Literature DB >> 11898038

Conversion of laparoscopic cholecystectomy to open cholecystectomy in acute cholecystitis: artificial neural networks improve the prediction of conversion.

Samuel Eldar1, Hava T Siegelmann, Daniel Buzaglo, Ibrahim Matter, Ayala Cohen, Edmond Sabo, Jack Abrahamson.   

Abstract

Laparoscopic cholecystectomy is now also performed for acute cholecystitis. In the presence of inflammatory conditions, technical difficulties leading to conversion to open cholecystectomy may occur and overshadow the advantages of the laparoscopic approach. Factors associated with these undue events combined with techniques capable of learning from them may help in determining when to completely avoid the laparoscopic procedure. In this study we determined predictors of conversion in acute cholecystitis and tested their predictive ability by means of statistical multivariate analysis and artificial neural networks. Between January 1994 and February 1997, 225 patients underwent laparoscopic cholecystectomy for acute cholecystitis. Preoperative and operative data were prospectively collected on standardized forms. The first 180 laparoscopically approached cases entered the training set, which was learned by both the statistical and the artificial neural networks methods. Conversion was first studied in relation to a set of preoperative data. Prediction models were then fitted by both of these methods. The last 45 operated cases, which remained unknown to the learning systems, served for testing the fitted models. The forward stepwise logistic regression technique, the forward stepwise linear discriminant analysis, and the artificial neural networks method enabled positive prediction of conversion in 0%, 27%, and 100% of the cases, and a negative prediction in 80%, 85.5%, and 97% respectively, in the training set. A positive prediction of conversion in 0%, 25%, and 67% of the cases, and a negative prediction in 82%, 88%, and 94%, respectively, in the untrained, validation set of patients. An artificial neural networks based model provides a practical tool for the prediction of successful laparoscopic cholecystectomies and their conversion. The high degree of certainty of prediction in untrained cases reveals its potential, and justifies, under appropriate conditions, the complete avoidance of laparoscopy and turning directly to open cholecystectomy.

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Year:  2001        PMID: 11898038     DOI: 10.1007/s00268-001-0185-2

Source DB:  PubMed          Journal:  World J Surg        ISSN: 0364-2313            Impact factor:   3.352


  6 in total

1.  Risk factors for conversion of laparoscopic cholecystectomy to open cholecystectomy.

Authors:  C Simopoulos; S Botaitis; A Polychronidis; G Tripsianis; A J Karayiannakis
Journal:  Surg Endosc       Date:  2005-05-04       Impact factor: 4.584

2.  Predicting Conversion from Laparoscopic to Open Cholecystectomy: A Single Institution Retrospective Study.

Authors:  Samer Al Masri; Yaser Shaib; Mostapha Edelbi; Hani Tamim; Faek Jamali; Nicholas Batley; Walid Faraj; Ali Hallal
Journal:  World J Surg       Date:  2018-08       Impact factor: 3.352

3.  Risk factors and outcomes of conversion in minimally invasive distal pancreatectomy: a systematic review.

Authors:  A Balduzzi; N van der Heijde; A Alseidi; S Dokmak; M L Kendrick; P M Polanco; D E Sandford; S V Shrikhande; C M Vollmer; S E Wang; H J Zeh; M Abu Hilal; H J Asbun; M G Besselink
Journal:  Langenbecks Arch Surg       Date:  2020-12-10       Impact factor: 3.445

4.  Prediction of conversion of laparoscopic cholecystectomy to open surgery with artificial neural networks.

Authors:  Changiz Gholipour; Mohammad Bassir Abolghasemi Fakhree; Rosita Alizadeh Shalchi; Mehrshad Abbasi
Journal:  BMC Surg       Date:  2009-08-21       Impact factor: 2.102

5.  Comparisons of prediction models of quality of life after laparoscopic cholecystectomy: a longitudinal prospective study.

Authors:  Hon-Yi Shi; Hao-Hsien Lee; Jinn-Tsong Tsai; Wen-Hsien Ho; Chieh-Fan Chen; King-Teh Lee; Chong-Chi Chiu
Journal:  PLoS One       Date:  2012-12-28       Impact factor: 3.240

Review 6.  WSES project on decision support systems based on artificial neural networks in emergency surgery.

Authors:  Andrey Litvin; Sergey Korenev; Sophiya Rumovskaya; Massimo Sartelli; Gianluca Baiocchi; Walter L Biffl; Federico Coccolini; Salomone Di Saverio; Michael Denis Kelly; Yoram Kluger; Ari Leppäniemi; Michael Sugrue; Fausto Catena
Journal:  World J Emerg Surg       Date:  2021-09-26       Impact factor: 5.469

  6 in total

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