Department of Surgery, Dentistry, Gynecology and Pediatrics, Division of General and Hepato-Biliary Surgery, University of Verona, P. le L.A. Scuro, 37134, Verona, Italy.
Surg Endosc. 2022 Dec;36(12):8869-8880. doi: 10.1007/s00464-022-09322-7. Epub 2022 May 23.
In the last decade, several difficulty scoring systems (DSS) have been proposed to predict technical difficulty in laparoscopic liver resections (LLR). The present study aimed to investigate the ability of four DSS for LLR to predict operative, short-term, and textbook outcomes.
Patients who underwent LLR at a single tertiary referral center from January 2014 to June 2020 were included in the present study. Four DSS for LLR (Halls, Hasegawa, Kawaguchi, and Iwate) were investigated to test their ability to predict operative and postoperative complications. Machine learning algorithms were used to identify the most important DSS associated with operative and short-term outcomes.
A total of 346 patients were included in the analysis, 28 (8.1%) patients were converted to open surgery. A total of 13 patients (3.7%) had severe (Clavien-Dindo ≥ 3) complications; the incidence of prolonged length of stay (> 5 days) was 39.3% (n = 136). No patients died within 90 days after the surgery. According to Halls, Hasegawa, Kawaguchi, and Iwate scores, 65 (18.8%), 59 (17.1%), 57 (16.5%), and 112 (32.4%) patients underwent high difficulty LLR, respectively. In accordance with a random forest algorithm, the Kawaguchi DSS predicted prolonged length of stay, high blood loss, and conversions and was the best performing DSS in predicting postoperative outcomes. Iwate DSS was the most important variable associated with operative time, while Halls score was the most important DSS predicting textbook outcomes. No one of the DSS investigated was associated with the occurrence of complication.
According to our results DDS are significantly related to surgical complexity and short-term outcomes, Kawaguchi and Iwate DSS showed the best performance in predicting operative outcomes, while Halls score was the most important variable in predicting textbook outcome. Interestingly, none of the DSS showed any correlation with or importance in predicting overall and severe postoperative complications.
在过去的十年中，已经提出了几种困难评分系统（DSS）来预测腹腔镜肝切除术（LLR）的技术难度。本研究旨在探讨四种用于 LLR 的 DSS 预测手术、短期和标准手术结果的能力。
本研究纳入了 2014 年 1 月至 2020 年 6 月在一家三级转诊中心接受 LLR 的患者。研究调查了四种用于 LLR 的 DSS（Halls、Hasegawa、Kawaguchi 和 Iwate）的能力，以测试它们预测手术和术后并发症的能力。机器学习算法用于确定与手术和短期结果最相关的最重要的 DSS。
共纳入 346 例患者，28 例（8.1%）患者转为开腹手术。13 例（3.7%）患者发生严重（Clavien-Dindo≥3）并发症；住院时间延长（>5 天）发生率为 39.3%（n=136）。无患者在手术后 90 天内死亡。根据 Halls、Hasegawa、Kawaguchi 和 Iwate 评分，分别有 65 例（18.8%）、59 例（17.1%）、57 例（16.5%）和 112 例（32.4%）患者行高难度 LLR。根据随机森林算法，Kawaguchi DSS 预测了住院时间延长、大量失血和转化，是预测术后结果的最佳 DSS。Iwate DSS 是与手术时间最相关的最重要变量，而 Halls 评分是预测标准手术结果的最重要 DSS。调查的 DSS 均与并发症的发生无关。
根据我们的结果，DDS 与手术复杂性和短期结果显著相关，Kawaguchi 和 Iwate DSS 在预测手术结果方面表现最佳，而 Halls 评分是预测标准手术结果的最重要变量。有趣的是，没有一个 DSS 显示与术后总体和严重并发症的相关性或重要性。
