Abstract
目的
慢性肾脏病(chronic kidney disease，CKD)患者的心血管疾病(cardiovascular disease，CVD)发生率高，动脉粥样硬化(atherosclerosis，AS)是CVD的主要病理基础。动脉粥样硬化性心血管疾病(atherosclerotic cardiovascular disease，ASCVD)是CKD患者的主要并发症和死亡原因之一。关注并监测AS风险是多学科综合治疗理念的体现。通过连续监测AS相关危险因素可早期发现高危人群，早期改变AS危险因素，以改善CKD患者的预后及生存质量，降低CKD患者的CVD病死率。纳入多因素分析的Framingham风险预测模型及国人10年ASCVD风险预测模型能综合评估、预测AS风险，其在普通人群中的使用价值已得到广泛证实，而在CKD人群的适用性需进一步探究。本研究旨在分析2种风险预测模型对CKD人群AS风险的预测价值，寻找预测CKD患者AS风险的简便且可靠的方法。
方法
本研究为横断面研究。选取CKD患者146例及健康对照者14例，应用颈动脉超声检查测量左、右侧颈动脉内膜-中膜厚度以评估AS患病情况。采集受试者病史、体格检查及实验室检查结果进行Framingham风险评分(Framingham Risk Score，FRS)及ASCVD评分以预测AS风险。比较CKD组与对照组的基线资料及AS阳性率，并将FRS及ASCVD评分检出的AS阳性率与颈动脉超声检查检出的AS阳性率进行Kappa一致性检验及真实性、可靠性及收益的综合比较。
结果
CKD组与对照组的性别、年龄及BMI差异均无统计学意义(均P>0.05)。CKD组的收缩压、舒张压、脉压、总胆固醇、三酰甘油、低密度脂蛋白胆固醇、血尿素氮、血肌酐及糖化血红蛋白水平显著高于对照组(均P<0.05)，而高密度脂蛋白胆固醇、肾小球滤过率均显著低于对照组(均P<0.05)。FRS、ASCVD评分及颈动脉超声检查3种方法检出的CKD患者AS阳性率分别为43.84%、53.42%及46.58%，均显著高于对照组(均P<0.01)。3种方法检出的CKD患者AS阳性率差异无统计学意义(P=0.24)。FRS与ASCVD评分检出的AS阳性率具有高度一致性(Kappa值=0.647，P<0.001)，FRS与颈动脉超声检查检出的AS阳性率具有中度一致性(Kappa值=0.447，P<0.001)，ASCVD评分与颈动脉超声检查检出的AS阳性率具有一致性(Kappa值=0.373，P<0.001)。FRS和ASCVD评分对预测CKD患者AS风险的灵敏度分别为67.65%和73.53%，特异度分别为76.92%和64.10%。FRS、ASCVD评分并联使用的灵敏度为91.44%、特异度为49.31%，串联使用的灵敏度为49.74%、特异度为91.71%。
结论
2种风险预测模型检出的CKD患者AS阳性率与颈动脉超声检查无明显差异且具有较好的一致性和符合率，对预测CKD患者的AS风险具有较好的应用价值，联合应用可进一步提高诊断的灵敏度或特异度。
Keywords: 慢性肾脏病, 动脉粥样硬化, 心血管疾病, Framingham风险预测模型, 国人ASCVD风险预测模型, 颈动脉超声检查
Abstract
Objective
Cardiovascular disease (CVD) remains a high morbidity in patients with chronic kidney disease (CKD), and atherosclerosis (AS) is the main pathological basis of CVD. Atherosclerotic cardiovascular disease (ASCVD) is one of the major complications and causes of death in CKD patients. Monitoring and paying attention to the risk of AS is the manifestation of multidisciplinary comprehensive treatment. Through continuous monitoring the risk factors of AS, patients with high AS risk can be detected early, and then risk factors can be treated to improve the prognosis and life quality of CKD patients and reduce the CVD mortality of CKD. The Framingham risk prediction model and Chinese 10-year ASCVD risk prediction model can comprehensively evaluate the AS risk. The predictive value of the 2 risk prediction models has been widely confirmed in the general population, but the application in CKD population needs to be further explored. Thus, the aim of this study is to analyze the value of the 2 risk prediction models in predicting AS risk of CKD population and to find a simple and reliable method to predict AS risk of CKD population.
Methods
This study was a cross-sectional study and 146 CKD patients (CKD group) and 14 healthy controls (control group) were recruited. Carotid ultrasound examination was performed to measure the intima-media thickness of left and right carotid arteries to evaluate the prevalence of AS. Medical history, physical examination, and clinical laboratory test data were collected to calculate Framingham Risk Score (FRS) and ASCVD score for all participants. We compared baseline data and AS positive rate between the CKD group and the control group. The AS positive rates detected by FRS, ASCVD score, and carotid ultrasound examination were compared in terms of Kappa consistency test, authenticity, reliability, and benefits.
Results
There were no significant differences in sex, age, and BMI between the CKD group and the control group (all P>0.05). The levels of systolic blood pressure, diastolic blood pressure, pulse pressure, total cholesterol, triglyceride, low density lipoprotein cholesterol, blood urea nitrogen, serum creatinine, and glycosylated hemoglobin in the CKD group were significantly higher than those in the control group (all P<0.05), while high density lipoprotein cholesterol and glomerular filtration rate were significantly lower than those in the control group (both P<0.05). The positive rates of AS in the CKD group detected by FRS, ASCVD score, and carotid ultrasound examination (43.84%, 53.42%, and 46.58%, respectively) were all significant higher than those in the control group (all P<0.01). There was no significant difference in the positive rate of AS in the CKD group among the 3 methods (P=0.24). The AS positive rate detected by FRS was highly consistent with ASCVD score (Kappa value was 0.647, P<0.001), FRS was moderately consistent with carotid ultrasound examination (Kappa value was 0.447, P<0.001), and ASCVD score was consistent with carotid ultrasound examination (Kappa value was 0.373, P<0.001). The sensitivities of FRS and ASCVD score were 67.65% and 73.53%, respectively, the specificities were 76.92% and 64.10%, respectively. The sensitivity was 91.44% and specificity was 49.31% when FRS and ASCVD score were used in parallel. And the sensitivity was 49.74% and the specificity was 91.71% when FRS and ASCVD score were used in series.
Conclusion
The positive rates of AS in CKD patients detected by risk prediction models are not different from that detected by carotid artery ultrasound, and there are good consistency and coincidence rate among risk prediction models and carotid artery ultrasound. Risk prediction models are suitable for predicting the risk of AS in patients with CKD, and the combined application of them can further improve the sensitivity or specificity of diagnosis.
Keywords: chronic kidney disease, atherosclerosis, cardiovascular disease, Framingham risk prediction model, Chinese ASCVD risk prediction model, carotid ultrasound examination
慢性肾脏病(chronic kidney disease，CKD)患者的心血管疾病(cardiovascular disease，CVD)发生率高[1-3]，改善全球肾病预后(Kidney Disease: Improving Global Outcomes，KDIGO)指南指出所有CKD患者都有更高的CVD风险(1A)[4]，降低CVD风险是CKD管理的主要组成部分[2]。中国肾脏疾病监测网络2015年度的报告[5]显示：27.8%的住院CKD患者合并CVD，而透析患者CVD患病率高达45.5%。目前，中国糖尿病发病率不断上升[6-7]，其主要并发症为糖尿病肾病[8]，也是CVD、CKD发病率剧增[2, 9]并进展至终末期肾病[10]的主要原因[11]。根据中国肾脏疾病监测网络公布的数据[5]，中国CKD疾病谱已由最常见的慢性肾小球肾炎转变为糖尿病肾病(27.0%)和高血压肾病(20.8%)[5]。糖尿病和高血压成为CKD最常见的病因[2]，并将导致CKD患者CVD发病率剧增[1, 12]。动脉粥样硬化(atherosclerosis，AS)是CVD的主要病理基础[13-14]，动脉粥样硬化性心血管疾病(atherosclerotic cardiovascular disease，ASCVD)是CKD的主要并发症[3]和死亡原因[14-16]之一。早期诊治ASCVD可显著改善CKD患者的预后[3]，及时识别和干预CVD进程也是降低CKD病死率的最有效措施[6]。
评价个体10年冠心病风险的Framingham风险预测模型Framingham风险评分(Framingham Risk Score，FRS)[17]及国人10年ASCVD风险预测模型[18]均为多因素分析预测模型，能综合评估、预测AS的风险，在普通人群中的使用价值已得到广泛证实[19-21]，而在CKD人群中的应用证据尚不足，其在CKD人群中的适用性需进一步研究证实。颈动脉粥样硬化可反映全身AS[22]，颈动脉超声检查可准确检出早期或无症状AS[23]。本研究拟将FRS及ASCVD风险预测模型检出的AS阳性率与颈动脉超声检查检出的AS患病率进行一致性、真实性、可靠性及收益的比较，以期寻找适合CKD人群的AS风险预测方法。
1. 对象与方法
1.1. 对象
本研究为横断面调查研究，选择2019年1至7月在西安交通大学第一附属医院(以下简称为我院)肾脏病医院肾内科住院的20~70岁的CKD患者，根据KDIGO指南推荐的慢性肾脏病流行病学合作研究(Chronic Kidney Disease Epidemiology Collaboration，CKD-EPI)公式[24]计算肾小球滤过率(glomerular filtration rate，GFR)并进行CKD分期[4]。排除标准：妊娠及哺乳期妇女；近期有急慢性感染、外伤、手术、器官移植者；近1个月内有输血或活动性出血史者；恶性肿瘤患者；有家族性高脂血症者；已发生过心脑血管疾病者(因FRS及ASCVD评分为预测模型）。最终纳入146例CKD患者(CKD组)，其中男99人，女47人，年龄22~69(46.97±12.19)岁；糖尿病肾病49人，慢性肾小球肾炎44人，慢性间质性肾炎8人，肾病综合征13人，IgA肾病13人，高血压肾病11人，多囊肾8人；CKD1期21人，CKD2期10人，CKD3期16人，CKD4期29人，CKD5期70人。对照组选取我院健康体检中心满足上述排除标准的健康对照者14例，其中男8人，女6人，年龄30~61(45.76±8.76)岁。本研究严格遵循《赫尔辛基宣言》中人体医学研究的伦理原则，获得我院医学伦理委员会批准(审批号：XJTU1AF2018LSK-049)。所有受试者均签署知情同意书。
1.2. 方法
收集所有受试者的病史资料，测量其体重、身高，计算体重指数(body mass index，BMI)。采用水银血压计测晨起同时间段收缩压(systolic blood pressure，SBP)、舒张压(diastolic blood pressure，DBP)3次，取平均值，并计算脉压(pulse pressure，PP)。采集所有受试者空腹12 h后晨起静脉血，使用全自动生化分析仪测定总胆固醇(total cholesterol，TC)、三酰甘油(triacylglycerol，TG)、低密度脂蛋白胆固醇(low density lipoprotein cholesterol，LDL-C)、高密度脂蛋白胆固醇(high density lipoprotein cholesterol，HDL-C)、糖化血红蛋白(glycosylated hemoglobin，HbA1c)、血尿素氮(blood urea nitrogen，BUN)、血肌酐(serum creatinine，SCr)。由同一有经验的超声科医师行颈动脉超声检查，采用美国GE公司的彩色超声诊断仪，选择频率3.5~10.0 MHz，测量左、右颈动脉内膜-中膜厚度(carotid intima-media thickness，CIMT)3次并取平均值，检查左、右颈总动脉分叉处及颈内、颈外动脉有无粥样硬化斑块形成。
根据FRS，将受试者的10年冠心病风险分为：低危(患病风险<10%)、中危(患病风险10%~20%)、高危(患病风险>20%)[17]。根据ASCVD评分将受试者的10年ASCVD风险分为：低危(患病风险<5%)、中危(患病风险5%~9%)、高危(患病风险≥10%)[18]。中危及以上为AS阳性。依据颈动脉粥样硬化诊断标准[25]：CIMT<1.0 mm定义为颈动脉超声检查阴性，CIMT≥1.0 mm或颈动脉粥样硬化斑块形成定义为颈动脉粥样硬化阳性。
将FRS、ASCVD评分检出的CKD患者AS阳性率与颈动脉超声检查检出的AS患病率从真实性、可靠性及收益3方面进行综合评价。灵敏度、特异度、假阴性率、假阳性率、约登指数、阳性似然比、阴性似然比属于真实性方面指标；符合率、Kappa值属于可靠性方面指标；阳性预测值、阴性预测值属于收益方面指标。并计算FRS、ASCVD评分串联使用和并联使用的灵敏度和特异度。
1.3. 统计学处理
应用SPSS 24.0统计学软件进行统计分析，符合正态分布的计量资料以均数±标准差( ±s)表示，采用t检验进行两样本间的比较；非正态分布的计量资料以中位数(四分位间距)[M(P 25，P 75)]表示，采用非参数秩和检验进行样本间的比较。采用χ2检验比较计数资料的组间差异。FRS、ASCVD评分、颈动脉超声检查3种方法的一致性比较采用Kappa一致性检验[26]。Kappa值<0为弱一致性，0~0.20为一致性较差，0.21~0.40为具有一致性，0.41~0.60为中度一致性，0.61~0.80为高度一致性，0.81~1.00为最强一致性[27]。P<0.05为差异有统计学意义。
2. 结 果
2.1. CKD组与对照组基线资料的比较
CKD组与对照组的性别、年龄及BMI差异均无统计学意义(均P>0.05)。CKD组的SBP、DBP、PP、TC、TG、LDL-C、BUN、SCr、HbA1c均显著高于对照组(均P<0.05)，HDL-C和eGFR均显著低于对照组(均P<0.05，表1)。
表1.
CKD患者与对照组基线资料比较
Table 1 Comparison of baseline data between patients with CKD and control group
| Group | n | Gender (male/female) | Age/year | BMI/(kg·m-2) | SBP/mmHg | DBP/mmHg | PP/mmHg | TC/(mmol·L-1) |
|---|---|---|---|---|---|---|---|---|
| P | 0.608 | 0.729 | 0.051 | <0.001 | <0.001 | <0.001 | 0.002 | |
| Control | 14 | 8/6 | 45.76±8.76 | 22.18±1.52 | 121.36±12.36 | 80.12±9.86 | 41.24±11.53 | 3.12±0.36 |
| CKD | 146 | 99/47 | 46.97±12.19 | 24.08±3.57 | 137.28±18.17 | 85.41±10.19 | 51.86±14.96 | 4.49±1.64 |
2.2. CKD组与对照组AS阳性率的比较
FRS、ASCVD评分、颈动脉超声检查检出的CKD组的AS阳性率分别为43.84%、53.42%、46.58%，对照组的AS阳性率3种方法均为7.14%。3种方法得到的AS阳性率CKD组均明显高于对照组(均P<0.01)；3种方法检出的CKD患者AS阳性率差异无统计学意义(P=0.24，表2)。
表2.
3种方法检出的AS阳性率比较
Table 2 Comparison of AS positive rate among the 3 kinds of methods
| Group |
LDL-C/ (mmol·L-1) |
HDL-C/ (mmol·L-1) |
TG/ (mmol·L-1) |
GFR/ (mL·min-1·1.73 m2) |
BUN/ (mmol·L-1) |
SCr/ (μmol·L-1) |
HbA1c/% |
|---|---|---|---|---|---|---|---|
| Control | 1.88±0.38 | 1.01±0.34 | 0.97±0.19 |
123.95, 115.66~127.36 |
3.93, 3.43~5.00 | 59.50, 57.00~60.75 | 5.00, 4.00~5.12 |
| CKD | 2.82±1.43 | 0.71±0.13 | 1.97±1.58 | 15.11, 7.28~35.85 | 15.28, 9.29~22.90 | 366.50, 176.75~621.00 | 5.40, 4.90~6.31 |
| P | 0.016 | 0.002 | 0.019 | <0.001 | <0.001 | <0.001 | 0.001 |
1 mmHg=0.133 kPa.
表2.
3种方法检出的AS阳性率比较
Table 2 Comparison of AS positive rate among the 3 kinds of methods
| Group | n | Result of FRS/[No.(%)] | Result of ASCVD score/[No.(%)] | Result of carotid ultrasound examination/[No.(%)] | ||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Low risk | Medium risk | High risk | Positive | Low risk | Medium risk | High risk | Positive | Positive | ||
| Control | 14 | 13(92.86) | 1(7.14) | 0(0.00) | 1(7.14) | 13(92.86) | 1(7.14) | 0(0.00) | 1(7.14) | 1(7.14) |
| CKD | 146 | 82(56.16) | 15(10.28) | 49(33.56) | 64(43.84)** | 68(46.58) | 13(8.90) | 65(44.52) | 78(53.42)** | 68(46.58)** |
Medium risk or above was defined as positive of AS in the mothod of FRS or ASCVD score. Carotid intima-media thickness≥ 1.0 mm or carotid atherosclerotic plaque formation was defined as positive in the mothod of carotid ultrasound. There was no significant difference in the positive rates of AS among the 3 kinds of methods (P=0.24). Compared with the Control, **P<0.01.
2.3. FRS、ASCVD评分与颈动脉超声检查检出的AS阳性率的一致性分析
采用FRS、ASCVD评分、颈动脉超声检查检出的CKD患者AS阳性情况见表3~5。FRS与ASCVD评分检出的AS阳性率具有高度一致性(Kappa值=0.647，P<0.001)，FRS与颈动脉超声检查检出的AS阳性率具有中度一致性(Kappa值=0.447，P<0.001)，ASCVD评分与颈动脉超声检查检出的AS阳性率具有一致性(Kappa值=0.373，P<0.001)。
表3.
FRS与ASCVD评分检出CKD患者AS情况
Table 3 AS of CKD patients obtained from FRS and
| FRS | ASCVD score/No. | Total/No. | |
|---|---|---|---|
| AS(+) | AS(-) | ||
| Total/No. | 78 | 68 | 146 |
| AS(+) | 58 | 6 | 64 |
| AS(-) | 20 | 62 | 82 |
ASCVD scores
表5.
ASCVD评分与颈动脉超声检查检出CKD患者AS情况
Table 5 AS of CKD patients obtained from ASCVD score and carotid ultrasound examination
| ASCVD score | Carotid ultrasound/No. | Total/No. | |
|---|---|---|---|
| AS(+) | AS(-) | ||
| Total/No. | 68 | 78 | 146 |
| AS(+) | 50 | 28 | 78 |
| AS(-) | 18 | 50 | 68 |
表4.
FRS与颈动脉超声检查检出CKD患者AS情况
Table 4 AS of CKD patients obtained from FRS and carotid ultrasound examination
| FRS | Carotid ultrasound/No. | Total/No. | |
|---|---|---|---|
| AS(+) | AS(-) | ||
| Total/No. | 68 | 78 | 146 |
| AS(+) | 46 | 18 | 64 |
| AS(-) | 22 | 60 | 82 |
2.4. FRS、ASCVD评分用于预测CKD患者AS风险的综合评价
FRS和ASCVD评分对预测CKD患者的AS风险的灵敏度分别为67.65%和73.53%，特异度分别为76.92%和64.10%，符合率分别为72.60%和68.49%，阳性预测值分别为71.88%和64.10%，阴性预测值分别为73.17%和73.53%(表6)。将FRS、ASCVD评分并联使用的灵敏度为91.44%、特异度为49.31%；串联使用的灵敏度为49.74%、特异度为91.71%。
表6.
FRS、ASCVD评分预测CKD患者AS风险的综合评价
Table 6 Comprehensive evaluation of FRS and ASCVD score for AS risk prediction in patients with CKD
| Indicators | FRS | ASCVD score | Indicators | FRS | ASCVD score |
|---|---|---|---|---|---|
| Validity | Reliability | ||||
| Sensitivity/% | 67.65 | 73.53 | Agreement rate/% | 72.60 | 68.49 |
| Specificity/% | 76.92 | 64.10 | Kappa value | 0.447 | 0.373 |
| False negative rate/% | 32.35 | 26.47 | Benefits | ||
| False positive rate/% | 23.08 | 35.90 | Positive predictive value/% | 71.88 | 64.10 |
| Youden index/% | 44.57 | 37.63 | Negative predictive value/% | 73.17 | 73.53 |
| Positive likelihood ratio | 2.93 | 2.05 | |||
| Negative likelihood ratio | 0.421 | 0.429 |
3. 讨 论
关注并监测CKD患者的CVD风险是多学科综合治疗理念的体现，通过连续监测危险因素可早期发现CVD高危人群，指导其改变生活方式，制订医疗措施早期干预可控危险因素，降低CKD相关CVD发病率和病死率，在防治CVD的同时也可延缓CKD进程，减轻CKD患者的疾病负担，改善CKD患者的预后及生存质量。本研究发现：CKD患者收缩压、舒张压、脉压、TC、TG、LDL-C、BUN、SCr、HbA1c等CVD危险因素显著高于对照组。由于CKD患者病情进展及CVD发生和发展是多因素交互作用的结果，采用纳入多因素的FRS、国人ASCVD风险预测模型预测CKD患者的ASCVD风险具有可行性。
采用FRS、ASCVD评分、颈动脉超声检查3种方法检出的CKD患者的AS阳性率均显著高于对照组，且3种方法的检出率差异无统计学意义。本研究入组年龄为22~69(46.97±12.19)岁的CKD人群颈动脉超声检查检出的颈AS阳性率为46.58%，显著高于流行病学调查[25]显示的>40(60.7±10.3)岁的中国普通人群颈AS阳性率(36.2%)，进一步印证了CKD人群AS的高发生率[3]。研究[28]表明FRS高估中国普通人群的ASCVD发病风险，而本研究中ASCVD评分检出CKD患者AS阳性率最高(53.42%)，FRS检出的AS阳性率最低(43.84%)，并未显示FRS高估CKD人群的AS风险，差异性检验也未显示FRS与ASCVD评分及颈动脉超声检查对检出CKD患者AS风险的差异，表明FRS及ASCVD评分与颈动脉超声检查检出的AS阳性率差异无统计学意义。
由于FRS及ASCVD评分多用于预测普通人群AS风险[29]，对CKD人群的应用甚少，因此本研究在FRS及ASCVD评分检出的CKD患者AS阳性率与颈动脉超声检查检出的AS阳性率无明显差异的情况下进一步行一致性分析，发现FRS与ASCVD评分具有高度一致性，FRS与颈动脉超声检查具有中度一致性，ASCVD与颈动脉超声检查具有一致性。一致性分析是反映3种方法预测CKD患者ASCVD风险可靠性的指标。在可靠性评价另一指标符合率上，FRS和ASCVD评分检出的AS阳性率均与颈动脉超声检查有较好的符合率(分别为72.60%和68.49%)，说明FRS及ASCVD风险预测模型对预测CKD患者AS风险的可靠性较好。
本研究还从真实性及收益方面对FRS、ASCVD评分用于预测CKD患者AS风险的适用性进行了综合评价。FRS及ASCVD评分的约登指数、阳性似然比均较高，而阴性似然比均较低，说明两者对CKD患者AS风险的预测价值较高。FRS的约登指数、阳性似然比均较ASCVD评分略高，说明FRS的评估价值可能优于ASCVD评分，但ASCVD评分的灵敏度更高，考虑进行风险预测的目的是筛选出CKD患者中AS高风险群体，以期及时诊断及干预，需要较高的灵敏度以降低漏诊率，故在筛选价值上ASCVD评分要优于FRS。两者的阴性似然比均较小，说明经2种方法检出的ASCVD低危人群罹患AS的可能性较小。在收益方面，FRS的阳性预测值高于ASCVD评分而ASCVD评分的阴性预测值高于FRS，因此ASCVD评分的漏诊风险要低于FRS。因目前尚未见CKD人群AS现患率的相关研究数据发表，因此本研究的阳性预测值及阴性预测值均是基于样本人群计算的结果。将FRS及ASCVD评分并联用于预测CKD患者的AS风险，诊断灵敏度升至91.44%；而将两者串联使用，特异度升至91.71%。在实际临床应用中，可将两者并联应用以提高早期诊断的能力，将两者串联使用以提高诊断特异度。在应用FRS及ASCVD评分预测ASCVD风险时，要根据具体应用目的综合考虑。在收益方面还要考虑生物学及卫生经济学效果评价。CVD是CKD的首位死因[3, 14]，早期发现CKD中CVD高危人群、及时诊治CVD并发症[2]，可显著改善CKD患者的预后及生存质量，降低CKD患者病死率[6]。2种模型涉及的评估内容通过病史采集、体格检查及常规实验室检查即可获得，这些评估内容可得性强、易掌握，且为量化指标，受医师主观因素影响小，故客观性好，真实度高。对患者来说，这2种预测方法无创、无害、省时且经济成本低而远期获益大，易于接受，具有较好的实用性与应用价值。
综上，本研究发现颈动脉超声检查显示CKD患者AS阳性率较高，普通人群FRS及ASCVD风险预测模型用于CKD患者检出的AS阳性率与颈动脉超声检查检出的AS阳性率无差异且具有较好的一致性和符合率，在综合评价方面也体现了较好的实用性和应用价值，联合应用FRS和ASCVD评分可进一步提高诊断的灵敏度或特异度。FRS、ASCVD风险预测模型值得在临床工作中推广用于预测CKD患者的ASCVD风险，并对高危人群进一步行CVD并发症的检查，使医疗卫生资源效益最大化。
基金资助
国家自然科学基金(81200541)；国家留学基金(201506285033)；中央高校基本科研业务费专项资金(xjj2017047)；陕西省科技计划项目(2020KW-043)；陕西省卫生和计划生育委员会项目(2016D064)；西安市科技计划项目(201805095YX3SF29)。
This work was supported by the National Natural Science Foundation (81200541), the National Scholarship Fund (201506285033),the Fundamental Research Funds for the Central Universities (xjj2017047), the Shaanxi Provincial Science and Technology Planning Project (2020KW-043), the Project of Shaanxi Provincial Health and Family Planning Commission (2016D064), and the Xi’an Science and Technology Planning Project (201805095YX3SF29), China.
利益冲突声明
作者声称无任何利益冲突。
原文网址
http://xbyxb.csu.edu.cn/xbwk/fileup/PDF/202109966.pdf
参考文献
- 1. Yuan J, Zou XR, Han SP, et al. Prevalence and risk factors for cardiovascular disease among chronic kidney disease patients: results from the Chinese cohort study of chronic kidney disease (C-STRIDE)[J]. BMC Nephrol, 2017, 18(1): 23. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2. Chen TK, Knicely DH, Grams ME. Chronic kidney disease diagnosis and management: A Review[J]. JAMA, 2019, 322(13): 294-304. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3. Mathew RO, Bangalore S, Lavelle MP, et al. Diagnosis and management of atherosclerotic cardiovascular disease in chronic kidney disease: A review[J]. Kidney Int, 2017, 91(4): 797-807. [DOI] [PubMed] [Google Scholar]
- 4. Stevens PE, Levin A. Evaluation and management of chronic kidney disease: synopsis of the kidney disease: improving global outcomes 2012 clinical practice guideline[J]. Ann Intern Med, 2013, 158(11): 825-830. [DOI] [PubMed] [Google Scholar]
- 5. Wang F, Yang C, Long JY, et al. Executive summary for the 2015 Annual Data Report of the China Kidney Disease Network (CKD-NET)[J]. Kidney Int, 2019, 95(3): 501-505. [DOI] [PubMed] [Google Scholar]
- 6. Gao BX, Zhang LX, Wang HY, et al. Chinese cohort study of chronic kidney disease: design and methods[J]. Chin Med J (Engl), 2014, 27(11): 2180-2185. [PubMed] [Google Scholar]
- 7. Zhang LX, Long JY, Jiang WS, et al. Trends in chronic kidney disease in China[J]. N Engl J Med, 2016, 375(9): 905-906. [DOI] [PubMed] [Google Scholar]
- 8. Szrejder M, Piwkowska A. AMPK signalling: Implications for podocyte biology in diabetic nephropathy[J]. Biol Cell, 2019, 111(5): 109-120. [DOI] [PubMed] [Google Scholar]
- 9. Alicic RZ, Rooney MT, Tuttle KR. Diabetic kidney disease: Challenges, progress, and possibilities[J]. Clin J Am Soc Nephrol, 2017, 12(12): 2032-2045. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10. Tuttle KR, Bakris GL, Bilous RW, et al. Diabetic kidney disease: a report from an ADA Consensus Conference[J]. Diabetes Care, 2014, 37(10): 2864-2883. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11. Perkovic V, Jardine MJ, Neal B, et al. Canagliflozin and renal outcomes in Type 2 diabetes and nephropathy[J]. N Engl J Med, 2019, 380(24): 2295-2306. [DOI] [PubMed] [Google Scholar]
- 12. Zhang LX, Wang F, Wang L, et al. Prevalence of chronic kidney disease in China: a cross-sectional survey[J]. Lancet, 2012, 379(9818): 815-822. [DOI] [PubMed] [Google Scholar]
- 13. Hansson G. Inflammation, atherosclerosis, and coronary artery disease[J]. N Engl J Med, 2005, 352(16): 1685-1695. [DOI] [PubMed] [Google Scholar]
- 14. Gisterå A, Hansson GK. The immunology of atherosclerosis[J]. Nat Rev Nephrol, 2017, 13(6): 368-380. [DOI] [PubMed] [Google Scholar]
- 15. 申燕, 王沛, 周娟, 等. 慢性肾功能不全患者血清及硫酸吲哚酚对巨噬细胞脂质聚集的影响[J]. 南方医科大学学报, 2015, 35(5): 631-638. [PubMed] [Google Scholar]; SHEN Yan, WANG Pei, ZHOU Juan, et al. Effect of serum from patients with chronic renal insufficiency and indoxyl sulfate on lipid accumulation in macrophages in vitro[J]. Journal of Southern Medical University, 2015, 35(5): 631-638. [PubMed] [Google Scholar]
- 16. Pasquel FJ, Gregg EW, Ali MK. The evolving epidemiology of atherosclerotic cardiovascular disease in people with diabetes[J]. Endocrinol Metab Clin North Am, 2018, 47(1): 1-32. [DOI] [PubMed] [Google Scholar]
- 17. Wilson PWF, D'Agostino RB, Levy D, et al. Prediction of coronary heart disease using risk factor categories[J]. Circulation, 1998, 97(18): 1837-1847. [DOI] [PubMed] [Google Scholar]
- 18. Yang XL, Li JX, Hu DS, et al. Predicting the 10-year risks of atherosclerotic cardiovascular disease in Chinese population: The China-PAR Project (Prediction for ASCVD Risk in China) [J]. Circulation, 2016, 134(19): 1430-1440. [DOI] [PubMed] [Google Scholar]
- 19. Zhang M, Jiang Y, Wang LM, et al. Prediction of 10-year atherosclerotic cardiovascular disease risk among adults aged 40-79 years in China: a nationally representative survey[J]. Biomed Environ Sci, 2017, 30(4): 244-254. [DOI] [PubMed] [Google Scholar]
- 20. 唐迅, 张杜丹, 何柳, 等. China-PAR模型在北方农村人群中预测动脉粥样硬化性心血管疾病发病风险的应用[J]. 北京大学学报(医学版), 2017, 49(3): 439-445. [Google Scholar]; TANG Xun, ZHANG Dudan, HE Liu, et al. Application of the China-PAR risk prediction model for atherosclerotic cardiovascular disease in a rural northern Chinese population[J]. Journal of Peking University. Health Sciences, 2017, 49(3): 439-445. [PubMed] [Google Scholar]
- 21. Pool LR, Ning H, Wilkins J, et al. Use of Long-term cumulative blood pressure in cardiovascular risk prediction models[J]. JAMA Cardiol, 2018, 3(11): 1096-1100. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22. van den Oord SC, Sijbrands EJ, ten Kate GL, et al. Carotid intima-media thickness for cardiovascular risk assessment: systematic review and meta-analysis[J]. Atherosclerosis, 2013, 228(1): 1-11. [DOI] [PubMed] [Google Scholar]
- 23. Naqvi TZ, Lee M. Carotid intima-media thickness and plaque in cardiovascular risk assessment[J]. JACC Cardiovasc Imaging, 2014, 7(10): 1025-1038. [DOI] [PubMed] [Google Scholar]
- 24. Levey AS, Stevens LA, Schmid CH, et al. A new equation to estimate glomerular filtration rate[J]. Ann Intern Med, 2009, 150(9): 604-612. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25. Wang XJ, Li WZ, Song FJ, et al. Carotid atherosclerosis detected by ultrasonography: a national cross-sectional study[J]. J Am Heart Assoc, 2018, 7(8): e008701. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26. Ren S, Chang Y, Zhang Q, et al. How to estimate renal function in patients with liver disease: choosing the most suitable equation[J]. Int Urol Nephrol, 2019, 51(4): 677-690. [DOI] [PubMed] [Google Scholar]
- 27. 颜虹, 郑全庆. 流行病学基本原理与方法[M]. 陕西: ：科学技术出版社, 2007: 145. [Google Scholar]; YAN Hong, ZHENG Quanqing. Basic principles and methods of epidemiology[M]. Shaanxi: Science and Technology Press, 2007: 145. [Google Scholar]
- 28. Liu J, Hong YL, SrD’Agostino RB, et al. Predictive value for the Chinese population of the Framingham CHD risk assessment tool compared with the Chinese multi-provincial cohort study[J]. JAMA, 2004, 291(21): 2591-2599. [DOI] [PubMed] [Google Scholar]
- 29. SrD’Agostino RB, Vasan RS, Pencina MJ, et al. General cardiovascular risk profile for use in primary care: the Framingham Heart Study[J]. Circulation, 2008, 17(6): 743-753. [DOI] [PubMed] [Google Scholar]