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Accuracy of conventional and novel scoring systems in predicting severity and outcomes of acute pancreatitis: a retrospective study

Lipids in Health and Disease volume 20, Article number: 41 (2021) Cite this article

Abstract

Background

Recently, several novel scoring systems have been developed to evaluate the severity and outcomes of acute pancreatitis. This study aimed to compare the effectiveness of novel and conventional scoring systems in predicting the severity and outcomes of acute pancreatitis.

Methods

Patients treated between January 2003 and August 2020 were reviewed. The Ranson score (RS), Glasgow score (GS), bedside index of severity in acute pancreatitis (BISAP), pancreatic activity scoring system (PASS), and Chinese simple scoring system (CSSS) were determined within 48 h after admission. Multivariate logistic regression was used for severity, mortality, and organ failure prediction. Optimum cutoffs were identified using receiver operating characteristic curve analysis.

Results

A total of 1848 patients were included. The areas under the curve (AUCs) of RS, GS, BISAP, PASS, and CSSS for severity prediction were 0.861, 0.865, 0.829, 0.778, and 0.816, respectively. The corresponding AUCs for mortality prediction were 0.693, 0.736, 0.789, 0.858, and 0.759. The corresponding AUCs for acute respiratory distress syndrome prediction were 0.745, 0.784, 0.834, 0.936, and 0.820. Finally, the corresponding AUCs for acute renal failure prediction were 0.707, 0.734, 0.781, 0.868, and 0.816.

Conclusions

RS and GS predicted severity better than they predicted mortality and organ failure, while PASS predicted mortality and organ failure better. BISAP and CSSS performed equally well in severity and outcome predictions.

Background

Acute pancreatitis (AP) is an inflammatory disease of the pancreas with a worldwide incidence varying from 33.2/100,000 to 45/100,000 in the general population [1,2,3]. Approximately 10% ~ 20% of patients with AP have a severe clinical course, with significant morbidity and mortality due to local and systemic complications [3,4,5,6]. Acute respiratory distress syndrome (ARDS) and acute renal failure (ARF) are common complications of severe acute pancreatitis, and result in worse outcomes [7,8,9]. Therefore, the early detection of ARDS and ARF in patients with AP is indispensable.

Many studies have compared biochemical markers and various scoring systems in the early stage to predict disease course and outcomes in AP [10,11,12,13]. Conventional scoring systems, including the Ranson score (RS), Glasgow score (GS), and Acute Physiology, Chronic Health Evaluation (APACHE) II score, and bedside index of severity in acute pancreatitis (BISAP) have been used to assess the severity of AP. However, these scores are complicated and require multiple difficult clinical parameters for risk stratification. Although biomarkers are easy to obtain, their ability in predicting outcomes varies [14,15,16,17]. Recently, some novel scoring systems have been developed. A prospective cohort study [18] showed that the pancreatic activity scoring system (PASS; Table 1), which was first reported by the Southern California Pancreas Study Group in 2017 [19], could predict important clinical events at different points during the course of AP. Another new scoring system called the Chinese simple scoring system (CSSS; Table 2) was proposed in 2020 [20]. Both scoring systems are not yet widely used.

Table 1 Pancreatic activity scoring system (PASS)
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Table 2 Chinese simple scoring system (CSSS)
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The present study aimed to specifically determine the accuracy of these conventional and novel scoring systems as well as biomarkers in predicting severity, mortality, and organ failure in patients with AP.

Materials and methods

Study design and patient selection

A retrospective study was conducted. Records of patients with AP who were treated in The First Affiliated Hospital of Guangxi Medical University, between January 2003 and July 2020, were reviewed.

Patients were diagnosed with AP if they met at least two of the following three criteria: (1) abdominal pain consistent with AP, (2) serum lipase activity or amylase activity at least three times greater than the upper limit of normal, and (3) characteristic findings on abdominal imaging. Patients younger than 16 years, those known to have chronic pancreatitis, or those without sufficient data were excluded from the study.

Definitions of severity and organ failure

Severity of AP was evaluated based on the revised Atlanta classification [21]. Mild AP was defined as AP in the absence of organ failure and local/systemic complications. Severe AP was characterized by the presence of organ failure and/or local complications. Organ failure was defined according to the modified Marshall scoring system [22].

Biochemical markers, scoring systems, and their cutoffs

Biochemical markers measured within 48 h after admission were analysed. RS [23], GS [24], BISAP [25], PASS [19], and CSSS [20] were calculated for each patient within 48 h after admission. Scores were compared for their accuracy in the prediction of disease severity, mortality, and development of organ failure (ARDS and ARF).

Statistical analysis

SPSS v23.0 (IBM Corp., Armonk, NY) was used for statistical analyses. Continuous variables were displayed as mean ± standard deviation. The Student t-test was used for continuous variables. The chi-square test was used for categorical variables. Univariate and multivariate logistic regression analyses were carried out to identify risk factors. Potential risk factors with P < 0.05 in the univariate analyses were enrolled into the binary logistic backward stepwise regression analysis. The results are presented as odds ratios (OR) with 95% confidence intervals (CIs). ROC curves of the scores were used for the prediction of severe AP, mortality, ARDS, and ARF. Areas under the curve (AUCs) were used to evaluate the predictive accuracy of each scoring system. All optimum cutoffs were identified on the basis of the highest sensitivity and specificity values generated from the ROC curves. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated. A P value of less than 0.05 was considered to indicate statistical significance.

Results

Baseline characteristics

Among 1848 patients enrolled, 1164 (62.99%) had mild AP and 684 (37.01%) had severe AP. The mean age of the patients was 48.22 ± 16.21 years. The mean age of severe group was significantly higher in the severe AP group than in the mild AP group (P < 0.001). A male preponderance (68.19%) was found. ARF was more common in male patients than in female patients (P < 0.001). A higher body-mass index (BMI) was observed in the severe AP group than in the mild AP group (P < 0.001). The BMI of patients with ARDS/ARF was higher than those of patients without ARDS/ARF (P < 0.05; Table 3). Gallstones (38.47%) were the most common cause of AP, followed by hypertriglyceridemia (16.72%) and alcohol consumption (10.77%). Alcohol-associated pancreatitis was more common in the severe AP group, ARDS group, and ARF group (Table 3). Hyperlipidemia (14.88%) and type-2 diabetes mellitus (7.52%) were common comorbidities. A history of smoking and alcohol intake history was present in 541 (29.27%) and 591 (31.98%) patients, respectively. Alcohol consumption was more common in patients with severe AP (P < 0.001), ARDS (P = 0.002), and ARF (P < 0.001; Table 3). Longer hospital stay was observed in patients with severe AP than in patients with mild AP (P < 0.001). The mortality rate was much higher in the severe AP group than in the mild AP group (P < 0.001; Table 3).

Table 3 Univariate analysis of factors associated with severity, mortality, ARDS, and ARF in AP
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Value of biomarkers in predicting severity, mortality, and organ failure

In the multivariate analysis, white blood cell count (WBC), serum albumin, lactate dehydrogenase (LDH), calcium, glucose, and C-reactive protein (CRP) predicted the severity of AP. Their ORs for predicting severe AP were 1.110 (95% CI, 1.040–1.184), 0.940 (95% CI, 0.894–0.989), 1.004 (95% CI, 1.002–1.006), 0.196 (95% CI, 0.065–0.592), 1.081 (95% CI, 1.016–1.150), and 1.007 (95% CI, 1.003–1.012), respectively. Serum total bilirubin was found to be an independent predictor of mortality (OR, 1.013; 95% CI, 1.004–1.023). For predicting organ failure, BMI, WBC and serum calcium were independent variables for ARDS, while blood urea nitrogen and serum triglyceride were independent variables for ARF. However, among them only serum calcium showed a better OR value than other variables (Table 4).

Table 4 Multivariate analysis of factors predicting severity, mortality, ARDS, and ARF in AP
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Value of scoring Systems in Predicting Severity, mortality, and organ failure

For severe AP prediction, the ROC curve indicated an AUC of 0.861 for RS, 0.865 for GS, 0.829 for BISAP, 0.778 for PASS, and 0.816 for CSSS. The cutoffs used were as follow: RS, at least 2; GS, at least 2; BISAP, at least 2; PASS, at least 90; and CSSS, at least 2 (Table 5, Fig. 1a). For mortality prediction, the AUCs of the scoring systems were as follow: 0.693 for RS, 0.736 for GS, 0.789 for BISAP, 0.858 for PASS, and 0.759 for CSSS. The cutoffs of the scoring systems for mortality prediction were as follow: RS, at least 3; GS, at least 2; BISAP, at least 3; PASS, at least 190; and CSSS, at least 3 (Table 5, Fig. 1b). For ARDS prediction, the AUCs of scoring systems were as follow: 0.745 for RS, 0.784 for GS, 0.834 for BISAP, 0.936 for PASS, and 0.820 for CSSS. The cutoffs for RS, GS, BISAP, and CSSS were all at least 2, and the cutoff for PASS was at least 195 (Table 5, Fig. 1c). For ARF prediction, the AUCs of the scoring systems were as follow: 0.707 for RS, 0.734 for GS, 0.781 for BISAP, 0.868 for PASS, and 0.816 for CSSS. The cutoffs for RS, GS, BISAP, and CSSS were all at least 3, and the cutoff for PASS was at least 65 (Table 5, Fig. 1d).

Table 5 Effectiveness of scoring systems for predicting severity, mortality, ARDS, and ARF in AP
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Fig. 1
figure 1

a Receiver operating characteristic curves of scoring systems to predict severe AP. b with AP. c Receiver operating characteristic curves of scoring systems to predict ARDS in patients with AP. d Receiver operating characteristic curves of scoring systems to predict ARF in patients with AP

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Discussion

In the present study, BMI was an independent factor for the development of ARDS in AP patients, which is consistent with the result of a meta-analysis, that demonstrated that obesity was an important risk factor for the development of ARDS [26]. Studies have shown that patients who are obese have higher levels of circulating neutrophils [27] and blood cytokines [28], and have low-grade chronic inflammation triggered by obesity [29]. Moreover, innate immune cell activation and endothelial injury in the pulmonary microvasculature are major contributors to increased cell permeability and pulmonary edema in obese patients [30, 31].

This study revealed that serum Ca2+ showed good ORs for severity and ARDS prediction. Abnormal regulation of Ca2+ signals acts as a crucial trigger in the pathogenesis of AP [32]. A study has shown that hypocalcemia is an independent risk factor of severe AP and for respiratory failure in AP [33]. According to the present study, the WBC predicted the development of severe AP and ARDS. Furthermore, serum albumin, glucose, LDH, and CRP were also predictive factors for severe AP. These biomarkers are commonly used factors to predict severe AP. In terms of mortality prediction, the multivariate analysis identified that an increase in serum total bilirubin was a risk factor. Although few studies have reported a definite relationship between total bilirubin and mortality in AP, some studies have found that the albumin-bilirubin score has a high predictive capacity for in-hospital mortality or prognosis in patients with critical diseases such as acute upper gastrointestinal bleeding due to liver cirrhosis [34], post-operative hepatic carcinoma [35, 36], and AP [37]. Moreover, the present study showed that the elevation of serum triglycerides was a risk factor for ARF in AP patients, which is consistent with the findings of a meta-analysis reported in 2018 [38].

RS, GS, and BISAP showed high accuracy in predicting the severity rather than the outcomes of AP in the present study. RS and GS predicted the severity and 3 outcomes of AP equally well, which was probably due to the similar parameters they share. Although simple, these scores are not repeatable. According to this study, BISAP was inferior to both RS and GS in predicting severity, which is consistent with the findings of other prospective studies [39, 40]. This is because the items in RS and GS cover more systems than those in BISAP. Nevertheless, BISAP was superior to RS and GS in predicting mortality in the present study. Hall et al. also found that RS and GS were not good indicators of mortality in AP [41]. BISAP was also better than RS and GS at predicting ARDS and ARF, possibly because it is based on 3 important items that are related to the renal and respiratory systems, namely, blood urea nitrogen (BUN), systemic inflammatory response syndrome (SIRS), and pleural effusion.

PASS is a system that assesses the activity of AP at any time during hospitalization. It contains not only objective items (organ failure and SIRS), but also subjective items (abdominal pain, morphine usage and ability to tolerate solid diet). The repeatable items make it available to be used at any time during hospitalization. A prospective study [18] demonstrated that a cutoff PASS score of > 140 on admission was associated with an AUC of 0.71 for predicting severe AP. The present study found a similar AUC for PASS for severe AP prediction. As the center in which this study was conducted rarely uses morphine to relieve abdominal pain in patients with AP, the cutoff for severity prediction was only 90. In the present study, PASS scores best predicted mortality and organ failure, especially ARDS prediction. This is because PASS contains organ failure items. However, its subjective items (such as abdominal pain, morphine usage and ability to tolerate solid diet) make it inferior to other scores in severity prediction. Thus, no study has reported the predictive ability of PASS for the outcomes of AP.

Four biomarkers, heart rate, and pancreatic imaging findings are included in CSSS. According to the present study, the AUCs of CSSS for severity and mortality prediction were 0.834 and 0.838, respectively. The cutoff points were 4 for severity and 6 for mortality. However, the AUCs and cutoff points in this study are smaller than those reported in the previous study [20], which is probably attributable to the larger sample size of the current study. In the present study, CSSS showed nearly the same ability in predicting the 4 outcomes of AP, and it shared nearly equal capacity with BISAP for predicting the outcomes of AP, which indicates that CSSS is a promising scoring system. However, no study evaluating CSSS was found. Hence, studies with larger sample size and prospective designed are required to verify the efficiency of this new scoring system.

Study strengths and limitations

The strengths of the present study are that it compared both conventional and novel scoring systems as well as biomarkers in a large sample of Chinese patients for the prediction of the severity and outcomes of AP.

This study does have some limitations. First, this was a retrospective single-center study. Second, there was diversity in the period between the onset of AP and admission. This probably resulted in heterogeneity in the timings of score calculations and biochemical marker measurements.

Conclusion

RS and GS predicted severity better than mortality and organ failure, while PASS predicted mortality and organ failure better. As a novel scoring system, PASS has potential, but some of its items are not that suitable for Chinese medical centers. BISAP and CSSS performed equally well in severity and outcome prediction.

Availability of data and materials

All data used in this study are available from the corresponding author.

Abbreviations

ARF:

Acute renal failure

ARDS:

Acute respiratory distress syndrome

RS:

Ranson score

GS:

Glasgow score

APACHE:

Acute physiology and chronic health evaluation

BISAP:

Bedside index of severity in acute pancreatitis

PASS:

Pancreatic activity scoring system

CSSS:

Chinese simple scoring system

AUC:

Area under the curve

ROC:

Receiver operating characteristic

BMI:

Body-mass index

T2DM:

Type-2 diabetes mellitus

WBC:

White blood cell count

BUN:

Blood urea nitrogen

SIRS:

Systemic inflammatory response syndrome

LDH:

Lactate dehydrogenase

CRP:

C-reactive protein

AST:

Aspartate transaminase

References

  1. Frey CF, Zhou H, Harvey DJ, White RH. The incidence and case-fatality rates of acute biliary, alcoholic, and idiopathic pancreatitis in California, 1994–2001. Pancreas. 2006;33:336–44. https://doi.org/10.1097/01.mpa.0000236727.16370.99.

    Article  PubMed  Google Scholar 

  2. Satoh K, Shimosegawa T, Masamune A, Hirota M, Kikuta K, Kihara Y, et al. Nationwide epidemiological survey of acute pancreatitis in Japan. Pancreas. 2011;40:503–7. https://doi.org/10.1097/MPA.0b013e318214812b.

    Article  PubMed  Google Scholar 

  3. Shen HN, Lu CL, Li CY. Epidemiology of first-attack acute pancreatitis in Taiwan from 2000 through 2009: a nationwide population-based study. Pancreas. 2012;41:696–702. https://doi.org/10.1097/MPA.0b013e31823db941.

    Article  PubMed  Google Scholar 

  4. Hamada S, Masamune A, Kikuta K, Hirota M, Tsuji I, Shimosegawa T, et al. Nationwide epidemiological survey of acute pancreatitis in Japan. Pancreas. 2014;43:1244–8. https://doi.org/10.1097/MPA.0000000000000200.

    Article  PubMed  Google Scholar 

  5. Frossard JL, Steer ML, Pastor CM. Acute pancreatitis. Lancet. 2008;371:143–52. https://doi.org/10.1016/S0140-6736(08)60107-5.

    Article  PubMed  Google Scholar 

  6. Zhu Y, Pan X, Zeng H, He W, Xia L, Liu P, et al. A Study on the Etiology, Severity, and Mortality of 3260 Patients With Acute Pancreatitis According to the Revised Atlanta Classification in Jiangxi, China Over an 8-Year Period. Pancreas. 2017;46:504–9. https://doi.org/10.1097/MPA.0000000000000776.

    Article  PubMed  Google Scholar 

  7. Naqvi R. Acute Kidney Injury in association with Acute Pancreatitis. Pak J Med Sci. 2018;34:606–9. https://doi.org/10.12669/pjms.343.14953.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Lin HY, Lai JI, Lai YC, Lin PC, Chang SC, Tang GJ. Acute renal failure in severe pancreatitis: A population-based study. Ups J Med Sci. 2011;116:155–9. https://doi.org/10.3109/03009734.2010.547636.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Manohar M, Verma AK, Venkateshaiah SU, Sanders NL, Mishra A. Chronic Pancreatitis Associated Acute Respiratory Failure. MOJ Immunol. 2017;5. https://doi.org/10.15406/moji.2017.05.00149.

  10. Tan YHA, Rafi S, Tyebally Fang M, Hwang S, Lim EW, Ngu J, et al. Validation of the modified Ranson versus Glasgow score for pancreatitis in a Singaporean population. ANZ J Surg. 2017;87:700–3. https://doi.org/10.1111/ans.13139.

    Article  PubMed  Google Scholar 

  11. Jones MJ, Neal CP, Ngu WS, Dennison AR, Garcea G. Early warning score independently predicts adverse outcome and mortality in patients with acute pancreatitis. Langenbecks. Arch Surg. 2017;402(5):811–9. https://doi.org/10.1007/s00423-017-1581-x.

    Article  Google Scholar 

  12. Vasudevan S, Goswami P, Sonika U, Thakur B, Sreenivas V, Saraya A. Comparison of Various Scoring Systems and Biochemical Markers in Predicting the Outcome in Acute Pancreatitis. Pancreas. 2018;47:65–71. https://doi.org/10.1097/MPA.0000000000000957.

    Article  CAS  PubMed  Google Scholar 

  13. Zheng J, Fan J, Huang C, Lu Y, Huang Z, Wang X, et al. Dynamic Detection of Monocyte Subsets in Peripheral Blood of Patients with Acute Hypertriglyceridemic Pancreatitis. Gastroenterol Res Pract. 2019;2019:5705782. https://doi.org/10.1155/2019/5705782.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Cho JH, Kim TN, Chung HH, Kim KH. Comparison of scoring systems in predicting the severity of acute pancreatitis. world. J Gastroenterol. 2015;21(8):2387–94. https://doi.org/10.3748/wjg.v21.i8.2387.

    Article  Google Scholar 

  15. Kiat TTJ, Gunasekaran SK, Junnarkar SP, Low JK, Woon W, Shelat VG. Are traditional scoring systems for severity stratification of acute pancreatitis sufficient? Ann Hepatobiliary Pancreat Surg. 2018;22:105–15. https://doi.org/10.14701/ahbps.2018.22.2.105.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Hagjer S, Kumar N. Evaluation of the BISAP scoring system in prognostication of acute pancreatitis - A prospective observational study. Int J Surg. 2018;54:76–81. https://doi.org/10.1016/j.ijsu.2018.04.026.

    Article  PubMed  Google Scholar 

  17. de Grooth HJ, Geenen IL, Girbes AR, Vincent JL, Parienti JJ, Oudemans-van Straaten HM. SOFA and mortality endpoints in randomized controlled trials: a systematic review and meta-regression analysis. Crit Care. 2017;21:38. https://doi.org/10.1186/s13054-017-1609-1.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Buxbaum J, Quezada M, Chong B, Gupta N, Yu CY, Lane C, et al. The pancreatitis activity scoring system predicts clinical outcomes in acute pancreatitis: findings from a prospective cohort study. am. J Gastroenterol. 2018;113(5):755–64. https://doi.org/10.1038/s41395-018-0048-1.

    Article  Google Scholar 

  19. Wu BU, Batech M, Quezada M, Lew D, Fujikawa K, Kung J, et al. Dynamic measurement of disease activity in acute pancreatitis: the pancreatitis activity scoring system. am. J Gastroenterol. 2017;112(7):1144–52. https://doi.org/10.1038/ajg.2017.114.

    Article  Google Scholar 

  20. Wang L, Zeng YB, Chen JY, Luo Q, Wang R, Zhang R, et al. A simple new scoring system for predicting the mortality of severe acute pancreatitis: A retrospective clinical study. Medicine (Baltimore). 2020;99:e20646. https://doi.org/10.1097/MD.0000000000020646.

    Article  CAS  Google Scholar 

  21. Banks PA, Bollen TL, Dervenis C, Gooszen HG, Johnson CD, Sarr MG, et al. Classification of acute pancreatitis--2012: revision of the Atlanta classification and definitions by international consensus. Gut. 2013;62:102–11. https://doi.org/10.1136/gutjnl-2012-302779.

    Article  PubMed  Google Scholar 

  22. Marshall JC, Cook DJ, Christou NV, Bernard GR, Sprung CL, Sibbald WJ. Multiple organ dysfunction score: a reliable descriptor of a complex clinical outcome. Crit Care Med. 1995;23:1638–52.

    Article  CAS  Google Scholar 

  23. Ranson JH, Rifkind KM, Roses DF, Fink SD, Eng K, Spencer FC. Prognostic signs and the role of operative management in acute pancreatitis. Surg Gynecol Obstet. 1974;139:69–81.

    CAS  PubMed  Google Scholar 

  24. Blamey SL, Imrie CW, O'Neill J, Gilmour WH, Carter DC. Prognostic factors in acute pancreatitis. Gut. 1984;25:1340–6. https://doi.org/10.1136/gut.25.12.1340.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Wu BU, Johannes RS, Sun X, Tabak Y, Conwell DL, Banks PA. The early prediction of mortality in acute pancreatitis: a large population-based study. Gut. 2008;57:1698–703. https://doi.org/10.1136/gut.2008.152702.

    Article  CAS  PubMed  Google Scholar 

  26. Zhi G, Xin W, Ying W, Guohong X, Shuying L. “Obesity Paradox” in Acute Respiratory Distress Syndrome: Asystematic Review and Meta-Analysis. PLoS One. 2016;11:e0163677. https://doi.org/10.1371/journal.pone.0163677.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Kim JA, Park HS. White blood cell count and abdominal fat distribution in female obese adolescents. Metabolism. 2008;57:1375–9. https://doi.org/10.1016/j.metabol.2008.05.005.

    Article  CAS  PubMed  Google Scholar 

  28. Ramos EJ, Xu Y, Romanova I, Middleton F, Chen C, Quinn R, et al. Is obesity an inflammatory disease? Surgery. 2003;134:329–35. https://doi.org/10.1067/msy.2003.267.

    Article  PubMed  Google Scholar 

  29. Saltiel AR, Olefsky JM. Inflammatory mechanisms linking obesity and metabolic disease. J Clin Invest. 2017;127:1–4. https://doi.org/10.1172/JCI92035.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Pontiroli AE, Frige F, Paganelli M, Folli F. In morbid obesity, metabolic abnormalities and adhesion molecules correlate with visceral fat, not with subcutaneous fat: effect of weight loss through surgery. Obes Surg. 2009;19:745–50. https://doi.org/10.1007/s11695-008-9626-4.

    Article  PubMed  Google Scholar 

  31. Cottam DR, Schaefer PA, Shaftan GW, Velcu L, Angus LD. Effect of surgically-induced weight loss on leukocyte indicators of chronic inflammation in morbid obesity. Obes Surg. 2002;12:335–42. https://doi.org/10.1381/096089202321088101.

    Article  CAS  PubMed  Google Scholar 

  32. Frick TW. The role of calcium in acute pancreatitis. Surgery. 2012;152:S157–63. https://doi.org/10.1016/j.surg.2012.05.013.

    Article  PubMed  Google Scholar 

  33. Peng T, Peng X, Huang M, Cui J, Zhang Y, Wu H, et al. Serum calcium as an indicator of persistent organ failure in acute pancreatitis. Am J Emerg Med. 2017;35:978–82. https://doi.org/10.1016/j.ajem.2017.02.006.

    Article  PubMed  Google Scholar 

  34. Zou D, Qi X, Zhu C, Ning Z, Hou F, Zhao J, et al. Albumin-bilirubin score for predicting the in-hospital mortality of acute upper gastrointestinal bleeding in liver cirrhosis: a retrospective study. Turk. J Gastroenterol. 2016;27(2):180–6. https://doi.org/10.5152/tjg.2016.15502.

    Article  Google Scholar 

  35. Mohammed MAA, Khalaf MH, Liang T, Wang DS, Lungren MP, Rosenberg J, et al. Albumin-Bilirubin Score: An Accurate Predictor of Hepatic Decompensation in High-Risk Patients Undergoing Transarterial Chemoembolization for Hepatocellular Carcinoma. J Vasc Interv Radiol. 2018;29:1527–34 e1. https://doi.org/10.1016/j.jvir.2018.06.016.

    Article  PubMed  Google Scholar 

  36. Ye L, Liang R, Zhang J, Chen C, Chen X, Zhang Y, et al. Postoperative albumin-bilirubin grade and albumin-bilirubin change predict the outcomes of hepatocellular carcinoma after hepatectomy. Ann Transl Med. 2019;7:367. https://doi.org/10.21037/atm.2019.06.01.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Shi L, Zhang D, Zhang J. Albumin-bilirubin score is associated with in-hospital mortality in critically ill patients with acute pancreatitis. Eur J Gastroenterol Hepatol. 2020. https://doi.org/10.1097/MEG.0000000000001753.

  38. Kiss L, Fur G, Matrai P, Hegyi P, Ivany E, Cazacu IM, et al. The effect of serum triglyceride concentration on the outcome of acute pancreatitis: systematic review and meta-analysis. Sci Rep. 2018;8:14096. https://doi.org/10.1038/s41598-018-32337-x.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Mounzer R, Langmead CJ, Wu BU, Evans AC, Bishehsari F, Muddana V, et al. Comparison of existing clinical scoring systems to predict persistent organ failure in patients with acute pancreatitis. Gastroenterology. 2012;142:1476–82; quiz e15–6. https://doi.org/10.1053/j.gastro.2012.03.005.

    Article  PubMed  Google Scholar 

  40. Papachristou GI, Muddana V, Yadav D, O'Connell M, Sanders MK, Slivka A, et al. Comparison of BISAP, Ranson’s, APACHE-II, and CTSI scores in predicting organ failure, complications, and mortality in acute pancreatitis. Am J Gastroenterol. 2010;105:435–41; quiz 42. https://doi.org/10.1038/ajg.2009.622.

    Article  PubMed  Google Scholar 

  41. Hall TC, Stephenson JS, Jones MJ, Ngu WS, Horsfield MA, Rajesh A, et al. Is Abdominal Fat Distribution Measured by Axial CT Imaging an Indicator of Complications and Mortality in Acute Pancreatitis? J Gastrointest Surg. 2015;19:2126–31. https://doi.org/10.1007/s11605-015-2972-3.

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

Not applicable.

Funding

This study was supported by the National Natural Science Foundation of China (81970558) and the Natural Science Foundation of Guangxi Province (2018GXNSFBA281154).

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Author notes

  1. Qing Wu and Jie Wang are contributed equally to this work and should be considered co-first authors.

Authors and Affiliations

  1. Department of Gastroenterology, the Second Affiliated Hospital of Guangxi Medical University, Nanning, China

    Qing Wu, Jie Wang & Mengbin Qin

  2. Department of Gastroenterology, the First Affiliated Hospital of Guangxi Medical University, Nanning, China

    Huiying Yang, Zhihai Liang & Guodu Tang

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Contributions

QW and JW contributed in the conception of the work, designing the study, collecting biochemical data and revising the draft. MQ and GDT contributed in the conception of the work and designing the study. HYY contributed in the conception of the work and collecting the biochemical data. ZHL contributed in the conception of the work, conducting the study and revising the draft. All authors approved the final manuscript.

Corresponding author

Correspondence to Guodu Tang.

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Ethics approval and consent to participate

This study was approved by the Medical Ethics Committee of First Affiliated Hospital of Guangxi Medical University (No. 2020(KY-E-177).

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Competing interests

The authors declare that they have no Competing interests.

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Wu, Q., Wang, J., Qin, M. et al. Accuracy of conventional and novel scoring systems in predicting severity and outcomes of acute pancreatitis: a retrospective study. Lipids Health Dis 20, 41 (2021). https://doi.org/10.1186/s12944-021-01470-4

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  • DOI: https://doi.org/10.1186/s12944-021-01470-4

Keywords

  • Acute pancreatitis
  • Severity
  • Acute respiratory distress syndrome
  • Acute renal failure
  • Mortality
  • Scoring system
  • Predict
  • Retrospective

Lipids in Health and Disease

ISSN: 1476-511X