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Predictive nomograms for postoperative 90-day morbidity and mortality in patients undergoing liver resection for various hepatobiliary diseases

Published:January 18, 2023DOI:https://doi.org/10.1016/j.surg.2022.11.009

      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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