经皮肾镜取石术患者大出血的预测因素:临床预测研究。

Teng Qiu, Xiao-Tao Hu
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引用次数: 0

摘要

目的:评价经皮肾镜取石术(PCNL)治疗上尿路结石的主要出血危险因素,并验证预测模型。研究设计:分析性研究。研究地点和时间:中国芜湖皖南医学院第一附属医院,2019年1月至2023年8月。方法:大出血定义为血红蛋白较术前水平下降≥20 g/L。回顾性分析468例PCNL患者,使用单变量、LASSO和logistic回归分析确定大出血的危险因素。采用R软件建立Nomogram模型,并用ROC和校正图评估模型的准确性。bootstrap方法提供内部验证,DCA评估临床效用。结果:独立危险因素包括糖尿病(OR = 4.17)、鹿角型结石(OR = 3.41)、手术时间(OR = 1.01)、分期手术(OR = 2.75)。该模型具有较高的判别能力(c统计量:0.783),与观察结果一致。内部验证证实稳健性(c统计量:0.728)。结论:PCNL术中及术后大出血的预测模型以糖尿病、鹿角型结石、手术时间、手术分期为重点,具有较高的准确性,有助于PCNL风险评估。关键词:上尿路结石,经皮肾镜取石术,大出血,危险因素,预测模型
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Predictive Factors for Major Bleeding in Patients Undergoing Percutaneous Nephrolithotomy: A Clinical Prediction Study.

Objective: To evaluate major bleeding risk factors in percutaneous nephrolithotomy (PCNL) for upper urinary tract calculi and validate a prediction model.

Study design: Analytical study. Place and Duration of the Study: The First Affiliated Hospital of Wannan Medical College, Wuhu, China, from January 2019 to August 2023.

Methodology: Major bleeding was defined as a decrease in haemoglobin of ≥20 g/L compared to preoperative levels. A retrospective analysis of 468 PCNL patients identified risk factors for major bleeding using univariate, LASSO, and logistic regression analyses. Nomogram models were developed using R software, with ROC and calibration plots assessing the model's accuracy. The bootstrap method provided internal validation, and DCA evaluated clinical utility.

Results: Independent risk factors included diabetes (OR = 4.17), staghorn calculi (OR = 3.41), operative duration (OR = 1.01), and staged surgery (OR = 2.75). The model showed high discriminative ability (C-statistic: 0.783) and alignment with observed outcomes. Internal validation confirmed robustness (C-statistic: 0.728).

Conclusion: The predictive model for major bleeding during and after PCNL, focusing on diabetes, staghorn calculi, operative duration, and staged surgery, is highly accurate, aiding in the PCNL risk assessment.

Key words: Upper urinary tract calculi, Percutaneous nephrolithotomy, Major bleeding, Risk factors, Prediction model.

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