Prediction of acute pancreatitis severity using a nomogram based on clinical features and body composition

Abstract

This study aimed to construct a new model based on quantitative computed tomography (QCT) body composition and clinical features for early prediction of acute pancreatitis (AP) severity. The clinical features and body composition of patients with clinical first-onset AP between January 1, 2024, and May 30, 2024, were analyzed. Concurrently, 100 healthy physical examination patients were included to collect the clinical characteristics and QCT parameters. AP was divided into mild AP (MAP, n = 66), moderate severe AP (MSAP, n = 18), and severe AP (SAP, n = 21), according to the revised Atlanta classification (RAC), subsequently, the patients were divided into the SAP (n = 21) and non-severe AP (NSAP; N = 84) groups. Clinical features and body composition parameters were used to determine risk factors for SAP using univariate and multivariate logistic regression methods. Efficacy was assessed using calibration curves, receiver operating characteristic (ROC) curves, and a decision curve analysis (DCA). A total of 105 patients with AP and 100 healthy individuals undergoing physical examinations were included in this study. Except for subcutaneous adipose tissue (SAT), all other body parameters showed statistically significant differences between the 2 groups (P < .05). Univariate and multivariate logistic regression analyses revealed that alcoholic etiology, C-reactive protein (CRP), total adipose tissue (TAT), skeletal muscle area (SMA) were independent predictive factors for SAP, and a model was derived. For the training cohort, the nomogram predicted SAP with area under the curve (AUC) of 0.87 (95% CI: 0.78–0.95), sensitivity of 0.80 (95% CI: 0.69–0.92), and specificity of 0.80 (95% CI: 0.64–0.96). For the validation cohort, the AUC was 0.81 (95% CI: 0.65–0.96), sensitivity was 0.56 (95% CI: 0.33–0.79), and specificity was 0.79 (95% CI: 0.57–1.00), indicating that the model had high discriminative power. The Hosmer–Lemeshow test P-value was .628, indicating that the nomogram performed well in calibration. Finally, the DCA demonstrated the clinical applicability of the model. The present study demonstrated that alcoholic etiology, CRP level, TAT, and SMA are independent risk factors for predicting SAP. The developed nomogram has good discrimination, calibration, and clinical applicability.

1. Introduction

Acute pancreatitis (AP) is a disease characterized by a local inflammatory response in the pancreas caused by various etiologies, with or without functional changes in other organs, and is one of the most common acute abdominal emergencies.^[1,2] The annual incidence of AP is approximately 34 cases per 100,000 individuals.^[3] Currently, AP could be assessed by many tools. The bedside index for severity in AP (BISAP) score based on clinical characteristics and imaging feature was developed to evaluate the severity and mortality risk in AP. It was first raised by Wu et al^[4] in 2008 and takes advantages of without expensive and time-consuming diagnostic tools, with high accuracy. The mortality of AP can be predicted by BISAP score, and the mortality of the highest score – 5 points – is 9.5%. In addition, according to the Atlanta classification, severe AP (SAP) experiences persistent organ failure, and its mortality rate is as high as 36% to 50%. Moreover, the imaging feature that pleural effusion in the BISAP score needs to be confirmed by X-ray or computerized tomography (CT) scan. Therefore, the CT evaluation of AP progression to SAP after admission can be used as a supplement to BISAP, which is conducive to personalized management of patients. Most patients present with mild AP, which is self-limiting, but 20% of patients can progress to SAP, with a mortality rate as high as 30%.^[5] There are many causes of AP, and in China, gallstone disease remains the main cause, followed by hypertriglyceridemia and excessive alcohol consumption. Hypertriglyceridemia and alcoholic AP are more common in young male patients, whereas older patients are more likely to have biliary origins.^[6] Excessive drinking and long-term alcohol abuse can lead to pancreatic ischemic injury and cell death.^[7] Compared to other causes, such as gallstones and hyperlipidemia, alcoholic etiology is more likely to lead to severe outcomes.^[8,9] With changes in people’s dietary structure, the incidence of obesity is also increasing, and body mass index (BMI) has been used as a substitute for obesity, with high BMI being an independent and established risk factor for SAP.^[10,11] Although BMI can represent obesity to some extent, it is not an accurate assessment of the abdominal fat content and distribution.^[12,13] CT, an important imaging method for the diagnosis and severity assessment of AP, can also quantitatively analyze body composition, such as visceral adipose tissue (VAT), subcutaneous adipose tissue (SAT), and skeletal muscle area (SMA). At present, studies on quantitative CT (QCT) body composition and AP are inconsistent. Most studies suggest that VAT can predict the severity of AP,^[14–16] but a few studies suggest that skeletal muscle, rather than VAT, is an independent prognostic factor for AP.^[17,18] In addition, many laboratory markers are also used to predict AP, and the biomarker C-reactive protein (CRP) is most commonly used in risk stratification of AP,^[19,20] the increase in CRP in serum within 48 hours is closely related to the severity indicators of pancreatic necrosis and peripancreatic exudate in patients with AP. Therefore, the present study aimed to construct a nomogram by prospectively analyzing clinical data, laboratory indicators, and QCT parameters of AP to predict the severity of SAP early and efficiently, thereby providing favorable evidence for the formulation of AP treatment plans.

2. Methods

2.1. Study population

This study was registered in the Chinese clinical trial registry (registration number ChiCTR2400082188). The study was led by Guiqian International Hospital and is titled with assessment of AP severity and prognosis with quantitative CT-measured abdominal fat. This study was conducted in accordance with the Declaration of Helsinki (revised 2013).

This study was approved by the hospital’s ethics committee (approval number: Gui Qian 2024 Ethics Review No. 04). We prospectively analyzed the imaging and clinical data of patients with first-onset AP admitted to our hospital between January 2024 and May 2024. All the enrolled patients agreed to participate in this study and signed an informed consent form. Inclusion criteria: At least 2 of the following inclusion criteria were met: typical AP clinical symptoms with persistent abdominal pain, serum amylase and/or lipase levels more than 3 times the normal upper limit, abdominal ultrasound and/or CT images showing characteristic changes in pancreatitis. All patients underwent the first abdominal CT scan with or without enhancement within 48 hours of the appearance of symptoms or signs; The same CT equipment was used for all patients. Exclusion criteria: history of abdominal surgery, a history of acute or chronic pancreatitis, non-AP patients with unclear acute abdominal diagnosis; other factors affecting CT measurement of abdominal fat and muscle, including poor image quality and significant artifacts. A total of 105 participants were included in the final study cohort (68 men and 37 women). To more comprehensively demonstrate the differences in body composition and clinical characteristics between AP patients and healthy individuals, and to provide baseline information for descriptive comparisons, a healthy control group was also included in this study. The healthy controls included patients who underwent low-dose chest CT scanning for lung cancer screening in our hospital and the scanning range included complete pancreas. One hundred healthy adults of matched age and sex were recruited as the control group during the same period, and the exclusion criteria were the same as those in the experimental group. Pancreas were confirmed no obvious disease. The flowchart of the study is presented in Figure 1.

Figure 1.

Flow diagram of participant enrollment. AP = acute pancreatitis.

2.2. Clinical data collection

Data were collected from the electronic medical record archives of the Guiqian International General Hospital and included age, sex, etiology, BMI, triglycerides (TG), CRP, procalcitonin, interleukin-6 (IL6), white blood cells (WBC), lipase (LPS), blood amylase (AMY), and length of stay (LOS). The clinical severity grading diagnosis of AP was divided into mild AP (MAP), moderately severe AP (MSAP), and SAP groups according to the revised 2012 Atlanta AP classification criteria.^[21]

2.3. Patient management

According to American gastroenterological association (AGA) institute guideline^[22] on initial management of AP, risk stratification and initiate basic treatment as early as possible (within 48–72 hours) should be performed on patients diagnosed with AP after admission. The treatment includes early fluid resuscitation, analgesic therapy and nutritional support.

2.4. CT scan and QCT analysis

All patients underwent abdominal CT scans with or without enhancement within 48 hours after the onset of the first abdominal pain symptoms, and no other parts of contrast-enhanced scanning were performed within 48 hours before the examination. CT was performed using a 256-row revolution CT scanner (GE Healthcare, Boston). The scan parameters were as follows: tube voltage, 120 kVp; tube current, Smart mA; slice thickness, 5 mm; slice spacing, 5 mm; pitch, 0.992:1. Enhanced CT scan parameters were the same as those of the plain CT scan, and the contrast agent used was 350 mgI/mL iopamidol solution, injected at 1.3 mL/kg body weight through the antecubital vein, with a total injection time maintained at 24 seconds, followed by 30 mL of physiological saline for flushing. Arterial phase using Smartprep trigger scanning technology; trigger threshold of 100 HU, collecting arterial phase (AP), venous phase (VP), and delayed phase (DP) dynamic enhanced images at 20 to 30 seconds, 60 to 65 seconds, and 120 to 150 seconds after injection, respectively. The scan range covered the entire pancreatic tissue area.

Body composition was measured using QCT (American Mindways Company, Austin) with daily calibration of the body membrane before measurement and a standard reconstruction algorithm. After the CT scan, the images were transferred to a post-processing workstation QCT pro 6.1 for post-processing. The measurement was centered on the lumbar 3 vertebra level, with the software automatically coloring the cross-sectional image to distinguish fat composition and skeletal muscle composition. Manual assistance was used to adjust and remove the default error parts to obtain the VAT, SAT, total adipose tissue (TAT), and SMA areas, as shown in Figure 2. To minimize inter-observer measurement bias as much as possible, the measurement results of the 2 radiologists were subjected to an interobserver consistency test.

Figure 2.

QCT measurement parameters. ROI was delineated in the plain CT image of the middle level of the lumbar 3 vertebral body and the abdominal fat area parameters of QCT were obtained. The blue area inside the green dotted line was visceral adipose tissue, the blue area outside the green dotted line was subcutaneous adipose tissue, and the yellow area of the abdominal wall was skeletal muscle area. CT = computed tomography, QCT = quantitative computed tomography, ROI = region of interest.

2.5. Statistical analysis

Data were organized and analyzed using the SPSS software (version 26.0; SPSS Inc., Chicago) and R 3.5.0. QCT was calculated as the average of the measured values from the 2 observers. The kappa statistic was used to assess inter-rater reliability between the 2 reviewers. Continuous variables conforming to a normal distribution were described using the mean and standard deviation, and the Student t test was used for intergroup comparisons. Variables with skewed distribution are presented as medians and interquartile ranges and were compared using the Mann–Whitney U test. Categorical variables were described as ratios and compared using the chi-squared test or Fisher exact test. Risk factors were screened through univariate and multivariate logistic regression analyses to screen for independent predictive factors of SAP and establish a nomogram. A receiver operating characteristic (ROC) curve was used to evaluate the ability of the nomogram to predict SAP. The higher the area under the curve (AUC), the higher the precision, and an AUC value >0.7 indicates a reasonable estimate. The optimal threshold value was determined by identifying the point with the highest sensitivity and specificity on the ROC curve. Calibration was evaluated using the Hosmer-Lemeshow test and calibration curve, while clinical effectiveness was evaluated using decision curve analysis (DCA). Statistical significance was set at P < .05.

3. Results

3.1. Comparison of clinical data and QCT parameters between AP group and control group

The Guiqian International General Hospital diagnosed 155 patients with AP between January 1, 2024, and May 30, 2024. Among them, 105 patients met the inclusion criteria (inclusion criteria are shown in Fig. 1). A total of 100 physically examined individuals were included in the control group. The 105 patients with AP were divided into 3 categories according to the revised Atlanta classification (RAC) standard: MAP (n = 66), MSAP (n = 18), and SAP (n = 21), subsequently, the patients were divided into the SAP (n = 21) and non-severe AP (NSAP; n = 84) groups, and the control group was compared. BMI, VAT, TAT, SMA, and V/S showed statistically significant differences among the 3 groups (P < .05). Age, sex, and SAT were not significantly different between the groups (P > .05). Further pairwise comparisons using the LSD method showed that the BMI, VAT, TAT, SMA, and V/S in the MAP and SAP groups were higher than those in the control group, and the VAT, TAT, and SMA parameters in the SAP group were significantly higher than those in the MAP group (P < .05). Analysis of the clinical characteristics of the MAP and SAP subgroups showed that IL6, CRP, LOS, and causes showed statistically significant differences (P < .05), and the rest of the variables showed no statistically significant differences (P > .05) (Table 1). All data were randomly selected for 30 cases at intervals of 2 weeks for repeat measurement and consistency analysis, with an Intraclass correlation coefficient (ICC) > 0.75, as shown in Table 2.

Table 1.

Clinical and imaging characteristics of control group and AP subgroup in the study.

Variables	Control group (n = 100)	MAP (n = 66)	MSAP (n = 18)	SAP (n = 21)	P
Demographic parameters
Gender
Female	48	25	5	7	.182
Male	52	41	13	14
Age	49.00 (38.00, 57.00)	45.50 (35.00, 53.00)	41.00 (36.00, 50.00)	44.00 (35.00, 9.00)	.377
BMI	23.61*,†,‡ (21.16, 26.17)	25.50§ (22.40, 27.28)	25.75§ (24.30, 27.00)	26.55§ (24.64, 29.05)	.011
Body composition
VAT	141.28 ± 68.99†,‡	169.53 ± 61.90†,‡	203.27 ± 50.92§,*	204.51 ± 59.83§,*	.001
SAT	114.90 (88.00, 147.50)	114.45 (88.80, 147.60)	113.85 (91.80, 165.20)	148.75 (110.8, 175.28)	.167
TAT	250.95*,†,‡ (193.95, 354.15)	291.50§ (238.60, 355.30)	348.10§ (288.70, 388.10)	360.10§,* (280.50, 434.85)	.001
SMA	220.2*,†,‡ (185.45, 254.20)	262.60§,‡ (215.10, 288.00)	267.90§ (225.30, 326.70)	314.60§,* (258.13, 341.20)	.000
VS	1.11*,†,‡ (0.67, 1.58)	1.40§ (1.04, 1.96)	1.75§ (1.27, 2.17)	1.43§ (1.06, 1.70)	.001
Clinical characteristics
Cause, n (%)
Biliary	NA	27 (40.91)	3 (16.67)	4 (19.05)	.054
Hyperlipemia		32 (48.48)	9 (50.00)	12 (57.14)
Alcoholism		7 (10.61)	6 (33.33)	5 (23.81)
TG	NA	6.01 (2.13, 18.80)	4.03 (1.93, 28.00)	5.82 (1.57, 21.43)	.943
WBC	NA	11.37 ± 4.14	12.78 ± 3.48	13.11 ± 4.61	.166
IL6	NA	31.21‡ (6.74, 57.08)	68.71 (31.62, 89.80)	138.75* (84.13, 247.10)	.001
CRP	NA	13.93‡ (3.28, 56.83)	86.09 (2.73, 118.44)	128.00* (17.90, 226.20)	.001
PCT	NA	0.14‡ (0.05, 0.30)	0.12‡ (0.07, 0.24)	0.50*,† (0.14, 3.66)	.017
AMY	NA	249.88 (127.54, 845.87)	179.50 (66.99, 319.30)	498.00 (164.00, 1210.76)	.168
LPS	NA	349.70 (128.28, 1037.38)	220.00 (81.24, 729.80)	909.50 (174.69, 1832.00)	.150
LOS	NA	7.00†,‡ (6.00, 8.00)	9.00*,‡ (7.00, 15.00)	17.00*,† (15.00, 22.00)	.001

AMY = amylase, BMI = body mass index, CRP = C-reactive protein, IL6 = leukocyte interleukin-6, LOS = length of stay, LPS = lipase, MAP = mild acute pancreatitis, MSAP = moderate severe acute pancreatitis, PCT = procalcitonin, SAP = severe acute pancreatitis, SAT = subcutaneous adipose tissue, SMA = skeletal muscle area, TAT = total adipose tissue (visceral adipose tissue plus SAT), TG = triglyceride, VAT = visceral adipose tissue, VS = visceral to SAT area ratio, WBC = white blood cells.

^*Versus MAP, P < .05.

^†Versus MSAP, P < .05.

^‡Versus SAP, P < .05.

^§Versus control group, P < .05.

Table 2.

Intraclass correlation coefficient (ICC) between 2 observers for all parameters from quantitative computed tomography (QCT).

Observers	VAT	SAT	SMA
1	174.07 ± 57.72	129.08 ± 74.95	253.85 ± 51.35
2	173.03 ± 58.3	129.83 ± 77.02	255.48 ± 52.79
ICC	0.991	0.996	0.984
95% CI	0.982–0.996	0.991–0.998	0.967–0.992
P	.000	.000	.000

The parameters in each group were >0.75 (P < .001).

SAT = subcutaneous adipose tissue, SMA = skeletal muscle area, VAT = visceral adipose tissue.

3.2. Univariate and multivariate logistic regression analysis for predicting SAP

The AP group was randomly divided into 2 cohorts (7:3): a training cohort (n = 73) and a validation cohort (n = 32). All variables were included in the univariate logistic regression, and the results showed that causes, CRP, TAT, and SMA were predictive factors of SAP (P < .05), and there was no significant statistical collinearity between the independent predictive factors. Multivariate logistic regression analysis results showed that alcoholic etiology, CRP, TAT, and SMA were independent risk factors for predicting SAP (P < .05), as shown in Table 3.

Table 3.

Univariate and multivariate logistic regression for the risk factors of SAP.

Variables	Univariate			Multivariate
	β	P	OR (95% CI)	β	P	OR (95% CI)
Gender
Female			1.00 (Reference)
Male	0.25	.641	1.29 (0.45–3.71)
Cause
0			1.00 (Reference)			1.00 (Reference)
1	0.99	.122	2.70 (0.77–9.5)	0.16	.849	1.20 (0.22–6.22)
2	1.69	.040	5.40 (1.08–26.93)	2.73	.020	15.38 (1.53–155.09)
Age	-0.02	.251	0.98 (0.94–1.02)
AMY	0.00	.842	1.00 (1.00–1.00)
TG	0.01	.645	1.01 (0.97–1.05)
IL6	0.00	.121	1.00 (1.00–1.01)
PCT	0.10	.513	1.11 (0.82–1.50)
LPS	0.00	.781	1.00 (1.00–1.00)
CRP	0.01	.015	1.01 (1.01–1.02)	0.01	.027	1.01 (1.01–1.02)
WBC	0.08	.177	1.08 (0.96–1.22)
BMI	0.13	.150	1.14 (0.95–1.36)
VAT	0.01	.078	1.01 (1.00–1.02)
SAT	0.01	.051	1.01 (1.00–1.02)
TAT	0.01	.007	1.01 (1.01–1.01)	0.01	.011	1.01 (1.01–1.02)
SMA	0.01	.002	1.02 (1.01–1.03)	0.02	.019	1.02 (1.01–1.03)
VS	−0.38	.355	0.68 (0.30–1.53)

“0” means biliary of cause, “1” means hyperlipemia of cause, “2” means alcoholism of cause.

AMY = amylase, BMI = body mass index, CRP = C-reactive protein, IL6 = leukocyte interleukin-6, LPS = lipase, PCT = procalcitonin, SAP = severe acute pancreatitis, SAT = subcutaneous adipose tissue, SMA = skeletal muscle area, TAT = total adipose tissue (visceral adipose tissue plus SAT), TG = triglyceride, VAT = visceral adipose tissue, VS = visceral to SAT area ratio, WBC = white blood cells.

3.3. Establishment and validation of a predictive nomogram for SAP

Four independent risk factors associated with SAP were obtained: alcoholic etiology, CRP, TAT, and SMA, which were integrated to establish a predictive nomogram (Fig. 3). In the nomogram model, each variable was projected upward to the first-row scoring ruler, and the scores of each variable were summed to obtain the total score. Finally, the severity of AP was predicted using the total score; the higher the score, the greater the possibility of SAP occurrence (Fig. 4A–C).

Figure 3.

The nomogram prediction model of the SAP. CRP = C-reactive protein, SAP = severe acute pancreatitis, SMA = skeletal muscle area, TAT = total adipose tissue (visceral adipose tissue plus subcutaneous adipose tissue). “0” means biliary of cause, “1” means hyperlipemia of cause, and “2” means alcoholism of cause.

Figure 4.

(A–C) Examples of the nomogram in clinical practice. (A and B) A 34-yr-old man with acute pancreatitis (AP) was admitted to hospital for alcohol consumption. Axial abdominal CT scan revealed swelling of the pancreatic tail with surrounding patchy exudation (A). Quantitative CT (QCT) showed that total adipose tissue (TAT) = 334.4 cm², and skeletal muscle area (SMA) = 406.8 cm². C-reactive protein (CRP) level of 242.80 mg/L. It corresponds to an severe AP (SAP) risk of >0.9. Within 48 h of admission, the patient’s condition worsened. Follow-up abdominal CT showed diffuse swelling and uneven density of the pancreas with a large amount of exudation around the pancreas (B, white arrow). The modified Marshall score was 3 points. The patient was transferred to the ICU for treatment and improved after 20 d, subsequently being discharged from the hospital. A 48-yr-old male, was admitted with AP for a high-fat diet. Emergency CT scan showed swelling of the pancreatic tail with small patchy exudation around it (C, white arrow). QCT revealed that TAT = 288.7 cm², and SMA = 145.0 cm². CRP level of 43.50 mg/L, it corresponds to an SAP risk <0.4. The patient was treated in the hospital for 6 d and recovered completely before being discharged.

The ROC curve of the nomogram model was plotted, with an AUC of 0.87 (95% CI: 0.78–0.95) for the training cohort and 0.81 (95% CI: 0.65–0.96) for the validation cohort (Fig. 5, Table 4), indicating that the model has good clinical discrimination ability. The calibration curve and nomogram prediction of SAP showed good consistency, and the actual curve was close to the ideal curve (Fig. 6), with a P-value of .628, indicating that the model had good clinical discrimination and calibration. DCA indicated that the model had good net clinical benefits and clinical application value. The bootstrap method was used for 1000 self-sampling bootstrap methods for internal validation, and the clinical DCA is presented by plotting, as shown in Figure 7. The final DCA indicated that at a threshold probability of 15% to 85%, SAP risk estimation based on the nomogram was more beneficial to the net benefits than the no-screening or all-screening strategy.

Figure 5.

ROC curves for the nomograms of the SAP. SAP = severe acute pancreatitis.

Table 4.

Accuracy of the nomogram prediction for SAP in training cohort and validation cohort.

Data	AUC (95% CI)	Accuracy (95% CI)	Sensitivity (95% CI)	Specificity (95% CI)	PPV (95% CI)	NPV (95% CI)	Cut off
Training	0.87 (0.78–0.95)	0.80 (0.69–0.89)	0.80 (0.69–0.92)	0.80 (0.64–0.96)	0.88 (0.78–0.98)	0.69 (0.52–0.86)	0.333
Validation	0.81 (0.65–0.96)	0.66 (0.47–0.81)	0.56 (0.33–0.79)	0.79 (0.57–1.00)	0.77 (0.54–1.00)	0.58 (0.36–0.80)	0.333

AUC = area under the curve, NPV = negative predictive value, PPV = positive predictive value, SAP = severe acute pancreatitis.

Figure 6.

Calibration plots for the nomograms of the SAP in the training cohort (A) as well as in the validation cohort (B). SAP = severe acute pancreatitis.

Figure 7.

Decision curve analysis for the nomograms of the SAP in the training cohort (A) as well as in the validation cohort (B). SAP = severe acute pancreatitis.

These findings indicate that the nomogram prediction model proposed in this study exhibits excellent potential for predicting the risk of SAP during early hospitalization.

4. Discussion

This study developed a visual nomogram model for predicting the severity of AP by integrating 4 indicators of alcoholic causes, CRP, TAT, and SMA. The model showed good discrimination for predicting the occurrence of SAP. By integrating clinical features and body composition parameters based on QCT, the model can provide clinicians with a rapid and accurate tool for assessing the risk of SAP, aiding early clinical decision-making. This may help identify high-risk patients in the early stages of the disease, thereby facilitating timely intervention and ICU triage and improving patient outcomes.

AP is an inflammatory disease originating from the pancreas itself and caused by various factors, such as gallstones, hyperlipidemia, and alcohol, which can affect other tissues and organs. The mortality rate of AP is positively correlated with its severity, with a good prognosis for MAP, whereas SAP often has various complications and can lead to death due to pancreatic necrosis and distant organ failure, with a high mortality rate.^[8] There are many clinical scoring systems, such as the BISAP, Ranson, and APACHE II score systems, which require at least 24 to 48 hours to provide a reliable prediction, and by this time, the patient’s condition is usually clearer, and it is still difficult to accurately stratify the disease and predict the progression using the existing assessment tools. Therefore, it is important to accurately assess the severity of AP early in the disease course.

Many studies have shown that alcohol consumption is a risk factor for SAP.^[7–9] This study shows that among the 105 AP patients, gallstone disease, hypertriglyceridemia, and alcoholic etiology were 34 cases (32.38%), 53 cases (50.48%), and 18 cases (17.14%), respectively. This is similar to the study^[23] (in the etiology of AP, alcoholism was 12% and biliary origin was 47%). Men were still the susceptible population for AP, and there was no statistical difference in the sex ratio of AP patients in this group, but there were more male patients (68/105) than females (37/105), and the proportion of males (27/39) was also higher than that of females (12/39) in the SAP group. Alcohol consumption is more common in young male patients^[23] and alcohol increases the burden on the pancreas, especially in Guizhou Province, China, where drinking culture is more prevalent. In this study, the proportion of patients with SAP who consumed alcohol was as high as 61.1% (11/18) and tended to be younger. The average age of the SAP group was 41 years, which was lower than that of the MAP and control groups (45.5 years and 49.0 years, respectively). Logistic regression analysis also found that alcohol consumption was an independent predictor of SAP. Some studies have used ethanol-fed rats, which can increase the adhesiveness of white blood cells, leading to pancreatic microcirculation disorders and necrotizing pancreatitis.^[24] Long-term excessive alcohol abuse can lead to pancreatic ischemic injury and cell death. Alcohol has the highest risk of pancreatic necrosis compared to other causes such as gallstones and hyperlipidemia.^[25]

The biomarker CRP has been used for the severity stratification of AP, with a stable indicator and “gold standard” for early severity stratification and monitoring of disease course changes.^[26] Related studies^[27] have shown that CRP can induce the expression of monocyte chemoattractant protein 21, vascular endothelial adhesion factor 21, and intercellular adhesion factor 12 on endothelial cells, which have obvious pro-inflammatory effects; it can also activate the complement, improve the phagocytic ability of white blood cells, and stimulate the activation of macrophages, lymphocytes, and monocytes, so when the body has an inflammatory response, the liver secretes a large amount of CRP, leading to an increase in serum CRP. Within 150 hours of the appearance of AP symptoms, a critical CRP level of 48 mg/dL indicated necrotizing AP, with a sensitivity and specificity of >80% and an accuracy of 86%.^[28] The increase in serum CRP level within 48 hours after the onset of AP is closely related to the severity indicators of pancreatic necrosis and peripancreatic exudate in patients with AP.^[29] In this study, the CRP level in SAP was significantly higher than that in MAP, with statistically significant differences, and was also an independent predictor of AP severity (P < .05).

With changes in eating habits, the prevalence of obesity and hyperlipidemia is increasing worldwide, with hyperlipidemia becoming the second leading cause of AP in China. In this study, the incidence of hyperlipidemia in AP was the highest, accounting for 50.48% (53/105). The world health organization (WHO) uses BMI as a specific parameter to define overweight and obesity, and a higher BMI is associated with higher mortality and complication rates.^[11,30] However, BMI cannot accurately assess the distributions of body fat and muscle mass. Even patients with the same BMI may have completely different body composition. Obese AP patients had a longer LOS, and this study found that the median LOS for SAP patients was 15 days, which was higher than that for MAP patients, which was 7 days, with statistically significant differences (P < .05). QCT can accurately determine body fat content and distribution, and adipocyte factors can participate in inflammatory responses and lipid metabolism and regulate the release of cell factors. Excessive secretion of pro-inflammatory and anti-inflammatory factors can cause long-term systemic inflammatory stress or hypersensitivity, and their levels are related to AP severity, which may produce a greater inflammatory response when AP occurs.^[31] Some studies have shown that, compared with non-obese patients, the levels of CRP and/or pro-inflammatory cytokines (IL1 or IL6) in obese AP are usually elevated. In our study, the levels of CRP, IL6, VAT, TAT, and SMA in the SAP group were all significantly higher than those in the MAP group (P < .05), and CRP, TAT, and SMA were also independent predictors of SAP. Previous studies have been inconsistent regarding whether the VAT is a predictor of AP severity.^[32–35] In a systematic review including 11 studies, only 9 studies showed a statistically significant correlation between VAT and the severity of AP. Only 4 studies found that VAT is a risk factor for AP, 2 studies found that VAT is related to an increased risk of local complications, and 2 studies found a correlation between VAT and mortality.^[31] In this study, VAT showed a statistically significant difference between the SAP and MAP groups, but it was not a predictor of SAP, which may be related to the small sample size and regional distribution of the disease. However, we found that TAT is an independent predictor of SAP, which indicates that total adipose content is still related to AP, which requires further study. In recent years, some scholars have studied the impact of abdominal wall and paraspinal muscle parameters on the prognosis of AP, but this has not been widely studied. It was previously believed that low SMA is usually associated with poor disease outcomes.^[33,35,36] Our study showed that the SMA in the SAP group was higher than that in the NSAP and control groups (P = .000). These results were surprising because low SMA is generally correlated with worse disease outcomes. One possible hypothesis is that SAP patients already at a very early stage of the disease demonstrated more fluid sequestration, and that edema consequently influenced the measurement, giving falsely high values. In details, edema leads to an increase in the CT attenuation of fat infiltrating the muscle. Since the muscle attenuation threshold (−29 to +50 HU) is close to the fat attenuation threshold (−190 to −30 HU), some fat may be mistakenly segmented as muscle. In addition, muscle swelling and increased volume due to elevated water content may also contribute to the increase in SMA. Although muscle edema may erroneously increase the measured SMA area, it also results in a decrease in muscle CT attenuation. This speculation is consistent with the results of the study by Hanna Sternby.^[17] Moreover, the level of SMA in this study was relatively high, which may be related to different populations and requires further study.

Our study has several limitations. First, this was a single-center study with a small sample size, which significantly reduces the generalizability of the study. Especially, the relatively small sample size for SAP did not allow a powerful multivariable analysis. Thus, we only performed comparison among the 3-type AP and healthy controls. Additionally, the lack of external validation limits the generalizability of our study results. Future research should expand the sample size and test the nomogram in independent, multicenter cohorts to further verify its applicability and accuracy in different populations. Second, the impact of muscle mass on the prognosis of AP is inconsistent with previous studies by other researchers, and the reasons are not well explained and require further study. We only established a nomogram for predicting SAP, and it remains to be determined whether early clinical treatment can reduce SAP complications or severity. Third, as the QCT automatic measurement method, only the areas of muscle and fat were provided, without the average attenuation values. Therefore, the study did not investigate the average attenuation of muscle and fat. The treatment methods have a certain impact on the development of SAP, but this was not explored in this study.

5. Conclusions

In summary, alcoholic cause, CRP, and SMA parameters based on body composition of QCT are independent risk factors for predicting the occurrence of SAP. The developed nomogram model showed good discrimination, calibration, and clinical applicability, and the results of this study can aid in the early prediction of SAP.

Acknowledgments

The authors would like to thank all the patients and researchers who participated in this study.

Author contributions

Conceptualization: Caijun Huang, Suping Chen.

Data curation: Chunyan He, Xiao Meng, Caihong Wang.

Formal analysis: Caijun Huang, Jianyu Li.

Investigation: Chunyan He, Xiao Meng.

Methodology: Caijun Huang, Suping Chen.

Resources: Ming Lu.

Supervision: Caijun Huang, Ming Lu.

Validation: Feiyu Wu.

Visualization: Feiyu Wu.

Writing – original draft: Caijun Huang, Suping Chen.

Writing – review & editing: Caijun Huang, Suping Chen.