence of SIC in critically ill patients is significantly associated with factors such as age, fibrinogen levels, prothrombin time, C-reactive protein, lactate concentration, and pediatric Sequential Organ Failure Assessment (pSOFA) scores [5]. Some patients with sepsis transition from not meeting overt DIC criteria to developing overt DIC within three days of admission, and DIC screening has been closely linked to reduced mortality in sepsis patients [6]. These findings collectively emphasize the dynamic nature of coagulation system activation in sepsis patients. Delayed intervention may accelerate disease progression and diminish the effectiveness of anticoagulant therapy [7, 8]. Therefore, early identification of high-risk patients with SIC and timely intervention are crucial for improving clinical outcomes.

Although anticoagulant therapy and the rational use of antibiotics have been shown to reduce mortality and improve clinical outcomes, current assessment tools have limitations in predicting the incidence and mortality of SIC at an early stage. These limitations may delay the identification and treatment of high-risk patients, underscoring the need for the development of accurate risk assessment models for SIC. While the SIC scoring system has demonstrated significant utility in the early and sensitive prediction of overt DIC, studies indicate that only approximately 50% of SIC-positive patients progress to overt DIC, suggesting room for improvement in its positive predictive value [9].

Nomograms are powerful visualization tools widely used in clinical prognostic research. Based on a large-scale database and modern statistical methods, this study aims to identify key risk factors associated with SIC and to develop an innovative nomogram model. This model is designed to assist clinicians in the early identification of high-risk SIC patients, optimize clinical decision-making, implement personalized management strategies, and ultimately improve patient outcomes.

Methods

Data source

All data used in this study were obtained from the MIMIC-IV database (version 2. 2)Footnote 1, a publicly available resource curated by the Massachusetts Institute of Technology Laboratory for Computational Physiology. The database encompasses detailed demographic, clinical, laboratory, and outcome information for over 70, 000 patients admitted to the Beth Israel Deaconess Medical Center in Boston, Massachusetts, between 2008 and 2019. This extensive dataset provides a valuable resource for clinical and epidemiological research.

For this analysis, the inclusion criteria were as follows: (1) age ≥ 18 years; (2) ICU stay duration ≥ 24 h; and (3) sepsis diagnosed upon ICU admission based on the Sepsis-3 diagnostic criteria. Exclusion criteria included: (1) patients aged < 18 years or > 100 years; (2) prior use of anticoagulant therapy before ICU admission; (3) a history of coagulation disorders or malignancies; (4) ICU stays shorter than 24 h; (5) an SIC score ≥ 4 upon ICU admission; and (6) non-initial ICU admissions.

The first author, JianYue Yang (certification number: 62316152), was authorized to access the MIMIC-IV database and conducted the study in compliance with the database usage agreement. The external validation cohort was generated from the single-center ICU database of Xingtai People’s Hospital in China (See Fig. 1).

Fig. 1
figure 1

The schematic diagram shows the data inclusion, exclusion, and grouping process for the study

Full size image

Data extraction

Using the PostgreSQL and Navicat 16. 1. 12 database management systems, we queried and extracted clinical data from the MIMIC-IV 2. 2 database, Data were divided into training cohorts (80%) and internal validation cohorts (20%) through cross-validation including: ① Demographic information. ② Unrestricted mortality nodes, retaining only patient outcomes. ③ SOFA scores. ④ The lowest values of vital signs(RBC、RDW、MCV、WBC 、Platelet、INR、PT、PTT、Lactate、Chloride、Anion Gap、Bicarbonate、Calcium、BUN、Sodium、Glucose、Creatinine)and laboratory test results on the first day of ICU admission(SBP、DBP、Heart rate、Spo2、Resp rate、Temperature). ⑤ Information on comorbidities, including hypertension, diabetes, coronary artery disease, cerebrovascular disease, peripheral vascular disease, renal disease, lung disease, and chronic obstructive pulmonary disease (COPD). ⑥ The use of vasoactive drugs, including adrenaline, norepinephrine, dopamine, dobutamine, and vasopressin.

Sepsis-induced coagulopathy (SIC)

Based on all sepsis patients, the diagnostic criteria for SIC were defined according to the 2017 recommendations of the International Society on Thrombosis and ISTH Scientific and Standardization Committee (SSC) on DIC. The criteria include: Platelet count: 1 point if 100–150 × 10⁹/L; 2 points if < 100 × 10⁹/L. International normalized ratio (INR): 1 point if 1. 2–1. 4; 2 points if > 1. 4. SOFA score: Summed scores for respiratory, hepatic, cardiovascular, and renal dysfunction, with 1 point for a score of 1 and 2 points for a score of ≥ 2. Patients were diagnosed with SIC if the total score reached 4 or more, and the platelet count and INR contributed more than 2 points.

Statistical analysis

All statistical analyses were performed using IBM SPSS Statistics 29. 0 and R version 4. 4. 1. Normality testing: The Kolmogorov-Smirnov test was used to assess the normality of variable distributions. Continuous variables: For normally distributed variables, unpaired Student’s t-tests were used, and results were expressed as means ± standard deviations (SD). For non-normally distributed variables, the Mann-Whitney U test was used, with results expressed as medians and interquartile ranges (IQR). Categorical variables: Percentages were used for representation, and comparisons between groups were performed using the chi-square test or Fisher’s exact test. In the data extraction phase, 78 variables were extracted. The study used multiple imputation to handle missing data, excluding variables with missing values ≥ 30%. After imputation, 47 variables remained and were imputed, generating 5 imputed datasets.

We used LASSO regression for variable selection and regularization. In this study, we developed a LASSO (Least Absolute Shrinkage and Selection Operator) regression model based on the training cohort. We initially included 47 variables and used 10 - fold cross - validation to optimize the model parameters. During model training, we used deviance as the evaluation metric, determining the optimal λ value of 0. 00023 by minimizing the deviance. Subsequently, we constructed a nomogram using the identified variables to predict the risk of SIC in sepsis patients and evaluated its performance through discrimination and calibration analyses. We assessed the nomogram’s discriminatory performance using receiver operating characteristic (ROC) curves and evaluated the area under the curve (AUC). The clinical usefulness and net benefit of the new predictive model were evaluated in both the training and validation sets using decision curve analysis (DCA). A P - value of less than 0. 05 was considered statistically significant.

Results

Comparison of general clinical data

This study systematically analyzed extensive clinical data from sepsis and sepsis-induced coagulopathy (SIC) patients, revealing significant differences in clinical outcomes, therapeutic interventions, and pathophysiological characteristics between the two groups (Table 1).

Firstly, the hospital mortality rate was significantly higher in SIC patients compared to sepsis patients (24. 7% vs. 16. 3%, p < 0. 001), with a prolonged hospital stay observed in the SIC group (20. 86 days vs. 18. 72 days, p < 0. 001). These findings highlight the increased disease burden and treatment complexity associated with SIC. Although no significant differences were found in gender distribution between the two groups, SIC patients exhibited a greater reliance on intensive hemodynamic support and renal replacement therapy. Specifically, the usage rates of norepinephrine (52. 4% vs. 35. 9%, p < 0. 001), vasopressin (23. 8% vs. 11. 1%, p < 0. 001), and continuous renal replacement therapy (CRRT) (15. 4% vs. 5. 9%, p < 0. 001) were significantly higher, indicating the substantial circulatory support needs of SIC patients.

In the analysis of laboratory parameters, SIC patients exhibited more pronounced coagulopathy and metabolic disturbances. Compared to the sepsis group, SIC patients demonstrated significantly elevated INR (1. 6 vs. 1. 2, p < 0. 001) and prolonged PT, indicating severe imbalances in the coagulation and fibrinolytic systems. Elevated lactate levels (1. 9 vs. 1. 3 mmol/L, p < 0. 001) suggested inadequate tissue perfusion and increased metabolic stress. Furthermore, a marked decrease in platelet count reflected the progression of systemic inflammatory response and coagulopathy in SIC patients.

Notably, the SOFA score was significantly higher in SIC patients compared to sepsis patients (6 [4, 9] vs. 4 [3, 6], p < 0. 001), emphasizing the severity of multi-organ dysfunction as a primary driver of the elevated mortality in SIC.

Table 1 Characteristics of patients in the sepsis and SIC groups
Full size table

Development of the predictive nomogram

We developed a LASSO regression model based on the training cohort, initially including 47 variables. These variables encompassed the scores for respiratory, cardiovascular, hepatic, and renal functions, which are included in the SOFA score used for diagnosing sepsis-induced coagulopathy (SIC). Variables such as the use of vasopressors (e.g., dopamine, adrenaline, noradrenaline, dobutamine), mechanical ventilation, creatinine, and bilirubin were excluded. We then performed 10-fold cross-validation on the remaining variables, using deviance as the evaluation metric. The optimal λ value was selected to be 0. 00023, which minimized the deviance. The 11 key variables selected by LASSO regression encompassed a wide range of factors, including basic patient characteristics (e.g., gender), chronic conditions (e.g., hypertension), the use of CRRT, the SOFA score system, and laboratory indicators (e.g., lactate levels, red blood cell count, red cell distribution width, white blood cell count, platelet count, and INR) (See Fig. 2).

Fig. 2
figure 2

(a) The cross-validation result of LASSO-Logistic regression. (b) LASSO coefficient profiles of the variables. (a) The figure illustrates the cross-validation results of the LASSO-Logistic regression. The x-axis represents the logarithmic values of the regularization parameter λ, while the y-axis represents the mean error from the cross-validation. The solid line shows the trend of the mean error, and the two dashed lines indicate the upper and lower limits of the error, reflecting the stability of the model’s performance

Full size image

The variables selected by LASSO were incorporated into a multivariable logistic regression model for further analysis to assess their association with prognosis. The results (Table 2) indicate that male gender (OR = 1. 15, 95% CI: 1. 01–1. 29, P < 0. 001) is an independent risk factor for the occurrence of the outcome, whereas a history of hypertension (OR = 0. 85, 95% CI: 0. 83–0. 89, P < 0. 001) is negatively correlated with the outcome, suggesting a protective effect. The use of continuous renal replacement therapy (CRRT) (OR = 1. 35, 95% CI: 1. 26–1. 45, P < 0. 001) significantly increases the risk of the outcome. For each 1-point increase in the Sequential Organ Failure Assessment (SOFA) score, the risk of the outcome increases by 13% (OR = 1. 13, 95% CI: 1. 09–1. 16, P < 0. 001). For each 1-unit increase in platelet count, the risk decreases by 1% (OR = 0. 95, 95% CI: 0. 92–0. 97, P < 0. 001); for each 1-unit increase in red blood cell count, the risk decreases by 39% (OR = 0. 61, 95% CI: 0. 56–0. 67, P < 0. 001), indicating a protective effect. Conversely, for each 1-unit increase in red cell distribution width (RDW), the risk increases by 24% (OR = 1. 24, 95% CI: 1. 20–1. 27, P < 0. 001); for each 1-unit increase in lactate levels, the risk increases by 46% (OR = 1. 46, 95% CI: 1. 36–1. 56, P < 0. 001), with these indicators positively correlating with the occurrence of the outcome. Additionally, for each 1-unit increase in the International Normalized Ratio (INR), the risk increases by 16% (OR = 1. 16, 95% CI: 1. 13–1. 18, P < 0. 001), suggesting that coagulation dysfunction is closely associated with the outcome.

Based on multivariate logistic regression analysis, the above 10 independent risk factors were included in the training group to construct a nomogram prediction model for predicting the in-hospital mortality rate of patients with sepsis and coagulopathy. (Fig. 3)

Table 2 Multivariate logistic analysis for the included variables in the training set
Full size table
Fig. 3
figure 3

Development of a predictive risk model for SIC

Full size image

Validation of the nomogram model

The predictive model developed in this study demonstrated excellent discriminative ability, calibration, and clinical applicability in the training, validation, and external validation cohorts. In terms of discriminative ability, the results of the ROC curve analysis (Fig. 4), indicate an AUC of 0. 81 (95% CI: 0. 79–0. 83) in the training set, 0. 83 (95% CI: 0. 82–0. 84) in the validation set, and 0. 79 (95% CI: 0. 74–0. 84) in the external validation set, indicating strong performance in distinguishing positive and negative outcomes.

The calibration curve (Fig. 5) shows that the bias-corrected curve in the training set closely aligns with the ideal reference line, suggesting good internal fitting of the model. In the validation set, the predicted probabilities also showed good consistency with the actual incidence, indicating the model’s strong generalization ability in external data. Furthermore, the calibration curve from the external validation confirmed the model’s excellent predictive performance on new datasets, further validating its robustness.

Additionally, decision curve analysis (DCA), as shown in Fig. 6, was used to assess the net benefit of the model at different threshold probabilities. The results showed that within the common clinical decision threshold range (approximately 0. 1–0. 7) for both the training and validation sets, the model outperformed the “all-intervention” or “no-intervention” strategies, demonstrating high clinical decision value. The DCA curve from the external validation further confirmed the model’s net benefit at different clinical decision thresholds, highlighting its potential value in practical clinical applications.

Fig. 4
figure 4

(a) The ROC curve of the training group; (b) The ROC curve of the validation group; (c) The external validation curve is shown in the figure

Full size image
Fig. 5
figure 5

(a) The calibration model of the training group; (b) The calibration model of the validation group; (c) The calibration model of the validation group

Full size image
Fig. 6
figure 6

(a) The decision curve analysis of the training group (b) The decision curve analysis of the validation group (c) The decision curve analysis of the external validation group

Full size image

Discussion

This study demonstrates that male gender, the absence of hypertension, the use of continuous renal replacement therapy (CRRT), as well as the SOFA score, RBC count, RDW, WBC count, platelet count, lactate levels, and INR, are independent predictors for the development of sepsis-induced coagulopathy (SIC). Based on these factors, we developed an easy-to-use nomogram to predict the individual probability of sepsis patients developing SIC, and performed both internal and external data validation.

In our cohort, the incidence of SIC was 41. 3% (5145/12455), higher than the 22. 1% and 24. 2% reported in the German HYPRESS and SISPCT trials, respectively [10], but lower than the 53. 3% reported in a retrospective study conducted in China [11]. The mechanisms of SIC are highly complex, involving thrombin [12, 13], monocytes [14], neutrophils [15], pathogen-associated molecular patterns [16, 17], damage-associated molecular patterns [18], platelets [19, 20, endothelial injury [21], and impaired anticoagulation mechanisms [22].

Previous studies have explored the risk factors for SIC in sepsis patients. Han et al. found that elevated serum sodium levels were independently associated with increased SIC incidence and poor prognosis in sepsis patients [23]. Tonai et al. demonstrated an independent association between hypomagnesemia and DIC in sepsis patients [24]. Zhao et al. developed predictive models for sepsis patients progressing to SIC using machine learning techniques, incorporating key variables such as SOFA, INR, platelets, and lactate [25]. Gao et al. developed a nomogram to predict SIC in pediatric ICU patients, finding that age, fibrinogen, prothrombin time, CRP, lactate, and SOFA score were independent predictors of SIC in pediatric sepsis [5].

Our study found a higher SIC incidence in male patients. Previous research also indicated sex-based differences in sepsis incidence. Adrie et al. reported a higher incidence of severe sepsis in males [26], while Wichmann et al. observed a significantly lower incidence of severe sepsis and septic shock in female ICU patients [27]. Sperry et al. found that females had lower rates of multi-organ failure and nosocomial infections compared to males of the same age [28]. These differences may be related to hormonal influences and other factors affecting female immune function and inflammatory responses, potentially reducing female susceptibility to SIC [29,30,31].

No prior studies have indicated a relationship between hypertension and SIC. We hypothesize that this may be attributed to the widespread use of antihypertensive medications in hypertensive individuals, which could alleviate oxidative stress and reduce endothelial dysfunction. Xie et al. conducted a retrospective analysis of 33, 213 sepsis patients on antihypertensive therapy and found that the use of ACE inhibitors (ACEIs) and angiotensin receptor blockers (ARBs) was associated with reduced in-hospital mortality [32]. A similar finding was reported in a retrospective study conducted by Kim et al. in South Korea [33].

Patients undergoing CRRT are typically administered anticoagulants to prevent thrombosis in the extracorporeal circuit, but debates remain regarding the selection and dosing of anticoagulants. Coagulation disorders are a common complication among CRRT patients [34]. Moreover, renal impairment, often present in these patients, further contributes to coagulopathy in sepsis [35].

SOFA scores have been strongly associated with sepsis outcomes in numerous studies [36, 37]. Specifically, increased coagulation scores reflect the worsening imbalance between coagulation and fibrinolysis. Sepsis disrupts the coagulation system, often leading to disseminated intravascular coagulation (DIC), where proinflammatory cytokines activate coagulation pathways, creating a cycle of inflammation and coagulation [38]. Li et al. conducted a retrospective analysis of 548 patients with sepsis and identified platelet count (PLT), international normalized ratio (INR), and shock as independent risk factors for SIC progression [11]. Elevated RDW indicates heightened inflammation and stress. Yi et al. found that RDW progression effectively predicts SIC incidence in sepsis-related DIC [39]. Increased RDW may exacerbate mortality risk in severe sepsis [39], while elevated lactate levels reflect tissue hypoxia and metabolic dysregulation [40]. Both markers indicate SIC progression and highlight its complex pathophysiology, providing valuable early risk indicators for clinical use.

In a study by Eunji Ko et al., abnormal RBC count was found to appear early in lipopolysaccharide (LPS)-induced endotoxemia, an important indicator of early clinical manifestations in sepsis [41]. Katia Donadello et al. also observed that changes in RBC rheology, including reduced deformability and increased aggregation, occur early in sepsis patients, and over time, decreased RBC deformability is associated with poor outcomes. These changes may be related to the decrease in RBC count during the development of SIC [42]. Alterations in RBC function lead to changes in microcirculatory blood flow and cellular hypoxia in sepsis [43]. David E. Joyce et al. studied the interactions between leukocytes and endothelial cells in sepsis and explored the role of the Protein C pathway in regulating innate immune responses through its anti-inflammatory properties [44]. The Protein C pathway acts as a mediator between endothelial cells and the innate immune system’s leukocyte response. Activated Protein C (APC) has fibrinolytic, anti-inflammatory, and anti-apoptotic properties, counteracting thrombin and pro-inflammatory cytokines, thus acting as a regulator of endothelial cells and microvascular tone. Therefore, when the inflammatory response occurs [45], endothelial cells and the Protein C pathway are prone to damage, leading to the development of SIC.

Although this study has some innovative aspects, it has certain limitations. First, this retrospective analysis based on electronic medical records may be subject to selection bias, which may limit the generalizability of the results. However, to validate the model’s generalization ability, we conducted an external validation study, and the results indicated that the model performed as expected in an independent dataset, enhancing its reliability. Despite the good predictive consistency of the SIC risk prediction model across different risk intervals, its precision in extreme risk intervals needs improvement. The calibration curve showed that there might be some overestimation in the high-risk interval and slight underestimation in the low-risk interval, suggesting that the model still has room for optimization. Future research should further explore the model’s performance in broader populations to ensure its applicability and robustness in different clinical settings. Furthermore, future studies should consider incorporating new biomarkers and dynamic variables to improve the predictive accuracy of the model. Additionally, a deeper investigation into the interactions between variables will be essential for optimizing the model’s performance, helping to provide more precise and personalized decision support for clinical diagnosis and treatment.

Conclusion

This study developed and validated a nomogram for predicting the risk of sepsis-induced coagulopathy (SIC) in ICU patients with sepsis. We identified several key predictive factors, including the SOFA score, red cell distribution width (RDW), white blood cell count, platelet count, International Normalized Ratio (INR), and lactate levels, which were significantly associated with the occurrence of SIC. Notably, hypertension showed a potential protective effect in this study, offering new perspectives on the prevention and treatment of SIC. The developed nomogram model performed excellently in the training, validation, and external validation cohorts, although further research should be conducted to verify its applicability and robustness in different medical institutions and patient populations. Additionally, incorporating more biomarkers and dynamic data into future studies is recommended to further enhance the model’s predictive capability. These efforts aim to provide strong support for improving the prognosis of sepsis-related SIC patients and offer valuable guidance for clinical practice.

Data availability

The datasets generated and/or analyzed during the current study are available in the MIMIC-IV 2.2 database (https:// physi onet. org/ conte nt/ mimic iv/2. 2/). This database can be used after obtaining the official permission of the database. The data from this study can be obtained by contacting the author(Jianyue yang, e-mail: y182328@163.com).

Notes

  1. Johnson A, Bulgarelli L, Pollard T, et al. Mimic-iv[J]. PhysioNet. Available online at: https://physionet.org/content/mimiciv/1. 0/(accessed August 23, 2021), 2020:49–55.

References

  1. T I, J H, M L, Jh L. Thromboinflammation in acute injury: infections, heatstroke, and trauma. Journal of thrombosis and haemostasis: JTH [homepage on the Internet] 2024 [cited 2024 Dec 6];22(1). Available from: https://pubmed.ncbi.nlm.nih.gov/37541590/

  2. Iba T, Levy JH, Yamakawa K, et al. Proposal of a two-step process for the diagnosis of sepsis-induced disseminated intravascular coagulation. J Thromb Haemost. 2019;17(8):1265–8.

    Article  PubMed  Google Scholar 

  3. Matsuoka T, Yamakawa K, Iba T, Homma K, Sasaki J. Persistent and Late-Onset disseminated intravascular coagulation are closely related to poor prognosis in patients with Sepsis. Thromb Haemost. 2024;124(5):399–407.

    Article  PubMed  Google Scholar 

  4. Helms J, Iba T, Connors JM, et al. How to manage coagulopathies in critically ill patients. Intensive Care Med. 2023;49(3):273–90.

    Article  PubMed  Google Scholar 

  5. Gao Y, Fu Y, Guo E, et al. Novel nomogram for the prediction of sepsis-induced coagulopathy in the PICU: A multicentre retrospective study. Thromb Res. 2024;243:109152.

    Article  CAS  PubMed  Google Scholar 

  6. Umemura Y, Yamakawa K, Hayakawa M, Hamasaki T, Fujimi S. Japan septic disseminated intravascular coagulation (J-Septic DIC) study group. Screening itself for disseminated intravascular coagulation May reduce mortality in sepsis: A nationwide multicenter registry in Japan. Thromb Res. 2018;161:60–6.

    Article  CAS  PubMed  Google Scholar 

  7. Gando S, Iba T, Eguchi Y, et al. A multicenter, prospective validation of disseminated intravascular coagulation diagnostic criteria for critically ill patients: comparing current criteria. Crit Care Med. 2006;34(3):625–31.

    Article  PubMed  Google Scholar 

  8. Yamakawa K, Umemura Y, Murao S, Hayakawa M, Fujimi S. Optimal timing and early intervention with anticoagulant therapy for Sepsis-Induced disseminated intravascular coagulation. Clin Appl Thromb Hemost. 2019;25:1076029619835055.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Yamakawa K, Yoshimura J, Ito T, Hayakawa M, Hamasaki T, Fujimi S. External validation of the two newly proposed criteria for assessing coagulopathy in Sepsis. Thromb Haemost. 2019;119(2):203–12.

    Article  PubMed  Google Scholar 

  10. Schmoch T, Möhnle P, Weigand MA, et al. The prevalence of sepsis-induced coagulopathy in patients with sepsis - a secondary analysis of two German multicenter randomized controlled trials. Ann Intensive Care. 2023;13(1):3.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Li Y, Zhang L, Wang Y, et al. Development and validation of a nomogram for predicting Sepsis-Induced coagulopathy in septic patients: mixed retrospective and prospective cohort study. Thromb Haemost; 2024.

  12. B O TF. Increased tissue thromboplastin activity in monocytes of patients with meningococcal infection: related to an unfavourable prognosis. Thrombosis and haemostasis [homepage on the Internet] 1983 [cited 2024 Dec 6];49(1). Available from: https://pubmed.ncbi.nlm.nih.gov/6845273/

  13. Panes O, Matus V, Sáez CG, Quiroga T, Pereira J, Mezzano D. Human platelets synthesize and express functional tissue factor. Blood. 2007;109(12):5242–50.

    Article  CAS  PubMed  Google Scholar 

  14. Grover SP, Mackman N. Tissue factor: an essential mediator of hemostasis and trigger of thrombosis. Arterioscler Thromb Vasc Biol. 2018;38(4):709–25.

    Article  CAS  PubMed  Google Scholar 

  15. Delabranche X, Helms J, Meziani F. Immunohaemostasis: a new view on haemostasis during sepsis. Ann Intensive Care. 2017;7(1):117.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Khakpour S, Wilhelmsen K, Hellman J. Vascular endothelial cell Toll-like receptor pathways in sepsis. Innate Immun. 2015;21(8):827–46.

    Article  CAS  PubMed  Google Scholar 

  17. Chen F, Zou L, Williams B, Chao W. Targeting Toll-Like receptors in sepsis: from bench to clinical trials. Antioxid Redox Signal. 2021;35(15):1324–39.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Ito T. PAMPs and damps as triggers for DIC. J Intensive Care. 2014;2(1):67.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Assinger A, Schrottmaier WC, Salzmann M, Rayes J. Platelets in sepsis: an update on experimental models and clinical data. Front Immunol. 2019;10:1687.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Stockschlaeder M, Schneppenheim R, Budde U. Update on von Willebrand factor multimers: focus on high-molecular-weight multimers and their role in hemostasis. Blood Coagul Fibrinolysis. 2014;25(3):206–16.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Fourrier F, Chopin C, Goudemand J, et al. Septic shock, multiple organ failure, and disseminated intravascular coagulation. Compared patterns of antithrombin III, protein C, and protein S deficiencies. Chest. 1992;101(3):816–23.

    Article  CAS  PubMed  Google Scholar 

  22. Chung M-C, Jorgensen SC, Popova TG, Bailey CL, Popov SG. Neutrophil elastase and syndecan shedding contribute to antithrombin depletion in murine anthrax. FEMS Immunol Med Microbiol. 2008;54(3):309–18.

    Article  CAS  PubMed  Google Scholar 

  23. Han Y, Duan J, Chen M, et al. Relationship between serum sodium level and sepsis-induced coagulopathy. Front Med (Lausanne). 2023;10:1324369.

    Article  PubMed  Google Scholar 

  24. Tonai K, Katayama S, Koyama K, et al. Association between hypomagnesemia and coagulopathy in sepsis: a retrospective observational study. BMC Anesthesiol. 2022;22(1):359.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Zhao QY, Liu LP, Luo JC, et al. A Machine-Learning approach for dynamic prediction of Sepsis-Induced coagulopathy in critically ill patients with Sepsis [J]. Front Med. 2020;7:637434.

    Article  Google Scholar 

  26. Adrie C, Alberti C, Chaix-Couturier C, et al. Epidemiology and economic evaluation of severe sepsis in France: age, severity, infection site, and place of acquisition (community, hospital, or intensive care unit) as determinants of workload and cost. J Crit Care. 2005;20(1):46–58.

    Article  PubMed  Google Scholar 

  27. Wichmann MW, Inthorn D, Andress HJ, Schildberg FW. Incidence and mortality of severe sepsis in surgical intensive care patients: the influence of patient gender on disease process and outcome. Intensive Care Med. 2000;26(2):167–72.

    Article  CAS  PubMed  Google Scholar 

  28. Sperry JL, Nathens AB, Frankel HL, et al. Characterization of the gender dimorphism after injury and hemorrhagic shock: are hormonal differences responsible? Crit Care Med. 2008;36(6):1838–45.

    Article  PubMed  Google Scholar 

  29. Schröder J, Kahlke V, Staubach KH, Zabel P, Stüber F. Gender differences in human sepsis. Arch Surg. 1998;133(11):1200–5.

    Article  PubMed  Google Scholar 

  30. Knöferl MW, Diodato MD, Angele MK, et al. Do female sex steroids adversely or beneficially affect the depressed immune responses in males after trauma-hemorrhage? Arch Surg. 2000;135(4):425–33.

    Article  PubMed  Google Scholar 

  31. Knöferl MW, Angele MK, Diodato MD, et al. Female sex hormones regulate macrophage function after trauma-hemorrhage and prevent increased death rate from subsequent sepsis. Ann Surg. 2002;235(1):105–12.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Hsieh M-S, How C-K, Hsieh VC-R, Chen P-C. Preadmission antihypertensive drug use and Sepsis outcome: impact of angiotensin-Converting enzyme inhibitors (ACEIs) and angiotensin receptor blockers (ARBs). Shock. 2020;53(4):407–15.

    Article  CAS  PubMed  Google Scholar 

  33. Kim J, Kim YA, Hwangbo B, et al. Effect of antihypertensive medications on Sepsis-Related outcomes: A Population-Based cohort study. Crit Care Med. 2019;47(5):e386–93.

    Article  CAS  PubMed  Google Scholar 

  34. Wu S, Xu T, Wu C, Lei X, Tian X. Continuous renal replacement therapy in sepsis-associated acute kidney injury: effects on inflammatory mediators and coagulation function. Asian J Surg. 2021;44(10):1254–9.

    Article  PubMed  Google Scholar 

  35. Helms J, Merdji H, Loewert S, et al. Disseminated intravascular coagulation is strongly associated with severe acute kidney injury in patients with septic shock. Ann Intensive Care. 2023;13(1):119.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Lee SY, Park MH, Oh DK, Lim C-M. Validation of adult Sepsis event and epidemiologic analysis of Sepsis prevalence and mortality using adult Sepsis event’s electronic health Records-Based sequential organ failure assessment criteria: A Single-Center study in South Korea. Crit Care Med. 2024;52(8):1173–82.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Alrawashdeh M, Klompas M, Rhee C. The impact of common variations in sequential organ failure assessment score calculation on Sepsis measurement using Sepsis-3 criteria: A retrospective analysis using electronic health record data. Crit Care Med. 2024;52(9):1380–90.

    Article  CAS  PubMed  Google Scholar 

  38. Ren C, Li Y-X, Xia D-M, et al. Sepsis-Associated coagulopathy predicts hospital mortality in critically ill patients with postoperative Sepsis. Front Med (Lausanne). 2022;9:783234.

    Article  PubMed  Google Scholar 

  39. Fan YW, Liu D, Chen JM, Li WJ, Gao CJ. Fluctuation in red cell distribution width predicts disseminated intravascular coagulation morbidity and mortality in sepsis: a retrospective single-center study. Minerva Anestesiol. 2021;87(1):52–64.

    Article  PubMed  Google Scholar 

  40. Zhang T, Chen L, Kueth G, et al. Lactate’s impact on immune cells in sepsis: unraveling the complex interplay. Front Immunol. 2024;15:1483400.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Ko E, Youn JM, Park HS, Song M, Koh KH, Lim CH. Early red blood cell abnormalities as a clinical variable in sepsis diagnosis. Clin Hemorheol Microcirc. 2018;70(3):355–63.

    Article  CAS  PubMed  Google Scholar 

  42. Donadello K, Piagnerelli M, Reggiori G, et al. Reduced red blood cell deformability over time is associated with a poor outcome in septic patients. Microvasc Res. 2015;101:8–14.

    Article  PubMed  Google Scholar 

  43. Piagnerelli M, Boudjeltia KZ, Vanhaeverbeek M, Vincent J-L. Red blood cell rheology in sepsis. Intensive Care Med. 2003;29(7):1052–61.

    Article  CAS  PubMed  Google Scholar 

  44. Joyce DE, Nelson DR, Grinnell BW. Leukocyte and endothelial cell interactions in sepsis: relevance of the protein C pathway. Crit Care Med. 2004;32(5 Suppl):S280–286.

    Article  PubMed  Google Scholar 

  45. Joffre J, Hellman J, Ince C, Ait-Oufella H. Endothelial responses in Sepsis. Am J Respir Crit Care Med. 2020;202(3):361–70.

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

Not applicable.

Funding

Not applicable.

Author information

Authors and Affiliations

  1. Department of Critical Care Medicine, Xingtai People’s Hospital, Hebei, 054001, China

    Jian-Yue Yang, Li-Li Li & Su-Zhen Fu

Authors
  1. Jian-Yue Yang
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Li-Li Li
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Su-Zhen Fu
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

Y research design, statistical analysis, manuscript writing. L extract data from the mimic database, build machine learning models. F guidance for research design, manuscript review. All authors reviewed the manuscript.

Corresponding author

Correspondence to Su-Zhen Fu.

Ethics declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yang, JY., Li, LL. & Fu, SZ. Association analysis of sepsis progression to sepsis-induced coagulopathy: a study based on the MIMIC-IV database. BMC Infect Dis 25, 573 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12879-025-10972-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12879-025-10972-w

Keywords

BMC Infectious Diseases

ISSN: 1471-2334

Contact us