Xu Zhijun, Huang Man
Department of Intensive Care Unit, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China.
PeerJ. 2024 Jan 23;12:e16723. doi: 10.7717/peerj.16723. eCollection 2024.
Septic shock is a severe life-threatening disease, and the mortality of septic shock in China was approximately 37.3% that lacks prognostic prediction model. This study aimed to develop and validate a prediction model to predict 28-day mortality for Chinese patients with septic shock.
This retrospective cohort study enrolled patients from Intensive Care Unit (ICU) of the Second Affiliated Hospital, School of Medicine, Zhejiang University between December 2020 and September 2021. We collected patients' clinical data: demographic data and physical condition data on admission, laboratory data on admission and treatment method. Patients were randomly divided into training and testing sets in a ratio of 7:3. Univariate logistic regression was adopted to screen for potential predictors, and stepwise regression was further used to screen for predictors in the training set. Prediction model was constructed based on these predictors. A dynamic nomogram was performed based on the results of prediction model. Using receiver operator characteristic (ROC) curve to assess predicting performance of dynamic nomogram, which were compared with Sepsis Organ Failure Assessment (SOFA) and Acute Physiology and Chronic Health Evaluation II (APACHE II) systems.
A total of 304 patients with septic shock were included, with a 28-day mortality of 25.66%. Systolic blood pressure, cerebrovascular disease, Na, oxygenation index (PaO/FiO), prothrombin time, glucocorticoids, and hemodialysis were identified as predictors for 28-day mortality in septic shock patients, which were combined to construct the predictive model. A dynamic nomogram (https://zhijunxu.shinyapps.io/DynNomapp/) was developed. The dynamic nomogram model showed a good discrimination with area under the ROC curve of 0.829 in the training set and 0.825 in the testing set. Additionally, the study suggested that the dynamic nomogram has a good predictive value than SOFA and APACHE II.
The dynamic nomogram for predicting 28-day mortality in Chinese patients with septic shock may help physicians to assess patient survival and optimize personalized treatment strategies for septic shock.
感染性休克是一种严重的危及生命的疾病，中国感染性休克的死亡率约为37.3%，且缺乏预后预测模型。本研究旨在开发并验证一种预测模型，以预测中国感染性休克患者的28天死亡率。
这项回顾性队列研究纳入了2020年12月至2021年9月期间浙江大学医学院附属第二医院重症监护病房（ICU）的患者。我们收集了患者的临床资料：入院时的人口统计学数据和身体状况数据、入院时的实验室数据以及治疗方法。患者按7:3的比例随机分为训练集和测试集。采用单因素逻辑回归筛选潜在预测因素，并进一步使用逐步回归在训练集中筛选预测因素。基于这些预测因素构建预测模型。根据预测模型的结果制作动态列线图。使用受试者工作特征（ROC）曲线评估动态列线图的预测性能，并与脓毒症器官功能衰竭评估（SOFA）和急性生理与慢性健康状况评估II（APACHE II）系统进行比较。
共纳入304例感染性休克患者，28天死亡率为25.66%。收缩压、脑血管疾病、钠、氧合指数（PaO/FiO）、凝血酶原时间、糖皮质激素和血液透析被确定为感染性休克患者28天死亡率的预测因素，并将其组合构建预测模型。开发了一个动态列线图（https://zhijunxu.shinyapps.io/DynNomapp/）。动态列线图模型在训练集中的ROC曲线下面积为0.829，在测试集中为0.825，显示出良好的区分度。此外，研究表明动态列线图比SOFA和APACHE II具有更好的预测价值。
用于预测中国感染性休克患者28天死亡率的动态列线图可能有助于医生评估患者的生存情况，并优化感染性休克的个性化治疗策略。
