Composite Score: prognostic tool to predict survival in patients undergoing surgery for colorectal liver metastases

Background: Several existing scoring systems predict survival of patients with colorectal liver metastases. Many lack validation, rely on old clinical data, and have been found to be less accurate since the introduction of chemotherapy. This study aimed to construct and validate a clinically relevant preoperative prognostic model for patients with colorectal liver metastases. Methods: A predictive model with data available before surgery was developed. Survival was analysed by Cox regression analysis, and the quality of the model was assessed using discrimination and calibration. The model was validated using multifold cross-validation. Results: The model included 1212 consecutive patients who underwent liver resection for colorectal liver metastases between 2005 and 2015. Prognostic factors for survival included advanced age, raised C-reactive protein level, hypoalbuminaemia, extended liver resection, larger number of metastases, and midgut origin of the primary tumour. A Composite Score was developed based on the prognostic variables. Patients were classified into those at low, medium, and high risk. Survival differences between the groups were significant; median overall survival was 87.4 months in the low-risk group, 50.1 months in the medium-risk group, and 22.6 months in the high-risk group. The discriminative performance, assessed by the concordance index, was 0.71, 0.67, and 0.67 respectively at 1, 3, and 5 years. Calibration, assessed graphically, was close to perfect. A multifold cross-validation of the model confirmed its internal validity (C-index 0.63 versus 0.62). Conclusion: The Composite Score categorizes patients into risk strata, and may help identify patients who have a poor prognosis, for whom surgery is questionable.

Automated summary

A clinically relevant preoperative prognostic model for colorectal liver metastases patients was constructed and validated, with prognostic factors and Composite Score effectively categorizing patients into low, medium, and high-risk groups.