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用于预测首次出现大脑小血管疾病急性表现后死亡率的列线图。

A nomogram to predict the probability of mortality after first-ever acute manifestations of cerebral small vessel disease.

机构信息

Stroke Unit-Neurology, Department of Neuroscience, Azienda Ospedaliera Universitaria Integrata, Verona, Italy.

Stroke Unit-Neurology, Department of Neuroscience, Azienda Ospedaliera Universitaria Integrata, Verona, Italy.

出版信息

J Neurol Sci. 2018 Feb 15;385:92-95. doi: 10.1016/j.jns.2017.12.020. Epub 2017 Dec 18.

DOI:10.1016/j.jns.2017.12.020
PMID:29406922
Abstract

BACKGROUND AND PURPOSE

Symptomatic lacunar stroke (LS) and deep intracerebral hemorrhage (dICH) represent the acute manifestations of type 1 cerebral small vessel disease (cSVD). Recently, two studies showed that the risk factor profile of dICH differs from that associated with LS in subjects with biologically plausible cSVD; however, the prognostic predictors after acute manifestations are currently lacking. We aimed to develop a nomogram for individualized prediction of the mortality probability in a cohort of patients with a first-ever acute manifestation of biologically plausible cSVD.

METHODS

We conducted a retrospective analysis of data collected from consecutive patients with acute symptomatic non-embolic LS or primary dICH. The outcome measure was 3-month mortality. Based on multivariate logistic model, the nomogram was generated.

RESULTS

Of the 288 patients who entered into the study for biologically plausible cSVD, 131 (45%) experienced a LS and 157 (55%) a dICH. After multivariate logistic regression, 5 variables remained predictors of mortality to compose the nomogram: dICH (OR:11.36; p=0.001), severe presentation (OR:8.08; p<0.001), age (OR:1.08; p=0.001), glucose (OR:1.23; p=0.003) and creatinine (OR:1.01; p=0.024) at admission were predictors of mortality. The discriminative performance of nomogram assessed by using the area under the receiver operating characteristic curve (AUC-ROC) was 0.898. The model was internally validated by using bootstrap (1000 samples) with AUC-ROC of 0.895 and cross-validation (deleted-d method repeated 1000 times) with AUC-ROC of 0.895.

CONCLUSIONS

We developed the first nomogram for prediction of the mortality probability in a cohort of patients with a first-ever acute manifestation of biologically plausible cSVD.

摘要

背景与目的

症状性腔隙性卒中(LS)和深部脑内出血(dICH)代表 1 型脑小血管病(cSVD)的急性表现。最近,两项研究表明,在具有生物学上合理的 cSVD 的受试者中,dICH 的危险因素谱与 LS 不同;然而,目前缺乏急性表现后的预后预测指标。我们旨在为首次发生生物学上合理的 cSVD 急性表现的患者队列开发一种用于个体化预测死亡率的列线图。

方法

我们对连续接受急性症状性非栓塞性 LS 或原发性 dICH 治疗的患者进行了回顾性数据分析。结局测量是 3 个月死亡率。基于多变量逻辑模型,生成了列线图。

结果

在纳入的 288 例生物学上合理的 cSVD 患者中,131 例(45%)发生 LS,157 例(55%)发生 dICH。经过多变量逻辑回归,5 个变量仍然是构成列线图的死亡率预测指标:dICH(OR:11.36;p=0.001)、严重表现(OR:8.08;p<0.001)、年龄(OR:1.08;p=0.001)、入院时的血糖(OR:1.23;p=0.003)和肌酐(OR:1.01;p=0.024)。使用接受者操作特征曲线下面积(AUC-ROC)评估的列线图的判别性能为 0.898。通过使用 bootstrap(1000 个样本)进行内部验证,AUC-ROC 为 0.895,通过交叉验证(重复 1000 次的删除-d 方法),AUC-ROC 为 0.895。

结论

我们开发了第一个列线图,用于预测首次发生生物学上合理的 cSVD 急性表现的患者的死亡率。

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