3 years ago

External Validation of the Prognostic Nomogram (COMPASS) for Patients with Peritoneal Carcinomatosis of Colorectal Cancer

Steffen Fieuws, Eric Van Cutsem, Albert Wolthuis, Anthony de Buck van Overstraeten, André D’Hoore, Karel Demey, Vincent Vandecaveye

Abstract

Background

To optimize outcome, selection of patients for cytoreductive surgery (CRS) and hyperthermic intraperitoneal chemotherapy (HIPEC) is critical. Recently, Simkens et al.7 evaluated the peritoneal surface disease severity score (PSDSS) and suggested a new prognostic nomogram, the colorectal peritoneal metastases prognostic surgical score (COMPASS) based on age, peritoneal carcinomatosis index score, locoregional lymph node status, and signet ring cell histology. This COMPASS nomogram had better discriminative ability according to the Harrell c-index than PSDSS (c = 0.72 vs. 0.62). This study aimed to validate the COMPASS nomogram externally.

Methods

Data were retrieved from a prospectively maintained database, and all patients who underwent surgery between May 2005 and May 2016 were included in the study. For each patient, the PSDSS and COMPASS were calculated and then divided into subgroups. The discriminative ability of both scores for overall survival were quantified using Harrell c indices.

Results

A total of 153 patients underwent CRS + HIPEC for peritoneal carcinomatosis (PC) of colorectal cancer. The median overall survival (OS) was 46 months, and the mean PSDSS was 7.8 ± 3.7. When the PSDSS was divided into subgroups, the c index was 0.67. The mean COMPASS was 55.5 ± 25.6. When the patients were divided into four groups according to cutpoints of Simkens et al.7 a c index of 0.72 was obtained, showing its significant superiority over the discriminative ability of the PSDSS (p = 0.016).

Conclusion

External validation of the COMPASS confirms its moderate to good discriminative ability and its superiority over the PSDSS. Nevertheless, discrimination with the COMPASS score remains suboptimal, and further research on prognostic variables is essential for optimal patient selection.

Publisher URL: https://link.springer.com/article/10.1245/s10434-017-6042-9

DOI: 10.1245/s10434-017-6042-9

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