| 老年脓毒症患者早期发生脓毒症性凝血病的预测模型构建及内部验证 |
| 投稿时间：2025-05-22 修订日期：2025-05-31 点此下载全文 |
| 引用本文：李晨,刘军.老年脓毒症患者早期发生脓毒症性凝血病的预测模型构建及内部验证[J].医学研究杂志,2025,54(10):72-78 |
| DOI：
10.11969/j.issn.1673-548X.2025.10.013 |
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| 基金项目:江苏省社会发展面上项目(BE2021660)；苏州市科技计划发展项目(SKY2023197)；南京医科大学姑苏学院临床研究项目(GSKY20240202) |
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| 中文摘要:目的 探讨老年脓毒症患者发生脓毒症性凝血病(sepsis-induced coagulopathy,SIC)的危险因素并构建风险预测模型。方法 本研究通过电子病历系统对2023年3月～2024年5月入住南京医科大学附属苏州医院重症医学科的老年脓毒症患者进行回顾性数据采集。研究采用7∶3的比例将纳入病例随机分配至模型构建队列(建模组)和验证队列(验证组)。本研究通过单因素及多因素Logistic回归分析确定老年脓毒症患者并发SIC的独立风险因子,并基于R语言rms包构建列线图预测模型。模型性能采用受试者工作特征(receiver operating characteristic,ROC)曲线、决策曲线分析(decision curve analysis, DCA)、校准曲线进行评价。结果 177例老年脓毒症患者纳入研究,其中建模组124例,验证组53例。建模组和验证组患者的临床资料比较,差异无统计学意义(P＞0.05)。基于单因素分析筛选的潜在危险变量,进一步构建多因素Logistic回归模型以校正混杂效应。结果显示,血小板计数与淋巴细胞计数比值(platelet-to-lymphocyte ratio,PLR)、血乳酸(lactic acid,Lac)、凝血酶原时间(prothrombin time,PT)、序贯器官衰竭评估(sequentia1 organ failure assessment,SOFA)评分是老年脓毒症患者发生SIC的独立危险因素。建模组中预测模型的曲线下面积(area under the curve,AUC)为0.914(95% CI:0.863~0.965),验证组中预测模型的AUC为0.751(95% CI:0.620~0.882)。列线图预测模型在建模组和验证组都具有良好的区分度、校准度、临床实用价值。结论基于PLR、Lac、PT、SOFA评分构建的列线图模型对老年脓毒症患者并发SIC具有良好的预测价值,可以帮助临床工作者早期识别老年脓毒症患者并发SIC。 |
| 中文关键词:老年 脓毒症性凝血病 列线图 预测模型 |
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| Construction and Internal Validation of A Predictive Model for Early Sepsis-induced Coagulopathy in Elderly Patients with Sepsis. |
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| Abstract:Objective To explore the risk factors of sepsis-induced coagulopathy (SIC) in elderly patients with sepsis and construct a risk prediction model. Methods In this study, retrospective data collection was conducted on elderly patients with sepsis who were admitted to the Intensive Care Unit of The Affiliated Suzhou Hospital of Nanjing Medical University from March 2023 to May 2024 through the electronic medical record system. The study randomly assigned the included cases to the model construction cohort (modeling group) and the validation cohort (validation group) in a ratio of 7∶3. In this study, the independent risk factors of elderly sepsis complicated with SIC were determined through univariate and multivariate Logistic regression analysis, and a nomogram prediction model was constructed based on the R language rms package. The model performance was evaluated by using the receiver operating characteristic (ROC) curve, decision curve analysis (DCA), and calibration curve. Results A total of 177 elderly patients with sepsis were included in the study, among which 124 cases were in the modeling group and 53 cases were in the validation group. There was no statistically significant difference in the clinical data of the modeling group and the validation group. Based on the potential risk variables screened by univariate analysis, a multivariate Logistic regression model was further constructed to correct for confounding effects. The results showed that platelet-to-lymphocyte ratio (PLR), lactic acid (Lac), prothrombin time (PT) and sequentia1 organ failure assessment (SOFA) score were independent risk factors for SIC in elderly patients with sepsis. The area under the curve (AUC) of the prediction model in the modeling group was 0.914 (95%CI:0.863-0.965), and the AUC of the prediction model in the validation group was 0.751 (95%CI:0.620-0.882). The nomogram prediction model has good discrimination, calibration and clinical practical value in both the modeling group and the validation group. Conclusion The nomogram model constructed based on PLR, Lac, PT and SOFA score has a good predictive value for elderly patients with sepsis complicated with SIC, which can help clinical workers identify elderly patients with sepsis complicated with SIC at an early stage. |
| keywords:Elderly Sepsis-induced coagulopathy Nomogram Prediction model |
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