Department of Clinical Laboratory Medicine, The First Affiliated Hospital of Jinan University, Guangzhou, 510630, People's Republic of China.
BMC Med Inform Decis Mak. 2022 Apr 6;22(1):92. doi: 10.1186/s12911-022-01836-3.
Stroke is a disease characterized by sudden cerebral ischemia and is the second leading cause of death worldwide. We aimed to develop and validate a nomogram model to predict mortality in intensive care unit patients with stroke.
All data involved in this study were extracted from the Medical Information Mart for Intensive Care III database (MIMIC-III). The data were analyzed using multivariate Cox regression, and the performance of the novel nomogram, which assessed the patient's overall survival at 30, 180, and 360 days after stroke, was evaluated using Harrell's concordance index (C-index) and the area under the receiver operating characteristic curve. A calibration curve and decision curve were introduced to test the clinical value and effectiveness of our prediction model.
A total of 767 patients with stroke were randomly divided into derivation (n = 536) and validation (n = 231) cohorts at a 7:3 ratio. Multivariate Cox regression showed that 12 independent predictors, including age, weight, ventilation, cardiac arrhythmia, metastatic cancer, explicit sepsis, Oxford Acute Severity of Illness Score or OASIS score, diastolic blood pressure, bicarbonate, chloride, red blood cell and white blood cell counts, played a significant role in the survival of individuals with stroke. The nomogram model was validated based on the C-indices, calibration plots, and decision curve analysis results.
The plotted nomogram accurately predicted stroke outcomes and, thus may contribute to clinical decision-making and treatment as well as consultation services for patients.
中风是一种以突发性脑缺血为特征的疾病，是全球范围内的第二大致死原因。我们旨在开发和验证一个列线图模型，以预测重症监护病房中风患者的死亡率。
本研究涉及的所有数据均从医疗信息集市重症监护 III 数据库（MIMIC-III）中提取。使用多变量 Cox 回归对数据进行分析，并使用 Harrell 一致性指数（C 指数）和接收者操作特征曲线下的面积评估新列线图的性能，该列线图评估了患者中风后 30、180 和 360 天的总体生存率。引入校准曲线和决策曲线来测试我们的预测模型的临床价值和效果。
总共 767 名中风患者以 7:3 的比例随机分为推导（n=536）和验证（n=231）队列。多变量 Cox 回归显示，12 个独立的预测因素，包括年龄、体重、通气、心律失常、转移性癌症、明确的败血症、牛津急性疾病严重程度评分或 OASIS 评分、舒张压、碳酸氢盐、氯、红细胞和白细胞计数，在中风患者的生存中起着重要作用。基于 C 指数、校准图和决策曲线分析结果验证了列线图模型。
绘制的列线图准确预测了中风的结果，因此可能有助于临床决策和治疗，以及为患者提供咨询服务。
