Development and validation of a comprehensive model to predict complications after hepatectomy

Area of Science:

• Hepatobiliary Surgery
• Surgical Oncology
• Intensive Care Medicine

Background:

• Hepatectomy carries significant morbidity (up to 40%) despite perioperative care advances.
• Existing nomograms for post-hepatectomy complications lack early postoperative data.
• Predicting severe outcomes after liver surgery is crucial for patient management.

Purpose of the Study:

• To identify risk factors for severe post-hepatectomy complications using routine Intensive Care Unit (ICU) data.
• To develop a practical scoring system for predicting severe postoperative complications.
• To enhance early warning and patient management strategies following liver resections.

Main Methods:

• Retrospective observational study of 411 adult patients undergoing elective hepatectomy.
• Analysis of preoperative and early ICU parameters (pH, lactate clearance, lactate levels, transfusions, ICU stay) as predictors of 30-day severe complications (Clavien-Dindo grade ≥3a).
• Development and validation of a nomogram scoring system.

Main Results:

• Severe complications occurred in 19% of patients.
• Key predictors included body mass index, preoperative bilirubin, and ICU data: pH, lactate clearance, arterial lactate at 12 hours, packed red blood cell transfusions, and ICU length of stay.
• The developed scoring system demonstrated good predictive accuracy (C-index=0.754) and stability.

Conclusions:

• An accurate and practical scoring system was developed using preoperative and early ICU data to predict poor outcomes after hepatectomy.
• This tool can aid in patient management and provide early warnings during ICU stays.
• Further external validation is recommended for routine clinical integration.