Eldar Samuel, Siegelmann Hava T, Buzaglo Daniel, Matter Ibrahim, Cohen Ayala, Sabo Edmond, Abrahamson Jack
Department of Surgery, Bnai Zion Medical Center, Israel Institute of Technology, Haifa, Israel.
World J Surg. 2002 Jan;26(1):79-85. doi: 10.1007/s00268-001-0185-2. Epub 2001 Nov 26.
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.
现在，腹腔镜胆囊切除术也用于治疗急性胆囊炎。在存在炎症的情况下，可能会出现导致转为开腹胆囊切除术的技术难题，从而使腹腔镜手术的优势黯然失色。与这些不良事件相关的因素以及能够从中吸取教训的技术，可能有助于确定何时应完全避免采用腹腔镜手术。在本研究中，我们确定了急性胆囊炎中转开腹手术的预测因素，并通过统计多变量分析和人工神经网络测试了它们的预测能力。1994年1月至1997年2月期间，225例患者因急性胆囊炎接受了腹腔镜胆囊切除术。术前和手术数据通过标准化表格进行前瞻性收集。最初的180例腹腔镜手术病例进入训练集，通过统计方法和人工神经网络方法进行学习。首先研究了与一组术前数据相关的中转开腹情况。然后用这两种方法拟合预测模型。最后45例手术病例，学习系统对其情况未知，用于测试拟合模型。在训练集中，向前逐步逻辑回归技术、向前逐步线性判别分析和人工神经网络方法对中转开腹的阳性预测分别为0%、27%和100%，阴性预测分别为80%、85.5%和97%。在未训练的患者验证集中，中转开腹的阳性预测分别为0%、25%和67%，阴性预测分别为82%、88%和94%。基于人工神经网络的模型为预测腹腔镜胆囊切除术的成功及中转开腹提供了一种实用工具。在未训练病例中预测的高度确定性揭示了其潜力，并在适当条件下证明完全避免腹腔镜手术而直接进行开腹胆囊切除术的合理性。
