Feasibility of applying data mining techniques for predicting technical difficulties during laparoscopic cholecystectomy based on routine patient work-up in a small community hospital

Stanisic, Veselin (35184186800)Veselin (35184186800)StanisicAndjelkovic, Igor (56034536500)Igor (56034536500)AndjelkovicVlaovic, Darko (40662203800)Darko (40662203800)VlaovicBabic, Igor (37004486500)Igor (37004486500)BabicKocev, Nikola (6602672952)Nikola (6602672952)KocevNikolic, Bosko (7006055343)Bosko (7006055343)NikolicMilicevic, Miroslav (7005565664)Miroslav (7005565664)Milicevic2025-06-122025-06-122013https://doi.org/10.5754/hge13213https://www.scopus.com/inward/record.uri?eid=2-s2.0-84893909749&doi=10.5754%2fhge13213&partnerID=40&md5=684583eb33f1f46b83419fdb846ebf2chttps://remedy.med.bg.ac.rs/handle/123456789/9032Background/Aims: Predicting technical difficulties in laparoscopic cholecystectomy (LC) in a small regional hospital increases efficacy, cost-benefit and safety of the procedure. The aim of the study was to assess whether it is possible to accurately predict a difficult LC (DLC) in a small regional hospital based only on the routine available clinical work-up parameters (patient history, ultrasound examination and blood chemistry) and their combinations. Methodology: A prospective, cohort, of 369 consecutive patients operated by the same surgeon was analyzed. Conversion rate was 10 (2.7%). DLC was registered in 55 (14.90%). Various data mining techniques were applied and assessed. Results: Seven significant predictors of DLC were identified: i) shrunken (fibrotic) gallbladder (GB); ii) ultrasound (US) GB wall thickness >4 mm; iii) >5 attacks of pain lasting >5 hours; iv) WBC >10×109 g/L; v) pericholecystic fluid; vi) urine amylase >380 IU/L, and vii) BMI >30kg/m2. Bayesian network was selected as the best classifier with accuracy of 94.57, specificity 0.98, sensitivity 0.77, AUC 0.96 and F-measure 0.81. Conclusion: It is possible to predict a DLC with high accuracy using data mining techniques, based on routine preoperative clinical parameters and their combinations. Use of sophisticated diagnostic equipment is not necessary. © H.G.E. Update Medical Publishing S.A.Bayesian networkDifficult laparoscopic cholecystectomyFeasibility of applying data mining techniques for predicting technical difficulties during laparoscopic cholecystectomy based on routine patient work-up in a small community hospital