Patient demographics
152 PMP subjects underwent CRS and HIPEC for the first time in our center, one patients of sigmoid colon origin and four died after CRS due to serious infection during hospitalization were excluded from this study. During the follow up period, 15 patients lost of follow up, afterwards, 132 patients were followed up, among of whom, whose follow up time less than 6 months were also excluded (n=13), ultimately, 119 PMP patients were included in present study, study schematic was shown in Figure 1.
In order to avoid bias in the study population as much as possible, comparative analysis of the baseline data was performed between the included (n=119) and excluded (n=33) subjects, there was no significant difference in sex ratio, age, PCI, Barthel Index Score, and the degree of radical operation between the two groups (all P>0.05), however, different proportion of histopathological grading was found between the two groups (P<0.05) (Table 1).
22 (18.49%) deaths occurred during the follow-up period of 119 included subjects, the present study were unable to calculate the overall cohort median survival time due to the low number of endpoints during follow-up period, the 1-year, 3-year, and 5-year survival rates were 95.4% (95%CI: 91.5-99.3), 75.4% (95%CI: 65.5-85.3), and 72.0% (95%CI: 60.5-83.5), respectively (Figure 2).
Correlation of serum tumor markers and PCI
The Spearman correlation between PCI and serum preoperative CEA, CA125, CA19-9, CA724, CA242 were 0.269 (P=0.003), 0.259 (P=0.005), 0.352 (P=0.001), 0.243 (P=0.008), 0.237 (P=0.012), respectively.
Impact of independent variables on patient survival
The ability to parse tumors into subsets based on biomarker expression has many clinical applications, many former studies employed the upper limit of reference range of tumor markers as the best cut-point. In 2004, Camp, R. L.[14] reported X-Tile plot could serve as a new bio-informatics tool to visualize the best cut-points for creating such divisions. In this study, X-Tile software was used to calculate the best cut-point of continuous variables (age, Barthel Index Score, albumin, D-dimer, CEA, CA125, CA19-9, CA724, CA242, and PCI ) in independent variables, however, we did not calculate the cut-off value for hemoglobin, because the hemoglobin level for anemia diagnosis in female and male is different (110g/L for female and 120g/L for male).
At univariate analysis, Barthel Index Score, albumin, D-dimer, CEA, CA125, CA19-9, CA724, CA242, PCI, degree of radical surgery, pathology were all significantly associated with OS rate in PMP. Although sex factor did not meet the criteria for inclusion in multivariate analysis, literature reported women tend to present at an earlier stage than men[1], we speculate that sex has a great influence on the prognosis of PMP, ultimately, sex factor was also included into Cox regression analysis. At multivariate analysis, sex, D-dimer, CA125, CA19-9, and degree of radical surgery were independently associated with OS rate in PMP.(Table 2). Cox regression analysis generated variables including SUR and XBE, afterwards, we calculated new variables risk based on generated variables, the formula was as follows: risk=1-SUR**EXP (XBE), according to the risk variable, ROC analysis was performed to calculate discrimination ability of prediction model, the area under curves (AUC) was 0.902 (95%CI: 0.823- 0.954) (Figure 3). Finally, the independent predicting factors were used for drawing nomogram for PMP (Figure 4).