Construction of Predictive Model in 9 037 Patients with Stroke

Xiaoxia XIE; Zhengning YANG; Zhen YAO; Shaowei LI; Ruoxue BAI; Xu ZHANG; Lan LI; Zhenliang HUI; Jun CHEN

doi:10.13422/j.cnki.syfjx.20221993

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Construction of Predictive Model in 9 037 Patients with Stroke

更新时间：2022-09-30

- Construction of Predictive Model in 9 037 Patients with Stroke
- Chinese Journal of Experimental Traditional Medical Formulae Vol. 28, Issue 21, Pages: 98-103(2022)
- 作者机构：
  
  1.陕西中医药大学，陕西咸阳 712046
  2.陕西省中医医院，西安 710000
- 作者简介：
- 基金信息：
- DOI：10.13422/j.cnki.syfjx.20221993
  CLC： R24;R25/278;R2-031;R255.2;R743
- Received：25 April 2022，
  
  Published Online：01 July 2022，
  
  Published：05 November 2022
- 稿件说明：
移动端阅览
解晓霞,杨正宁,姚震等.9 037例中风患者预测模型的构建[J].中国实验方剂学杂志,2022,28(21):98-103.

XIE Xiaoxia,YANG Zhengning,YAO Zhen,et al.Construction of Predictive Model in 9 037 Patients with Stroke[J].Chinese Journal of Experimental Traditional Medical Formulae,2022,28(21):98-103.
解晓霞,杨正宁,姚震等.9 037例中风患者预测模型的构建[J].中国实验方剂学杂志,2022,28(21):98-103. DOI： 10.13422/j.cnki.syfjx.20221993.

XIE Xiaoxia,YANG Zhengning,YAO Zhen,et al.Construction of Predictive Model in 9 037 Patients with Stroke[J].Chinese Journal of Experimental Traditional Medical Formulae,2022,28(21):98-103. DOI： 10.13422/j.cnki.syfjx.20221993.

摘要

目的

旨在开发和验证一个预测模型，以个体化预测eICU协同研究数据库中中风患者的风险，以便临床早期识别和干预。

方法

选取一项多中心队列研究（eICU数据库）的个体患者数据（200 859例），最终纳入神经系统疾病中的卒中患者（9 037例）进行统计分析，主要结果是医院死亡率。采用格拉斯哥评分（GCS）将所有中风患者划分为中经络和中脏腑［格拉斯哥昏迷评分（GCS）≤14分为中脏腑，GCS=15分为中经络］，分别按7∶3划分为训练集和测试集，评估两组中风患者住院死亡率的差异，并采用多变量logistic回归分析影响两组预后的相关因素，并建立预测模型。使用受试者工作特征（ROC）曲线来评估预测模型的鉴别度。

结果

建立了基于9 037例中风患者的预测模型。中经络（4 475例）预测因素包括是否合并肺部感染、是否机械通气（MV）、急性生理与慢性健康状况评分系统Ⅳ（APACHE Ⅳ）评分。中脏腑（4 562例）预测因素包括是否接受抗凝治疗（AT）、是否MV、APACHE Ⅳ评分。根据这些预测因素分别构建中经络与中脏腑的预测模型。中经络预测模型训练集和测试集ROC曲线下面积（AUC）分别为0.845［95%置信区间（CI） 0.811~0.879］和0.807（95%CI 0.751~0.863）。中脏腑预测模型训练集和测试集AUC分别为0.799（95%CI 0.781~0.817）］和0.805（95%CI 0.778~0.832）。预测模型训练集和测试集AUC均在0.7以上。

结论

本研究建立的模型可方便、直观、准确地预测中风患者的医院死亡风险。医师和其他保健专业人员可以使用这种预测方法为中风患者在住院期间提供早期护理计划及临床干预。

Abstract

Objective

To develop and validate a predictive model to individually predict the risk of patients with stroke in the eICU Collaborative Research Database for early clinical identification and intervention.

Method

Individual patient data （200 859 cases） from a national multicenter cohort study （eICU database） were selected， and the patients with stroke in neurological diseases （9 037 cases） were selected for statistical analysis. The main outcome was hospital mortality. The Glasgow Coma scale （GCS） was used to divide all patients with stroke into stroke in meridian and stroke in viscera （GCS≤14 for stroke in viscera and GCS=15 for stroke in meridian）. The patients were then divided into a training set and a test set according to 7∶3， respectively， to evaluate the differences in hospital mortality between the two groups. The multivariate logistic regression was used to analyze the related factors affecting the prognosis of the two groups， and a predictive model was established. Receiver operator characteristic （ROC） curves were used to assess the discrimination of the predictive model.

Result

The predictive model based on 9 037 patients with stroke was established. The predictors of the stroke in meridian （4 475 cases） included pulmonary infection， mechanical ventilation， acute physiology， and chronic health status scoring system Ⅳ （APACHE Ⅳ） score. The predictors of the stroke in viscera （4 562 cases） included anticoagulation therapy （AT）， mechanical ventilation， acute physiology， and APACHE Ⅳ score. According to the predictors， the predictive models of the stroke in meridian and the stroke in viscera were constructed， respectively. The areas under the curve （AUC） of ROC of the training set and the test set of the predictive models of the stroke in meridian were 0.845 ［95% confidence interval （CI）（0.811， 0.879）］ and 0.807 ［95% CI （0.751， 0.863）］， respectively. The areas under the ROC curve of the training set and test set of the predictive models of the stroke in viscera were 0.799 ［95% CI （0.781， 0.817）］ and 0.805 ［95% CI （0.778， 0.832）］， respectively. The AUC of the predictive model of the training set and the test set were both above 0.7.

Conclusion

The model established in this study can conveniently， directly， and accurately predict the hospital mortality risk of patients with stroke. Physicians and other healthcare professionals can use this predictive approach to provide early care planning and clinical interventions for patients with stroke during their hospital stay.

关键词

Keywords

references

GBD 2019 Stroke Collaborators . Global， regional， and national burden of stroke and its risk factors， 1990-2019： A systematic analysis for the Global Burden of Disease Study 2019 ［J］. Lancet Neurol ， 2021 ， 20 （ 10 ）： 795 - 820 .

ESKIOGLOU E ， HUCHMANDZADEH MILLOTTE M ， AMIGUET M ， et al . National institutes of health stroke scale zero strokes ［J］. Stroke ， 2018 ， 49 （ 12 ）： 3057 - 3059 .

KASNER S E . Clinical interpretation and use of stroke scales ［J］. Lancet Neurol ， 2006 ， 5 （ 7 ）： 603 - 612 .

FIL BALKAN A ， SALCI Y ， KEKLICEK H ， et al . The trunk control： Which scale is the best in very acute stroke patients？［J］. Top Stroke Rehabil ， 2019 ， 26 （ 5 ）： 359 - 365 .

POLLARD T J ， JOHNSON A ， RAFFA J D ， et al . The eICU Collaborative Research Database， a freely available multi-center database for critical care research ［J］. Sci Data ， 2018 ， 5 ： 180178 .

O'HALLORAN H M ， KWONG K ， VELDHOEN R A ， et al . Characterizing the patients， hospitals， and data quality of the eICU Collaborative Research Database ［J］. Crit Care Med ， 2020 ， 48 （ 12 ）： 1737 - 1743 .

AUSTIN P C ， WHITE I R ， LEE D S ， et al . Missing data in clinical research： A tutorial on multiple imputation ［J］. Can J Cardiol ， 2021 ， 37 （ 9 ）： 1322 - 1331 .

王楚林，吴强，徐名伟，等 . 无创机械通气在急性心肌梗死合并低氧血症中的应用［J］. 齐齐哈尔医学院学报， 2016 ， 37 （ 10 ）： 1281 - 1282 .

BENNETT C E ， WRIGHT R S ， JENTZER J ， et al . Severity of illness assessment with application of the APACHE Ⅳ predicted mortality and outcome trends analysis in an academic cardiac intensive care unit ［J］. J Crit Care ， 2019 ， 50 ： 242 - 246 .

VANDENBRANDE J ， VERBRUGGE L ， BRUCKERS L ， et al . Validation of the acute physiology and chronic health evaluation （APACHE） Ⅱ and Ⅳ score in COVID-19 patients ［J］. Crit Care Res Pract ， 2021 ， 2021 ： 5443083 .

DOSI R ， JAIN G ， JAIN N ， et al . The predictive ability of SAPS Ⅱ， APACHE Ⅱ， SAPS Ⅲ， and APACHE Ⅳ to assess outcome and duration of mechanical ventilation in respiratory intensive care unit ［J］. Lung India ， 2021 ， 38 （ 3 ）： 236 - 240 .

XU C Y ， YE H W ， CHEN B ， et al . Analysis of risk factors and prognosis of post-stroke pulmonary infection in integrated ICU ［J］. Eur Rev Med Pharmacol Sci ， 2021 ， 25 （ 2 ）： 856 - 865 .

杜长芬，谭越 . 分析急性脑梗死并发肺部感染的危险因素及预后情况［J］. 中外医疗， 2017 ， 36 （ 13 ）： 54 - 55，61 .

震华，金兰英，高峰，等 . 急性期脑卒中并发肺部感染86例临床分析［J］. 内科急危重症杂志， 2009 ， 15 （ 05 ）： 260 - 261 .

BIFFI A ， KURAMATSU J B ， LEASURE A ， et al . Oral Anticoagulation and functional outcome after intracerebral hemorrhage ［J］. Ann Neurol ， 2017 ， 82 （ 5 ）： 755 - 765 .

HASEGAWA S ， OHTA K ， UEDA Y ， et al . The timing of the resumption of anticoagulants and its relationship to clinical outcomes in patients with intracerebral hemorrhage ［J］. Nosotchu ， 2017 ， 39 （ 5 ）： 339 - 343 .

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