Objective

The study aim was to develop and validate models to predict clinically significant post-hepatectomy liver failure (PHLF) and serious complications (a Comprehensive Complication Index® [CCI®]>40) using preoperative and intraoperative variables.

Summary background data

PHLF is a serious complication after major hepatectomy but does not comprehensively capture a patient's postoperative course. Adding the CCI® as an additional metric can account for complications unrelated to liver function.

Methods

The cohort included adult patients who underwent major hepatectomies at twelve international centers (2010-2020). After splitting the data into training and validation sets (70:30), models for PHLF and a CCI®>40 were fit using logistic regression with a lasso penalty on the training cohort. The models were then evaluated on the validation dataset.

Results

Among 2,192 patients, 185 (8.4%) had clinically significant PHLF and 160 (7.3%) had a CCI®>40. The PHLF model had an area under the curve (AUC) of 0.80, calibration slope of 0.95, and calibration-in-the-large of -0.09, while the CCI® model had an AUC of 0.76, calibration slope of 0.88, and calibration-in-the-large of 0.02. When the models were provided only preoperative variables to predict PHLF and a CCI®>40, this resulted in similar AUCs of 0.78 and 0.71, respectively. Both models were used to build two risk calculators with the option to include or exclude intraoperative variables (PHLF Risk Calculator; CCI®>40 Risk Calculator).

Conclusions

Using an international cohort of major hepatectomy patients, we used preoperative and intraoperative variables to develop and internally validate multivariable models to predict clinically significant PHLF and a CCI®>40 with good discrimination and calibration.