随着硬膜下血肿发病率的增加,在该人群中,了解与治疗结果和死亡率相关的症状学和临床变量非常重要;我们从2016-2020年的国家住院患者样本(NIS)数据库中选择了硬膜下血肿患者,使用国际疾病分类第10版(ICD10)代码.使用格拉斯哥昏迷量表(GCS)鉴定中度至重度硬膜下血肿患者。首先使用多元回归来确定住院死亡率的预测因子,然后使用β系数来创建加权死亡率评分。在29,915例中重度硬膜下血肿患者中,12,135(40.6%)在同一医院住院期间死亡。在相关人口统计学和临床协变量的多变量模型中,年龄大于70岁,糖尿病,机械通气,脑积水,和疝是死亡率的独立预测因子(均p<0.001)。年龄大于70岁,糖尿病,机械通气,脑积水,和疝在加权死亡率评分中被分配为“1”。我们模型的ROC曲线显示曲线下面积为0.64。年龄大于70岁,糖尿病,机械通气,脑积水,和疝是死亡率的预测因素。我们创建了第一个可用于风险分层的临床相关加权死亡率评分,指导预后,并告知家庭讨论。
As the incidence of
subdural hematoma is increasing, it is important to understand symptomatology and clinical variables associated with treatment outcomes and mortality in this population; patients with
subdural hematoma were selected from the National Inpatient Sample (NIS) Database between 2016 and 2020 using International Classification of Disease 10th Edition (ICD10) codes. Moderate-to-severe
subdural hematoma patients were identified using the Glasgow Coma Scale (GCS). Multivariate regression was first used to identify predictors of in-hospital mortality and then beta coefficients were used to create a weighted mortality score. Of 29,915 patients admitted with moderate-to-severe
subdural hematomas, 12,135 (40.6%) died within the same hospital admission. In a multivariate model of relevant demographic and clinical covariates, age greater than 70, diabetes mellitus, mechanical ventilation, hydrocephalus, and herniation were independent predictors of mortality (p < 0.001 for all). Age greater than 70, diabetes mellitus, mechanical ventilation, hydrocephalus, and herniation were assigned a \"1\" in a weighted mortality score. The ROC curve for our model showed an area under the curve of 0.64. Age greater than 70, diabetes mellitus, mechanical ventilation, hydrocephalus, and herniation were predictive of mortality. We created the first clinically relevant weighted mortality score that can be used to stratify risk, guide prognosis, and inform family discussions.