Constructing a predictive model for high intraoperative excessive bleeding in patients undergoing posterior lumbar...

1. Construct a risk prediction model to predict the factors of high intraoperative bleeding in patients undergoing posterior lumbar decompression and fusion internal fixation surgery during outpatient visits. 2. Implement pre-hospital blood management for surgery patients, to improve clinical outcomes.

We collected patients who underwent two-segment and three-segment posterior lumbar decompression and fusion internal fixation surgery in our hospital from 2016 to 2021. A total of 24 preoperative indicators were analyzed, covering medical history, demographic characteristics, segment, operator and laboratory test results. We used a logistic regression model to optimize the model's feature selection. The predictive model was constructed using the multivariable logistic regression method with all included methods, and a nomogram was created to display the model. Activated partial thromboplastin time, surgeon volume, American Society of Anesthesiologists classification, body mass index, and the number of fusion and fixation lumbar segments were used to construct the predictive model. The predictive model's discrimination, calibration, clinical applicability, and rationality were evaluated.

The predictive model's area under the receiver operating characteristic curve is 0.723, with a 95% confidence interval of (0.685-0.760). The training set's decision curve analysis demonstrates that applying this diagnostic curve will increase the net benefit when the threshold probability is between 5% and 40%.

This study developed a novel nomogram with relatively good accuracy to assist clinical doctors in assessing the high intraoperative bleeding risk in patients undergoing posterior lumbar decompression and fusion internal fixation surgery during outpatient visits. By evaluating individual risk, surgeons can develop an individualized treatment plan to reduce the risk of intraoperative bleeding for each patient.