Lv Bo, Hu Linhui, Fang Heng, Sun Dayong, Hou Yating, Deng Jia, Zhang Huidan, Xu Jing, He Linling, Liang Yufan, Chen Chunbo
Department of Critical Care Medicine, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China.
The Second School of Clinical Medicine, Southern Medical University, Guangzhou, China.
Front Med (Lausanne). 2021 Dec 24;8:765818. doi: 10.3389/fmed.2021.765818. eCollection 2021.
The plasma colloid osmotic pressure (COP) values for predicting mortality are not well-estimated. A user-friendly nomogram could predict mortality by incorporating clinical factors and scoring systems to facilitate physicians modify decision-making when caring for patients with serious neurological conditions. Patients were prospectively recruited from March 2017 to September 2018 from a tertiary hospital to establish the development cohort for the internal test of the nomogram, while patients recruited from October 2018 to June 2019 from another tertiary hospital prospectively constituted the validation cohort for the external validation of the nomogram. A multivariate logistic regression analysis was performed in the development cohort using a backward stepwise method to determine the best-fit model for the nomogram. The nomogram was subsequently validated in an independent external validation cohort for discrimination and calibration. A decision-curve analysis was also performed to evaluate the net benefit of the insertion decision using the nomogram. A total of 280 patients were enrolled in the development cohort, of whom 42 (15.0%) died, whereas 237 patients were enrolled in the validation cohort, of which 43 (18.1%) died. COP, neurological pathogenesis and Acute Physiology and Chronic Health Evaluation II (APACHE II) score were predictors in the prediction nomogram. The derived cohort demonstrated good discriminative ability, and the area under the receiver operating characteristic curve (AUC) was 0.895 [95% confidence interval (CI), 0.840-0.951], showing good correction ability. The application of this nomogram to the validation cohort also provided good discrimination, with an AUC of 0.934 (95% CI, 0.892-0.976) and good calibration. The decision-curve analysis of this nomogram showed a better net benefit. A prediction nomogram incorporating COP, neurological pathogenesis and APACHE II score could be convenient in predicting mortality for critically ill neurological patients.
用于预测死亡率的血浆胶体渗透压（COP）值估计不佳。一个用户友好的列线图可以通过纳入临床因素和评分系统来预测死亡率，以便在照顾患有严重神经系统疾病的患者时帮助医生修改决策。2017年3月至2018年9月，从一家三级医院前瞻性招募患者以建立列线图内部测试的开发队列，而2018年10月至2019年6月从另一家三级医院前瞻性招募的患者构成列线图外部验证的验证队列。在开发队列中使用向后逐步法进行多变量逻辑回归分析，以确定列线图的最佳拟合模型。随后在独立的外部验证队列中对列线图进行验证，以评估其区分度和校准度。还进行了决策曲线分析，以评估使用列线图进行插入决策的净效益。开发队列共纳入280例患者，其中42例（15.0%）死亡；而验证队列纳入237例患者，其中43例（18.1%）死亡。COP、神经发病机制和急性生理与慢性健康状况评分系统II（APACHE II）评分是预测列线图中的预测因素。推导队列显示出良好的区分能力，受试者操作特征曲线（AUC）下的面积为0.895[95%置信区间（CI），0.840 - 0.951]，显示出良好的校正能力。将该列线图应用于验证队列也显示出良好的区分度，AUC为0.934（95%CI，0.892 - 0.976）和良好的校准度。该列线图的决策曲线分析显示出更好的净效益。一个纳入COP、神经发病机制和APACHE II评分的预测列线图在预测重症神经科患者的死亡率方面可能很方便。
