Literature DB >> 27303124

Developing Risk Prediction Models for Postoperative Pancreatic Fistula: a Systematic Review of Methodology and Reporting Quality.

Zhang Wen1, Ya Guo1, Banghao Xu1, Kaiyin Xiao1, Tao Peng1, Minhao Peng1.   

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.

Entities:  

Keywords:  Methodology; Postoperative pancreatic fistula; Prediction model; Systematic review

Year:  2016        PMID: 27303124      PMCID: PMC4875907          DOI: 10.1007/s12262-015-1439-9

Source DB:  PubMed          Journal:  Indian J Surg        ISSN: 0973-9793            Impact factor:   0.656


  39 in total

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Authors:  E W Steyerberg; M J Eijkemans; F E Harrell; J D Habbema
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2.  What you see may not be what you get: a brief, nontechnical introduction to overfitting in regression-type models.

Authors:  Michael A Babyak
Journal:  Psychosom Med       Date:  2004 May-Jun       Impact factor: 4.312

Review 3.  Systematic review of prognostic models in patients with acute stroke.

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Journal:  Cerebrovasc Dis       Date:  2001       Impact factor: 2.762

4.  Translating clinical research into clinical practice: impact of using prediction rules to make decisions.

Authors:  Brendan M Reilly; Arthur T Evans
Journal:  Ann Intern Med       Date:  2006-02-07       Impact factor: 25.391

5.  Development and validation of a prediction model with missing predictor data: a practical approach.

Authors:  Yvonne Vergouwe; Patrick Royston; Karel G M Moons; Douglas G Altman
Journal:  J Clin Epidemiol       Date:  2009-07-12       Impact factor: 6.437

Review 6.  Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors.

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Journal:  Stat Med       Date:  1996-02-28       Impact factor: 2.373

7.  Inappropriate use of bivariable analysis to screen risk factors for use in multivariable analysis.

Authors:  G W Sun; T L Shook; G L Kay
Journal:  J Clin Epidemiol       Date:  1996-08       Impact factor: 6.437

8.  Automated variable selection methods for logistic regression produced unstable models for predicting acute myocardial infarction mortality.

Authors:  Peter C Austin; Jack V Tu
Journal:  J Clin Epidemiol       Date:  2004-11       Impact factor: 6.437

9.  How do we predict the clinically relevant pancreatic fistula after pancreaticoduodenectomy?--an analysis in 244 consecutive patients.

Authors:  Manabu Kawai; Masaji Tani; Seiko Hirono; Shinomi Ina; Motoki Miyazawa; Hiroki Yamaue
Journal:  World J Surg       Date:  2009-12       Impact factor: 3.352

10.  Comparison of techniques for handling missing covariate data within prognostic modelling studies: a simulation study.

Authors:  Andrea Marshall; Douglas G Altman; Patrick Royston; Roger L Holder
Journal:  BMC Med Res Methodol       Date:  2010-01-19       Impact factor: 4.615

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  3 in total

1.  Poor reporting of multivariable prediction model studies: towards a targeted implementation strategy of the TRIPOD statement.

Authors:  Pauline Heus; Johanna A A G Damen; Romin Pajouheshnia; Rob J P M Scholten; Johannes B Reitsma; Gary S Collins; Douglas G Altman; Karel G M Moons; Lotty Hooft
Journal:  BMC Med       Date:  2018-07-19       Impact factor: 8.775

2.  Analysis of pancreatic fistula risk in patients with laparoscopic pancreatoduodenectomy: what matters.

Authors:  Kate Nong; Yue Zhang; Shengyong Liu; Yue Yang; Donglin Sun; Xuemin Chen
Journal:  J Int Med Res       Date:  2020-07       Impact factor: 1.671

3.  Protocol for a systematic review on the methodological and reporting quality of prediction model studies using machine learning techniques.

Authors:  Constanza L Andaur Navarro; Johanna A A G Damen; Toshihiko Takada; Steven W J Nijman; Paula Dhiman; Jie Ma; Gary S Collins; Ram Bajpai; Richard D Riley; Karel Gm Moons; Lotty Hooft
Journal:  BMJ Open       Date:  2020-11-11       Impact factor: 2.692

  3 in total

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