预测降尿酸治疗期间痛风频繁发作的临床模型建立与评价
Establishment and Evaluation of a Clinical Model to Predict Frequent Gout Attacks during Urate-Lowering Therapy
DOI: 10.12677/acm.2024.1451628, PDF, HTML, XML, 下载: 25  浏览: 46 
作者: 周 睿:青岛大学附属医院,山东 青岛
关键词: 痛风痛风急性发作预测模型Gout Flares Predict Model
摘要: 目的:确定降尿酸治疗过程中影响痛风患者痛风频繁发作风险的因素。方法:本研究为单中心、观察性研究,纳入2022年8月至2023年8月在青岛大学附属医院痛风门诊初诊且确诊为痛风的患者165名。入组后,所有患者开始降尿酸治疗(ULT),并在3个月、6个月、12个月时接受随访。在12个月内出现至少2次急性发作的患者(频繁痛风发作患者)与小于两次急性发作的患者(非频繁痛风发作患者)进行比较,计算痛风发作风险的比值比(ORs)。利用多因素logistic回归建立临床预测模型,并绘制受试者工作曲线(ROC)评价模型预测性能。结果:111名患者被归类为频繁痛风发作患者,54名患者被归类为非频繁痛风发作患者。痛风频繁发作患者的血沉、肉眼痛风石、痛风家族史较非频繁发作患者显著增高(P < 0.05)。两组患者在是否患有高血压、是否患有脂肪肝、年龄、尿酸和饮酒史方面差异无统计学意义(P > 0.05)。结论:本研究建立的预测痛风频繁发作预测模型的预测效能较好,为临床及时识别频繁痛风发作患者提供了一个参考工具。
Abstract: Objective: To determine the factors that affect the risk of frequent gout attacks in gout patients during uric acid-lowering treatment. Methods: This study is a single-center, observational study, including 165 patients who were first diagnosed with gout at the Gout Clinic of the Affiliated Hospital of Qingdao University from August 2022 to August 2023. After enrollment, all patients started urate-lowering therapy (ULT) and were followed up at 3 months, 6 months, and 12 months. Odds ratios (ORs) were calculated for the risk of gout attacks in patients who had at least two exacerbations within 12 months (frequent gout attack patients) compared with those who had less than two exacerbations (infrequent gout attack patients). Multi-factor logistic regression was used to establish a clinical prediction model, and a receiver operating curve (ROC) was drawn to evaluate the prediction performance of the model. Results: 111 patients were classified as having frequent gout attacks, and 54 patients were classified as having infrequent gout attacks in remission. The erythrocyte sedimentation rate, gross tophi, and family history of gout in patients with frequent gout attacks were significantly higher than those in the non-frequent attack group (P < 0.05). There was no statistically significant difference between the two groups in terms of whether they had hypertension, fatty liver, age, uric acid and drinking history (P > 0.05). Conclusion: The prediction model established in this study for predicting frequent gout attacks has good prediction performance and provides a reference tool for clinical timely identification of patients with frequent gout attacks.
文章引用:周睿. 预测降尿酸治疗期间痛风频繁发作的临床模型建立与评价[J]. 临床医学进展, 2024, 14(5): 1865-1870. https://doi.org/10.12677/acm.2024.1451628

1. 引言

痛风是由于尿酸单钠晶体沉积于关节及其周围组织引起的炎症性关节炎,当血尿酸水平长期处于较高水平(达到6.8 mg/mL)或不断波动状态,尿酸结晶将从血浆中沉淀出来,并沉积在软组织、关节及肾脏等组织中。由于下肢血液循环相对较差,体温较低,局部尿酸浓度较高,MSU晶体会优先沉积在下肢关节及其周围结构中,特别是在第一跖趾关节、脚踝和膝盖处 [1] ,通常表现为这些区域的急性、剧烈疼痛和炎症性关节炎,即局部的“红、肿、热、痛”。此种急性发作具有自限性,通常在7~10天内自行消退 [2] 。

2. 方法

患者纳入与研究设计

这项为期12个月的前瞻性、观察性研究于2022年8月至2023年8月进行。在过去12个月内首次就诊的痛风患者在中国青岛青岛大学附属医院的专门痛风门诊连续入组。所有患者均符合2015年美国风湿病学会(ACR)/欧洲抗风湿病联盟(EULAR)痛风分类标准。所有患者均在观察期间接受降尿酸治疗,排除标准如下:① 病史采集困难。② 合并其他关节炎或足部关节有外伤史或手术史。③ 对研究方案的依从性低。

入组后所有患者开启降尿酸治疗,并进行为期12个月的随访,随访期间所有患者规律服用适量降尿酸药物,根据欧洲风湿病协会的建议,降尿酸治疗从最低剂量开始,滴剂加量,直至血尿酸水平达标,并给予小剂量秋水仙碱预防痛风急性发作,如果患者在随访期间痛风急性发作,则使用秋水仙碱或非甾体抗炎药物缓解症状 [3] 。165例患者完成12个月随访被纳入本次研究。对患者进行人口统计学特征、血清学检测。在3、6、9、12个月时对患者进行痛风发作随访,并进行血清学检测,痛风急性发作根据患者以往的痛风发作经验由患者自己描述,并由研究人员根据对发作的描述进行回顾性验证。

3. 数据分析

定性变量描述为百分比(%);定量变量以均数 ± 标准偏差表示。定性变量选择卡方检验或者Fisher精确检验,连续变量呈正态分布时使用Student’s t检验,不满足正态分布时使用Wilcoxon秩和检验。使用二元logistic回归筛选出通过多元logistic回归分析建立痛风急性发作的临床预测模型。采用受试者工作特征(ROC)曲线方法评预测模型性能。SPSS 26.0 (IBM, Armonk, NY, USA)用于统计分析,统计学显著性差异表示为P < 0.05和P < 0.01。

4. 结果

165名患者完成了随访被纳入研究,并对其进行统计学分析。根据随访期间是否有痛风发作将患者分为两组,其中频繁发作组(急性发作 ≥ 2次)患者111名,不发作组(急性发作 < 2次)患者54名。如表1所示,频繁发作组患者的血沉[8 (4, 18) VS 5.5 (2.25, 8.75), P < 0.05]、合并痛风家族史(58% VS 33, P < 0.05)、合并肉眼痛风石比例(36% VS 11%, P < 0.05)明显高于不频繁发作组。2组患者在血尿酸水平、年龄、合并高血压、合并脂肪肝、饮酒史方面差异无统计学意义(P > 0.05) (表1)。多因素Logistic回归分析表明,合并痛风家族史[2.336, 95%CI (1.002~5.445), P < 0.05]、合并肉眼痛风石[5.588, 95%CI (1.543~20.246), P < 0.05]是痛风急性发作的独立危险因素(表2)。以临床预测模型识别患者时AUC值为0.726 [(95%CI) 0.639~0.813],敏感度为0.805,特异度为0.602,准确率为0.874,阳性预测概率为0.671,阴性预测概率为0.733 (图1表3)。

Table 1. Demographic characteristics of gout patients in different groups

表1. 不同分组痛风患者人口特征

Table 2. Multi-factor logistic regression analysis

表2. 多因素logistic回归分析

Table 3. Performance evaluation of clinical prediction models

表3. 临床预测模型的性能评价

Figure 1. ROC curve

图1. ROC曲线

5. 讨论

痛风是一种与关节和软组织中尿酸盐(MSU)晶体沉积相关的代谢疾病,导致NLRP炎性小体引导的复发性关节炎 [4] 。尽管MSU晶体会永久沉积,但炎症反应的爆发只是间歇性的,难以预测。痛风发作的风险因素是已知的或怀疑的,但因果关系知之甚少 [5] 。开始和调整降尿酸治疗是急性发作的关键时期。所有国际指南都建议在开始治疗后6个月内使用秋水仙碱、非甾体抗炎药(NSAID)甚至口服皮质类固醇预防发作 [3] [6] [7] 。这些建议是基于在大多数涉及ULT与安慰剂的随机对照试验中观察到的前6个月内复发率增加 [8] [9] 。预防性治疗,尤其是秋水仙碱,已被证明可有效降低发作风险 [10] [11] 。普遍认为血清尿酸盐(SU)浓度降低会诱导沉积的MSU负荷的活动,从而可能使晶体暴露于先天免疫系统 [11] [12] 。然而,这种解释仍然是假设性的。因此需要建立痛风急性发作的风险预测模型,这对于科学管理痛风患者,提高患者治疗依从性具有很大的帮助。

痛风性关节炎作为一种自身炎症性疾病,是由MSU晶体直接或间接触发细胞坏死和炎症反应引起的 [13] 。受损细胞或组织释放的MSU晶体可被先天免疫细胞的模式识别受体识别为损伤相关分子模式,从而在初始阶段产生免疫应答,刺激许多促炎细胞因子的分泌,以及激活和启动适应性免疫应答 [14] 。先天性和适应性免疫细胞,如中性粒细胞、单核细胞、淋巴细胞、血小板和促炎介质,在痛风性关节炎的发病机制中起关键作用。各种炎症细胞和细胞因子协同作用,诱导痛风炎症发生、发展和消退的过程 [15] 。因此,血沉作为临床常见的炎症活动性指标其水平能够确定痛风急性发作中炎症反应的程度,本研究表明痛风患者基线血沉每升高1 μmol/L,痛风急性发作的概率增加1.051倍(95%CI, 0.996~1.019, P < 0.05)。

显微镜下,痛风石是慢性异物肉芽肿样结构,由周围炎症细胞和结缔组织内含大量MSU晶体形成。在痛风石内部的MSU晶体活动后,中性粒细胞等免疫细胞以及因子迅速对其做出反应,引起机体炎症反应,从而导致痛风急性发作。Pascart T等人进行了一项单中心、前瞻性、小样本研究,表明MSU体积是痛风发作的独立危险因素,患者基线时DECT扫描下的MSU体积每增加1 cm3,在第六个月随访时,痛风急性发作的风险增加2.03倍(OR 2.03, 95%CI 1.15~4.38, P < 0.05) [16] ,这与本研究结果相一致,本研究表明肉眼痛风石与痛风急性发作之间关系密切,是痛风急性发作的独立危险因素之一(P < 0.05)。

有研究表明有痛风家族史的青少年高尿酸血症患者痛风发作的概率更高,同时转化为痛风的时间也更早 [17] ,同时,有痛风家族史的患者更易出现心脏舒张功能的损伤 [18] ,在本研究中,有痛风家族史患者痛风急性发作的概率是无痛风家族史患者的2.336倍(95CI%, 1.002~5.445, P < 0.05),这与之前的研究结果是一致的。

本研究发现较大的血沉水平、痛风家族史、肉眼痛风石与开启降尿酸治疗后痛风频繁发作密切相关。本研究建立的临床预测模型可以预测降尿酸治疗开启后1年内痛风频繁发作的风险,为痛风患者的临床管理提供了参考,提示临床降尿酸治疗初始阶段应注意对合并痛风家族史和肉眼痛风石的患者进行痛风急性发作预防。

本研究的局限性如下:首先,本研究的样本量较小,随访时间比较短,由于患者数量较少,数据缺失,可能忽略了其他影响较小的因素。其次,在随访期间痛风急性发作是由患者自己描述再由临床医师进行回顾性验证,这一过程可能受到患者经验的影响,产生回忆偏倚,进而影响数据的准确性。此外有一部分患者在随访过程中失访,这可能会导致对经历过急性发作的患者的选择偏倚。最后,本研究属单中心研究,存在研究人群单一、样本量较少等问题,未来需要扩大样本量,以验证结果的准确性。

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