一个基于网络的脓毒症早期诊断动态图:开发和验证与现实世界的临床应用。

IF 2.9 3区 医学 Q2 INFECTIOUS DISEASES
Infection and Drug Resistance Pub Date : 2025-09-03 eCollection Date: 2025-01-01 DOI:10.2147/IDR.S532869
Chaochao Chen, Zhengxian Su, Yuwei Zheng, Minya Jin, Xiaojie Bi
{"title":"一个基于网络的脓毒症早期诊断动态图:开发和验证与现实世界的临床应用。","authors":"Chaochao Chen, Zhengxian Su, Yuwei Zheng, Minya Jin, Xiaojie Bi","doi":"10.2147/IDR.S532869","DOIUrl":null,"url":null,"abstract":"<p><strong>Purpose: </strong>Sepsis has high mortality and progresses rapidly, requiring early diagnosis; traditional scoring and lab parameters are limited in non-ICU settings, highlighting the need for biomarker integration and continuous monitoring to enhance diagnostic accuracy.</p><p><strong>Patients and methods: </strong>A retrospective analysis of 1,098 patients at Taizhou Hospital of Zhejiang Province identified sepsis and non-sepsis groups per Sepsis 3.0 criteria, Logistic regression analyses were used to identify the risk factors. A dynamic nomogram was built, and predictive accuracy was evaluated using calibration and decision curves. External validation for 94 patients occurred from January to March 2024, using Receiver operating characteristic (ROC) curve analysis for diagnostic evaluation.</p><p><strong>Results: </strong>Multivariate logistic regression analysis revealed eight independent risk factors significantly associated with sepsis development: hypertension (odds ratio [OR] = 1.6278, 95% confidence interval [CI], 1.2079-2.1937), renal insufficiency (OR=1.7002, 95% CI, 1.2840-2.2513), cardiac insufficiency (OR=1.8927, 95% CI, 1.2979-2.7599), interleukin-6 levels (OR=1.0003 95% CI, 1.0002-1.0005), basophil percentage (OR=0.4319, 95% CI, 0.2353-0.7926), platelet-to-lymphocyte ratio (PLR) (OR=1.0025, 95% CI, 1.0011-1.0040), platelet count (PLT) (OR=0.9939, 95% CI, 0.9912-0.9959) and D-dimer levels (OR=1.0796, 95% CI, 1.0273-1.1347). The prognostic nomogram showed significant discriminative power, with a concordance index of 0.746 (95% CI 0.709-0.772). ROC analysis further revealed a negative predictive value (NPV) of 0.832 and a positive predictive value (PPV) of 0.511. Decision curve analysis validated the clinical utility of the model, demonstrating a substantial net benefit for predicting disease progression within a clinically relevant probability threshold range of 30% - 70%. The model maintained satisfactory discriminative performance in external validation, demonstrating an area under the curve (AUC) of 0.663 (95% CI, 0.549-0.776). The interactive web-based nomogram is available at https://bixiaojie-1987.shinyapps.io/DynNomapp/.</p><p><strong>Conclusion: </strong>This web-based dynamic nomogram incorporating eight clinically readily available predictors demonstrates robust diagnostic performance for sepsis, which helps doctors make quicker decisions by providing real-time risk assessments for each patient in non-ICU departments.</p>","PeriodicalId":13577,"journal":{"name":"Infection and Drug Resistance","volume":"18 ","pages":"4667-4676"},"PeriodicalIF":2.9000,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12414447/pdf/","citationCount":"0","resultStr":"{\"title\":\"A Web-Based Dynamic Nomogram for Early Diagnosis in Sepsis: Development and Validation with Real-World Clinical Utility.\",\"authors\":\"Chaochao Chen, Zhengxian Su, Yuwei Zheng, Minya Jin, Xiaojie Bi\",\"doi\":\"10.2147/IDR.S532869\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Purpose: </strong>Sepsis has high mortality and progresses rapidly, requiring early diagnosis; traditional scoring and lab parameters are limited in non-ICU settings, highlighting the need for biomarker integration and continuous monitoring to enhance diagnostic accuracy.</p><p><strong>Patients and methods: </strong>A retrospective analysis of 1,098 patients at Taizhou Hospital of Zhejiang Province identified sepsis and non-sepsis groups per Sepsis 3.0 criteria, Logistic regression analyses were used to identify the risk factors. A dynamic nomogram was built, and predictive accuracy was evaluated using calibration and decision curves. External validation for 94 patients occurred from January to March 2024, using Receiver operating characteristic (ROC) curve analysis for diagnostic evaluation.</p><p><strong>Results: </strong>Multivariate logistic regression analysis revealed eight independent risk factors significantly associated with sepsis development: hypertension (odds ratio [OR] = 1.6278, 95% confidence interval [CI], 1.2079-2.1937), renal insufficiency (OR=1.7002, 95% CI, 1.2840-2.2513), cardiac insufficiency (OR=1.8927, 95% CI, 1.2979-2.7599), interleukin-6 levels (OR=1.0003 95% CI, 1.0002-1.0005), basophil percentage (OR=0.4319, 95% CI, 0.2353-0.7926), platelet-to-lymphocyte ratio (PLR) (OR=1.0025, 95% CI, 1.0011-1.0040), platelet count (PLT) (OR=0.9939, 95% CI, 0.9912-0.9959) and D-dimer levels (OR=1.0796, 95% CI, 1.0273-1.1347). The prognostic nomogram showed significant discriminative power, with a concordance index of 0.746 (95% CI 0.709-0.772). ROC analysis further revealed a negative predictive value (NPV) of 0.832 and a positive predictive value (PPV) of 0.511. Decision curve analysis validated the clinical utility of the model, demonstrating a substantial net benefit for predicting disease progression within a clinically relevant probability threshold range of 30% - 70%. The model maintained satisfactory discriminative performance in external validation, demonstrating an area under the curve (AUC) of 0.663 (95% CI, 0.549-0.776). The interactive web-based nomogram is available at https://bixiaojie-1987.shinyapps.io/DynNomapp/.</p><p><strong>Conclusion: </strong>This web-based dynamic nomogram incorporating eight clinically readily available predictors demonstrates robust diagnostic performance for sepsis, which helps doctors make quicker decisions by providing real-time risk assessments for each patient in non-ICU departments.</p>\",\"PeriodicalId\":13577,\"journal\":{\"name\":\"Infection and Drug Resistance\",\"volume\":\"18 \",\"pages\":\"4667-4676\"},\"PeriodicalIF\":2.9000,\"publicationDate\":\"2025-09-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12414447/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Infection and Drug Resistance\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.2147/IDR.S532869\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2025/1/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q2\",\"JCRName\":\"INFECTIOUS DISEASES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Infection and Drug Resistance","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.2147/IDR.S532869","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/1/1 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"INFECTIOUS DISEASES","Score":null,"Total":0}
引用次数: 0

摘要

目的:脓毒症死亡率高,进展迅速,需要早期诊断;传统的评分和实验室参数在非icu环境中是有限的,这突出了生物标志物整合和持续监测以提高诊断准确性的必要性。患者和方法:回顾性分析浙江省台州市医院1098例脓毒症和非脓毒症患者的脓毒症3.0标准,采用Logistic回归分析确定危险因素。建立了动态模态图,并利用标定曲线和决策曲线对预测精度进行了评价。2024年1月至3月对94例患者进行了外部验证,采用受试者工作特征(ROC)曲线分析进行诊断评价。结果:多因素logistic回归分析揭示了与脓毒症发展显著相关的8个独立危险因素:高血压(比值比(或)= 1.6278,95%可信区间(CI), 1.2079 - -2.1937),肾功能不全(OR = 1.7002, 95% CI, 1.2840 - -2.2513),心脏功能不全(OR = 1.8927, 95% CI, 1.2979 - -2.7599)、白细胞介素- 6的水平(或= 1.0003 95% CI, 1.0002 - -1.0005),嗜碱细胞百分比(OR = 0.4319, 95% CI, 0.2353 - -0.7926), platelet-to-lymphocyte比率(PLR) (OR = 1.0025, 95% CI, 1.0011 - -1.0040),血小板计数(PLT) (OR = 0.9939, 95% CI, 0.9912 - -0.9959)和肺动脉栓塞(OR = 1.0796, 95% CI, 1.0273 - -1.1347)。预后nomogram具有显著的判别能力,一致性指数为0.746 (95% CI 0.709-0.772)。ROC分析显示阴性预测值(NPV)为0.832,阳性预测值(PPV)为0.511。决策曲线分析验证了该模型的临床效用,证明在30% - 70%的临床相关概率阈值范围内预测疾病进展具有实质性的净收益。该模型在外部验证中保持了令人满意的判别性能,曲线下面积(AUC)为0.663 (95% CI, 0.549-0.776)。这种基于网络的交互式nomogram(基于网络的动态nomogram)图可在https://bixiaojie-1987.shinyapps.io/DynNomapp/.Conclusion上获得:这种基于网络的动态nomogram(基于网络的动态nomogram)图结合了8种临床可用的预测指标,展示了对败血症的强大诊断性能,通过为非icu科室的每位患者提供实时风险评估,帮助医生更快地做出决策。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

A Web-Based Dynamic Nomogram for Early Diagnosis in Sepsis: Development and Validation with Real-World Clinical Utility.

A Web-Based Dynamic Nomogram for Early Diagnosis in Sepsis: Development and Validation with Real-World Clinical Utility.

A Web-Based Dynamic Nomogram for Early Diagnosis in Sepsis: Development and Validation with Real-World Clinical Utility.

A Web-Based Dynamic Nomogram for Early Diagnosis in Sepsis: Development and Validation with Real-World Clinical Utility.

Purpose: Sepsis has high mortality and progresses rapidly, requiring early diagnosis; traditional scoring and lab parameters are limited in non-ICU settings, highlighting the need for biomarker integration and continuous monitoring to enhance diagnostic accuracy.

Patients and methods: A retrospective analysis of 1,098 patients at Taizhou Hospital of Zhejiang Province identified sepsis and non-sepsis groups per Sepsis 3.0 criteria, Logistic regression analyses were used to identify the risk factors. A dynamic nomogram was built, and predictive accuracy was evaluated using calibration and decision curves. External validation for 94 patients occurred from January to March 2024, using Receiver operating characteristic (ROC) curve analysis for diagnostic evaluation.

Results: Multivariate logistic regression analysis revealed eight independent risk factors significantly associated with sepsis development: hypertension (odds ratio [OR] = 1.6278, 95% confidence interval [CI], 1.2079-2.1937), renal insufficiency (OR=1.7002, 95% CI, 1.2840-2.2513), cardiac insufficiency (OR=1.8927, 95% CI, 1.2979-2.7599), interleukin-6 levels (OR=1.0003 95% CI, 1.0002-1.0005), basophil percentage (OR=0.4319, 95% CI, 0.2353-0.7926), platelet-to-lymphocyte ratio (PLR) (OR=1.0025, 95% CI, 1.0011-1.0040), platelet count (PLT) (OR=0.9939, 95% CI, 0.9912-0.9959) and D-dimer levels (OR=1.0796, 95% CI, 1.0273-1.1347). The prognostic nomogram showed significant discriminative power, with a concordance index of 0.746 (95% CI 0.709-0.772). ROC analysis further revealed a negative predictive value (NPV) of 0.832 and a positive predictive value (PPV) of 0.511. Decision curve analysis validated the clinical utility of the model, demonstrating a substantial net benefit for predicting disease progression within a clinically relevant probability threshold range of 30% - 70%. The model maintained satisfactory discriminative performance in external validation, demonstrating an area under the curve (AUC) of 0.663 (95% CI, 0.549-0.776). The interactive web-based nomogram is available at https://bixiaojie-1987.shinyapps.io/DynNomapp/.

Conclusion: This web-based dynamic nomogram incorporating eight clinically readily available predictors demonstrates robust diagnostic performance for sepsis, which helps doctors make quicker decisions by providing real-time risk assessments for each patient in non-ICU departments.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Infection and Drug Resistance
Infection and Drug Resistance Medicine-Pharmacology (medical)
CiteScore
5.60
自引率
7.70%
发文量
826
审稿时长
16 weeks
期刊介绍: About Journal Editors Peer Reviewers Articles Article Publishing Charges Aims and Scope Call For Papers ISSN: 1178-6973 Editor-in-Chief: Professor Suresh Antony An international, peer-reviewed, open access journal that focuses on the optimal treatment of infection (bacterial, fungal and viral) and the development and institution of preventative strategies to minimize the development and spread of resistance.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信