Prediction of poor prognosis in patients with sepsis-induced coagulopathy

Abstract

This study aims to evaluate the prognostic value of procalcitonin (PCT), lactate (Lac), and sequential organ failure assessment (SOFA) score in predicting poor outcomes in sepsis-induced coagulopathy (SIC) and develop a predictive model. Data from 96 SIC patients in Hebei General Hospital (September 2020–2023) were analyzed. Patients were divided into survival and death groups based on 28-day outcomes. General data, complications, infection sites, and laboratory parameters were compared. Logistic regression identified independent risk factors, and receiver operating characteristic curve analysis assessed the predictive value of PCT, Lac, SOFA scores, and the combined model. The 28-day mortality was 36.46%. Non-survivors had higher PCT, Lac, SOFA scores, prothrombin time-international normalized ratio, white blood cell count, and intensive care unit stays (P < .05). PCT, Lac, and SOFA scores were independent predictors. The predictive model equation was logit (P = death group) = 0.157 × SOFA score + 0.027 × PCT + 0.187 × Lac − 4.112. The areas under the curve (AUC) of the combined predictive model was 0.809 (95%, confidence intervals [95% CI]: 0.716–0.882), outperformed individual indicators with improved sensitivity and specificity. A combined model of PCT, Lac, and SOFA scores effectively predicts SIC prognosis, aiding clinical decision-making.

1. Introduction

Sepsis is a serious condition where the body’s inappropriate response to infection can lead to organ failure.^[1] It is a major reason for patients being admitted to the intensive care unit (ICU), with around 20% to 30% of those in the ICU affected by sepsis.^[2] If not treated promptly, the mortality rate can be shockingly high, ranging from 70% to 90%.^[3,4] Despite advancements in critical care, sepsis continues to pose a significant challenge due to its complexity, varied symptoms, and high mortality risk.

Recent research emphasizes the critical importance of coagulation abnormalities in sepsis, with studies showing that 50% to 70% of patients with sepsis have coagulation abnormalities, and nearly 35% may develop disseminated intravascular coagulation (DIC), a serious and often deadly complication.^[5,6] A milder form of coagulopathy, known as sepsis-induced coagulopathy (SIC), often occurs in the early stages of sepsis and is associated with an increased risk of organ failure and death.^[7] Therefore, recognizing and treating SIC early is crucial for improving patient outcomes and lowering mortality rates.

In clinical settings, the sequential organ failure assessment (SOFA) score is commonly used to predict mortality in the ICU and is included in the diagnostic criteria for sepsis.^[8] While the SOFA score is effective in evaluating overall organ dysfunction, it does not specifically address the risks associated with coagulopathy, especially for early detection and prognosis of SIC.^[9] To overcome the limitations of relying solely on the SOFA score, incorporating additional biomarkers could improve the accuracy of risk assessment for patients with SIC.^[10]

Recent studies indicate a correlation between serum procalcitonin (PCT) and lactate (Lac) levels and the severity of sepsis, suggesting that these markers may significantly enhance prognostic accuracy.^[11,12] PCT is a recognized biomarker that signifies systemic inflammation and bacterial infection, while elevated lactate levels point to impaired tissue perfusion and cellular hypoxia, both of which are linked to unfavorable clinical outcomes in patients with sepsis.^[13] By integrating these biomarkers with the SOFA score, a more comprehensive assessment of SIC prognosis can be achieved, allowing for earlier detection and intervention.

To meet this clinical need, our study, conducted in the ICU of Hebei General Hospital, aims to develop and assess a combined prognostic model that incorporates PCT, Lac, and the SOFA score specifically for SIC patients. This model aspires to enhance the accuracy of risk evaluation, thereby improving early decision-making and ultimately increasing survival rates in this vulnerable patient population.

2. Materials and methods

2.1. Ethics statement

The study was approved by the Hebei General Hospital Ethics Committee, with the Institutional Review Board Approval Number 2023178, granted on November 29, 2023. This approval ensured that all research procedures adhered to ethical guidelines and complied with the Declaration of Helsinki and relevant national regulations.

2.2. Data collection

A retrospective analysis was conducted on 96 SIC patients admitted to the ICU of Hebei General Hospital between September 2020 and September 2023. Inclusion criteria were as follows: meeting the diagnosis of sepsis according to Sepsis-3.0 criteria,^[1] with a clear site of infection and a SOFA score ≥ 2 points; meeting the diagnostic criteria for SIC proposed by the International Society for Thrombosis and Haemostasis Scientific Standardization Committee in 2017.^[14] SOFA score was calculated based on the evaluation of 6 organ systems, with a scoring range of 0 to 4 for each system, according to the Sepsis-3 criteria.^[1] The assessed organ systems included respiratory (PaO₂/FiO₂ ratio), cardiovascular (mean arterial pressure and vasopressor use), hepatic (bilirubin levels), renal (creatinine or urine output), coagulation (platelet count), and neurological (Glasgow Coma Scale) functions. Exclusion criteria were: age under 18 years old; history of hematologic disorders, immune system disorders, tumors, psychiatric disorders, pregnancy, or other conditions deemed unsuitable for inclusion; ICU stay <24 hours or death within 24 hours of ICU admission; incomplete clinical data or laboratory data, cases with unclear ICU prognosis, or patients who refused treatment. The patient screening process is shown in Figure 1.

Figure 1.

Patient screening flow.

2.3. Research methodology

A retrospective study method was employed to collect general data from all patients diagnosed with SIC within 24 hours of admission. This included age, gender, comorbidities, site of infection, as well as laboratory parameters such as PCT and Lac levels. SOFA scores were also collected.

2.4. PCT and Lac testing

Blood specimens were collected from the patients and analyzed using the PCT assay kit purchased from Guangzhou Wondfo Biotech Co., Ltd., following the instructions provided, on a compatible dry fluorescence immunoassay analyzer. Lac levels were measured using a fully automated blood gas, electrolyte, and biochemical analyzer, manufactured by Roche Diagnostics GmbH, with the model cobas b 123 POC system. Specimen handling and instrument operation were conducted according to the manufacturer’s instructions. All patients received standard treatments including antimicrobial therapy, fluid resuscitation, mechanical ventilation, as per the sepsis guidelines. They were categorized into survival and non-survival groups based on their survival status at 28 days (patients who had critical conditions after rescue efforts and were discharged due to the difficulty in maintaining circulation were considered part of the non-survival group).

2.5. Observational indicators

Clinical data, PCT, Lac levels, and SOFA scores were compared between the 2 groups of patients. A multivariable logistic regression analysis was conducted to explore the risk factors associated with 28-day mortality in SIC patients. Additionally, the predictive value of PCT, Lac, SOFA scores, and their combination for the prognosis of SIC patients was compared.

2.6. Statistical analysis

Statistical analysis was performed using SPSS 27.0 (Chicago) and MedCalc 22.0 software (Ostend, Belgium). The Shapiro–Wilk test was used to assess the normality of continuous variables. Normally distributed continuous variables were presented as mean ± standard deviation ($\overline{X}$± SD), and between-group comparisons were conducted using the independent samples t-test. Non-normally distributed continuous variables were expressed as median and interquartile range (M[P25, P75]), and between-group comparisons were performed using the Mann–Whitney U test. Categorical variables were presented as frequency (percentage) (n[%]) and compared between groups using the chi-square test. Binary logistic regression analysis was utilized to identify independent risk factors for 28-day mortality in SIC patients. receiver operating characteristic curve analysis was employed to determine the area under the curve (AUC) for each risk factor, and the Delong nonparametric method was applied to compare the AUC of different indicators’ receiver operating characteristic curves, assessing the predictive value of PCT, Lac, SOFA scores, and their combined model for adverse outcomes in SIC patients. A P-value < .05 was considered statistically significant.

3. Results

3.1. Baseline characteristics

This study included a total of 96 patients, who were divided into a survival group of 61 cases and a death group of 35 cases based on their 28-day survival status, with an overall mortality rate of 36.46% (35/96). The results showed that patients in the death group had higher age, PCT, Lac, SOFA scores, PT-INR, white blood cell, and ICU stay time compared to those in the survival group, while platelet (PLT) count was lower in the survival group, with statistically significant differences (P < .05). The comparison of baseline characteristics between the 2 groups of patients is shown in Table 1.

Table 1.

Characteristics of patients in survival and death group of SIC patients.

Variable	Survival group (n = 61)	Death group (n = 35)	t/Z/χ²	P
Age, years	69 (60–78)	80 (62–85)	−2.007	.045*
Male sex	38 (62.30)	21 (60.00)	0.049	.824
Complication, %
Coronary heart disease	12 (19.67)	11 (31.43)	1.687	.194
Hypertension	30 (49.18)	17 (48.57)	0.003	.954
Diabetes	23 (37.70)	8 (22.86)	2.242	.134
Cerebrovascular disease	19 (31.15)	17 (48.57)	2.881	.09
Chronic renal disease	8 (13.11)	8 (22.86)	1.520	.218
Primary infection site, %
Lung	30 (49.18)	23 (65.71)	2.459	.117
Abdomen	28 (45.90)	16 (45.71)	0.000	.986
Urinary tract	16 (26.23)	4 (11.43)	2.954	.086
Others	23 (37.70)	15 (42.86)	0.247	.619
Length of stay in ICU, d	8 (3–16)	12 (6.5–23)	−2.106	.035*
SOFA score, total	8 (7–11)	13 (9–14)	−3.447	.001*
Lac, mmol/L	3.41 (2.06–5.84)	6.26 (3.84–8.38)	−3.555	<.001*
Albumin, g/L	26.50 ± 4.24	24.73 ± 4.87	1.864	.066
Potassium, mmol/L	3.8 (3.6–4.3)	4.2 (3.6–4.55)	−1.457	.145
PCT, ng/mL	19.64 (4.71–40.94)	47.31 (20.92–67.59)	−3.483	<.001*
INR	1.5 (1.37–1.7)	1.63 (1.41–2.24)	−2.094	.036*
WBC,109/L	13.30 (6.93–18.54)	16.37 (11.36–19.41)	−2.025	.043*
PLT, 109/L	82 (52–15)	67 (42.5–93.5)	−2.147	.032*
NEU, 109/L	11.47 (6.41–21.42)	13.55 (6.61–17.24)	−0.453	.651
LYM, 109/L	0.56 (0.35–0.72)	0.47 (0.29–0.82)	−0.259	.796
MON, 109/L	0.30 (0.13–0.52)	0.28 (0.09–0.46)	−1.062	.288
Hb, g/L	105.00 (93.00–123.00)	92.00 (84.00–106.50)	−1.953	.051
RDW, fL	47.50 (43.40–53.90)	49.50 (46.25–56.55)	−1.774	.076
PDW, fL	14.30 (11.85–18.05)	14.60 (12.60–17.50)	−0.175	.861
PCT	0.10 (0.07–0.12)	0.10 (0.07–0.13)	−0.413	.68
MPV, fL	11.79 ± 1.21	11.88 ± 1.15	−0.337	.737
LPR	38.56 ± 9.28	39.22 ± 9.05	−0.329	.743

Hb = hemoglobin, ICU = intensive care unit, INR = International Normalized Ratio of Prothrombin Time, Lac = lactate, LPR = large platelet ratio, LYM = lymphocyte count, MON = monocyte count, MPV = mean platelet volume, NEU = neutrophil count, PCT = plateletcrit, PCT = procalcitonin, PDW = platelet distribution width, PLT = platelet count, RDW = red blood cell distribution width, SIC = sepsis-induced coagulopathy, SOFA score = sequential organ failure assessment score, WBC = white blood cell count.

^* P < .05 indicates statistical significance.

3.2. Logistic regression analysis

To avoid multicollinearity issues, a multivariable logistic regression analysis was conducted with the outcome as the dependent variable and age, PCT, Lac, SOFA score, and white blood cell as independent variables. The results indicated that PCT (odds ratios [OR] = 1.026; 95% CI = 1.006–1.047), Lac (OR = 1.194; 95% CI = 1.018–1.400), and SOFA score (OR = 1.169; 95% CI = 1.025–1.333) were independent risk factors for 28-day mortality in SIC patients. A joint prediction model was constructed using these independent risk factors, with the equation: logit (P = death group) = 0.157 × SOFA score + 0.027 × PCT + 0.187 × Lac −4.112. Please refer to Table 2 for details.

Table 2.

Multivariate logistics regression results of 28-day prognosis in SIC patients.

Factors	β	SE	Wald	P	OR	95% CI
						Minimum	Maximum
Age	0.026	0.018	1.975	.16	1.026	0.990	1.064
SOFA score	0.156	0.067	5.388	.02*	1.169	1.025	1.333
PCT	0.026	0.01	6.342	.012*	1.026	1.006	1.047
Lac	0.177	0.081	4.765	.029*	1.194	1.018	1.400
WBC	0.035	0.038	0.824	.364	1.035	0.961	1.116
Quantity	−6.352	1.693	14.069	0	0.002

95% CI = 95% confidence intervals, Lac = lactate, OR = odds ratio, PCT = procalcitonin, SIC = sepsis-induced coagulopathy, SOFA score = sequential organ failure assessment score, WBC = white blood cell count.

^* P < .05 indicates statistical significance.

3.3. Predictive value of PCT, Lac and SOFA score

The AUC analysis was performed to evaluate the 28-day survival outcome in SIC patients. For PCT predicting the risk of 28-day mortality in SIC patients, the AUC was 0.714 (95% CI = 0.613–0.802), with a sensitivity of 0.543, specificity of 0.836, optimal cutoff value of 46.36 ng/mL, and Youden index of 0.379. For Lac predicting the risk of 28-day mortality in SIC patients, the AUC was 0.719 (95% CI = 0.618–0.806), with a sensitivity of 0.543, specificity of 0.787, optimal cutoff value of 6.09 mmol/L, and Youden index of 0.330. For SOFA score predicting the risk of 28-day mortality in SIC patients, the AUC was 0.711 (95% CI = 0.610–0.799), with a sensitivity of 0.600, specificity of 0.820, optimal cutoff value of 12, and Youden index of 0.420. The efficacy of the combined prediction of the 3 factors for predicting the risk of 28-day mortality in SIC patients was superior to that of individual indicators, with an AUC of 0.809 (95% CI = 0.716–0.882), and high sensitivity (0.800) and specificity (0.800). Please refer to Table 3 and Figure 2 for details.

Table 3.

Predictive value of different indicators and prediction models for 28-day prognosis of SIC patients.

	AUC	Cutoff value	Sensitivity, %	Specificity, %	Youden index	P	95% CI
							Minimum	Maximum
SOFA score, total	0.711	12	60.00	81.97	0.420	<.001*	0.610	0.799
PCT, ng/mL	0.714	46.36	54.29	83.61	0.379	<.001*	0.613	0.802
Lac, mmol/L	0.719	6.09	54.29	78.69	0.330	<.001*	0.618	0.806
All	0.809	0.293	80.00	80.00	0.538	<.001*	0.716	0.882

95% CI = 95% confidence intervals, AUC = area under the curve, Lac = lactate, PCT = procalcitonin, ROC = receiver operating characteristic, SIC = sepsis-induced coagulopathy, SOFA score = sequential organ failure assessment score.

^* P < .05 indicates statistical significance.

Figure 2.

Predictive value (ROC) of different indicators and prediction models for 28-day prognosis of SIC patients. ROC = receiver operating characteristic, SIC = sepsis-induced coagulopathy.

The DeLong test results showed that the combined predictive model exhibited superior predictive performance for the 28-day prognosis of SIC patients compared to individual predictors (P < .05). Please refer to Table 4 for details.

Table 4.

Quality comparison of ROC curve AUC of PCT, Lac, SOFA score and the combination of the 3 to evaluate the prognosis of SIC patients.

Contrast factor or model	AUC difference	95% CI		Z	P
		Minimum	Maximum
PCT and all	0.095	0.004	0.186	2.049	.040*
Lac and all	0.091	0.002	0.180	1.998	.046*
SOFA score and all	0.098	0.004	0.192	2.042	.041*

95% CI = 95% confidence intervals, AUC = area under the curve, Lac = lactate, PCT = procalcitonin, ROC = receiver operating characteristic, SIC = sepsis-induced coagulopathy, SOFA score = sequential organ failure assessment score.

^* P < .05 indicates statistical significance.

4. Discussion

Sepsis is a severe medical emergency that poses a significant threat to human health.^[15] As a systemic inflammatory response syndrome, any infection in the body has the potential to progress to sepsis, with pulmonary infections being considered the primary site of origin in septic patients.^[16] The alterations in coagulation function in septic patients represent a dynamic process, starting with coagulation dysfunction, gradually progressing to SIC, and ultimately culminating in DIC.^[17] Nearly all septic patients experience coagulation abnormalities, which are closely associated with the occurrence of multiple organ failure and increased mortality rates.^[18,19]

During SIC, pathogens and inflammatory factors promote widespread thrombus formation through mechanisms such as upregulation of the procoagulant pathway, downregulation of physiological anticoagulant pathways, and inhibition of fibrinolysis.^[20] Thrombin, as a key mediator of inflammation and coagulation regulation, can bind to protease-activated receptor-1 (PAR-1) expressed by substances such as monocytes and neutrophils, thereby upregulating proinflammatory and procoagulant responses.^[21,22] Additionally, the release of large amounts of inflammatory factors can lead to neutrophil-endothelial cell adhesion, activating complement and coagulation cascade reactions.^[23] Complement C5a, by dissolving cells and/or bacterial pathogens, releases damage-associated molecular patterns and/or pathogen-associated molecular patterns and other cellular components that promote coagulation dysfunction.^[24] Monocytes/macrophages, as the frontline responders to phagocytosis of pathogens, detect, capture, and localize bacteria by sensing specific molecular patterns.^[25] They also express pattern recognition receptors, including toll-like receptors, Fcγ receptors, etc, to identify pathogen-associated molecular patterns and trigger the release of proinflammatory and anti-inflammatory mediators, thereby transmitting damage-associated molecular patterns and initiating a series of vicious cycles of inflammation and coagulation.^[26,27] Therefore, early identification of SIC patients, understanding their prognostic risk factors, and adopting targeted therapies for active intervention are crucial.

Studies have shown that PCT, Lac, and SOFA score have certain reference value for assessing the severity of SIC in patients, but each has limitations in predicting their prognosis.^[28–30] Therefore, this study explores the predictive value of a combined model of these 3 factors in hopes of finding a more accurate method for assessing the prognosis of SIC. Iba et al conducted a study on 2516 SIC patients in Japan and found a high 28-day mortality rate of 38.4%.^[14] This study collected data from 96 SIC patients, with a 28-day mortality rate of 36.46% (35/96), which is consistent with recent research results. Multivariate logistic regression analysis showed that PCT, Lac, and SOFA score were independent risk factors for 28-day mortality in SIC patients.

Procalcitonin is a glycoprotein related to calcium homeostasis and serves as the precursor of calcitonin, produced by the C cells in the thyroid gland.^[31] Under physiological conditions, PCT is almost exclusively produced by the thyroid gland and is undetectable in serum samples from healthy individuals. However, in pathological states, upregulation of proinflammatory factors leads to increased expression of the CALC-1 gene, resulting in a significant elevation of PCT levels up to 10,000 times.^[32] During the process of SIC, elevated PCT levels lead to increased levels of inflammatory factors, which can further stimulate the release of von Willebrand factor from vascular endothelium, thereby promoting microthrombus formation and exacerbating coagulation dysfunction.^[33] Research has found that PCT levels in SIC patients are significantly higher than those in septic patients without obvious coagulation dysfunction.^[34] Chen Zhenhua et al also found that the higher the PCT levels in confirmed SIC patients, the higher the mortality rate.^[35] This study shows that in the deceased group, PCT levels are significantly higher than those in the survival group. Therefore, high PCT levels are a poor prognostic factor for SIC patients. This further confirms the potential role of PCT in predicting mortality rates, but more multicenter, large-sample, prospective, and long-term follow-up high-quality randomized controlled trials are needed for validation.

Lactate is a key marker used to assess microcirculatory dysfunction in sepsis. It indicates tissue perfusion and oxygen metabolism and is recommended by several guidelines for evaluating the prognosis of patients with sepsis.^[36] In sepsis, changes in the microcirculation increase heterogeneity of blood flow, further limiting tissue oxygenation. Additionally, hepatic and renal dysfunction in septic patients may decrease lactate clearance, leading to reduced lactate utilization and excretion, resulting in elevated blood lactate levels.^[37] High lactate levels are negatively correlated with the prognosis of patients with SIC, possibly due to 2 main reasons: firstly, elevated lactate impairs endothelial cells, altering endothelial cell permeability, thereby initiating exogenous coagulation and causing coagulation dysfunction.^[38,39] Secondly, elevated lactate can lead to acidosis, and studies have found that acidosis can inhibit thrombin generation. Since impaired coagulation function in the body may form microthrombi, further exacerbating peripheral circulation hypoperfusion, lactate continues to rise, and the vicious cycle of metabolism and coagulation will adversely affect patients.^[40,41] This study showed that lactate levels in the deceased group were significantly higher than those in the survival group, and multifactorial logistic regression analysis suggested that lactate is an independent risk factor for 28-day mortality in SIC patients. Therefore, monitoring changes in lactate levels during the course of SIC can early identify tissue hypoperfusion, understand the degree of tissue organ hypoxia, and guide clinicians to actively provide fluid resuscitation to maintain internal environment stability and improve prognosis.

The SOFA score is a systematic tool for assessing the severity of the condition based on biochemical indicators and organ function. It plays a significant role in predicting the prognosis of critically ill patients. Studies have demonstrated that compared to the systemic inflammatory response syndrome score, the Quick SOFA (qSOFA) score, and the acute physiology and chronic health evaluation II score, the SOFA score has higher accuracy in predicting in-hospital mortality in adult patients with suspected infection in the ICU.^[42,43] Iba et al found that the SOFA score showed the strongest correlation with 28-day mortality in septic DIC patients undergoing anticoagulant therapy, compared to the Japanese Association of Acute Medicine DIC score.^[44] This study showed that in the deceased group, the SOFA score was significantly higher than in the survival group, and this indicator had high specificity for assessing patient prognosis, although its sensitivity was not high. Therefore, improving predictive accuracy requires considering other clinical indicators.

The results of this study demonstrate that PCT, Lac, and SOFA score are independent risk factors affecting the prognosis of SIC patients. The combined predictive model comprising these 3 factors exhibits good calibration with an AUC of 0.809 (95% CI: 0.716–0.882), and both sensitivity and specificity are 0.800, higher than the AUC obtained by individual predictors alone. Therefore, during the diagnosis and treatment of this disease, utilizing this predictive model for prognostic assessment enables proactive clinical interventions, thereby preventing deterioration of the condition and improving patient outcomes.

Over the past few years, the COVID-19 outbreak has posed an unprecedented challenge to global healthcare systems.^[45] While COVID-19 primarily affects the respiratory system, it is also known to cause hematological abnormalities and hyperactivation of the immune system, leading to multi-organ damage.^[46,47] These features are relevant to the pathophysiology of SIC, particularly in terms of coagulation dysfunction and systemic inflammation. For instance, the neutrophil-to-lymphocyte ratio has been shown to be a reliable marker of systemic inflammation and immune response in COVID-19.^[48] Similarly, in SIC, the imbalance between neutrophils and lymphocytes can reflect the severity of the inflammatory response and coagulation dysfunction. The systemic inflammatory index, which incorporates platelet count along with neutrophil-to-lymphocyte ratio, provides a more comprehensive assessment of systemic inflammation, relevant to both COVID-19 and SIC.^[48] Additionally, the CoMPred tool, which integrates multiple laboratory findings to predict mortality in COVID-19 patients, highlights the importance of combining various biomarkers to assess disease severity.^[49] This approach is also applicable to SIC, where a combination of markers can provide a more accurate prognosis. This study is about modeling based on biomarkers and disease severity score multimetrics commonly used in ICUs to assess the prognosis of SIC patients.

This study has several limitations that should be acknowledged. Firstly, it is a single-center, retrospective study with a relatively small sample size meeting the inclusion criteria. Efforts are currently underway to collect multicenter case data to validate the study results with an external dataset in the future. Secondly, the SIC diagnostic criteria used in this study are based on the Sepsis-3.0 diagnostic criteria, resulting in a smaller cohort compared to other standards such as the Japanese Society on Thrombosis and Hemostasis, the Chinese DIC scoring system, and the Korean Society of Thrombosis and Hemostasis. Again, our study did not include other biological parameters such as C-reactive protein (CRP), interleukin-6 (IL-6), and other inflammatory markers, which have been shown to play significant roles in the pathophysiology of SIC and sepsis.^[34,50,51] The exclusion of these markers may limit the comprehensiveness of our predictive model. Future studies should consider incorporating a broader range of biomarkers to improve the predictive accuracy of prognostic models in SIC. Lastly, dynamic assessment of patient prognosis is more valuable; however, this study only collected indicators within 24 hours after SIC diagnosis and explored their impact on the prognosis of SIC patients.

5. Conclusions

In summary, patients in the SIC mortality group exhibited higher levels of PCT, Lac, and SOFA scores compared to the survival group. The combined use of PCT, Lac, and SOFA scores demonstrated higher predictive efficacy for SIC prognosis, thereby enhancing the accuracy of early prediction. This study, for the first time, applies the aforementioned indicators in combination to assess the prognosis of SIC patients, offering valuable insights for guiding the timing of clinical interventions.

Author contributions

Conceptualization: Ruimin Tan, Zinan Yang.

Formal analysis: Zhe Li.

Funding acquisition: Quansheng Du.

Investigation: Zhe Li.

Methodology: Ruimin Tan, He Guo.

Resources: Ruimin Tan, Quansheng Du.

Supervision: Xumin Han.

Writing – original draft: Ruimin Tan, Chen Ge.

Writing – review & editing: Ruimin Tan.