Literature DB >> 31693518

A Useful Model for Predicting Intraoperative Blood Loss in Metastatic Spine Tumor Surgery.

Xin Gao1, Tianqi Fan, Shaohui He, Wei Wan, Chenglong Zhao, Dongsheng Wang, Liang Tang, Yan Lou, Zhenxi Li, Tielong Liu, Jianru Xiao.   

Abstract

STUDY
DESIGN: A retrospective study was performed.
OBJECTIVE: As predictors of intraoperative blood loss have not yet been well defined, the objective of the present study is to develop a model to predict the amount of intraoperative blood loss in metastatic spine tumor surgery. SUMMARY OF BACKGROUND DATA: Massive blood loss is a huge challenge in metastatic spine tumor surgery. Misjudgment of intraoperative blood loss in preoperative planning may result in disastrous consequences.
MATERIALS AND METHODS: Enrolled in this retrospective analysis were 392 patients who received 423 surgeries of vertebrectomy and reconstruction in our hospital between 2011 and 2017. Risk factors for high-volume blood loss were identified by univariate and multivariate linear regression. The optimal regression model was selected to predict the amount of intraoperative blood loss. Correlation analysis between predicted and actual blood loss in the test cohort was performed to verify the performance of the new model.
RESULTS: The overall mean blood loss was 1756±1218 mL, with spinal metastases from thyroid cancer most prominent, followed by renal cancer. The model was developed based on 5 independent risk factors influencing intraoperative blood loss: primary tumor, tumor site, level of instrumentation, level of vertebrectomy, and resection method. In the test cohort, the correlation coefficient (r) between predicted and actual blood loss was 0.606.
CONCLUSIONS: This study presented a relatively reliable method to predict the amount of intraoperative blood loss in metastatic spine tumor surgery, which may help surgeons address blood loss-related issues in preoperative planning.

Entities:  

Year:  2020        PMID: 31693518     DOI: 10.1097/BSD.0000000000000911

Source DB:  PubMed          Journal:  Clin Spine Surg        ISSN: 2380-0186            Impact factor:   1.876


  1 in total

1.  Clinical-Deep Neural Network and Clinical-Radiomics Nomograms for Predicting the Intraoperative Massive Blood Loss of Pelvic and Sacral Tumors.

Authors:  Ping Yin; Chao Sun; Sicong Wang; Lei Chen; Nan Hong
Journal:  Front Oncol       Date:  2021-10-25       Impact factor: 6.244

  1 in total

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