Perioperative blood transfusion does not affect recurrence-free and overall survivals after curative resection for intrahepatic cholangiocarcinoma: a propensity score matching analysis

The study enrolled 758 consecutive ICC patients who underwent curative surgery between 2000 and 2012 at the Liver Cancer Institute, Zhongshan Hospital, Fudan University. All resections were performed or supervised by experienced hepatobiliary surgeons and used standardized procedures [13]. Additionally, all surgical specimens were confirmed by pathologic histology [2]. The following exclusion criteria were used: pre-interventional therapy before liver surgery (n = 35, 27 underwent transcatheter arterial chemoembolization (TACE), 1 underwent radiofrequency ablation(RFA), 2 underwent radiotherapy, and 5 underwent RFA plus TACE); hemoglobin less than 70 g/L (n = 1); widespread metastasis (n = 4); TNM staging IVb (n = 89); missing data of hemoglobin before surgery (n = 14); missing data of blood transfusion (n = 5); and clinical source loss (n = 5) (Fig. 1). The eligible 605 patients included 93 cases who received perioperative allogeneic blood transfusion and 512 cases without transfusion. We defined the perioperative period as the time between the third preoperative day and the seventh postoperative day.

All data on the patients’ demographics, morbidity, postoperative mortality, and histological results were obtained from the hospital medical system. The TNM classification was based on the AJCC Cancer Staging Manual, Seventh edition (2010) by springer New York, Inc. All patients were followed-up regularly at outpatient clinics and the Liver Cancer Institute, Zhongshan Hospital, Fudan University. The follow-up results were obtained via telephone by an experienced researcher working in the Liver Cancer Institute. All patients were regularly followed in the outpatient department and tumor markers were measured every 3 months during the first 3 years and thereafter every 6 months until the study end or loss of follow-up. An abdominal ultrasound was performed every 3 months, and abdominal computed tomography or MRI was performed 6 months postoperatively or upon suspected recurrence. The median follow-up time was 20 months (range 0–134 months), and the end follow-up time was November 2015. The primary research endpoint was the death of patient or the end follow-up time, and the secondary endpoint was follow-up dropout. The OS was defined as the period from surgery until death due to any cause. DFS was defined as the duration from surgery until the date of intrahepatic cholangiocarcinoma recurrence. The transfusion of any blood visible components including red blood cells and blood plasma were considered blood transfusion. Blood management, including processing, testing, and transporting, were quality controlled by Shanghai Blood Center. The ABO and Rh status as well as blood cross matching were conducted by the blood department of Zhongshan hospital [14].

The categorical variables are shown as whole numbers and proportions, and the continuous variables are described as the means with standard deviation as appropriate. Two-sided p values of <0.05 were considered statistically significant. Statistical analyses were performed using IBM SPSS Statistics 22 and R statistical software. To compare continuous variables that followed Gaussian distributions, t tests were used; the K-Independent-Samples Test (Kruskal Wallis H(K) test) was used for those variables did not follow Gaussian distributions. To compare proportional variables, a Two-Independent-Samples Test (Mann-Whitney U test) was used, and a Two-Related-Samples Test (Wilcoxon Signed Ranks Test) was applied to matched propensity scores [15]; all missing data are reflected in the available data [16]. We examined the following parameters: age, gender, preoperative hemoglobin (Hb), platelets (PLT), aspartate aminotransferase (AST), alanine transaminase (ALT), alpha fetoprotein (AFP), carcinoembryonic antigen (CEA), carbohydrate antigen 19–9 (CA19–9), prothrombin time (PT), international normalized ratio (INR), hepatitis B surface antigen (HBsAg), anti-hepatitis C virus (Anti-HCV), tumor maximum dimension (TMD), tumor node metastasis (TNM), intraoperative blood loss (IBL), degree of differentiation (DD), and transcatheter arterial chemoembolization (TACE). The confounders were measured accurately using univariate Cox regression through an enter variable selection procedure. The full variable selection logistic regression was used to specify a mathematical relationship for the variables related to blood transfusion [17]. The regression models were based on Akaike’s information criterion. We then adjusted for further confounding parameters in the propensity score analysis. This is a useful technique that focuses on the relationship between confounders and the treatment [18, 19]. The “PS MATCHING 3.03” and “SPSS Statistics R Essentials 22.0” and “R-2.15.3-win” R packages [20] were used to perform the matching propensity score analysis. The matching confounders was estimated by the regression models described above, and balance matching showed the values of absolute standard mean difference (SMD). The demographics and characteristics of the matched patients were compared to ensure that there were no significant differences in the baseline settings. We used univariate and multivariate Cox regressions to assess the prognostic value of blood transfusion through balanced data. The GraphPad Prism 6 software was used to draw the survival curves depicting OS and DFS.

Of these patients, 93 (15.37%) received blood transfusion, and 512 (84.63%) did not. We described the patient demographics and clinical characteristics for the two groups separately (Table 1). The clinical data for 9 of the 21 variables differed significantly (P < 0.05) as a result of a conspicuous bias with pre-described PS (0.096 ± 0.104 vs. 0.479 ± 0.372 P < 0.001).

To investigate whether the existing confounders led to any bias, a univariate Cox regression was conducted to filter out the 8 variables that were associated with the outcome without considering treatment. In the OS Cox regression model, the preoperative Hb, total bilirubin, ALT, CA19–9, anti-HCV, TMD, TNM stage, intraoperative blood loss variables were selected as independent prognostics for ICC patients. In the DFS Cox regression model, CEA, which was an extended prognostic, was picked out. Then, a univariate logistic regression was performed between patients who received blood transfusion and those who did not; the gender, preoperative Hb, PLT, total bilirubin, CA19–9, HBsAg, and intraoperative blood loss differed significantly. After multivariate analysis, only the preoperative Hb, total bilirubin, and intraoperative blood loss were left and were thus considered confounders that had to be adjusted to synthesize all of the regression models (Table 2).

Before performing propensity score matching analysis, we used a calculation called “PS Power and Sample Size Calculations(Version 3.0, January 2009)” [21, 22] to estimate the ideal sample size. We are planning a study with 1 PBT subject matched to 4 no-PBT subjects, an accrual interval of 1 month, and additional follow-up after the accrual interval of 133 months. Prior data indicate that the median survival time on the no-PBT is 26 months. If the true median survival times on the PBT and no-PBT are 26 and 12 months, respectively, we will need to study 31 PBT subjects and 124 no-PBT subjects to be able to reject the null hypothesis that the experimental and control survival curves are equal with probability (power) 0.9(β = 0.1). The Type I error probability associated with this test of this null hypothesis is 0.01(α).

The propensity score matching procedure was performed to reduce confounding variables based on the three identified factors. The caliper was set at 0.05, and we used an optimal match ratio of 1:4. We found 52 of the 93 transfused patients were matched with 163 of the 512 no-transfused patients, which is more than the ideal sample size we calculated previously to obtain the significant conclusion. The propensity score suggests there were no biases in the matched groups (0.136 ± 0.117 vs. 0.193 ± 0.167, P = 0.785). Figure 2 shows the matched data absolute standardized mean difference (SMD), and the SMD of all three confounders and PS decreased to less than 0.2. In Table 3, the matched patient characteristics were compared, and no significant differences were shown between the groups, considering all 21 variables.

The univariate Cox proportional hazards regression analysis indicated PBT has a poor effect on OS and DFS before propensity score matching, which forebodes an 88.9% risk of overall mortality (HR: 1.889, 95% CI: 1.446–2.468, p < 0.001) and 58.9% risk of DFS (HR: 1.589, 95% CI: 1.221–2.067, p < 0.001). Additionally, the Kaplan-Meier curve of OS showed the no-transfused group has a significant benefit compared with transfused group (p < 0.0001) (Fig. 3), the median survival months(MSMs) of PBT (12 months) is obviously less than non-PBT (26 months), Of note, no difference was found in DFS (p = 0.3807), same as the MSMs (PBT = 15 months, non-PBT = 16 months). However, after performing a multivariate risk dependent Cox regression we found that neither OS (HR: 1.172, 95% CI: 0.756–1.816, p = 0.479) nor DFS (HR: 0.944, 95% CI: 0.608–1.466, p = 0.799) was significantly different due to blood transfusion. Our findings suggest it was not a statistically independent prognostic risk. After propensity score matching, PBT had no significant effect on the risk of OS (HR: 1.429, 95% CI: 0.972–2.103, p = 0.070) and DFS (HR: 1.262, 95% CI: 0.858–1.856, p = 0.238) (Table 4). Furthermore, the Kaplan-Meier plot showed similar trends for OS (P < 1.000) and DFS (P < 0.230). Both PBT and non-PBT MSMs to OS is 21 months and no significant difference (PBT = 12 months, non-PBT = 15 months) to DFS.