Several inflammation-related factors (IRFs) have been reported to predict organ failure of acute pancreatitis (AP) in previous clinical studies. However, there are a few shortcomings in these models. The aim of this study was to develop a new prediction model based on IRFs that could accurately identify the risk for organ failure in AP.
Generally, acute pancreatitis (AP) commonly presents as a mild and self-limiting course with slighter clinical symptoms. However, nearly 20% AP patients present with severe disease, which is correlated with higher rates of organ failure and mortality [
In previous studies, many factors have been reported as independent risk factors for the development of OF in AP, such as lipase, albumin, BUN, and pleural effusion. Moreover, the role of inflammation-related factors (IRFs) in secondary OF is getting more and more attention [
In general, various types of IRFs were potential predictive indicators of OF in patients with AP. However, the forecast performance of each indicator was insufficient in clinical practice. Therefore, the purpose of this work was to develop and validate a simple and effective risk model based on IRFs for the early recognition of OF.
Consecutive patients with AP treated in the First Affiliated Hospital of Nanjing Medical University (Nanjing, Jiangsu province, China) from 2017.06 to 2018.06 were retrospectively enrolled in our study, and patients referred from other hospitals were excluded in our study. Three criteria were applied to diagnose AP, including persistent abdominal pain, serum amylase or lipase elevation of more than 3 times upper limit of normal, and characteristic ultrasound and/or CT findings. Patients were diagnosed as AP when two or more signs mentioned above were observed. Furthermore, patients with recent surgery (less than 1 week), chronic, traumatic, endoscopic, or recurrent acute pancreatitis, immunosuppression, or immune deficiency were excluded. The principles of the Helsinki Declaration were applied in the performance of our study, and this study was approved by Ethics Committee of the First Affiliated Hospital of Nanjing Medical University.
According to the modified Marshall scoring system, serum creatinine was applied to define renal failure (more than 1.9 mg/dL), systolic blood pressure was used to define cardiovascular failure (less than 90 mmHg), and ratio of PaO2/FiO2 was used to confirm respiratory failure (less than 300 mmHg). The duration was used to distinguish transient OF (≤48 h) from persistent OF (>48 h) [
The peripheral blood samples of all patients were obtained within 24 h after admission to hospital. Peripheral blood lymphocyte subset assay was applied to detect the percentage of CD3+T lymphocytes, CD3+ CD4+T lymphocytes, CD3+ CD8+ cytotoxic T lymphocytes, CD16+ CD56+ natural killer cells, and CD19+ B lymphocytes. Blood routine examination was used to obtain white blood cell count and percentage. Blood samples were routinely collected on AP patients admitted to our hospital and then stored and subsequently analyzed for the concentration of 10 cytokines (IL1a, IL1b, IL4, IL6, IL8, IL10, IL13, MCP-1, IFNg, and TNFa) by using the Human Inflammation Array Q1 (QAH-INF-1-1) (RayBiotech, Norcross, America). Moreover, other IRF (CRP and PCT) data, several biochemical parameters, and basic clinical information were also collected.
All statistical analyses were conducted by Stata/SE version 10.0 for Windows. Descriptive data were presented as
Predictive scoring system for OF.
Preoperative factor | Points contributed | ||
---|---|---|---|
IL 6 (pg/mL) | <7 | 0 | 0 |
7-150 | 0.015257141 | 1 | |
150-250 | 3 | ||
>250 | 4 | ||
IL 8 (pg/mL) | <8.1 | 0 | 0 |
8.1-21.3 | 0.248266704 | 2 | |
>21.3 | 4 | ||
CD19+ B lymphocyte s(%) | <9 | 0 | 0 |
9-14 | 0.122927762 | 1 | |
>14 | 3 |
A total of 100 patients were enrolled in this study, 94 patients were finally enrolled in the statistical analysis, and 6 patients were excluded due to the lack of IRF data (3 patients for PCT, 1 patient for CRP, 2 patients for PCT and CRP). Related information of clinical parameters was shown in Table
Characteristics of enrolled patients.
Variable | NOF ( | OF ( | ||
---|---|---|---|---|
Age (years) ( | 0.774 | |||
Sex, male/female | 33/46 | 7/8 | 0.821 | |
Time of onset (hours) | 0.705 | |||
0-24 h | 28 | 7 | ||
24-48 h | 27 | 4 | ||
48-72 h | 24 | 4 | ||
Etiology | 0.417 | |||
Biliary, | 43 | 11 | ||
Alcohol, | 8 | 2 | ||
Hypertriglyceridemia, | 13 | 1 | ||
Other, | 15 | 1 | ||
APPACHE II score | 0.011 | |||
RANSON score | ||||
Length of hospital stay (days) | 9 (7, 12) | 16 (15, 36) | -4.06 | |
Hospital cost (CNY | 32435 (22203, 41689) | 75075 (62198, 174771) | ||
Inflammatory markers (pg/mL) | Median (P25, P75) | Median (P25, P75) | ||
IL1a | 2.35 (1.55, 4.25) | 1.55 (1.28, 2.56) | -1.76 | 0.474 |
IL1b | 3.32 (1.73, 8.18) | 3.16 (1.69, 11.38) | -0.03 | 0.895 |
IL4 | 2.33 (1.42, 4.75) | 2.08 (1.57, 3.67) | -0.49 | 0.431 |
IL6 | 24.43 (10.18, 42.3) | 106.9 (78.97, 188.13) | -4.82 | |
IL8 | 4.52 (2.99, 7.86) | 14.14 (5.68, 18.03) | -3.76 | 0.002 |
IL10 | 1.51 (0.83, 2.62) | 8.25 (4.33, 14.98) | -4.67 | 0.003 |
IL13 | 0.58 (0.32, 0.76) | 0.31 (0.2, 0.65) | -1.36 | 0.348 |
MCP1 | 152.64 (125.18, 192.46) | 234.11 (165.02, 280.94) | -1.01 | 0.001 |
IFNg | 1.56 (0.93, 2.49) | 1.21 (0.78, 2.24) | -2.71 | 0.505 |
TNFa | 5.78 (2.34, 11.16) | 4.4 (2.2, 11.55) | -0.45 | 0.624 |
PCT (ng/mL) | 0.42 (0.12, 1.18) | 2.07 (0.33, 23.38) | -2.65 | |
CRP (mg/mL) | 90 (40.1, 90) | 90 (84.9, 90) | -1.02 | 0.84 |
Routine blood test | ||||
WBC, ×109/L ( | 0.244 | |||
LY, ×109/L median (P25, P75) | 1.13 (0.85, 1.57) | 0.99 (0.85, 1.25) | -1.172 | 0.241 |
MO, ×109/L median (P25, P75) | 0.53 (0.44, 0.78) | 0.45 (0.34, 0.95) | -0.749 | 0.454 |
NE, ×109/L ( | 0.161 | |||
Immunity markers (%) | ||||
CD3 T lymphocytes | 0.002 | |||
CD3CD4 T lymphocytes | 0.007 | |||
CD3CD8 cytotoxic T lymphocytes | 23.06 (17.56, 28.53) | 21.56 (13.21, 26.84) | -1.1 | 0.272 |
CD16+ CD56+ natural killer cells | 10.9 (6.7, 18.39) | 14.67 (7.63, 17.39) | -0.49 | 0.624 |
CD19+ B lymphocytes | 14.5 (10.2, 19.8) | 25.57 (17.9, 29.97) | -3.929 | |
CD4CD8 cytotoxic T lymphocytes | 1.76 (1.29, 2.24) | 1.55 (1.09, 2.87) | -0.083 | 0.934 |
All patients enrolled in final analysis were divided into two groups according to the occurrence of OF (NOF vs. OF). Univariate analysis was selected to evaluate the association between OF occurrence and IRF levels (Table
Univariate analyses of factors predicting OF.
Odds ratio | Std. err. | 95% conf. interval | |||
---|---|---|---|---|---|
Age | 0.9868881 | 0.0171669 | -0.76 | 0.448 | 0.9538086, 1.021115 |
Gender | 0.8198758 | 0.4637188 | -0.35 | 0.725 | 0.2705899, 2.484189 |
APACHE II score | 1.275259 | 0.1280648 | 2.42 | 0.015 | 1.047415, 1.552667 |
RANSON score | 3.570297 | 1.186867 | 3.83 | <0.001 | 1.860978, 6.849637 |
IL1a | 0.9204542 | 0.1064778 | -0.72 | 0.474 | 0.7337286, 1.154699 |
IL1b | 0.9975549 | 0.0183145 | -0.13 | 0.894 | 0.9622973, 1.034104 |
IL4 | 0.9486596 | 0.0635995 | -0.79 | 0.432 | 0.8318493, 1.081873 |
IL6 | 1.014311 | 0.0049215 | 2.93 | 0.003 | 1.004711, 1.024003 |
IL8 | 1.200382 | 0.0638983 | 3.43 | 0.001 | 1.081455, 1.332386 |
IL10 | 1.263606 | 0.0983619 | 3.01 | 0.003 | 1.084806, 1.471875 |
IL13 | 0.5039705 | 0.3617765 | -0.95 | 0.34 | 0.123415, 2.057986 |
MCP1 | 1.011875 | 0.0041537 | 2.88 | 0.004 | 1.003767, 1.020049 |
IFNg | 0.8752206 | 0.1774049 | -0.66 | 0.511 | 0.5882761, 1.302129 |
TNFa | 0.9844869 | 0.0314253 | -0.49 | 0.624 | 0.9247815, 1.048047 |
PCT | 1.144502 | 0.0563309 | 2.74 | 0.006 | 1.039253, 1.260409 |
CRP | 1.01183 | 0.0111544 | 1.07 | 0.286 | 0.990202, 1.03393 |
Cause | 0.6276013 | 0.1894851 | -1.54 | 0.123 | 0.3472878, 1.13417 |
CD3 T lymphocytes | 0.9298539 | 0.0238692 | -2.83 | 0.005 | 0.8842285, 0.9778336 |
CD3CD4 T lymphocytes | 0.9241535 | 0.0283805 | -2.57 | 0.01 | 0.8701696, 0.9814864 |
CD3CD8 cytotoxic T lymphocytes | 0.9575243 | 0.0332156 | -1.25 | 0.211 | 0.8945867, 1.02489 |
CD16+ CD56+ natural killer cells | 1.021315 | 0.032097 | 0.67 | 0.502 | 0.9603039, 1.086201 |
CD19+ B lymphocytes | 1.110688 | 0.0364459 | 3.2 | 0.001 | 1.041504, 1.184468 |
CD4CD8 cytotoxic T lymphocytes | 1.050004 | 0.2806481 | 0.18 | 0.855 | 0.6218407, 1.772975 |
WBC, ×109/L | 1.078487 | 0.0699162 | 1.17 | 0.244 | 0.9498018, 1.224606 |
LY, ×109/L | 1.516559 | 0.9935976 | 0.64 | 0.525 | 0.4199339, 5.476934 |
MO, ×109/L | 0.3913318 | 0.253425 | -1.45 | 0.147 | 0.1099791, 1.392451 |
NE, ×109/L | 1.101204 | 0.0761925 | 1.39 | 0.164 | 0.961552, 1.261138 |
Ca | 0.0473802 | 0.0504324 | -2.86 | 0.004 | 0.0058825, 0.3816217 |
To assess whether the potential risk factors (
Multivariate analyses of factors predicting OF.
Odds ratio | Std. err. | 95% conf. interval | |||
---|---|---|---|---|---|
RANSON score | 9.380673 | 7.650668 | 2.74 | 0.006 | 1.896762, 46.39328 |
PCT | 1.099312 | 0.0612663 | 1.7 | 0.089 | 0.9855586, 1.226196 |
IL6 | 1.015374 | 0.0074628 | 2.08 | 0.038 | 1.000852, 1.030107 |
IL8 | 1.281802 | 0.1574817 | 2.02 | 0.043 | 1.007494, 1.630794 |
IL10 | 1.056482 | 0.1449462 | 0.4 | 0.689 | 0.8073833, 1.382434 |
MCP1 | 0.982185 | 0.0102192 | -1.73 | 0.084 | 0.9623586, 1.00242 |
CD19+ B lymphocytes | 1.130803 | 0.069377 | 2 | 0.045 | 1.002684, 1.275292 |
ROC curve analysis were applied to evaluate the predictive value of IRF model for OF, as well as APACHE II score and RANSON score. As shown in Table
predictive value of IRFs model and other clinical factors for OF.
Preoperative factor | AUC | Std. err. | 95% conf. interval | |
---|---|---|---|---|
Lower limit | Upper limit | |||
APACHEII | 0.684 | 0.072 | 0.541 | 0.826 |
RANSON | 0.821 | 0.055 | 0.712 | 0.929 |
IL6 | 0.895 | 0.044 | 0.808 | 0.981 |
IL8 | 0.808 | 0.056 | 0.699 | 0.916 |
CD19+ B lymphocytes | 0.821 | 0.047 | 0.729 | 0.913 |
IFRs | 0.905 | 0.036 | 0.835 | 0.976 |
ROC curve analysis of the IRFs and relative clinical factors in prediction of OF.
Therefore, these three inflammation-related variables (IL6, IL8, and CD19+ B lymphocytes) were finally selected to develop a prediction model (IRFs model). All details of IRF model are shown in Table
Results of multivariate logistic regression analysis suggested that IL6, IL8, and CD19+ B lymphocytes are independent predictors for organ failure. Therefore, these three factors were selected into the final model for the construction of the prediction score (IRF score). The prediction scores based on this model range from 1 to 11, and AP patients with higher score had increasing risk to suffer with OF. The risk of AP patients with OF progressively increased as the score increased. ROC curve analysis was applied to evaluate the predictive value of new score system for OF, as well as APACHE II score and RANSON score. The AUC 0.86 (95% CI: 0.78-0.94) of ROC analysis demonstrated a satisfactory discrimination of new score system and higher than other two criteria (APACHE II and RANSON score) (Figure
Comparisons of the AUCs between scoring systems in prediction of OF.
The mean ROC curve of tenfold crossvalidation of the new prediction score (IRF score) is shown in Figure
Mean receiver-operating characteristic (ROC) curve of the new prediction score for prediction of OF in tenfold crossvalidation.
As reported in previous studies, organ failure was a common but dangerous clinical manifestations in AP, and organ failure was also regarded as the main reason for the death of AP patients. Therefore, the identification of individuals with organ failure in the early stage was the key step for the treatment of these patients. Different kinds of IRF-induced SIRS were reported to be responsible for the occurrence and development of organ failure in AP, and it has been confirmed that some of these IRFs could function as independent indicators for such pathogenetic process. Several researches suggested that abnormal alteration of some immune cells could play the predictive role in the organ failure process, such as CD4+ lymphocyte, CD19+ B lymphocyte, neutrophil-lymphocyte ratio, and CD14hiCD16-monocytes [
In our study, we firstly assess the predictive value of several IRFs for the development of organ failure in AP, including the percentages and/or cell counts of immune cells, the levels of 10 cytokines, and 2 common inflammation biomarkers. Similar to previous studies, the results based on this aspect suggested that the increased levels of IL6, IL8, and CD19+ B lymphocyte were, respectively, correlated with the increased incidence rate of organ failure [
In clinical practice, two AP scoring systems, APACHE II and RANSON, were widely used to evaluate to the severity of such disease. In fact, these two models were relatively cumbersome due to the excessive parameters enrolled. Therefore, researchers specialized in AP have been trying to build simple and feasible models to replace them. In recent years, several simple models have been developed to evaluate the severity of acute pancreatitis in previous investigations. For example, Hong et al. published a prediction model for severe acute pancreatitis by using SIRS, albumin, BUN, and pleural effusion [
However, our study also has some limitations. Firstly, there were data missing in this study, especially the information of some other IRFs related to AP which were not enrolled, such as IL17, IL12, and M2 monocyte, which might be responsible for the selection bias. Secondly, all data applied in our study were collected retrospectively, which might reduce the reliability of results based on these data. Lastly, because of the funding problem, the sample size of our study was insufficient, not only the total number of all AP patients but also the number of AP patients with organ failure, and the validation cohort for IRF model was also absent. In the future research, we will expand the sample size and set up a validation cohort to further confirm the working efficiency of our prediction model, as well as enroll more potential IRFs.
In conclusions, IL6, IL8, and CD19+ B lymphocyte were reliable predictors for the organ failure of AP, and the prediction model has satisfactory working efficiency to identify AP patients with high risk of organ failure. This study may improve clinical prevention and treatment strategies of AP progression.
All data in our study are available from the corresponding authors upon reasonable request.
This study was approved by the institutional review board at The First Affiliated Hospital of Nanjing Medical University.
Informed consent from individual participants was obtained.
The authors declare that they have no competing interests.
QL, YM, DYH, ZPL, CQH, CYS, XLZ, and YPP all have made substantial contributions to conception, acquisition of data, analysis, and interpretation of data. All of them have been involved in drafting the manuscript and revising it critically for important intellectual content. All authors read and approved the final manuscript and take public responsibility for appropriate portions of the content and agreed to be accountable for all aspects of work. Yunpeng Peng, Xiaole Zhu, and Chaoqun Hou have contributed equally to this work.
This work was supported by grants from the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD, JX10231801), the National Science Foundation for Young Scientists of China (Grant no. 81802408), the Innovation Capability Development Project of Jiangsu Province (No. BM2015004), Jiangsu Province “333” Project (2019(RS19)), and Jiangsu Biobank of Clinical Resources.