Abstract
目的
探索影响首发缺血性脑卒中幸存者复发的危险因素，建立复发预测模型并使用列线图将预测结果可视化。
方法
收集2010年1月~2018年12月入住四川大学华西医院神经内科且符合纳入标准的首发缺血性脑卒中幸存者，共821例幸存者纳入研究，利用R软件对初始人群进行随机抽样，70% 纳入训练集建立模型，30% 纳入验证集。应用Cox比例风险回归模型对影响首发缺血性脑卒中幸存者复发的因素进行多因素分析，并使用R软件的rms包构造列线图，建立可视化的预测模型。利用C- index和校准曲线来评价模型的预测效果。
结果
821例幸存者中，患者3年复发率为16.81%，5年复发率为19.98%；训练集经Cox回归模型多因素分析，年龄 > 65岁(HR=2.596，P=0.024)、年龄45~64岁(HR=2.510，P=0.006)、mRS评分≥3分(HR=2.284，P=0.004)、冠心病史(HR=1.353，P=0.034)是影响复发的危险因素；并由此建立列线图，3年(5年)复发预测模型的C-index为0.640(0.671)。
结论
首发缺血性脑卒中幸存者复发的影响因素是年龄、mRS评分和周围血管病史，列线图在首发缺血性脑卒中复发预测中有较高的区分度和预测力。
Keywords: 缺血性卒中, Nomogram, Cox比例风险回归模型
Abstract
Objective
To explore the risk factors for recurrence in first-episode ischemic stroke survivors and establish a model for predicting stroke recurrence using a nomogram.
Methods
We collected the data from a total of 821 first-episode ischemic stroke survivors admitted in the Department of Neurology, West China Hospital, Sichuan University from January, 2010 to December, 2018. R software was used for random sampling of the patients, and 70% of the patients were included in the training set to establish the prediction model and 30% were included in the validation set. Cox proportional risk regression model was used to analyze the factors affecting stroke recurrence, and R software rms package was used to construct the histogram and establish the visual prediction model. C-index and calibration curve were used to evaluate the performance of the model for predicting stroke occurrence.
Results
Among the 821 survivors, the recurrence rate was 16.81% at 3 years and 19.98% at 5 years. Multivariate analysis of the training set by Cox regression model showed that an age over 65 years (HR= 2.596, P=0.024), an age of 45-64 years (HR=2.510, P=0.006), a mRS score beyond 3 (HR=2.284, P=0.004) and a history of coronary heart disease (HR=1.353, P=0.034) were all risk factors for stroke recurrence. The C-indexes of the nomogram for the 3-and 5-year relapse prediction model were 0.640 and 0.671, respectively.
Conclusion
Age, mRS score and peripheral vascular disease are the factors affecting stroke recurrence in first-episode ischemic stroke survivors, and the nomogram has a high discrimination and predictive power for predicting ischemic stroke recurrence.
Keywords: recurrence of ischemic stroke, nomogram, Cox proportional hazards regression model
“脑卒中”(CS)又称“脑血管意外”(CVA)或“中风”，是一种常见的急性脑血管疾病，包括缺血性和出血性卒中。其中，急性缺血性卒中占发病率的60%以上[1]。脑卒中的高发病率、高复发率、高致残率、高死亡率不仅给患者及其家庭带来严重疾病负担，也增加了社会负担以及国家投入的卫生费用。积极的二级预防可以减少约80% 的缺血性脑卒中复发[2]。对脑卒中幸存者进行复发预测是合理有效的二级预防措施之一，预测结果可以提示卒中幸存者复发的相关影响因素以及风险，为临床合理开展卒中预后治疗提供参考信息。防止复发也是降低脑卒中死亡率和致残率、提高患者生命质量的重要手段。在国内外的既往研究中，常建立单因素分析模型、Logistic回归分析和Cox比例风险回归模型以研究某些特定因素对脑卒中复发的影响[3-4]; 这些常见的疾病预测模型缺少直观性，而将评分系统以及所对应的图表结合更适用于临床实际。列线图，由变量和所对应的评分线条、生存(或复发概率)线条所组成，读取简单、适用性强，在国内外常用于肿瘤预后研究，目前国内外仍缺乏缺血性卒中复发相关的直观预测模型[5-6]。为此，本研究将其应用于脑卒中复发预测模型中后，可以将幸存者的复发预测结果以图表的形式展示，简化对预测模型的公式的解读，增强结果可视化程度。另一方面，将列线图用于临床和社区服务中，有利于对幸存者可控的复发危险因素进行管理。
1. 资料和方法
1.1. 研究对象
研究资料来源于国家自然科学基金项目“慢性疾病动态离散事件微模拟决策分析模型的构建——以脑卒中二级预防为例(项目编号：81673273)”。资料收集于2010年1月~2018年12月，以在四川大学华西医院进行首发缺血性脑卒中治疗的住院患者为研究对象。纳入标准：[7](1)经影像学和神经内科医生确诊为首发缺血性脑卒中和(或)短暂性脑缺血发作(TIA); (2)患者首次发生缺血性脑卒中或TIA; (3)患者的病情稳定，患者和家属自愿参加，依从性良好。排除标准：(1)患者拒绝参加; (2)因心脑血管手术(如颈动脉内膜切除术、血管造影术)手术而诱发卒中的患者以及心源性卒中患者; (3)患者处于严重的疾病状态。
本研究已通过四川大学医学伦理委员会审查(审批号：2009A50)，全体患者均自愿参加并签署知情同意书。
1.2. 数据收集与预处理
对患者住院期间的基线调查和出院后每3月1次的电话随访调查。基线调查包括利用医院的HIS系统查阅患者的电子病历和对患者以及家属进行面对面问卷调查。采用自行设计的问卷对调查对象进行面对面访谈，包括患者的基本信息(姓名、性别、年龄、民族、身高、卒中前1年体质量、卒中后体质量、腰围、婚姻、职业、文化程度、家庭人口数、保险类型、家庭收入等)、行为生活方式信息(吸烟史、饮酒史、饮食习惯、卒中前一年体育锻炼情况等)、脑卒中患病信息(疾病严重程度评分、缺血性卒中病灶部位)、疾病史及合并用药信息(卒中家族史、周围血管疾病、高血压、糖尿病、高血脂、冠心病等)、抗血栓药物(抗凝、抗血小板药物)服用情况等。随访调查包括对纳入基线的患者进行电话随访调查，每3月随访1次。电话随访内容主要包括患者的健康状况(疾病严重程度评分即rms评分以及死亡)、个人生活习惯(吸烟、饮酒、饮食习惯、体育锻炼等)、疾病史及合并用药史(周围血管疾病、高血压、糖尿病、高血脂、冠心病等)、体格及实验室检查(体质量、腰围、血压、血糖、血脂等)、抗血栓药物服药依从情况、缺血性脑卒中复发情况等。将有信息缺失的部分患者删除，包括身高数据缺失(9例)、体质量数据缺失(17例)。
用R version3.6.1对删除缺失信息后的821例患者进行随机抽样，将70%的病例(578例)构建成训练集建立Cox比例风险回归模型，并分析缺血性卒中幸存者复发的影响因素; 剩余30%的病例(243例)构成验证集对模型进行外部验证。
1.3. 统计方法
根据现有资料和脑卒中二级预防指南，本研究纳入年龄、性别、吸烟史、饮酒史、出院后第3月卒中严重程度(改良Rankin评分，即mRS评分)、高血压史、糖尿病史、高血脂史、冠心病史、周围血管疾病史、抗栓药物服用依从性等因素作为协变量，以探讨其对卒中复发的影响。在构建Cox比例风险回归模型之前检验其是否满足等比例风险假定; 以所构造的风险函数建立列线图。利用Bootstrap自抽样产生的样本评价模型的准确性并画出校准曲线，利用C-index衡量模型的区分度。P < 0.05认为差异具有统计学意义。
2. 结果
2.1. 缺血性卒中幸存者复发的Cox回归预测模型
1.
训练集与验证集的基线及临床特征比较
Baseline demographic and clinical characteristics of the training set and validation set[n(%)]
| Variable | Training set (n=578) | Validation set (n=243) | All (n=821) | P |
| *CHD: Coronary heart disease PVD: Peripheral vascular disease. | ||||
| Age (year) | 61.5±13 | 61.4±13 | 61.4±13 | 0.862 |
| Gender | ||||
| Male | 359(62.1%) | 147(60.5%) | 506(61.6%) | 0.72 |
| Female | 219(37.9%) | 96(39.5%) | 315(38.4%) | |
| BMI (kg/m2) | ||||
| < 18.5 | 38(6.6%) | 12(4.9%) | 50(6.1%) | 0.787 |
| 18.5- | 275(47.6%) | 122(50.2%) | 397(48.4%) | |
| 24- | 199(34.4%) | 81(33.3%) | 280(34.1) | |
| 28- | 66(11.4%) | 28(11.5%) | 94(11.4%) | |
| Smoke status | ||||
| No | 508(59.5%) | 217(89.3%) | 725(88.3%) | 0.649 |
| Yes | 70(12.1%) | 26(10.7%) | 96(11.7%) | |
| Drinking status | ||||
| No | 509(88.1%) | 223(91.8%) | 732(89.2%) | 0.151 |
| Yes | 69(11.9%) | 20(8.2%) | 89(10.8%) | |
| Hypertension | ||||
| No | 203(35.1%) | 108(44.4%) | 311(37.9%) | 0.015 |
| Yes | 375(64.9%) | 135(55.6%) | 510(62.1%) | |
| Diabetes | ||||
| No | 424(73.4%) | 165(67.9%) | 589(71.7%) | 0.134 |
| Yes | 154(26.6%) | 78(32.1%) | 232(28.3%) | |
| CHD | ||||
| No | 442(76.5%) | 187(77.0%) | 629(76.6%) | 0.953 |
| Yes | 136(23.5%) | 56(23.0%) | 192(23.4%) | |
| Pyperlipmia | ||||
| No | 385(66.6%) | 45(67.5%) | 549(66.9%) | 0.870 |
| Yes | 193(33.4%) | 79(32.5%) | 272(33.1%) | |
| PVD | ||||
| No | 558(96.5%) | 227(93.4%) | 785(95.6%) | 0.070 |
| Yes | 20(3.5%) | 16(6.6%) | 36(4.4%) | |
| Adherence | ||||
| Yes | 19(3.3%) | 7(2.9%) | 26(3.2%) | 0.932 |
| No | 559(96.7%) | 236(97.1%) | 795(96.8%) | |
| mRS | ||||
| < 3 | 389(67.3%) | 162(66.7%) | 551(67.1%) | 0.924 |
| 3- | 189(32.7%) | 81(33.3%) | 270(32.9%) | |
| Three-year recurrence rate | 16.93% | 16.88% | 16.81% | 0.284 |
| Five-year recurrence rate | 19.52% | 21.39% | 19.98% | 0.524 |
3.
卒中幸存者复发多因素Cox分析结果(训练集)
Multivariate analysis of stroke-free survival(training set)
| Variable | HR | SE | P | 95% CI | |
| Gender | |||||
| Female | Reference | ||||
| Male | 0.908 | 0.262 | 0.712 | (0.543, 1.516) | |
| Age(year) | |||||
| < 45 | Reference | ||||
| 45- | 2.467 | 0.399 | 0.024 | (1.129, 5.391) | |
| 65- | 2.994 | 0.398 | 0.006 | (1.371, 6.537) | |
| Smoke status | |||||
| No | Reference | ||||
| Yes | 0.892 | 0.463 | 0.805 | (0.360, 2.209) | |
| Drinking status | |||||
| No | Reference | ||||
| Yes | 0.563 | 0.516 | 0.265 | (0.205, 1.547) | |
| BMI (kg/m2) | |||||
| < 18.5 | Reference | ||||
| 18.5- | 1.368 | 0.418 | 0.453 | (0.603, 3.105) | |
| 24- | 1.549 | 0.433 | 0.313 | (0.662, 3.621) | |
| 28- | 1.144 | 0.541 | 0.804 | (0.396, 3.304) | |
| mRS | |||||
| < 3 | Reference | ||||
| ≤3 | 1.877 | 0.216 | 0.004 | (1.228, 2.869) | |
| Hypertension | |||||
| No | Reference | ||||
| Yes | 0.901 | 0.228 | 0.679 | (0.582, 1.422) | |
| Diabetes | |||||
| No | Reference | ||||
| Yes | 0.969 | 0.250 | 0.899 | (0.549, 1.580) | |
| Hyperlipidemia | |||||
| No | Reference | ||||
| Yes | 0.679 | 0.171 | 0.679 | (0.583, 1.580) | |
| CHD | |||||
| No | Reference | ||||
| Yes | 1.620 | 0.228 | 0.034 | (1.038, 2.529) | |
| PVD | |||||
| No | Reference | ||||
| Yes | 2.106 | 0.433 | 0.085 | (0.902, 4.917) | |
| Adherence | |||||
| Yes | Reference | ||||
| No | 0.427 | 0.449 | 0.058 | (0.177, 1.028) |
2.
卒中幸存者复发单因素COX分析结果(训练集)
Univariate analysis of stroke-free survival(training set)
| Variable | HR | SE | P | 95% CI | |
| Gender | |||||
| Female | Reference | ||||
| Male | 1.229 | 0.173 | 0.233 | (0.876, 1.724) | |
| Age(year) | |||||
| < 45 | Reference | ||||
| 45- | 2.467 | 0.399 | 0.024 | (1.129, 5.391) | |
| 65- | 2.994 | 0.398 | 0.006 | (1.371, 6.537) | |
| Smoking status | |||||
| Never | Reference | ||||
| Yes | 0.763 | 0.154 | 0.079 | (0.564, 1.032) | |
| Drinking | |||||
| Never | Reference | ||||
| Yes | 0.745 | 0.158 | 0.063 | (0.547, 1.016) | |
| BMI | |||||
| < 18.5 | Reference | ||||
| 18.5- | 1.083 | 0.266 | 0.765 | (0.643, 1.822) | |
| 24- | 1.045 | 0.150 | 0.771 | (0.779, 1.402) | |
| 28- | 1.210 | 0.155 | 0.219 | (0.893, 1.639) | |
| mRS score | |||||
| < 3 | Reference | ||||
| 3- | 2.197 | 0.172 | 0.000 | (1.567, 3.079) | |
| Hypertension | |||||
| No | Reference | ||||
| Yes | 1.164 | 0.183 | 0.405 | (0.814, 1.665) | |
| Diabetes | |||||
| No | Reference | ||||
| Yes | 1.150 | 0.193 | 0.467 | (0.789, 1.678) | |
| Hyperlipidemia | |||||
| No | Reference | ||||
| Yes | 0.994 | 0.177 | 0.744 | (0.667, 1.336) | |
| CHD | |||||
| No | Reference | ||||
| Yes | 1.445 | 0.189 | 0.051 | (0.998, 2.094) | |
| PVD | |||||
| No | Reference | ||||
| Yes | 2.400 | 0.365 | 0.017 | (1.173, 4.910) | |
| Adherence | |||||
| Yes | Reference | ||||
| No | 0.295 | 0.391 | 0.002 | (0.137, 0.635) |
根据表 3的结果，拟将年龄、mRS评分、高血压史、糖尿病史、冠心病史、周围血管病史作为协变量纳入列线图的建立。比例风险假定检验结果如表 4。
4.
回归模型的比例风险假定诊断
Diagnosis of proportional risk assumption
| Variable | Wald χ2 | P |
| Age (year) | 0.5667 | 0.753 |
| hypertension | 0.0639 | 0.800 |
| diabetes | 2.0428 | 0.153 |
| pvd | 0.0419 | 0.838 |
| chd | 4.3919 | 0.66 |
| mrs | 0.0289 | 0.865 |
| GLOBAL | 7.6847 | 0.361 |
2.2. 列线图的建立及其验证
在软件中基于构建的Cox比例风险模型绘制如下列线图。
在以训练集的数据建立的Cox比例风险模型基础上绘制列线图(图 1)。3年复发预测模型的C-index= 0.640，(95% CI：0.587，0.693);利用验证集的数据(对原始数据随机抽样的30%)对列线图进行内部验证，C-index=0.603，(95% CI：0.529，0.677)。5年复发预测模型的C-index=0.671，(95% CI：0.607，0.735);内部验证C-index为0.635，(95% CI：0.540，0.730)。列线图的内部验证采取Bootstrap重抽样的方法，得到3年和5复发预测模型的校准曲线(图 2、3)。
1.
缺血性卒中幸存者复发预测列线图
Nomograms of the training set showing composition of associated factors and probability of 3-year and 5-year survival in stroke survivors.
2.
3年复发预测模型的校准曲线
Training set validation curves of the nomogram for 3-year recurrence-free survival.
3.
5年复发预测模型的校准曲线
Training set validation curves of the nomogram for 5-year recurrence-free survival.
3. 讨论
目前，缺血性卒中复发率较高，预后较首发更差，之前有研究表明脑血管病幸存者1年内复发率约为7%~ 20%，5年复发率约16%~35%[8-10]。本研究患者3年复发率为16.81%，5年复发率为19.98%。早期准确评估缺血性卒中幸存者复发的风险可以增加药物和体育锻炼等方式的预防机会，延长患者生存期并改善生命质量。因此，甄别复发危险因素是预防和治疗的关键。虽然此前有基于大数据的脑卒中影响因素分析和复发预测模型有较高的AUC值(AUC=0.88)，但是不能将预测结果形象展示，预测结果也为分类变量，难以将预测结果个体化，给出某一患者个体复发风险的高低。在实际预防和临床工作中，除了要探明相关复发危险因素，还需要增强结果的可视化程度和临床应用价值。
本研究中年龄(大于65岁，45~64岁)、mRS评分 > 3分、周围血管病史是缺血性卒中复发的独立影响因素。既往国内外研究表明发现年龄是缺血性脑卒中患者复发的独立危险因素(HR=1.025，95% CI：1.003~1.048)[3, 11-12]。多因素分析中，高血压史、高血糖史与卒中复发风险增加之间没有统计学意义(P > 0.05)，但二者HR均大于一，呈现出与复发风险增加的正相关关系。众多先前的研究也表明，高血压是脑卒中不良预后事件的危险因素，血压降低可有效降低TIA或脑卒中幸存者其他脑血管及周围血管疾病发生概率[13-16]。对卒中患者，控制其血压已是卒中二级预防中的常规临床治疗方案，也是预防复发的重要方式[17-18]。同时急性高血糖或糖尿病与缺血性卒中幸存者较差的预后相关，包括较高结论[22-23]。冠心病史作为独立影响因素之一的结果与先的死亡率、较差的神经功能、较长的住院时间、较高的再前研究的结果一致，将其纳入缺血性卒中复发预测模型入院率和中风复发[19-21]。故在建立Cox风险模型时将高中有利于对疾病风险分层的判断[24-25]。mRS评分结果血压史和高血糖史纳入。虽然在多因素模型中，脑血管显示，大于等于3分以上的患者较低于3分的患者更易疾病史与缺血性卒中复发的关系没有统计学意义，但是复发，并且可作为独立的预后影响因素。mRS评分作可以看到HR仍大于一且达到1.353(0.925，1.980)，对为较为简便的量表在神经外科的工作中得到广泛使用，缺血性卒中复发的风险贡献较大，也验证了先前研究的其可靠性和真实性也得到了众多先前研究的验证，也多于Cox回归模型建立了列线图; 3年(5年)复发预测模型的C-index=0.640(0.671)并采用Bootstrap法画出校准曲线; 同时将30% 的数据用于内部验证，C-index为0.603(0.635)，提示列线图的预测五年复发概率的能力略高于3年复发概率。。因量表常用于短期复发预测，且卒中复发风险在首发后短期内的风险更高而后逐步下降，所以与先前研究RRE-90、ESRS评分和CHA2DS2-VASc评分预测缺血性卒中1年复发AUC值0.588、0.587和0.558相比[4, 29]，本研究预测准确度和价值更高。根据标准曲线的结果，两种模型的预测结果在实际生存概率较高的时候会略微低估幸存者的生存概率，在较低的生存概率时其结果会略微高估风险。总体来说，校准曲线较好的与实际值重合，提示模型的预测效果较好。列线图将Cox模型的结果以分数加无复发生存概率展示，它较之于PI方程的优点在于，可以直接从各变量对应线段的长短看出其对回归方程贡献的大小，并可更简便地得到个体化的预测概率[30-31]。
此外，本研究存在一定的局限性。因本研究纳入的患者例数有限，属于小样本单中心研究，Cox回归分析的结果可能存在偏倚; 在研究的变量选择时，可控的行为生活方式因素较少，如：饮食习惯、体育锻炼情况等。以后的相关研究可以加入相关方面的信息，对模型的预测能力进行加强。
综上，本研究利用全年龄段首发缺血性卒中人群数据，基于Cox比例风险回归模型建立的列线图模型辅助诊断的直观性较强、性能较好，可以用于指导临床和社区医生筛选高风险卒中幸存者，及时进行早期积极干预。
Biography
刘瑾，硕士，E-mail：610110812@qq.com
Funding Statement
国家自然科学基金(81673273，30600511，82173618)
Supported by National Natural Science Foundation of China(81673273, 30600511, 82173618)
Contributor Information
刘 瑾 (Jin LIU), Email: 610110812@qq.com.
蒋 敏 (Min JIANG), Email: 43761128@qq.com.
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