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Background Hidden blood loss (HBL) represents an important complication of unilateral biportal endoscopic (UBE) spine surgery. This study aimed to evaluate HBL and its possible risk factors among patients undergoing UBE surgery for lumbar degenerative diseases. Methods This multicentric retrospective study was conducted in 3 different medical centers between July 2020 and April 2021. Data of patients who underwent UBE surgery were extracted by electronic medical record system. The patient’s demographic characteristics and blood loss-related parameters were recorded. We calculated the amount of HBL and explored the association between patient’s characteristics and HBL using Pearson or Spearman correlation analysis. Multivariate linear regression analysis was conducted to identify independent risk factors of HBL. Results A total of 136 patients (55 females and 81 males, age range 43 to 74 years) were included in this study. A substantial amount of HBL (469.5 ± 195.3 ml, 57.6% of TBL, total blood loss) occurred following UBE surgery. Multiple linear regression analysis indicated that the risk factors of HBL were as follows: age (P = 0.000), number of fusion levels (P = 0.015), American Society of Anesthesiologists (ASA) classification (P = 0.046), surgery time (P = 0.017), patient’s blood volume (PBV, P = 0.026), total blood loss (TBL, P = 0.001), postoperative (i.e., day 2 or 3) hematocrit (Hct, P = 0.034), Hct loss (P = 0.005), and fibrinogen (P = 0.028). Conclusions A certain amount of HBL occurs in UBE surgery and cannot be ignored in daily clinical practice. The age, number of fusion levels, ASA classification, surgery time, PBV, TBL, postoperative Hct, Hct loss, and fibrinogen are independent risk factors for HBL.

BackgroundRegional lymph node metastasis is a contributor for poor prognosis in osteosarcoma. However, studies on risk factors for predicting regional lymph node metastasis in osteosarcoma are scarce. This study aimed to develop and validate a model based on machine learning (ML) algorithms.MethodsA total of 1201 patients, with 1094 cases from the surveillance epidemiology and end results (SEER) (the training set) and 107 cases (the external validation set) admitted from four medical centers in China, was included in this study. Independent risk factors for the risk of lymph node metastasis were screened by the multifactorial logistic regression models. Six ML algorithms, including the logistic regression (LR), the gradient boosting machine (GBM), the extreme gradient boosting (XGBoost), the random forest (RF), the decision tree (DT), and the multilayer perceptron (MLP), were used to evaluate the risk of lymph node metastasis. The prediction model was developed based on the bestpredictive performance of ML algorithm and the performance of the model was evaluatedby the area under curve (AUC), prediction accuracy, sensitivity and specificity. A homemade online calculator was capable of estimating the probability of lymph node metastasis in individuals.ResultsOf all included patients, 9.41% (113/1201) patients developed regional lymph node metastasis. ML prediction models were developed based on nine variables: age, tumor (T) stage, metastasis (M) stage, laterality, surgery, radiation, chemotherapy, bone metastases, and lung metastases. In multivariate logistic regression analysis, T and M stage, surgery, and chemotherapy were significantly associated with lymph node metastasis. In the six ML algorithms, XGB had the highest AUC (0.882) and was utilized to develop as prediction model. A homemade online calculator was capable of estimating the probability of CLNM in individuals.ConclusionsT and M stage, surgery and Chemotherapy are independent risk factors for predicting lymph node metastasis among osteosarcoma patients. XGB algorithm has the best predictive performance, and the online risk calculator can help clinicians to identify the risk probability of lymph node metastasis among osteosarcoma patients.