4.5 Article

Early Prediction of Post-hepatectomy Liver Failure in Patients Undergoing Major Hepatectomy Using a PHLF Prognostic Nomogram

Journal

WORLD JOURNAL OF SURGERY
Volume 44, Issue 12, Pages 4197-4206

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SPRINGER
DOI: 10.1007/s00268-020-05713-w

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Background Liver resection (LR) is the main modality of treatment for hepatocellular carcinoma (HCC) and colorectal liver metastasis (CRLM). Post-hepatectomy liver failure (PHLF) remains the most dreaded complication. We aim to create a prognostic score for early risk stratification of patients undergoing LR. Methodology Clinical and operative data of 472 patients between 2000 and 2016 with HCC or CRLM undergoing major hepatectomy were extracted and analysed from a prospectively maintained database. PHLF was defined using the 50-50 criteria. Results Liver cirrhosis and fatty liver were histologically confirmed in 35.6% and 53% of patients. 4.7% (n = 22) of patients had PHLF. A 90-day mortality was 5.1% (n = 24). Pre-operative albumin-bilirubin score (p = 0.0385), prothrombin time (p < 0.0001) and the natural logarithm of the ratio of post-operative day 1 to pre-operative serum bilirubin (SB) (ln((POD1)Bil/(pre-op)Bil);p < 0.0001) were significantly independent predictors of PHLF. The PHLF prognostic nomogram was developed using these factors with receiver operating curve showing area under curve of 0.88. Excellent sensitivity (94.7%) and specificity (95.7%) for the prediction of PHLF (50-50 criteria) were achieved at cut-offs of 9 and 11 points on this model. This score was also predictive of PHLF according to (Peak)Bil > 7 and International Study Group for Liver Surgery criteria, intensive care unit admissions, length of stay, all complications, major complications, re-admissions and mortality (p < 0.05). Conclusions The PHLF nomogram (https://tinyurl.com/SGH-PHLF-Risk-Calculator) can serve as a useful tool for early identification of patients at high risk of PHLF before the 'point of no return'. This allows enforcement of closer monitoring, timely intervention and mitigation of adverse outcomes.

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