Abstract
Background
Postoperative complications affect the long-term survival and quality of life in patients
undergoing liver resection. No model has yet been validated to predict 90-day severe
morbidity and mortality.
Methods
The prospective recruitment of patients undergoing liver resection for various indications
was performed. Preoperative clinical and laboratory data, including liver stiffness,
indocyanine green retention, and intraoperative parameters, were analyzed to develop
predictive nomograms for postoperative severe morbidity and mortality. Calibration
plots were used to perform external validation.
Results
The most common indications in 418 liver resections performed were colorectal metastases
(N = 149 [35.6%]), hepatocellular carcinoma (N = 106 [25.4%]), and benign liver tumors
(N = 60 [14.3%]). Major liver resections were performed in 164 (39.2%) patients. Severe
morbidity and mortality were observed in 87 (20.8%) and 9 (2.2%) of patients, respectively,
during the 90-day postoperative period. Post-hepatectomy liver failure was observed
in 19 (4.5%) patients, resulting in the death of 4. The independent predictors of
90-day severe morbidity were age (odds ratio:1.02, P = .06), liver stiffness (odds ratio: 1.23, P = .04], number of resected segments (odds ratio: 1.28, P = .004), and operative time (odds ratio: 1.01, P = .01). Independent predictors of 90-day mortality were diabetes mellitus (odds ratio:
6.6, P = .04), tumor size >50 mm (odds ratio:4.8, P = .08), liver stiffness ≥22 kPa (odds ratio:7.0, P = .04), and operative time ≥6 hours (odds ratio: 6.1, P = .05). Nomograms were developed using these independent predictors and validated
by testing the Goodness of fit in calibration plots (P = .64 for severe morbidity; P = .8 for mortality).
Conclusion
Proposed nomograms would enable a personalized approach to identifying patients at
risk of complications and adapting surgical treatment according to their clinical
profile and the center’s expertise.
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Article info
Publication history
Published online: January 18, 2023
Accepted:
November 13,
2022
Identification
Copyright
© 2022 Published by Elsevier Inc.
