Model to predict major complications following liver resection for HCC in patients with metabolic syndrome

Abstract

Background: Metabolic syndrome (MS) is rapidly growing as risk factor for HCC. Liver resection for HCC in patients with MS is associated with increased postoperative risks. There are no data on factors associated with postoperative complications.

Aims: The aim was to identify risk factors and develop and validate a model for postoperative major morbidity after liver resection for HCC in patients with MS, using a large multicentric Western cohort.

Materials and methods: The univariable logistic regression analysis was applied to select predictive factors for 90 days major morbidity. The model was built on the multivariable regression and presented as a nomogram. Performance was evaluated by internal validation through the bootstrap method. The predictive discrimination was assessed through the concordance index.

Results: A total of 1087 patients were gathered from 24 centers between 2001 and 2021. Four hundred and eighty-four patients (45.2%) were obese. Most liver resections were performed using an open approach (59.1%), and 743 (68.3%) underwent minor hepatectomies. Three hundred and seventy-six patients (34.6%) developed postoperative complications, with 13.8% major morbidity and 2.9% mortality rates. Seven hundred and thirteen patients had complete data and were included in the prediction model. The model identified obesity, diabetes, ischemic heart disease, portal hypertension, open approach, major hepatectomy, and changes in the nontumoral parenchyma as risk factors for major morbidity. The model demonstrated an AUC of 72.8% (95% CI: 67.2%-78.2%) ( https://childb.shinyapps.io/NomogramMajorMorbidity90days/ ).

Conclusions: Patients undergoing liver resection for HCC and MS are at high risk of postoperative major complications and death. Careful patient selection, considering baseline characteristics, liver function, and type of surgery, is key to achieving optimal outcomes.

FIGURE 1

Multivariable model and nomogram to predict 90 days major morbidity following surgery for hepatocellular carcinoma on metabolic syndrome. The nomogram maps the predicted probability of 90 days postoperative major morbidity in a scale of 0–550. For each covariate, please draw a vertical line upwards and note down the corresponding points (ie, major hepatectomy = 100 points). This is repeated for each covariate ending with a total score that corresponds to a predicted probability of morbidity at the bottom of the nomogram. Please visit https://childb.shinyapps.io/NomogramMajorMorbidity90days/. Model equation on logarithmic scale was equal to: −3.2+0.26*Obesity(BMI ≥ 30)+0.34*Diabetes +0.48*Ischemic heart disease+0.98*Portal Hypertension–0.58*Approach(minimally invasive)+1.52*type of hepatectomy(major)+(if EASL classification = NAFL the coefficient was −0.32; if EASL classification = NASH the coefficient was +0.58; if EASL classification = Cirrhosis the coefficient was +0.97). Lower and upper confidence limit of the constant: −4.04 to −2.55. Abbreviations: BMI, body mass index; EASL, European Association for the Study of the Liver.

FIGURE 2

Calibration plot of the nomogram. Ideal line estimated probabilities correspond to the actual observed; apparent line, prediction capability of the model obtained after data analysis; bias-corrected line, prediction capability of the model obtained after bootstrap correction. Vertical lines at the top of the figure represent number of patients.