Abstract
Postoperative pancreatic fistula is still a major complication after pancreatic surgery, despite improvements of surgical technique and perioperative management. We sought to systematically review and critically access the conduct and reporting of methods used to develop risk prediction models for predicting postoperative pancreatic fistula. We conducted a systematic search of PubMed and EMBASE databases to identify articles published before January 1, 2015, which described the development of models to predict the risk of postoperative pancreatic fistula. We extracted information of developing a prediction model including study design, sample size and number of events, definition of postoperative pancreatic fistula, risk predictor selection, missing data, model-building strategies, and model performance. Seven studies of developing seven risk prediction models were included. In three studies (42 %), the number of events per variable was less than 10. The number of candidate risk predictors ranged from 9 to 32. Five studies (71 %) reported using univariate screening, which was not recommended in building a multivariate model, to reduce the number of risk predictors. Six risk prediction models (86 %) were developed by categorizing all continuous risk predictors. The treatment and handling of missing data were not mentioned in all studies. We found use of inappropriate methods that could endanger the development of model, including univariate pre-screening of variables, categorization of continuous risk predictors, and model validation. The use of inappropriate methods affects the reliability and the accuracy of the probability estimates of predicting postoperative pancreatic fistula.
Similar content being viewed by others
References
Pratt WB, Maithel SK, Vanounou T, Huang ZS, Callery MP, Vollmer CM Jr (2007) Clinical and economic validation of the international study group of pancreatic fistula (ISGPF) classification scheme. Ann Surg 245(3):443–451
Reid-Lombardo KM, Farnell MB, Crippa S, Barnett M, Maupin G, Bassi C et al (2007) Pancreatic anastomotic leakage after pancreaticoduodenectomy in 1,507 patients: a report from the pancreatic anastomotic leak study group. J Gastrointest Surg 11(11):1451–1458, discussion 59
Bassi C, Dervenis C, Butturini G, Fingerhut A, Yeo C, Izbicki J et al (2005) Postoperative pancreatic fistula: an international study group (ISGPF) definition. Surgery 138(1):8–13
Buchler MW, Friess H, Wagner M, Kulli C, Wagener V, Z’Graggen K (2000) Pancreatic fistula after pancreatic head resection. Br J Surg 87(7):883–889
Wellner U, Makowiec F, Fischer E, Hopt UT, Keck T (2009) Reduced postoperative pancreatic fistula rate after pancreatogastrostomy versus pancreaticojejunostomy. J Gastrointest Surg 13(4):745–751
Ferrone CR, Warshaw AL, Rattner DW, Berger D, Zheng H, Rawal B et al (2008) Pancreatic fistula rates after 462 distal pancreatectomies: staplers do not decrease fistula rates. J Gastrointest Surg 12(10):1691–1697, Discussion 97–8
Goh BK, Tan YM, Chung YF, Cheow PC, Ong HS, Chan WH et al (2008) Critical appraisal of 232 consecutive distal pancreatectomies with emphasis on risk factors, outcome, and management of the postoperative pancreatic fistula: a 21-year experience at a single institution. Arch Surg 143(10):956–965
Yamamoto Y, Sakamoto Y, Nara S, Esaki M, Shimada K, Kosuge T (2011) A preoperative predictive scoring system for postoperative pancreatic fistula after pancreaticoduodenectomy. World J Surg 35(12):2747–2755
Hubbard TJ, Lawson-McLean A, Fearon KC (2011) Nutritional predictors of postoperative outcome in pancreatic cancer. Br J Surg 98:268–274, Br J Surg 2011;98(7):1032; author reply 32–3
Gaujoux S, Cortes A, Couvelard A, Noullet S, Clavel L, Rebours V et al (2010) Fatty pancreas and increased body mass index are risk factors of pancreatic fistula after pancreaticoduodenectomy. Surgery 148(1):15–23
Braga M, Capretti G, Pecorelli N, Balzano G, Doglioni C, Ariotti R et al (2011) A prognostic score to predict major complications after pancreaticoduodenectomy. Ann Surg 254(5):702–707, Discussion 07–8
Belyaev O, Munding J, Herzog T, Suelberg D, Tannapfel A, Schmidt WE et al (2011) Histomorphological features of the pancreatic remnant as independent risk factors for postoperative pancreatic fistula: a matched-pairs analysis. Pancreatology 11(5):516–524
Wellner UF, Kayser G, Lapshyn H, Sick O, Makowiec F, Hoppner J et al (2010) A simple scoring system based on clinical factors related to pancreatic texture predicts postoperative pancreatic fistula preoperatively. HPB Oxford 12(10):696–702
Konstadoulakis MM, Filippakis GM, Lagoudianakis E, Antonakis PT, Dervenis C, Bramis J (2005) Intra-arterial bolus octreotide administration during Whipple procedure in patients with fragile pancreas: a novel technique for safer pancreaticojejunostomy. J Surg Oncol 89(4):268–272
Alghamdi AA, Jawas AM, Hart RS (2007) Use of octreotide for the prevention of pancreatic fistula after elective pancreatic surgery: a systematic review and meta-analysis. Can J Surg 50(6):459–466
Moher D, Liberati A, Tetzlaff J, Altman DG (2009) Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. BMJ 339:b2535
Lee SE, Jang JY, Lim CS, Kang MJ, Kim SH, Kim MA et al (2010) Measurement of pancreatic fat by magnetic resonance imaging: predicting the occurrence of pancreatic fistula after pancreatoduodenectomy. Ann Surg 251(5):932–936
Kawai M, Tani M, Hirono S, Ina S, Miyazawa M, Yamaue H (2009) How do we predict the clinically relevant pancreatic fistula after pancreaticoduodenectomy?--an analysis in 244 consecutive patients. World J Surg 33(12):2670–2678
Peduzzi P, Concato J, Kemper E, Holford TR, Feinstein AR (1996) A simulation study of the number of events per variable in logistic regression analysis. J Clin Epidemiol 49(12):1373–1379
Babyak MA (2004) What you see may not be what you get: a brief, nontechnical introduction to overfitting in regression-type models. Psychosom Med 66(3):411–421
Altman DG, Vergouwe Y, Royston P, Moons KG (2009) Prognosis and prognostic research: validating a prognostic model. BMJ 338:b605
Mallett S, Royston P, Dutton S, Waters R, Altman DG (2010) Reporting methods in studies developing prognostic models in cancer: a review. BMC Med 8:20
Royston P, Altman DG, Sauerbrei W (2006) Dichotomizing continuous predictors in multiple regression: a bad idea. Stat Med 25(1):127–141
Counsell C, Dennis M (2001) Systematic review of prognostic models in patients with acute stroke. Cerebrovasc Dis 12(3):159–170
Jacob M, Lewsey JD, Sharpin C, Gimson A, Rela M, van der Meulen JH (2005) Systematic review and validation of prognostic models in liver transplantation. Liver Transpl 11(7):814–825
Hukkelhoven CW, Rampen AJ, Maas AI, Farace E, Habbema JD, Marmarou A et al (2006) Some prognostic models for traumatic brain injury were not valid. J Clin Epidemiol 59(2):132–143
Mushkudiani NA, Hukkelhoven CW, Hernandez AV, Murray GD, Choi SC, Maas AI et al (2008) A systematic review finds methodological improvements necessary for prognostic models in determining traumatic brain injury outcomes. J Clin Epidemiol 61(4):331–343
Lagakos SW (1988) Effects of mismodelling and mismeasuring explanatory variables on tests of their association with a response variable. Stat Med 7(1–2):257–274
Collins GS, Mallett S, Omar O, Yu LM (2011) Developing risk prediction models for type 2 diabetes: a systematic review of methodology and reporting. BMC Med 9:103
Harrell FE Jr, Lee KL, Mark DB (1996) Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Stat Med 15(4):361–387
Marshall A, Altman DG, Royston P, Holder RL (2010) Comparison of techniques for handling missing covariate data within prognostic modelling studies: a simulation study. BMC Med Res Methodol 10:7
Vergouwe Y, Royston P, Moons KG, Altman DG (2010) Development and validation of a prediction model with missing predictor data: a practical approach. J Clin Epidemiol 63(2):205–214
Sun GW, Shook TL, Kay GL (1996) Inappropriate use of bivariable analysis to screen risk factors for use in multivariable analysis. J Clin Epidemiol 49(8):907–916
Austin PC, Tu JV (2004) Automated variable selection methods for logistic regression produced unstable models for predicting acute myocardial infarction mortality. J Clin Epidemiol 57(11):1138–1146
Steyerberg EW, Eijkemans MJ, Harrell FE Jr, Habbema JD (2000) Prognostic modelling with logistic regression analysis: a comparison of selection and estimation methods in small data sets. Stat Med 19(8):1059–1079
Steyerberg EW, Harrell FE Jr, Borsboom GJ, Eijkemans MJ, Vergouwe Y, Habbema JD (2001) Internal validation of predictive models: efficiency of some procedures for logistic regression analysis. J Clin Epidemiol 54(8):774–781
Altman DG, Royston P (2000) What do we mean by validating a prognostic model? Stat Med 19(4):453–473
Bleeker SE, Moll HA, Steyerberg EW, Donders AR, Derksen-Lubsen G, Grobbee DE et al (2003) External validation is necessary in prediction research: a clinical example. J Clin Epidemiol 56(9):826–832
Reilly BM, Evans AT (2006) Translating clinical research into clinical practice: impact of using prediction rules to make decisions. Ann Intern Med 144(3):201–209
Acknowledgments
The study was supported by the National Natural Science Foundation of China (Grant No. 81560387).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of Interest
The authors declare that they have no conflict of interest.
Additional information
Zhang Wen and Ya Guo contributed equally to this work.
Rights and permissions
About this article
Cite this article
Wen, Z., Guo, Y., Xu, B. et al. Developing Risk Prediction Models for Postoperative Pancreatic Fistula: a Systematic Review of Methodology and Reporting Quality. Indian J Surg 78, 136–143 (2016). https://doi.org/10.1007/s12262-015-1439-9
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12262-015-1439-9