Artif Intell Med - Prediction of intraoperative complexity from preoperative patient data for laparoscopic cholecystectomy.

Tópicos

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Resumo

JECTIVE: Different reasons may cause difficult intraoperative surgical situations. This study aims to predict intraoperative complexity by classifying and evaluating preoperative patient data. The basic prediction problem addressed in this paper involves the classification of preoperative data into two classes: easy (Class 0) and complex (Class 1) surgeries.METHODS AND MATERIAL: preoperative patient data were collected from 337 patients admitted to the Klinikum rechts der Isar hospital in Munich, Germany for laparoscopic cholecystectomy (LAPCHOL) in the period of 2005-2008. The data include the patient's body mass index (BMI), sex, inflammation, wall thickening, age and history of previous surgery, as well as the name and level of experience of the operating surgeon. The operating surgeon was asked to label the intraoperative complexity after the surgery: '0' if the surgery was easy and '1' if it was complex. For the classification task a set of classifiers was evaluated, including linear discriminant classifier (LDC), quadratic discriminant classifier (QDC), Parzen and support vector machine (SVM). Moreover, feature-selection was applied to derive the optimal preoperative patient parameters for predicting intraoperative complexity.RESULTS: Classification results indicate a preference for the LDC in terms of classification error, although the SVM classifier is preferred in terms of results concerning the area under the curve. The trained LDC or SVM classifier can therefore be used in preoperative settings to predict complexity from preoperative patient data with classification error rates below 17%. Moreover, feature-selection results identify bias in the process of labelling surgical complexity, although this bias is irrelevant for patients with inflammation, wall thickening, male sex and high BMI. These patients tend to be at high risk for complex LAPCHOL surgeries, regardless of labelling bias.CONCLUSIONS: Intraoperative complexity can be predicted before surgery according to preoperative data with accuracy up to 83% using an LDC or SVM classifier. The set of features that are relevant for predicting complexity includes inflammation, wall thickening, sex and BMI score.

Resumo Limpo

jectiv differ reason may caus difficult intraop surgic situat studi aim predict intraop complex classifi evalu preoper patient data basic predict problem address paper involv classif preoper data two class easi class complex class surgeriesmethod materi preoper patient data collect patient admit klinikum recht der isar hospit munich germani laparoscop cholecystectomi lapchol period data includ patient bodi mass index bmi sex inflamm wall thicken age histori previous surgeri well name level experi oper surgeon oper surgeon ask label intraop complex surgeri surgeri easi complex classif task set classifi evalu includ linear discrimin classifi ldc quadrat discrimin classifi qdc parzen support vector machin svm moreov featureselect appli deriv optim preoper patient paramet predict intraop complexityresult classif result indic prefer ldc term classif error although svm classifi prefer term result concern area curv train ldc svm classifi can therefor use preoper set predict complex preoper patient data classif error rate moreov featureselect result identifi bias process label surgic complex although bias irrelev patient inflamm wall thicken male sex high bmi patient tend high risk complex lapchol surgeri regardless label biasconclus intraop complex can predict surgeri accord preoper data accuraci use ldc svm classifi set featur relev predict complex includ inflamm wall thicken sex bmi score

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