Study Population and Baseline Characteristics
A total of 154 patients (95 male, 59 female) were included in the study, with an average age of 64.9±11.4 years. Within days postoperatively, complications occurred in 80 patients (51.9%), and two patients died.
Analysis of Possible Risk Factors for Postoperative Complications
Univariate analysis revealed that sex, preoperative albumin levels, preoperative CRP, use of neoadjuvant therapy, operative time, preoperative mGPS, preoperative CAR, POD3 CAR, POD3 poGPS, POD3 CRP and POD3 albumin were significantly associated with the 30-day incidence of postoperative complications (P<0.05) (Table 2). Both POD3 CAR and poGPS were derived from the same CRP and albumin in the same patients and highly interrelated, and the same for preoperative CAR and mGPS. We do not put them in the same multivariate analysis. On multivariate analysis, POD3 CRP (OR=1.015; 95% CI=1.006–1.024;P=0.001) and operative time (OR=1.006; 95% CI=1.001–1.010; P=0.018) were independent risk factors for postoperative complications (Table 3). While preoperative CRP appeared to have similar predictive value as that of POD3 CRP (OR=1.045; 95% CI=0.983–1.112), the association of preoperative CRP with the 30-day incidence of postoperative complications did not reach statistical significance (P=0.160). As POD3 CRP was an independent risk factor for postoperative complications, we speculated that the diagnostic value of POD3 CAR and poGPS was higher than that of preoperative indicators.
Usefulness of CAR, mGPS, and poGPS as Predictors of Postoperative Complications
Because POD3 CRP was previously reported to serve as a predictor of postoperative complications,9 we conducted receiver operating characteristic curve analysis for all five indicators of inflammatory response (preoperative mGPS, preoperative CAR, as well as CRP, CAR, and poGPS on POD3). For POD3 CAR, the AUC was 0.711 and an optimal cutoff of 2.6 was identified (sensitivity, 51.3%; specificity, 87.8%; PPV, 83.2%; NPV, 61.3%). For preoperative mGPS (Figure 1, Table 4), the AUC was 0.613 and an optimal cutoff of 0.1 was identified (sensitivity, 66.3%; specificity, 50.0%; PPV, 58.9%; NPV, 57.8%). For preoperative mGPS, the AUC was 0.600, with the cutoff value at 0 (sensitivity, 73.8%; specificity, 6.9%; PPV, 46.1%; NPV, 19.2%), 1 (sensitivity, 18.8%; specificity, 93.2%; PPV, 75.0%; NPV, 51.5%) and 2 (sensitivity, 7.5%; specificity, 0.0%; PPV, 100.0%; NPV, 50.0%). For poGPS on POD3, the AUC was 0.599, with cutoff value at 0 (sensitivity, 77.5%; specificity, 2.7%; PPV, 46.3%; NPV, 10.0%), 1 (sensitivity, 21.3%; specificity, 97.3%; PPV, 89.5%; NPV, 53.3%) and 2 (sensitivity, 1.3%; specificity, 0.0%; PPV, 100.0%; NPV, 48.4%). For CRP on POD3, the AUC was 0.706 and an optimal cutoff of 65.5 mg/L was identified (sensitivity, 48.8%; specificity, 79.7%; PPV, 73.2%; NPV, 60.2%).
The AUC was above 0.5 for all four variables analyzed, indicating good predictive value for postoperative complications (Figure 1, Table 4). From the perspective of clinical practice, PPV is the most impactful parameter because it reflects the likelihood that the screening will correctly identify patients with the outcome (high risk of postoperative complications). The present findings suggest that there is an 83.2% probability of correctly identifying the high risk of early complications among patients with CAR≥2.6 on POD3. Compared to preoperative CAR, preoperative mGPS, and POD3 poGPS, POD3 CAR provided significantly superior predictive value. Moreover, it reflected in the increased chance to correctly identify patients at risk. Therefore, we further examined the usefulness of POD3 CAR as an early predictor of specific postoperative complications.
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