Brief Article
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World J Gastroenterol. Jan 7, 2014; 20(1): 274-281
Published online Jan 7, 2014. doi: 10.3748/wjg.v20.i1.274
An accurate predictor of liver failure and death after hepatectomy: A single institution’s experience with 478 consecutive cases
Zheng-Gui Du, Yong-Gang Wei, Ke-Fei Chen, Bo Li
Zheng-Gui Du, Yong-Gang Wei, Ke-Fei Chen, Bo Li, Department of Liver Surgery, Liver Transplantation Center, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
Author contributions: Li B and Du ZG contributed equally to this work; Li B and Du ZG designed the study; Du ZG, Li B, Wei YG and Chen KF performed the experiments; Du ZG and Wei YG contributed new reagents/analytic tools; Du ZG and Wei YG analyzed the data; and Du ZG and Chen KF wrote the paper.
Correspondence to: Bo Li, MD, Department of Liver Surgery, Liver Transplantation Centre, West China Hospital, Sichuan University, 37 Guoxue Street, Chengdu 610041, Sichuan Province, China. doclibo@gmail.com
Telephone: +86-28-85422476 Fax: +86-28-85423724
Received: August 1, 2013
Revised: October 25, 2013
Accepted: November 12, 2013
Published online: January 7, 2014
Processing time: 172 Days and 23 Hours
Abstract

AIM: To establish a reliable definition of postoperative liver failure (PLF) and allow the prediction of outcomes after hepatectomy.

METHODS: The clinical data of 478 consecutive patients who underwent hepatectomy were retrospectively analyzed. The examined prognostic factors included the ratio of total bilirubin (TBIL) on postoperative day (POD) X to TBIL on POD 1 (TBIL-r1) and the ratio of the international normalized ratio (INR) on POD X to the INR on POD 1 (INR-r1) for PODs 3, 5 and 7. Student’s t test, the χ2 test, logistic regression, survival analysis and receiver operating curve analysis were used to evaluate risk factors and establish the definition of postoperative liver failure (PLF).

RESULTS: Fourteen patients (2.9%) died of liver failure within 3 mo of surgery. Significant differences were found between patients who died of liver failure and the remaining patients in terms of TBIL-r1 and INR-r1 on PODs 3, 5 and 7. The combination of TBIL-r1 and INR-r1 on POD 5 showed strong predictive power for liver failure-related death (sensitivity 92.9% and specificity 90.1%). The hepatic damage score (HDs), which was derived from TBIL-r1 and INR-r1, was used to define the degree of metabolic functional impairment after resection as mild (HDs = 0), reversible hepatic “dysfunction” (HDs = 1) or fatal hepatic failure (HDs = 2). Furthermore, the indocyanine green retention rate at 15 min (ICG-R15) and the number of resected segments (RSs) were identified as independent predictors of the HDs. A linear relationship was found between ICG-R15 and RSs in the HDs = 2 group. The regression equation was: RSs = -0.168 × ICG-R15 + 5.625 (r2 = 0.613, F = 14.257, P = 0.004).

CONCLUSION: PLF can be defined by the HDs, which accurately predicts liver failure-related death after liver resection. Furthermore, the ICG-R15 and RSs can be used as selection criteria for hepatectomy.

Keywords: Liver failure; Hepatectomy; Mortality; Morbidity; Hepatic dysfunction

Core tip: We derived a new definition of postoperative liver failure (PLF) termed the hepatic damage score (HDs). The HDs was an ideal definition of PLF and reflected the degree of liver impairment after resection as mild (HDs = 0), reversible hepatic “dysfunction” (HDs = 1) to fatal hepatic failure (HDs = 2).