Derivation and Validation of the Silent Brain Infarction Risk Score in the Healthy

doi:10.3969/j.issn.1673-5765.2020.09.013

Abstract

Abstract:

Objective To investigate the independent risk factors of silent brain infarction (SBI) in the healthy people, and to develop and validate a predictive scale for SBI risk. Methods In a cross-section study from National Clinical Research Center for Neurological Diseases, the consecutive eligible participants from the Health Management Center of Beijing Tiantan Hospital were enrolled. The clinical data and laboratory tests results were collected. MRI examination was performed according to a standard protocol and evaluated by radiologists using a blind method. All the subjects were divided to SBI group and non-SBI group based on MRI results. All the subjects were randomly divided into a derivation set and a validation set at a ratio of 3:1.Univariable and multivariable logistic analysis were conducted in the derivation set to analyze the independent risk factors of SBI, and a SBI risk score (SBI-RS) was built according to the adjusted OR. ROC curve was used to evaluate the discrimination of the SBI-RS, and the calibration of the scale was evaluated using the Hosmer-Lemeshow test. Results A total of 633 eligible participants were enrolled, with a mean age of 52.0±10.5 years old and 272 (43.0%) females. The clinical features were balanced between the derivation set (n =475) and validation set (n =158). After adjusting for the confounding factors, multivariable logistic analysis showed that age over 45 years old (OR 8.37, 95%CI 1.12-62.80, P =0.039), hypertension (OR 2.30, 95%CI 1.08-4.90, P =0.032), and homocysteine (Q2-Q3: OR 6.89, 95%CI 0.89-53.10, P =0.064; Q4: OR 13.6, 95%CI 1.74-105.87, P =0.013) were independently associated with SBI. The SBI-RS was derivated according to the adjusted OR , every variable was assigned a value as follows: 8 points for ≥45 years old, 2 points for hypertension and 0, 7 or 14 points for the quartiles of the homocysteine level. The discrimination of the SBI-RS was reasonable, the area under the curve (AUC) was 0.77 (95%CI 0.69-0.84, P< 0.001) in the derivation set and 0.76 (95%CI 0.63- 0.88, P< 0.001) in the validation set. The Hosmer-Lemeshow correlation analysis showed a good calibration of the SBI-RS (P >0.05). Conclusions The SBI-RS can help to identify the SBI high-risk people in the healthy, with a reasonable discrimination and calibration.

Key words: Silent brain infarction; Risk score; Discrimination; Calibration

摘要：

【摘要】目的调查与静止性脑梗死（silent brain infarction，SBI）相关的独立影响因素，构建SBI风险预测量表并验证。方法在单中心横断面研究中，前瞻性连续纳入无神经系统疾病既往史的体检者，收集其人口学信息，高血压、糖尿病等血管危险因素，血脂、糖化血红蛋白、血浆同型半胱氨酸等化验结果录入数据库。采用标准影像学操作规范进行头颅MRI扫描，并由影像学医师盲法判读，将受试者分为SBI组和无SBI组。将所有受试者按照3∶1比例随机分为训练集和验证集，在训练集中采用单因素和多因素 Logistic回归分析SBI的独立影响因素，构建SBI预测量表。在训练集和验证集中应用ROC曲线检验量表的区分度，应用Hosmer-Lemeshow分析检验量表的校准度。结果共有633例研究对象纳入研究，平均年龄52.0±10.5岁，女性272例（43.0%）。训练集（475 例）和验证集（158例）两个样本集合的基线特征均衡。校正混杂因素后多因素分析显示，年龄≥45 岁（OR 8.37，95%CI 1.12～62.80，P =0.039），高血压（OR 2.30，95%CI 1.08～4.90，P =0.032），同型半胱氨酸（Q2～Q3：OR 6.89，95%CI 0.89～53.10，P =0.064；Q4：OR 13.6，95%CI 1.74～105.87，P =0.013）与SBI风险独立相关。根据OR 值构建SBI危险评分（SBI risk score，SBI-RS）量表，量表赋值为：年龄 ≥45岁赋值8分；有高血压赋值2分；同型半胱氨酸根据四分位分层分别赋值为0分、7分和14分。SBIRS 在训练集和验证集中ROC曲线显示曲线下面积分别为0.77（95%CI 0.69～0.84，P＜0.001）和0.76 （95%CI 0.63～0.88，P＜0.001），区分度良好。Hosmer-Lemeshow相关分析提示SBI-RS具有较好的校准度（P＞0.05）。结论在健康体检人群中，SBI -RS具有较好的区分度和校准度，可以帮助识别SBI高危人群。

关键词: 静止性脑梗死; 风险预测量表; 区分度; 校准度

ZHENG Hua-Guang,ZHANG Wei-Wei, ZHANG Long-You, WANG Rui-Qing, MENG Qing-Ying, LIU Xiao-Nan, DUAN Yun-Yun, LIU Ya-Ou,WANG Yong-Jun. Derivation and Validation of the Silent Brain Infarction Risk Score in the Healthy[J]. Chinese Journal of Stroke, 2020, 15(09): 999-1005.

郑华光，张蔚韡，张龙友，王瑞青，孟庆颖，刘晓楠，段芸芸，刘亚欧，王拥军. 静止性脑梗死风险预测量表的构建和验证[J]. 中国卒中杂志, 2020, 15(09): 999-1005.

References

[1] SMITH E E，SAPOSNIK G，BIESSELS G J，et

al. Prevention of stroke in patients with silent

cerebrovascular disease：a scientific statement for

healthcare professionals from the American Heart

Association/American Stroke Association[J/OL].

Stroke，2017，48（2）：e44-e71[2019-12-19]. http：//

doi.org/10.1161/STR.0000000000000116.

[2] DAS A S，REGENHARDT R W，VERNOOIJ M

W，et al. Asymptomatic cerebral small vessel disease：

insights from population-based studies[J]. J Stroke，

2019，21（2）：121-138.

[3] REGENHARDT R W，DAS A S，LO E H，et al.

Advances in lacunar stroke pathophysiology：a

review[J]. JAMA Neurol，2018，75（10）：1273-1281.

[4] MEINEL T R，KAESMACHER J，ROTEN L，et al.

Covert brain infarction：towards precision medicine

in research，diagnosis，and therapy for a silent

pandemic[J]. Stroke，2020，51（8）：2597-2606.

[5] VERMEER S E，KOUDSTAAL P J，OUDKERK

M，et al. Prevalence and risk factors of silent brain

infarcts in the population-based rotterdam scan

study[J]. Stroke，2002，33（1）：21-25.

[6] PRABHAKARAN S，WRIGHT C B，YOSHITA

M，et al. Prevalence and determinants of subclinical

brain infarction：the Northern Manhattan Study[J].

Neurology，2008，70（6）：425-430.

[7] DAS R R，SESHADRI S，BEISER A S，et al.

Prevalence and correlates of silent cerebral infarcts

in the Framingham Offspring Study[J]. Stroke，2008，

39（11）：2929-2935.

[8] FEI H，ZHAI F F，WANG Q，et al. Prevalence and

risk factors of cerebral small vessel disease in a

Chinese population-based sample[J]. J Stroke，2018，

20（2）：239-246.

[9] VERMEER S E，PRINS N D，HEIJER T D，et al.

Silent brain infarcts and the risk of dementia and

cognitive decline[J]. N Engl J Med，2003，348（13）：

1215-1222.

[10] ANAND S S，FRIEDRICH M G，DESAI D，et

al. Reduced cognitive assessment scores among

individuals with magnetic resonance imaging-detected vascular brain injury[J]. Stroke，2020，51

（4）：1158-1165.

[11] GUPTA A，GIAMBRONE A E，GIALDINI G，et

al. Silent brain infarction and risk of future stroke：a

systematic review and meta-analysis[J]. Stroke，2016，

47（3）：719-725.

[12] CANNISTRARO R J，BADI M，EIDELMAN B H，

et al. CNS small vessel disease：a clinical review[J].

Neurology，2019，92（24）：1146-1156.

[13] NAKANISHI K，JIN Z，HOMMA S，et al. Nighttime

systolic blood pressure and subclinical

cerebrovascular disease：the Cardiovascular

Abnormalities and Brain Lesions（CABL）study[J].

Eur Heart J Cardiovasc Imaging，2019，20（7）：765-

771.

[14] CONEN D，RODONDI N，MÜLLER A，et al.

Relationships of overt and silent brain lesions

with cognitive function in patients with atrial

fibrillation[J]. J Am Coll Cardiol，2019，73（3）：989-

999.

[15] NAM K W，KWON H M，JEONG H Y，et al. Serum

homocysteine level is related to cerebral small vessel

disease in a healthy population[J/OL]. Neurology，

2019，92（4）：e317-e325[2019-12-19]. https：//doi.

org/10.1212/wnl.0000000000006816.

[16] SESHADRI S，WOLF P A，BEISER A S，et al.

Association of plasma homocysteine levels with

subclinical brain injury：cerebral volumes，white

matter hyperintensity and silent brain infarcts

on volumetric MRI in the Framingham Offspring

Study[J]. Arch Neurol，2008，65（5）：642-649.

[17] LARSSON S C，TRAYLOR M，MARKUS H

S，et al. Homocysteine and small vessel stroke：a

Mendelian randomization analysis[J]. Ann Neurol，

2019，85（4）：495-501.

[18] CHAUHAN C，ADAMS H H H，SATIZABAL

C L，et al. Genetic and lifestyle risk factors

for MRI-defined brain infarcts in a populationbased

setting[J/OL]. Neurology，2019，92（5）：

e486-e503[2019-12-19]. https：//doi.org/10.1212/

wnl.0000000000006851.

[19] 郑华光，刘晓楠，张蔚韡，等. 高血压与高同型半胱

氨酸血症与静止性脑梗死的相关性分析[J]. 中国卒

中杂志，2020，15（8）：859-866.

[20] ANAND S S，J TU J V，DESAI D D，et al.

Cardiovascular risk scoring and magnetic resonance

imaging detected subclinical cerebrovascular

disease[J]. Eur Heart J Cardiovasc Imaging，2020，21

（6）：692-700.

[21] WILLIAMS B，MANCIA G，SPIERING W，et al.

2018 ESC/ESH Guidelines for the management of

arterial hypertension[J]. Eur Heart J，2018，39（33）：

3021-3104.

[22] American Diabetes Association. Classification and

diagnosis of diabetes：standards of medical care in

diabetes-2019[J/OL]. Diabetes Care，2019，42（Suppl

1）：S13-S28[2019-12-19]. https：//doi.org/10.2337/

dc19-s002.

[23] KNUUTI J，WIJNS W，SARASTE A，et al. 2019

ESC Guidelines for the diagnosis and management

of chronic coronary syndromes[J]. Eur Heart J，2020，

41（3）：407-477.

[24] DING N，SANG Y Y，CHEN J S，et al. Cigarette

smoking，smoking cessation，and long-term risk of 3

major atherosclerotic diseases[J]. J Am Coll Cardiol，

2019，74（4）：498-507.

[25] WARDLAW J M，SMITH E E，BIESSELS G J，et

al. Neuroimaging standards for research into small

vessel disease and its contribution to ageing and

neurodegeneration[J]. Lancet Neurol，2013，12（8）：

822-838.

[26] WESSLER B S，LAI YH L，KRAMER W，et

al. Clinical prediction models for cardiovascular

disease：tufts predictive analytics and comparative

effectiveness clinical prediction model database[J].

Circ Cardiovasc Qual Outcomes，2015，8（4）：368-

375.

[27] ALBA A C，AGORITSAS T，WALSH M，et al.

Discrimination and calibration of clinical prediction

models users’guides to the medical literature[J].

JAMA，2017，318（14）：1377-1384.

[28] SPRINT Research Group，WRIGHT J T JR，

WILLIAMSON J D，et al. A randomized trial of

intensive versus standard blood-pressure control[J].

N Engl J Med，2015，373（22）：2103-2116.

[29] SPRINT MIND Investigators for the SPRINT

Research Group，WILLIAMSON J D，PAJEWSKI

N M，et al. Effect of intensive vs standard blood

pressure control on probable dementia：a randomized

clinical trial[J]. JAMA，2019，321（6）：553-561.