Keywords sepsis - sepsis-induced coagulopathy - nomogram - prediction - intensive care unit
Background
Sepsis is life-threatening organ dysfunction caused by a dysregulated host response
to infection.[1 ] It poses a significant threat to the survival of patients admitted to the intensive
care unit (ICU). The incidence and mortality of sepsis remain high and it is one of
the leading causes of death in the ICU worldwide.[2 ] The incidence of coagulopathy, which is reportedly responsible for poor outcomes,
is commonly seen among patients with sepsis.[3 ]
[4 ] Sepsis-induced coagulopathy (SIC) was proposed in 2017 by the Scientific Standardization
Committee on Disseminated Intravascular Coagulopathy (DIC) of the International Society
on Thrombosis and Haemostasis to categorize patients with “sepsis and coagulation
disorders” and was designed to fit the new sepsis definition. So far, the diagnostic
criteria of SIC consist of three items, namely, platelet count, international normalized
ratio (INR), and total Sequential Organ Failure Assessment (SOFA) score which only
contains four items (respiratory SOFA, cardiovascular SOFA, hepatic SOFA, and renal
SOFA). The score of total SOFA is defined as 2 if the total score exceeded 2. SIC
is defined as a score ≥4 and the score system for SIC is listed in [Supplementary Table S1 ] (available in the online version).[5 ]
In a recent observational survey conducted in Japan, 29% of 1,895 sepsis patients
treated in ICUs were diagnosed with SIC.[6 ] A secondary analysis of two randomized controlled trials in Europe demonstrated
that SIC prevalence was 22.1% (the HYPRESS trial) and 24.2% (the SISPCT trial).[7 ] Coagulation abnormalities are a serious complication in almost all septic patients.[8 ] The clinical manifestations of coagulation abnormalities range from early thrombocytopenia
to late DIC, which often leads to multiple organ dysfunction syndrome and a higher
mortality rate.[9 ] Previous multicenter retrospective observational trials have shown a significant
correlation between SIC and poor prognosis.[10 ]
[11 ] SIC is regarded as an early phase of DIC because it includes most cases of overt
DIC, which provides the possibility for early clinical intervention of sepsis.[12 ]
Although several studies have shown that coagulopathy is one of the major complications
of sepsis, leading to a higher risk of thrombosis, the deterioration of organ failure,
and an increased mortality rate,[13 ]
[14 ]
[15 ] so far there are almost no tools specifically designed for predicting the occurrence
of SIC in septic patients earlier. Since SIC is associated with poor prognosis in
patients with sepsis, this study aimed to develop a predictive nomogram incorporating
clinical markers and scoring systems to individually predict the probability of SIC
in septic patients, so as to provide evidence for early diagnosis and treatment of
SIC.
Materials and Methods
Study Design
This study was conducted in two stages. In the development stage, a retrospective
research approach was employed to screen all patients admitted to the ICU of a tertiary
general hospital (The First Hospital of Jilin University in Changchun, China) from
January 2022 to April 2023. Clinical data of septic patients were collected through
the electronic medical records system. Patients who met the inclusion criteria and
did not meet the exclusion criteria were included in the development cohort to establish
a clinical model for predicting SIC. In the validation stage, a prospective observational
study was conducted, including septic patients admitted to the ICU of the First Hospital
of Jilin University from May 2023 to November 2023. All participants in validation
cohort provided written informed consent. This study has been approved by the Ethics
Committee of the First Hospital of Jilin University [Approval number: 2022(013)].
Study Population
Adult patients fulfilling the diagnostic criteria for sepsis stated in the third international
consensus definitions for sepsis and septic shock (Sepsis-3)[1 ] were collected.
The inclusion criteria are: (1) adult (≥18 years old); (2) met the definition of Sepsis
3.0 criteria, which is defined as a suspected infection combined with an acute increase
in SOFA score ≥2; (3) the length of ICU stay is greater than 48 hours.
The exclusion criteria are: (1) age <18 years; (2) ICU length of stay <48 hours; (3)
history of heparin-induced thrombocytopenia; (4) patients with various cancers combined
with abnormal coagulation function; (5) decompensated liver cirrhosis; (6) concomitant
anticoagulant treatment of warfarin; (7) missing data >10%; (8) patient refusal to
sign the informed consent form or request for withdrawal during the second stage.
The patients were divided into SIC group and non-SIC group according to whether the
SIC score was ≥4.[5 ]
Data Collection
The collected data included age, gender, body weight, the Acute Physiology and Chronic
Health Evaluation (APACHE) II score based on the worst values obtained within 24 hours
after the onset of sepsis, SOFA score, past medical history, and site of infection.
In addition, procalcitonin, C-reactive protein, white blood cells, platelets, INR,
fibrinogen, prothrombin time (PT), creatinine, D-dimer, fibrin degradation products,
neutrophil-to-lymphocyte ratio, total bilirubin, lactate, and oxygenation index (PaO2 /FiO2 ) were also collected within 24 hours after the onset of sepsis. Furthermore, we also
collected continuous renal replacement therapy (CRRT) proportion, mechanical ventilation
proportion, and shock ratio. According to Sepsis-3, patients with septic shock can
be clinically identified by a vasopressor requirement to maintain a mean arterial
pressure of 65 mmHg or greater and serum lactate level greater than 2 mmol/L (>18 mg/dL)
in the absence of hypovolemia.[1 ]
Sample Size Consideration
The sample size was calculated based on the rule of thumb proposed by Harrell et al[16 ] and Peduzzi et al,[17 ] which suggest a minimum of 20 outcome events per predictor variable in a multivariate
regression model. In our model development, we considered approximately 2 to 3 critical
clinical factors and 2 to 3 scores.
To accurately predict the occurrence of the SIC, a minimum sample size of 120 patients
(6 × 20) with the event (SIC) was required. This ensured an adequate number of patients
who experienced the outcome event relative to the predictor variables and allows for
more reliable predictions of SIC incidence.
Statistical Analysis
The Shapiro–Wilk test was employed to identify the normal distribution of continuous
variables,[18 ]
[19 ] expressed as the median and standard deviation. The Wilcoxon–Mann–Whitney U-test
was performed on the skew distribution, defined as the median and interquartile range.
Categorical variables were described using frequency (percentage) and compared using
Pearson's Chi-square tests or Fisher precision tests according to appropriateness.
Variables showing significance at the 0.1 level in the univariate analysis were taken
into account. Spearman correlation and Belsley collinearity tests were used to assess
collinearity across all covariables.
To develop a predictive nomogram indicating the probability of developing SIC in patients
with sepsis, we initially performed a multivariate logistic regression analysis using
a backward stepwise approach. The analysis aimed to identify simplified models in
the development cohort. The covariates considered in the analysis included diabetes
mellitus, shock, platelet, and INR. We obtained estimated odds ratios (ORs) and 95%
confidence intervals (CIs). A nomogram was then constructed based on the simplified
model, including the identified predictors. Each predictor in the nomogram was assigned
points by drawing a vertical line from the corresponding factor to the point axis.
The sum of all points from all the predictors was then calculated to generate the
total points. By drawing a vertical line from the total point axis to the risk of
SIC axis, the probability of SIC occurrence can be estimated. The differentiation
was assessed by calculating the area under the receiver operating characteristic curve
(AUROC) derived from the conventional receiver operating characteristic curves (ROC).
In order to evaluate the classification accuracy of these two models, AUROC was compared
using the nonparametric method of DeLong and Clarke-Pearson.[20 ]
The best-fitting model and the nomogram were verified and calibrated using bootstrap
techniques.[17 ] The bootstrap method was applied to 1,000 resamples, and the AUROC and 95% CI of
the obtained bootstrap correction were reported. We used Hosmer–Lemeshow test to evaluate
the calibration plot of the nomogram. The identification and calibration of the nomogram
models were verified in an independent external validation cohort. In addition, in
the validation cohort, decision curve analysis was performed using a nomogram at different
threshold probabilities to assess the net benefit of SIC treatment decisions. All
statistical analyses were performed using SPSS 26.0 and Stata 16.0.
Results
Development Cohort
Of 697 patients recruited in the first stage, 33 patients with incomplete data were
excluded from the analysis. In addition, 67 patients were excluded due to hospitalization
in the ICU for less than 48 hours, 21 patients were excluded due to diagnosis of hematological
malignancy, and 28 patients were excluded due to diagnosis of Child–Pugh grade C cirrhosis.
Thus, a total of 548 patients were included in the development cohort, in which 302
(55.1%) patients occurred SIC ([Table 1 ]).
Table 1
Clinical and demographic data for development and validation cohort
Variables
Development cohort
Validation cohort
Total (n = 548)
SIC (n = 302)
Non-SIC (n = 246)
p -Value
Total (n = 245)
SIC
(n = 121)
Non-SIC (n = 124)
p -Value
Age (y)
60.4 ± 16.7[a ]
60.5 ± 16.9
60.1 ± 16.4
0.158
63.5 ± 15.5[a ]
63.9 ± 15.1
63.1 ± 16.0
0.451
Gender
0.880
0.128
Male, n (%)
334 (60.9)
177 (58.6)
157 (63.8)
154 (62.9)
80 (66.1)
74 (59.7)
Female, n (%)
214 (39.1)
125 (41.4)
89 (36.2)
91 (37.1)
41 (33.9)
50 (40.3)
Body weight (kg)
67.4 ± 13.8[b ]
67.2 ± 13.3
67.7 ± 14.4
0.990
73.5 ± 15.1[b ]
73.8 ± 14.9
73.2 ± 15.4
0.652
Past medical history, n (%)
Diabetes mellitus
167 (30.5)
86 (28.5)
81 (32.9)
0.011
62 (25.3)
40 (33.1)
22 (17.7)
0.078
Hypertension
246 (44.9)
129 (41.7)
117 (47.6)
0.235
116 (47.3)
54 (44.6)
62 (50.0)
0.208
Coronary artery disease
99 (18.1)
60 (19.9)
39 (15.9)
0.067
49 (20.0)
20 (16.5)
29 (23.3)
0.085
Site of infection, n (%)
0.628
0.607
Blood
55 (10.0)
41 (13.6)
14 (5.7)
30 (12.2)
22 (18.2)
8 (6.5)
Pulmonary
346 (63.1)
179 (59.3)
167 (67.9)
150 (61.2)
66 (54.5)
84 (67.7)
Intra-abdominal
85 (15.5)
46 (15.2)
39 (15.9)
27 (11.0)
20 (16.5)
7 (5.6)
Genitourinary
22 (4.0)
15 (5.0)
7 (2.8)
7 (2.9)
3 (2.5)
4 (3.2)
Others
40 (7.3)
21 (7.0)
19 (7.7)
31 (12.7)
10 (8.3)
21 (16.9)
APACHE II
15.0 ± 6.1
16.0 ± 6.4
13.8 ± 5.6
0.608
14.8 ± 6.0
16.5 ± 6.1
13.1 ± 5.5
0.152
SOFA
5.8 ± 3.2
6.4 ± 3.5
5.1 ± 2.6
0.859
6.4 ± 3.5
7.6 ± 4.0
5.2 ± 2.6
0.207
Mechanical ventilation, n (%)
307 (56.0)
185 (61.3)
122 (49.6)
0.236
164 (66.9)
91 (75.2)
73 (58.9)
0.385
CRRT, n (%)
144 (26.3)
107 (35.4)
37 (15.0)
0.530
55 (22.4)
37 (30.6)
18 (14.5)
0.175
Shock, n (%)
236 (43.1)
174 (57.6)
62 (25.2)
0.019
120 (49.0)
84 (69.4)
36 (29.0)
0.002
PCT (ng/mL)
12.8 ± 25.0
18.1 ± 29.7
5.9 ± 14.6
0.704
12.4 ± 23.3
18.4 ± 28.8
6.6 ± 14.3
0.174
Lac (mmol/L)
2.1 ± 2.2
2.6 ± 2.7
1.5 ± 1.1
0.320
2.2 ± 2.0
2.7 ± 2.5
1.7 ± 1.2
0.510
CRP (mg/L)
134.0 ± 11.9
150.9 ± 120.5
113.7 ± 99.7
0.516
125.8 ± 97.2
142.8 ± 100.9
109.5 ± 91.0
0.503
WBC (×109 /L)
12.8 ± 11.9
13.7 ± 15.1
11.6 ± 5.9
0.150
12.6 ± 6.5
12.9 ± 7.0
12.4 ± 6.0
0.176
NLR
20.0 ± 69.8
24.5 ± 93.2
14.4 ± 12.2
0.115
19.7 ± 16.8
22.1 ± 18.7
17.4 ± 14.3
0.679
RDW (%)
14.5 ± 2.4
14.8 ± 2.5
14.1 ± 2.1
0.915
14.1 ± 2.3
14.4 ± 2.8
13.7 ± 1.5
0.472
MCV (fL)
91.0 ± 7.1[c ]
91.2 ± 7.9
90.72 ± 6.0
0.014
89.6 ± 7.6[c ]
90.9 ± 5.6
88.4 ± 9.0
0.040
PLT (×109 /L)
170.7 ± 94.4
130.0 ± 82.1
220.8 ± 84.0
<0.001
187.0 ± 100.3
155.8 ± 109.0
217.8 ± 80.1
0.035
AST (U/L)
25.9 (15.9–52.5)
27.3 (18.6–62.2)
24.6 (14.4–45.2)
0.440
39.9 (26.1–76.5)
53.1 (33.0–103.0)
32.0 (23.0–47.1)
0.685
ALT (U/L)
38.7 (22.7–69.2)
47.4 (27.4–89.5)
30.0 (19.3–52.6)
0.831
31.5 (19.6–58.3)
39.2 (22.2–88.3)
25.6 (16.8–43.3)
0.682
Variables
Development cohort
Validation cohort
Total (n = 548)
SIC (n = 302)
Non-SIC (n = 246)
p- Value
Total (n = 245)
SIC (n = 121)
Non-SIC (n = 124)
p -Value
ALP (U/L)
106.1 ± 70.3
107.9 ± 73.6
104.0 ± 66.2
0.073
96.8 ± 76.7
98.5 ± 90.9
95.1 ± 59.9
0.072
Albumin (g/L)
29.5 ± 5.8
28.4 ± 5.6
30.8 ± 5.8
0.910
30.1 ± 5.6
28.4 ± 5.9
31.7 ± 4.7
0.727
TBIL (µmol/L)
27.7 ± 50.8
35.6 ± 64.9
18.0 ± 20.5
0.995
25.9 ± 43.1
36.2 ± 58.9
15.9 ± 9.7
0.594
CRE (µmol/L)
202.7 ± 253.1[a ]
229.8 ± 247.6
169.0 ± 258.3
0.300
135.1 ± 164.8[a ]
160.4 ± 153.6
110.2 ± 172.1
0.405
BUN (mmol/L)
14.0 ± 11.3[c ]
15.8 ± 11.7
11.8 ± 10.5
0.741
11.3 ± 7.6[c ]
13.7 ± 8.3
9.0 ± 6.1
0.144
INR
1.22 ± 0.45
1.33 ± 0.57
1.08 ± 0.12
0.020
1.21 ± 0.51
1.36 ± 0.69
1.06 ± 0.12
0.037
FBG (g/L)
4.6 ± 2.3
4.4 ± 2.4
4.9 ± 2.2
0.486
4.9 ± 2.1
4.7 ± 2.0
5.0 ± 2.1
0.963
PT (s)
14.1 ± 4.6
15.2 ± 5.9
12.8 ± 1.4
0.361
13.9 ± 5.9
15.7 ± 8.0
12.2 ± 1.2
0.610
D-D (mg/L)
10.4 ± 21.0
12.7 ± 24.7
7.5 ± 14.7
0.389
8.5 ± 12.6
11.7 ± 14.9
5.4 ± 8.8
0.526
FDP (ug/mL)
29.2 ± 61.1
36.3 ± 71.5
20.5 ± 43.6
0.466
23.4 ± 34.2
32.1 ± 40.0
14.7 ± 25.1
0.605
PaO2 /FiO2 (mmHg)
246.8 ± 124.3
232.3 ± 121.6
264.5 ± 125.4
0.273
228.0 ± 130.2
226.5 ± 131.4
229.5 ± 129.6
0.964
Abbreviations: ALP, alkaline phosphatase; ALT, alanine aminotransferase; APACHE II,
Acute Physiology and Chronic Health Evaluation II; AST, aspartate aminotransferase;
BUN, blood urea nitrogen; CRE, creatinine; CRP, C-reactive protein; CRRT, continuous
renal replacement therapy; D-D, D-dimer; FBG, fibrinogen; FDP, fibrin degradation
product; INR, international normalized ratio; IQR, interquartile range; Lac, lactate;
MCV, mean corpuscular volume; NLR, neutrophil-to-lymphocyte ratio; PaO2 /FiO2 , oxygenation index; PCT, procalcitonin; PLT, platelets; PT, prothrombin time; RDW,
red blood cell volume distribution width; SOFA, Sequential Organ Failure Assessment;
TBIL, total bilirubin; WBC, white blood cells.
Note: Data presented as mean ± standard deviation, median (IQR) or n (%).
a Represents p < 0.01.
b Represents p < 0.001.
c Represents the difference between the development cohort and the validation cohort;
represents p < 0.05.
Validation Cohort
Of 265 patients prospectively recruited in the second stage, 12 patients were excluded
due to hospitalization in the ICU for less than 48 hours, 4 patients were excluded
due to diagnosis of hematological malignancy, and 4 patients were excluded due to
diagnosis of Child–Pugh grade C cirrhosis. Then a total of 245 patients were involved
in the validation cohort, in which 121 (49.4%) patients occurred SIC ([Table 1 ]). The clinical and demographic data differences between development and validation
cohorts are also presented in [Table 1 ]. The flowchart for the patient selection is shown in [Fig. 1 ] ([Fig. 1A ] is the flowchart of development cohort; [Fig. 1B ] is the flowchart of validation cohort).
Fig. 1 The flowchart for the patient selection.
Development of the Nomogram Model
As shown in [Table 1 ], variables presenting significance including diabetes, shock, average red blood
cell volume, platelets, and INR were selected to the univariate logistic regression.
After univariate logistic regression analysis, shock, platelets, and INR might represent
the risk factors for SIC (p < 0.05) ([Table 2 ]). Shock, platelets, and INR were recognized as independent predictors in the multivariate
logistic regression analysis ([Table 3 ]). Patients with shock (OR: 4.499; 95% CI: 2.730–7.414; p < 0.001) or higher INR (OR: 349.384; 95% CI: 62.337–1958.221; p < 0.001) had higher probabilities of SIC. By contrast, the higher the platelet (OR:
0.985; 95% CI: 0.982–0.988; p < 0.001), the less likely the SIC was to be occurred ([Table 3 ]).
Table 2
Univariate logistic regression analysis of predictors for SIC in the development cohort
Predictive factors
OR (95% CI)
p- Value
Diabetes
1.233 (0.856–1.776)
0.261
MCV
1.011 (0.987–1.035)
0.384
Shock
4.034 (2.794–5.825)
<0.001
Platelets
0.988 (0.986–0.991)
<0.001
INR
470.555 (110.903–1996.529)
<0.001
Abbreviations: CI, confidence interval; INR, international normalized ratio; MCV,
mean corpuscular volume; OR, odds ratio; SIC, sepsis-induced coagulopathy.
Table 3
Multivariate logistic regression analysis of predictors for SIC in the development
cohort
Predictive factors
OR (95% CI)
p- Value
Shock
4.499 (2.730–7.414)
<0.001
Platelets
0.985 (0.982–0.988)
<0.001
INR
349.384 (62.337–1,958.221)
<0.001
Abbreviations: CI, confidence interval; INR, international normalized ratio; OR, odds
ratio.
The nomogram, which incorporated these predictors, was developed and presented as
shown ([Fig. 2 ]). To obtain the nomogram-predicted probability, whether the patient is in shock,
the patient's platelet and INR should be mapped onto the axes of the nomogram-predictive
factors. A vertical line is drawn on the axes to identify the score for each variable
value. By summing up the scores for all variables and locating the corresponding total
on the total point line, the individual probability of SIC occurrence can be assessed.
For example, let's consider a patient with shock, platelet 100 × 109 /L, and an INR of 1.01. The corresponding points on the axes of the nomogram-predictive
factors are as follows: 1 point for the shock, 3 points for platelet, and 3 points
for INR. Adding up these points, the total score is 7 (1 + 3 + 3) points. According
to this nomogram, the probability of SIC occurrence for this patient is over 80%.
Fig. 2 Nomogram of sepsis-induced coagulopathy.
Validation of the Nomogram Model
ROC analysis was conducted on the predictors of SIC occurrence in both the development
cohort and validation cohort. The area under the curve (AUC) of the development group
was 0.879 (95% CI: 0.850–0.908) ([Fig. 3 ]), while the AUC of the validation group was 0.872 (95% CI: 0.826–0.917) ([Fig. 4 ]). There was no significant difference observed (DeLong test, p = 0.372). These results initially confirm the favorable discriminative ability of
the nomogram model. This model enables the prediction of SIC occurrence probability
in diverse septic patients.
Fig. 3 Receiver operating characteristic curve analysis of predictors for SIC in the development
cohort. SIC, sepsis-induced coagulopathy.
Fig. 4 Receiver operating characteristic curve analysis of predictors for SIC in the validation
cohort. SIC, sepsis-induced coagulopathy.
To further evaluate the calibration performance of the nomogram model, the calibration
curve was described using the bootstrap method for both the development cohort ([Fig. 5 ]) and validation cohort ([Fig. 6 ]). The x -axis represents the predicted risk of SIC occurring, while the y -axis represents the actual risk of SIC occurring. The diagonal dotted lines represent
prediction models with perfect predictive power. A closer alignment between the calibration
curve and the diagonal dashed line indicates a higher prediction accuracy of the nomogram
model. It is worth noting that both curves show slight linearity, indicating that
the model has excellent calibration performance.
Fig. 5 Calibration plot for nomogram in the development cohort.
Fig. 6 Calibration plot for nomogram in the validation cohort.
Clinical Use
The decision curve analysis of the nomogram of SIC occurrence risk in the development
cohort is shown in [Fig. 7 ]. The y -axis represents the net benefit, while the x -axis represents the threshold probability that the ICU physician believes SIC is
likely to occur. The blue dashed line represents a scenario in which all patients
receive the intervention, while the red dashed line represents a scenario in which
no patients receive the intervention, resulting in a net benefit of 0. The net benefit
is calculated by subtracting the percentage of patients with false positives from
the percentage of patients with true positives, weighted according to the relative
harm of refusing treatment versus the negative consequences of unnecessary treatment.
The threshold probability indicates the likelihood that SIC will occur and guides
the critical care physician in deciding whether to treat SIC based on this probability.
Fig. 7 Decision curve analysis of the nomogram of SIC occurrence risk in the development
cohort. SIC, sepsis-induced coagulopathy.
The decision curve shows that if the threshold probability of SIC occurrence is 8%
or higher, covering the clinically acceptable range (the incidence of SIC is about
50%), employing the nomogram for SIC intervention yields greater benefits compared
to no intervention.
The decision curve analysis of the nomogram of SIC occurrence risk in the validation
cohort is shown in [Fig. 8 ]. The decision curve shows that if the threshold probability of SIC occurrence is
17% or higher, employing the nomogram for SIC intervention yields greater benefits
compared to no intervention.
Fig. 8 Decision curve analysis of the nomogram of SIC occurrence risk in the validation
cohort. SIC, sepsis-induced coagulopathy.
Discussion
This study demonstrated that shock, platelets, and INR were independent predictors
for the occurrence of SIC, and developed an user-friendly nomogram with clinical usefulness
to predict the individual probability of SIC in septic patients. This mixed retrospective
and prospective cohort study indicated that the incidence of SIC is 53.3% (423/793)
in patients with sepsis, this is slightly higher than the previously reported incidence
of SIC in Japan (29%) and Europe (22.1% in the HYPRESS trial and 24.2% in the SISPCT
trial).[6 ]
[7 ] Coagulation dysfunction is common in sepsis and is often associated with poor prognosis
caused by multiple organ dysfunction syndrome and microvascular thrombosis.[21 ] Coagulopathy in sepsis may take the form of SIC or sepsis-associated DIC. About
93.9% of patients who were diagnosed with SIC went on to develop sepsis-associated
DIC within the next 2 to 4 days.[10 ] Coagulopathy in septic patients is caused by a complex relationship between immune,
inflammatory, and coagulation systems, characterized by coagulation activation, disorder
of the anticoagulant system, and excessive inhibition of fibrinolysis. The activation
of coagulation and inflammation is a necessary response for host defense during sepsis.[22 ] Engelmann and Massberg proposed the concept of “immunothrombosis,” which refers
to the close interaction between coagulation and innate immunity.[23 ] The combined effects of these processes lead to coagulation disorders worsening
into sepsis-related DIC.[24 ] Since SIC is closely associated with poor prognosis in septic patients and the incidence
of which is high, it is important to identify SIC early, as at this stage of coagulopathy
anticoagulants may be of the greatest benefit.[25 ]
The current SIC criteria are a scoring system designed to identify patients with sepsis
and coagulation disorders. With the concept of “infection-induced organ dysfunction
and coagulopathy, ” SIC diagnostic criteria include SOFA score, platelet count, and
INR. SIC is defined as a score of ≥4 points.[5 ] The SOFA score included in the SIC diagnostic criteria is used to confirm the presence
of sepsis. Because the SOFA score is limited to two points, it does not reflect the
severity of sepsis.[26 ] Coagulopathy in sepsis may take the form of SIC (early stage) or sepsis-associated
DIC (late stage). Thrombocytopenia is usually a clue to the presence of DIC, with
reported platelet counts below 50 × 109 /L is closely related to poor prognosis in patients with sepsis.[27 ] Study suggests that INR is a moderate diagnostic tool for infectious shock and sepsis.
In addition, INR has been proven to be an appropriate prognostic tool for 30-day all-cause
mortality. INR >1.5 is associated with an increased risk of all-cause death at 30
days, as observed in patients with sepsis and septic shock.[28 ] During sepsis, the hemostatic balance is significantly disrupted. The coagulation
process is activated, while anticoagulant mechanisms are suppressed. Traditional laboratory
findings of sepsis, including thrombocytopenia, increased PT and fibrin degradation
products, and decreased fibrinogen, only present late in the course of sepsis.[29 ] This nomogram can predict the incidence of SIC through platelet and INR levels at
an earlier stage when changes to coagulation status are still reversible. Different
from our study, another nomogram including 13 conventional clinical variables provided
an optimal prediction of the 28-day mortality risk in SIC patients through the internal
validation. Using this model, the 28-day mortality risk of an individual SIC patient
can be determined, which may lead to an improved mortality assessment.[30 ]
There were 144 patients in the development cohort and 55 patients in the validation
cohort who received CRRT. These patients received local external anticoagulant therapy
with sodium citrate. Sodium citrate is an anticoagulant drug whose pharmacological
action is mainly to exert anticoagulant effect by inhibiting coagulation factors in
the blood. Specifically, citrate ions in sodium citrate combine with calcium ions
in the blood to form the refractory soluble complex calcium citrate. Although this
complex is soluble in water, it is not easily dissociated, resulting in a decrease
in calcium ions in the blood, which inhibits the clotting process and prevents the
blood from clotting. Citrate is partially removed by filtration or dialysis, and the
remaining amount is rapidly metabolized in the citric acid cycle, especially in the
liver, muscle, and renal cortex, while the chelated calcium is released and the lost
calcium is replaced. Systemic coagulation is unaffected.[31 ] Therefore, sodium citrate has little effect on INR and platelets. In addition, there
was no statistical difference in the proportion of CRRT between SIC and non-SIC patients
in both the development and validation cohorts. As a result, the ratios of sodium
citrate use in SIC and non-SIC patients in the development and validation cohorts
were also matched.
INR has provided higher value for predicting occurrence of SIC than platelets in the
nomogram, which may be due to several reasons. First, INR can reflect the coagulation
state more comprehensively. INR takes into account not only the number of platelets,
but also the synthesis and function of other clotting factors, thus providing a more
complete picture of a patient's clotting status. Second, INR is more sensitive to
coagulation dysfunction. Since INR is an indicator of PT, it is more sensitive to
coagulation disorders and can detect coagulation abnormalities earlier. Third, INR
is less disturbed. Compared with platelets, INR is less affected by some interfering
factors (such as drugs, blood transfusion, etc.), so it can more accurately reflect
the patient's clotting status. Previous study has found that there was a strong correlation
between INR value and SOFA score.[32 ] The SOFA score was correlated with the prognosis of SIC, which also suggested that
INR had a good predictive value of SIC from another perspective.
Shock was also an independent predictive factor for SIC in our study. Septic shock
is commonly associated with a wide spectrum of coagulation abnormalities with the
most severe form being DIC. This is the result of a complex interplay between proinflammatory
cytokines, procoagulant factors, anticoagulant factors, and endothelial dysfunction.[13 ] Patients who needed vasopressors were considered to have septic shock. Previous
study has demonstrated that SIC developed in 66.4% of patients who used vasopressors
and 42.2% of patients who did not. The in-hospital mortality difference between the
SIC and non-SIC groups was statistically significant in those who needed vasopressors
(35.8% vs. 27.9%). In addition, SIC was significantly correlated with mortality risk
in patients who used vasopressors.[33 ] Although shock does not have a high score in our nomogram, patients with septic
shock need to be highly vigilant about the occurrence of SIC.
There are several limitations in our study. First, it is a mixed retrospective and
prospective cohort study, the participants with missing variables in the retrospective
cohort study are excluded, hence suffers from potential selection and ascertainment
bias. Second, the variables included in the model are mainly common indicators of
SIC; some new coagulation markers and examinations, including soluble thrombomodulin,
thrombin–antithrombin complex, tissue plasminogen activator–inhibitor complex, α2-plasmin
inhibitor–plasmin complex, antithrombin III, and thromboelastography, are becoming
useful tools in coagulopathy diagnosis.[34 ]
[35 ]
[36 ]
[37 ] However, these tests have not been widely and routinely conducted in clinical practice
at present, so complete results cannot be obtained. Third, due to the single-center
nature and small heterogeneous patient population, the generalizability of our results
is limited. Therefore, larger-scale and multicenter research is still required in
the future. Fourth, it is crucial to assess the significance of SIC in less severe
patients, not only the patients treated in the ICU but also the patients in the emergency
room or general ward should be evaluated and screened for early warning in the future
study.
Conclusion
By incorporating shock, platelets, and INR in the model, this useful nomogram could
be accessibly utilized to predict SIC occurrence in septic patients earlier. However,
external validation is still required for further generalizability improvement of
this nomogram.
What is known about this topic?
Sepsis-induced coagulopathy (SIC) is a common cause of poor prognosis in critically
ill patients in the intensive care unit (ICU).
So far the diagnostic criteria of SIC consist of three items, namely, platelet count,
international normalized ratio (INR), and Sequential Organ Failure Assessment (SOFA)
score.
However, currently there are no tools specifically designed for predicting the occurrence
of SIC in septic patients earlier.
What does this paper add?
This study developed an user-friendly nomogram with clinical usefulness to predict
the individual probability of SIC in septic patients.
By incorporating shock, platelets, and INR in the model, this useful nomogram could
be accessibly utilized to predict SIC occurrence in septic patients earlier.
