Background/Aims: Artificial neural networks (ANNs) are non-linear pattern recognition techniques, which can be used as a tool in medical decision-making. The aim of this study was to construct and validate an ANN model for early prediction of the severity of acute pancreatitis (AP). Methods: Patients treated for AP from 2002 to 2005 (n = 139) and from 2007 to 2009 (n = 69) were analyzed to develop and validate the ANN model. Severe AP was defined according to the Atlanta criteria. Results: ANNs selected 6 of 23 potential risk variables as relevant for severity prediction, including duration of pain until arrival at the emergency department, creatinine, hemoglobin, alanine aminotransferase, heart rate, and white blood cell count. The discriminatory power for prediction of progression to a severe course, determined from the area under the receiver-operating characteristic curve, was 0.92 for the ANN model, 0.84 for the logistic regression model (p = 0.030), and 0.63 for the APACHE II score (p < 0.001). The numbers of correctly classified patients for a sensitivity of 50 and 75% were significantly higher for the ANN model than for logistic regression (p = 0.002) and APACHE II (p < 0.001). Conclusion: The ANN model identified 6 risk variables available at the time of admission, including duration of pain, a finding not being presented as a risk factor before. The severity classification developed proved to be superior to APACHE II.

Keywords:
Acute pancreatitis, APACHE II, Artificial neural network, Classification, Logistic regression, Severity of acute pancreatitis
1.
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.
2.
Balthazar EJ, Robinson DL, Megibow AJ, Ranson JH: Acute pancreatitis: Value of CT in establishing prognosis. Radiology 1990;174:331–336.
3.
Imrie CW, Benjamin IS, Ferguson JC, McKay AJ, Mackenzie I, O’Neill J, Blumgart LH: A single-centre double-blind trial of Trasylol therapy in primary acute pancreatitis. Br J Surg 1978;65:337–341.
4.
Knaus WA, Draper EA, Wagner DP, Zimmerman JE: APACHE II: a severity of disease classification system. Crit Care Med 1985;13:818–829.
5.
Mentula P, Kylanpaa ML, Kemppainen E, Jansson SE, Sarna S, Puolakkainen P, Haapiainen R, Repo H: Early prediction of organ failure by combined markers in patients with acute pancreatitis. Br J Surg 2005;92:68–75.
6.
Neoptolemos JP, Kemppainen EA, Mayer JM, Fitzpatrick JM, Raraty MG, Slavin J, Beger HG, Hietaranta AJ, Puolakkainen PA: Early prediction of severity in acute pancreatitis by urinary trypsinogen activation peptide: a multicentre study. Lancet 2000;355:1955–1960.
7.
Riche FC, Cholley BP, Laisne MJ, Vicaut E, Panis YH, Lajeunie EJ, Boudiaf M, Valleur PD: Inflammatory cytokines, C reactive protein, and procalcitonin as early predictors of necrosis infection in acute necrotizing pancreatitis. Surgery 2003;133:257–262.
8.
Ueda T, Takeyama Y, Yasuda T, Matsumura N, Sawa H, Nakajima T, Ajiki T, Fujino Y, Suzuki Y, Kuroda Y: Simple scoring system for the prediction of the prognosis of severe acute pancreatitis. Surgery 2007;141:51–58.
9.
Dybowski R, Gant V: Clinical applications of artificial neural networks. Cambridge, Cambridge University Press, 2001.
10.
Cucchetti A, Vivarelli M, Heaton ND, Phillips S, Piscaglia F, Bolondi L, La Barba G, Foxton MR, Rela M, O’Grady J, Pinna AD: Artificial neural network is superior to MELD in predicting mortality of patients with end-stage liver disease. Gut 2007;56:253–258.
11.
Nilsson J, Ohlsson M, Thulin L, Hoglund P, Nashef SA, Brandt J: Risk factor identification and mortality prediction in cardiac surgery using artificial neural networks. J Thorac Cardiovasc Surg 2006;132:12–19.
12.
Halonen KI, Leppaniemi AK, Lundin JE, Puolakkainen PA, Kemppainen EA, Haapiainen RK: Predicting fatal outcome in the early phase of severe acute pancreatitis by using novel prognostic models. Pancreatology 2003;3:309–315.
13.
Keogan MT, Lo JY, Freed KS, Raptopoulos V, Blake S, Kamel IR, Weisinger K, Rosen MP, Nelson RC: Outcome analysis of patients with acute pancreatitis by using an artificial neural network. Acad Radiol 2002;9:410–419.
14.
Mofidi R, Duff MD, Madhavan KK, Garden OJ, Parks RW: Identification of severe acute pancreatitis using an artificial neural network. Surgery 2007;141:59–66.
15.
Pofahl WE, Walczak SM, Rhone E, Izenberg SD: Use of an artificial neural network to predict length of stay in acute pancreatitis. Am Surg 1998;64:868–872.
16.
Yoldas O, Koc M, Karakose N, Kilic M, Tez M: Prediction of clinical outcomes using artificial neural networks for patients with acute biliary pancreatitis. Pancreas 2008;36:90–92.
17.
Bradley EL 3rd: A clinically based classification system for acute pancreatitis. Summary of the International Symposium on Acute Pancreatitis, Atlanta, Ga, September 11 through 13, 1992. Arch Surg 1993;128:586–590.
18.
Schemper M, Smith TL: Efficient evaluation of treatment effects in the presence of missing covariate values. Stat Med 1990;9:777–784.
19.
Cross SS, Harrison RF, Kennedy RL: Introduction to neural networks. Lancet 1995;346:1075–1079.
20.
Lippmann RP, Shahian DM: Coronary artery bypass risk prediction using neural networks. Ann Thorac Surg 1997;63:1635–1643.
21.
Hosmer DW, Lemeshow S: Applied Logistic Regression. New York, Wiley, 2000.
22.
Pepe MS: The Statistical Evaluation of Medical Tests for Classification and Prediction. New York, Oxford University Press, 2003, pp 92–94.
23.
DeLong ER, DeLong DM, Clarke-Pearson DL: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 1988;44:837–845.
24.
McMahon MJ, Playforth MJ, Pickford IR: A comparative study of methods for the prediction of severity of attacks of acute pancreatitis. Br J Surg 1980;67:22–25.
25.
Dervenis C, Johnson CD, Bassi C, Bradley E, Imrie CW, McMahon MJ, Modlin I: Diagnosis, objective assessment of severity, and management of acute pancreatitis. Santorini consensus conference. Int J Pancreatol 1999;25:195–210.
26.
Larvin M, McMahon MJ: APACHE-II score for assessment and monitoring of acute pancreatitis. Lancet 1989;ii:201–205.
27.
King NK, Powell JJ, Redhead D, Siriwardena AK: A simplified method for computed tomographic estimation of prognosis in acute pancreatitis. Scand J Gastroenterol 2003;38:433–436.
28.
Singh VK, Wu BU, Bollen TL, Repas K, Maurer R, Johannes RS, Mortele KJ, Conwell DL, Banks PA: A prospective evaluation of the bedside index for severity in acute pancreatitis score in assessing mortality and intermediate markers of severity in acute pancreatitis. Am J Gastroenterol 2009;104:966–971.
29.
Bollen TL, van Santvoort HC, Besselink MG, van Leeuwen MS, Horvath KD, Freeny PC, Gooszen HG: The Atlanta classification of acute pancreatitis revisited. Br J Surg 2008;95:6–21.
30.
Besselink MG, van Santvoort HC, Bollen TL, van Leeuwen MS, Lameris JS, van der Jagt EJ, Strijk SP, Buskens E, Freeny PC, Gooszen HG: Describing computed tomography findings in acute necrotizing pancreatitis with the Atlanta classification: an interobserver agreement study. Pancreas 2006;33:331–335.
31.
Bartosch-Harlid A, Andersson B, Aho U, Nilsson J, Andersson R: Artificial neural networks in pancreatic disease. Br J Surg 2008;95:817–826.
32.
Bone RC: Immunologic dissonance: a continuing evolution in our understanding of the systemic inflammatory response syndrome (SIRS) and the multiple organ dysfunction syndrome (MODS). Ann Intern Med 1996;125:680–687.
33.
Lankisch PG, Assmus C, Pflichthofer D, Struckmann K, Lehnick D: Which etiology causes the most severe acute pancreatitis? Int J Pancreatol 1999;26:55–57.
34.
Stimac D, Lenac T, Marusic Z: A scoring system for early differentiation of the etiology of acute pancreatitis. Scand J Gastroenterol 1998;33:209–211.
© 2011 S. Karger AG, Basel and IAP
2011
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