A preoperative prediction model based on Lymphocyte-C-reactive protein ratio predicts postoperative anastomotic leakage in patients with colorectal carcinoma: a retrospective study.

BACKGROUND & AIMS: Lymphocyte-C-reactive Protein Ratio (LCR) has been demonstrated as a promising new marker for predicting surgical and oncological outcomes in colorectal carcinoma (CRC). However, anastomotic leakage (AL) is also likely related to this inflammatory marker. Herein, we aimed to identify preoperative predictors of AL and build and develop a novel model able to identify patients at risk of developing AL.

METHODS

We collected 858 patients with CRC undergoing elective radical operation between 2007 and 2018 at a single center were retrospectively reviewed. We performed univariable and multivariable analyses and built a multivariable model that predicts AL based on preoperative factors. Propensity adjustment was used to correct the bias introduced by non-random matching of the LCR. The model's performance was evaluated by using the area under the receiver operator characteristic curves (AUROCs), decision curve analysis (DCA), Brier scores, D statistics, and R2 values.

RESULTS

Age, nutrition risk screening 2002 (NRS2002) score, tumor location and LCR, together with hemoglobin < 90 g/l, were independent predictors of AL. The models built on these variables showed good performance (internal validation: c-statistic = 0.851 (95%CI 0.803-0.965), Brier score = 0.049; temporal validation: c-statistic = 0.777 (95%CI 0.823-0.979), Brier score = 0.096). A regression equation to predict the AL was also established by multiple linear regression analysis: [Age(≥ 60 year) × 1.281] + [NRS2002(≥ 3) × 1.341] + [Tumor location(pt.) × 1.348]-[LCR(≤ 6000) × 1.593]-[Hemoglobin(< 90 g/L) × 1.589]-6.12.

CONCLUSION

Preoperative LCR is an independent predictive factor for AL. A novel model combining LCR values, age, tumor location, and NRS2002 provided an excellent preoperative prediction of AL in patients with CRC. The nomogram can help clinical decision-making and support future research.