脓毒症相关性谵妄风险预测模型研究方法学与报告质量评价-东南大学学报医学版

脓毒症相关性谵妄风险预测模型研究方法学与报告质量评价

单位：1. 南京医科大学附属逸夫医院神经外科, 江苏南京 211100;
2. 南京医科大学附属脑科医院 ICU, 江苏南京 210029;
3. 南京医科大学附属逸夫医院老年科, 江苏南京 211100

分类号：R631

出版年·卷·期（页码）：2025·44·第六期（932-940）

摘要：

目的: 评价现有脓毒症相关性谵妄(SAD)风险预测模型的研究方法学与报告质量。方法: 系统检索中英文数据库,检索时限从建库至2024年8月31日。纳入以构建或验证SAD风险预测模型为研究主题的文献。由两名研究者独立进行文献筛选,采用预测建模研究系统评价的批判性评估和数据提取清单(CHARMS)提取数据,分析模型的预测性能指标和关键预测因子。使用预测模型偏倚风险评估工具(PROBAST)评估模型的偏倚风险和适用性,应用个体预后与诊断预测模型研究报告规范(TRIPOD)声明评价报告质量。结果: 共纳入9项研究,纳入模型受试者工作特征曲线下面积(AUC)为0.742~0.910,模型总体预测性能良好,其中出现频率较高的预测因子有年龄、序贯器官衰竭评估(SOFA)、呼吸频率及使用镇静药物咪达唑仑等。方法学评价结果显示:在偏倚风险方面,8项研究为高风险,1项为低风险;在适用性方面,3项研究为高风险,6项为低风险。TRIPOD报告质量评价平均完成度为77.78%(范围:63.64%~90.91%),其中“样本量计算”“风险分层”“补充分析”和“代码共享”等条目报告缺失严重。结论: 现有SAD风险预测模型在预测性能和临床适用性方面整体表现良好,但普遍存在较高的方法学偏倚风险,且报告质量有待提高, 尤其在关键方法学细节和透明化报告方面存在显著不足。未来研究应严格遵循PROBAST和TRIPOD指南,以提升模型的可靠性、适用性和可重复性。

Objective: To evaluate the methodological and reporting quality of existing risk prediction models for sepsis-associated delirium(SAD). Methods: A systematic search was conducted in both English and Chinese databases, covering all records up to August 31, 2024. Studies focusing on the development or validation of SAD risk prediction models were included. Two reviewers independently screened the literature. Data were extracted using the CHARMS checklist to analyze model performance metrics and key predictors. The Prediction Model Risk of Bias Assessment Tool(PROBAST) was used to assess risk of bias and applicability. Reporting quality was evaluated using the transparent reporting of a multivariable prediction model for individual prognosis or diagnosis(TRIPOD) statement. Results: A total of nine studies were included. The area under the receiver operating characteristic curve(AUC) for the included models ranged from 0.742 to 0.910, indicating good predictive performance. Frequently reported predictors included age, Sequential Organ Failure Assessment(SOFA), respiratory rate, and use of the sedative midazolam. Methodological assessment showed that eight studies had a high risk of bias and one had a low risk. Regarding applicability, three studies were rated high risk and six low risk. The average reporting completeness based on TRIPOD was 77.78%(range: 63.64%-90.91%). Items such as sample size calculation, risk stratification, supplementary analyses, and code sharing were frequently underreported. Conclusion: Current SAD risk prediction models generally exhibit good predictive performance and clinical applicability. However, they commonly suffer from high methodological bias and suboptimal reporting quality, especially in key methodological details and transparency. Future studies should adhere strictly to PROBAST and TRIPOD guidelines to improve model reliability, applicability, and reproducibility.

参考文献：

[1] FAIAD Y,KHOURY B,DAOUK S,et al.Frequency of use of the International Classification of Diseases ICD-10 diagnostic categories for mental and behavioural disorders across world regions[J].Epidemiol Psychiatr Sci,2018,27(6):568-576.
[2] VAN DER POLL T,SHANKAR-HARI M,WIERSINGA W J.The immunology of sepsis[J].Immunity,2021,54(11):2450-2464.
[3] EVANS L,RHODES A,ALHAZZANI W,et al.Surviving sepsis campaign:international guidelines for management of sepsis and septic shock 2021[J].Intensive Care Med,2021,47(11):1181-1247.
[4] RUDD K E,JOHNSON S C,AGESA K M,et al.Global,regional,and national sepsis incidence and mortality,1990-2017:analysis for the Global Burden of Disease Study[J].Lancet,2020,395(10219):200-211.
[5] WILSON J E,MART M F,CUNNINGHAM C,et al.Delirium[J].Nat Rev Dis Primers,2020,6:90.
[6] ALıCı Š,ÖZTüRK BIRGE A.The frequency of sepsis-associated delirium in intensive care unit and its effect on nurse workload[J].J Clin Nurs,2025,34(4):1383-1397.
[7] BRüCK E,SCHANDL A,BOTTAI M,et al.The impact of sepsis,delirium,and psychological distress on self-rated cognitive function in ICU survivors-a prospective cohort study[J].J Intensive Care,2018,6:2.
[8] PAULEY E,LISHMANOV A,SCHUMANN S,et al.Delirium is a robust predictor of morbidity and mortality among critically ill patients treated in the cardiac intensive care unit[J].Am Heart J,2015,170(1):79-86,86.e1.
[9] 谷鸿秋,周支瑞,章仲恒,等.临床预测模型:基本概念、应用场景及研究思路[J].中国循证心血管医学杂志,2018,10(12):1454-1456,1462.
[10] MOONS K G M,DE GROOT J A H,BOUWMEESTER W,et al.Critical appraisal and data extraction for systematic reviews of prediction modelling studies:the CHARMS checklist[J].PLoS Med,2014,11(10):e1001744.
[11] FERNANDEZ-FELIX B M,LÓPEZ-ALCALDE J,ROQUÉ M,et al.CHARMS and PROBAST at your fingertips:a template for data extraction and risk of bias assessment in systematic reviews of predictive models[J].BMC Med Res Methodol,2023,23(1):44.
[12] 单娇,包小源,龚志忠,等.医院感染预测模型研究方法学与报告质量评价[J].中国医院管理,2024,44(11):55-59.
[13] CAVAILLON J M,CHRÉTIEN F.From septicemia to sepsis 3.0-from ignaz semmelweis to Louis Pasteur[J].Genes Immun,2019,20(5):371-382.
[14] KIM Y,JIN Y,JIN T,et al.Risk factors and outcomes of sepsis-associated delirium in intensive care unit patients:a secondary data analysis[J].Intensive Crit Care Nurs,2020,59:102844.
[15] ZHAO Q,XIAO J,LIU X,et al.The nomogram to predict the occurrence of sepsis-associated encephalopathy in elderly patients in the intensive care units:a retrospective cohort study[J].Front Neurol,2023,14:1084868.
[16] ZHANG Y,HU J,HUA T,et al.Development of a machine learning-based prediction model for sepsis-associated delirium in the intensive care unit[J].Sci Rep,2023,13(1):12697.
[17] GU Q,YANG S,FEI D,et al.A nomogram for predicting sepsis-associated delirium:a retrospective study in MIMIC Ⅲ[J].BMC Med Inform Decis Mak,2023,23(1):184.
[18] 杨思敏,卞兴航,邓开基,等.脓毒症患者谵妄发生风险预测模型构建[J].中华急诊医学杂志,2024,33(6):784-791.
[19] 柏晓奇,顾琼,许俊,等.脓毒症相关谵妄患者预后预测模型构建及验证[J].中华急诊医学杂志,2024,33(5):651-657.
[20] ZHAO L,WANG Y,GE Z,et al.Mechanical learning for prediction of sepsis-associated encephalopathy[J].Front Comput Neurosci,2021,15:739265.
[21] WANG G,JIANG X,FU Y,et al.Development and validation of a nomogram to predict the risk of sepsis-associated encephalopathy for septic patients in PICU:a multicenter retrospective cohort study[J].J Intensive Care,2024,12(1):8.
[22] JIN J,YU L,ZHOU Q,et al.Improved prediction of sepsis-associated encephalopathy in intensive care unit sepsis patients with an innovative nomogram tool[J].Front Neurol,2024,15:1344004.
[23] 张文彤.SPSS 统计分析基础教程[M].第3版.北京:高等教育出版社,2017:203-204.
[24] 王俊峰,章仲恒,周支瑞,等.临床预测模型:模型的验证[J].中国循证心血管医学杂志,2019,11(2):141-144.
[25] ALBA A C,AGORITSAS T,WALSH M,et al.Discrimination and calibration of clinical prediction models:users' guides to the medical literature[J].JAMA,2017,318(14):1377-1384.
[26] STEYERBERG E W,HARRELL F E Jr.Prediction models need appropriate internal,internal-external,and external validation[J].J Clin Epidemiol,2016,69:245-247.
[27] BAUER M,GERLACH H,VOGELMANN T,et al.Mortality in sepsis and septic shock in Europe,North America and Australia between 2009 and 2019-results from a systematic review and meta-analysis[J].Crit Care,2020,24(1):239.
[28] 朱震寒,姜婷婷,徐宪辉.利用决策树算法建立严重脓毒症院内死亡的风险预测模型及其效能验证[J].东南大学学报(医学版),2025,44(1):90-98.
[29] JIN G,WANG S,CHEN J,et al.Identification of sepsis-associated encephalopathy risk factors in elderly patients:a retrospective observational cohort study[J].Turk J Med Sci,2022,52(5):1513-1522.
[30] WENG J,HOU R,ZHOU X,et al.Development and validation of a score to predict mortality in ICU patients with sepsis:a multicenter retrospective study[J].J Transl Med,2021,19(1):322.
[31] ZAAL I J,DEVLIN J W,HAZELBAG M,et al.Benzodiazepine-associated delirium in critically ill adults[J].Intensive Care Med,2015,41(12):2130-2137.
[32] COLLINS G S,REITSMA J B,ALTMAN D G,et al.Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis(TRIPOD):the TRIPOD statement[J].BMJ,2015,350:g7594.

服务与反馈：

【文章下载】【发表评论】【查看评论】【加入收藏】

提示：您还未登录，请登录！点此登录