The study protocol was approved by the Baoji City Hospital of Traditional Chinese Medicine (Baoji, China) and the Research Ethics review board (NO. 2021GYKJD5N). Due to the retrospective nature of the study, a waiver of informed consent was obtained. From March 2013 to March 2019, patients with symptomatic TBF were obtained and analyzed. The inclusion criteria for the study were as follows: 1) age of ≥18; 2); 2) burst fracture (AO classification); 3) fracture level: T11-L2; 4) Thoracolumbar Injury Classification and Severity Score (TLICS) ≥ 5[14, 15]) surgical approach: posterior instrumentation. The exclusion criteria were as follows: 1) organ with dysfunction such as liver or kidney, abnormal bleeding or abnormal coagulation function; 2) history of surgery for spinal disorder; 3) cerebrospinal fluid leakage (CSF fistula) or spontaneous; 4) intraoperative and postoperative use of hemostatic drugs; 5) patients with incomplete medical records. HBL ≥ 470ml was defined as an HBL positive group, according to the definition of HBL, calculated by Gross equation, and HBL < 470ml was an HBL negative group[16].
2.2 Data collections
We retrospectively collected the patients’ data, including age, sex, body mass index (BMI), underlying diseases (hypertension, diabetes, chronic obstructive pulmonary disease (COPD), history of smoking, history of alcohol, history of blood transfusion, and chronic steroid use. The preoperative visual analogue score (VAS), Japanese Orthopaedic Association (JOA) scores, and 36-Item Short Form Health Survey (SF-36) were recorded.
Surgery-related parameters included duration from admission to surgery, length of surgical incision, levels of fusion and duration of operation were obtained from an electronic medical record system. The parameters relevant to the perioperative fluid management strategy, including intraoperative infusion of crystalloids, intraoperative infusion of colloids, autologous blood transfusion, and allogeneic blood transfusion, were recorded. Meanwhile, routine preoperative laboratory test data, including hematologic tests, blood chemistry, coagulation tests, and liver function tests, was obtained from the clinical laboratory database of the Baoji City Hospital of Traditional Chinese Medicine. In addition, to assess the influence of fractured vertebral height on hidden blood loss, we calculated percentages of vertebral height restoration (P1%) and vertebral height loss (P2%) by measuring preoperative and postoperative X-ray parameters[7]. The formulae for P1% and P2% calculation are as follows:
Here, a is the height of the fractured vertebra; b is the upper anterior vertebral height adjacent to the fractured vertebra, c is the lower anterior vertebral height adjacent to the fractured vertebra; d is the predicted height of each fractured vertebra which is calculated according to the average height of the two adjacent vertebrae (b and c); e is the postoperative vertebral body height. These parameters measurement method is shown in Fig. 1.
2.3 Management and calculation of HBL
The anesthesiologists, operating room nurses, and blood equipment managers within the surgical team collaborate to determine the blood loss from the surgical site. Postoperative use of a lower extremity pump, and elastic stockings. The drainage tube was routinely placed intraoperatively and removed within 48-72 h postoperatively. According to the formula by Nader et at[17], patient’s blood volume (PBV) was calculated as follows:
PBV=k1×height (m) + k2×weight (kg)+k3
Here, for male, k1 = 0.3669, k2 = 0.03219, and k3 = 0.6041; for female, k1 = 0.3561, k2 = 0.03308, and k3 = 0.1833.
According to the Gross formula, total blood loss (TBL) was calculated as follows:
TBL= PBV × (Hctpre – Hctpost) / Hctave
Where, Hctpre was defined as the Hct on preoperative day 1, Hctpost was defined as the Hct on postoperative day 2 or 3, and Hctave was defined was the average of Hctpre and Hctpost. Subsequently, intraoperative blood loss was determined as follows:
Intraoperative blood loss = estimating the volume of blood in the suction container+ weighing blood-soaked guaze
Visible blood loss (VBL) = intraoperative blood loss + postoperative drainage
Therefore, HBL was defined as follows:
HBL = TBL –VBL
When there was perioperative blood transfusion, HBL could be defined as follows:
HBL = TBL – VBL + autologous blood transfusion + allogeneic blood transfusion
2.4 Statistical analysis
Continuous data is presented as the means ± standard deviations. Categorical variables were grouped and compared using the χ2 test or Fisher’s exact test. Continuous variables were compared using Student’s t-test. Risk factor analysis was performed using univariate and multivariate logistic regression analyses. In order to facilitate the establishment of the prediction model, cut-off values for continuous variables were determined by the receiver operating characteristic (ROC) curve (Figure S1). Variables showing statistical significance in the univariate analysis were included in the multivariate logistic regression analysis, and the forward stepwise method was used to select the variables that were eventually included in the model. According to the results of the regression coefficients of independent variables, an individual nomogram prediction model for HBL was established. The performance of the model was assessed in terms of discrimination and calibration. The discrimination of our prediction model is usually evaluated by calculating the area under the curve (AUC) of the ROC curve. Typically, the AUC values of the models were between 0-1. A prediction model with an AUC in the range of 0.5-0.75 was considered acceptable, and AUC > 0.75 indicated that the models possessed excellent discriminative power. The calibration of the nomogram was evaluated with a calibration curve. The Hosmer–Lemeshow test was conducted to assess the goodness-of-fit of the nomogram. A relatively corrected C-index (1000 bootstrap resamples) of the nomogram was also determined in this dataset. Furthermore, the decision curve analysis (DCA) was performed to evaluate the net benefit of the nomogram to the decision. All the analyses were performed by using IBM SPSS 23.0 (SPSS Inc) and R software version 4.0.2 (http://www.r-project.org) with the "rms", "SimDesign" and "AICcmodavg" packages[18-20].