A novel model for prediction of pure laparoscopic liver resection surgical difficulty

Yasushi Hasegawa, Go Wakabayashi, Hiroyuki Nitta, Takeshi Takahara, Hirokatsu Katagiri, Akira Umemura, Kenji Makabe, Akira Sasaki

Research output: Contribution to journalArticlepeer-review

101 Citations (Scopus)


Background: Extending the clinical indications for laparoscopic liver resection (LLR) should be carefully considered based on a surgeon’s experience and skill. However, objective indexes to help surgeons assess the estimated difficulty of LLR are scarce. The aim of our study was to develop the first objective numerical rating scale to predict the surgical difficulty of various LLR procedures. Methods: We performed a retrospective review of the operative outcomes of 187 patients who underwent a pure LLR. First, the value of preoperative factors for predicting surgical time was evaluated by multivariate linear regression analyses, and a scoring system was constructed. Next, the integrity of our predictive linear model was evaluated against the documented operative outcomes for patients forming our study group. Results: Four predictive factors were identified and scored based on the weighted contribution of each factor predicting surgical time: extent of resection (scored 0, 2, or 3); location of tumor (scored 0, 1, or 2); obesity (scored 0 or 1); and platelet count (scored 0 or 1). The scores were summed to classify surgical difficulty into three levels: low (total score ≤1); medium (total score 2–3); and high (total score ≥4). Operative outcomes, including surgical time, volume of blood loss, length of hospital stay, and rate of morbidity, were significantly different between the three surgical difficulty levels. Conclusion: Our novel model will be useful for surgeons to predict the difficulty of an LLR procedure relative to their own experience and skill.

Original languageEnglish
Pages (from-to)5356-5363
Number of pages8
JournalSurgical endoscopy
Issue number12
Publication statusPublished - 01-12-2017
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Surgery


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