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


METHODS
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 >10x109 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.


CONCLUSIONS
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.
Veselin
V
Stanisic
Igor
I
Andjelkovic
Darko
D
Vlaovic
Igor
I
Babic
Nikola
N
Kocev
Bosko
B
Nikolic
Miroslav
M
Milicevic
D000328:Adult; D000368:Aged; D000465:Algorithms; D001185:Artificial Intelligence; D015415:Biomarkers / Q000097:blood; D016009:Chi-Square Distribution; D057225:Data Mining / Q000379:methods*; D003661:Decision Support Techniques; D005240:Feasibility Studies; D005260:Female; D042882:Gallstones / Q000097:blood / Q000175:diagnosis / Q000601:surgery*; D006764:Hospitals, Community; D006801:Humans; D010535:Laparoscopy / Q000009:adverse effects*; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D015999:Multivariate Analysis; D018579:Patient Selection; D011237:Predictive Value of Tests; D011446:Prospective Studies; D018570:Risk Assessment; D012307:Risk Factors; D016896:Treatment Outcome
D015415:Biomarkers