Prediction of intraoperative complexity from preoperative patient data for laparoscopic cholecystectomy
Introduction
Pattern recognition is gaining increasing attention in the medical domain, as it has proven more effective than common clinical statistical tools in the prediction of clinical outcomes. Pattern recognition has been used for decades for segmentation purposes in medical imaging applications [1]. Classification methods have recently been used for decision support in computer-aided diagnosis. For example, Lin [2] designed a diagnosis model for the treatment of liver disease using classification and regression trees, and Lee et al. [3] developed a computer-aided diagnosis system for evaluating pulmonary nodules using feature selection and a linear discriminant classifier (LDC).
The vast amount of preoperative patient data generated before surgery motivates the construction of pattern recognition tools that are able to improve the accuracy of predictions regarding complexity factors that may occur during surgery. Nevertheless, the use of surgical preoperative data for prediction has been overlooked in the literature. This challenge has recently been addressed by conventional clinical statistical approaches, which lack the power of pattern recognition when using ranking and classification algorithms as prediction tools.
Surgical complications have been associated with increased inpatient hospital costs [4]. As a result, reducing complications has become a desirable objective for quality-improvement initiatives aimed at improving efficiency and safety in health care. Davenport et al. [5] have shown that preoperative risk factors and surgical procedure complexity are more effective predictors of hospital costs than complications are. This dependence between risk and procedure complexity is to be expected, as these measures were designed to predict complications. Nevertheless, the use of raw preoperative patient data in predicting complexity factors has been overlooked in the clinical literature. In most clinical studies [6], complexity factors often are based on nothing more than surgical expertise. Complexity factors are pre-designed and classified according to the surgeon's knowledge about possible complications of a specific procedure. Moreover, in the clinical literature, surgical complexity is estimated in per procedure and not with regard to the relative ease or difficulty of a procedure for a given patient [5]. Nonetheless, the literature does contain a small number of studies that assess the surgical complexity of individual procedures according to readily available patient data. Jenkins et al. [7] used patient demographics to predict the operative time for ventral hernia repair. In most cases, however, these approaches are statistical, concentrating only on evaluating the significance of individual parameters as independent variables. They lack the power of pattern recognition tools in performing classification and selecting a subset of parameters for which the classification performance improves the most.
Laparoscopic cholecystectomy (LAPCHOL) is one of the most commonly performed surgical procedures worldwide [8]. LAPCHOL is accepted as the gold standard in the treatment of symptomatic gallstones. Up to 700 000 LAPCHOL procedures are performed in the US each year [8]. The preoperative assessment of complexity factors is needed for frequent procedures such as LAPCHOL in order to avoid complications and delays and to guarantee an efficient course of surgery.
This study aims to estimate the intraoperative complexity of LAPCHOL according to readily available preoperative patient data. Resource planning is a crucial topic in research on surgical efficiency. Tools are prepared in essentially the same way for all surgeries. In many cases, however, surgeons need advanced tools in case of complications. These tools can be prepared preoperatively for surgeries that are predicted to be complex. Furthermore, any complex procedure always involves the risk of conversion to an open procedure. This measure can be taken preoperatively to avoid intraoperative delays during surgery. The members of the surgical team can be considered another example of resources. If a surgery is identified as complex, it can be assigned to a more experienced team (including the surgeon, the surgeon-assistant or the operative-nurse), thereby allowing for a safer and efficient surgical procedure. Moreover, although the average time required for LAPCHOL is about 60 min, in practice, it may vary from 20 min to 5 h, depending on the level of complexity [9]. The variation in operative time is due to uncertainties regarding the complexity of the LAPCHOL procedure across a diverse patient population [7]. Estimating the level of complexity beforehand may improve the flexibility and accuracy of preoperative planning. The scheduled time can be increased for complex procedures and decreased for easy ones, thus allowing flexible preoperative planning.
In complex surgical situations, the surgeon gets into dilemma weather to continue the intended operation, or to deviate from the planned procedure. Complexity estimation before surgery can aid surgeons in decisions regarding whether to proceed with a minimally invasive approach, perform an open procedure or make a referral to a more experienced surgeon. It may also be useful as an informative tool for communicating about details of the intervention with the patient and for explaining the various risks of laparoscopic and open procedures.
This study aims to delineate the relevant demographic and sonographic patient data that are predictive of the intraoperative complexity of LAPCHOL interventions.
This work contributes in several ways. First, it introduces pattern recognition tools for processing surgical data; in the medical literature, these data are usually processed using common clinical statistical tools. Second, this study provides an evaluation of a number of classifiers based on a 337-patient dataset that was collected in the period of 2005–2008. Third, it provides an analysis indicating which set of preoperative features is effective for predicting intraoperative complexity, thus filtering out insignificant features. The study also measures objectivity bias in the assessment of surgical complexity by surgeons with various levels of experience. Finally, the study evaluates the conformity of the results with clinical practice.
Section snippets
Materials and methods
The preoperative features used in the experiments were collected in the period of 2005–2008 from N = 337 patients who had been admitted for elective LAPCHOL procedures, which involve removing the patient's gallbladder in case of symptomatic gallstones.
Experimental validation
To address the problem of predicting surgical complexity, Section 3.1 focuses on the identification of the optimal classifier for the preoperative dataset described in Table 1 and the number of samples required for optimal training. Section 3.2 aims to identify which subsets of features allow the prediction of complexity with accuracy comparable to, or better than, the complete set of features.
To answer these questions, we conducted both classification and feature selection. For the experiments
Discussion and conclusions
Intraoperative complexity can be predicted before surgery according to readily available preoperative data. The problem addressed in this paper involves the classification of preoperative data in two classes: Easy (Class 0) and complex (Class 1) surgeries. Learning-curve results indicated a preference for the LDC classifier in terms of classification error, although the SVM classifier is preferable in terms of AUC error.
Feature selection was used to identify the preoperative features that are
Acknowledgments
We would like to thank David Tax for the useful discussions and advice. The data-collection process was supported in part by the German Research Foundation DFG, FE 585/1-1. This research was supported by the Dutch STW Technology Foundation, project number: 07320.
References (21)
- et al.
A survey of medical image registration
Medical Image Analysis
(1998) An intelligent model for liver disease diagnosis
Artificial Intelligence in Medicine
(2009)- et al.
Computer-aided diagnosis of pulmonary nodules using a two-step approach for feature selection and classifier ensemble construction
Artificial Intelligence in Medicine
(2010) - et al.
Hospital costs associated with surgical complications: a report from the private-sector national surgical quality improvement program
Journal of the American College of Surgeons
(2004) - et al.
Laser ureterolithotripsy for cystine calculi
AORN
(1996) - et al.
Preoperative risk factors and surgical complexity are more predictive of costs than postoperative complications: a case study using the National Surgical Quality Improvement Program (NSQIP) database
Annals of Surgery
(2005) - et al.
Classification of surgical complications: a new proposal with evaluation in a cohort of 6336 patients and results of a survey
Annals of Surgery
(2004) - et al.
Clinical predictors of operative complexity in laparoscopic ventral hernia repair: a prospective study
Surgical Endoscopy
(2010) - et al.
Evaluation of orientation strategies in laparoscopic cholecystectomy
Annals of Surgery
(2010) - et al.
Mean operating room times differ by 50% among hospitals in different countries for laparoscopic cholecystectomy and lung lobectomy
Journal of Anesthesia
(2006)
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