Constructing a predictive model for high intraoperative excessive bleeding in patients undergoing posterior lumbar decompression and fusion internal fixation surgery during outpatient visits.

IF 2.5 3区 医学 Q2 MEDICAL LABORATORY TECHNOLOGY Clinical biochemistry Pub Date : 2025-01-01 Epub Date: 2024-12-01 DOI:10.1016/j.clinbiochem.2024.110856
Zhenmin Sun, Nan Yang, Lei Wang, Jiansuo Zhou, Hua Zhang, Jun Wang
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Abstract

Objective: 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.

Design & methods: 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.

Results: 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%.

Conclusion: 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.

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建立腰椎后路减压融合内固定手术患者门诊时术中大量大出血的预测模型。
目的:1。建立风险预测模型,预测门诊腰椎后路减压融合内固定手术患者术中高出血因素。2. 对手术患者实施院前血液管理,提高临床疗效。设计与方法:我们收集2016年至2021年在我院行二节段和三节段后路腰椎减压融合内固定手术的患者。共分析24项术前指标,包括病史、人口统计学特征、手术部位、手术操作者和实验室检查结果。我们使用逻辑回归模型来优化模型的特征选择。采用多变量logistic回归方法建立了预测模型,并建立了模型的模态图。激活部分凝血活素时间、外科医生体积、美国麻醉医师学会分类、体重指数、融合固定腰椎节段数量被用于构建预测模型。对预测模型的鉴别、校正、临床适用性和合理性进行评价。结果:预测模型的受试者工作特征曲线下面积为0.723,95%置信区间为(0.685 ~ 0.760)。训练集的决策曲线分析表明,当阈值概率在5% ~ 40%之间时,应用该诊断曲线可以提高净效益。结论:本研究开发了一种准确度相对较高的新型nomogram方法,可帮助临床医生在门诊期间评估后路腰椎减压融合内固定手术患者的高术中出血风险。通过评估个体风险,外科医生可以制定个性化的治疗计划,以减少每个患者术中出血的风险。
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来源期刊
Clinical biochemistry
Clinical biochemistry 医学-医学实验技术
CiteScore
5.10
自引率
0.00%
发文量
151
审稿时长
25 days
期刊介绍: Clinical Biochemistry publishes articles relating to clinical chemistry, molecular biology and genetics, therapeutic drug monitoring and toxicology, laboratory immunology and laboratory medicine in general, with the focus on analytical and clinical investigation of laboratory tests in humans used for diagnosis, prognosis, treatment and therapy, and monitoring of disease.
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