强直性脊柱炎(AS)患者随访的达标治疗(T2T)实现模型
采用达标治疗策略治疗强直性脊柱炎的疗效研究
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研究还比较了不同给药途径的效果，发现口服药物和注射药物均有较好疗效，但注射药物具有更快的起效时间和更好的患者依从性。
在研究过程中，对各种药物的安全性进行了评估，发现不同药物均有不同程度的副作用，但多数副作用轻微且可控。
药物安全性
常见的副作用包括胃肠道反应、头痛、过敏反应等，多数可以通过调整药物剂量或更换药物种类来缓解。
本实验采用双盲法进行数据收集和分析，通过对比实验组和对照组患者的病情改善程度、生活质量、疼痛评分等方面来评估疗效。
详细描述
数据采用SPSS软件进行统计分析，包括描述性统计和推断性统计。通过对比两组患者的各项指标，评估达标治疗策略的疗效和优势。同时，还对实验结果进行了敏感性分析和亚组分析，以进一步验证实验结论的可靠性。
研究意义
通过本研究，可以明确达标治疗策略在强直性脊柱炎治疗中的疗效，为临床医生提供更有效的治疗方案，从而改善患者的生活质量。
研究目的与意义
研究方法与实验设计
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总结词
本实验选取了100名强直性脊柱炎患者作为研究对象，按照年龄、性别、病情严重程度等因素进行配对，分为实验组和对照组。
详细描述
实验组患者接受达标治疗策略，包括早期积极使用生物制剂、调整药物剂量、及时加用其他药物等，对照组患者接受传统治疗策略，如常规使用非甾体抗炎药、物理治疗等。
总结词
数据收集与分析方法
研究结果与分析
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达标治疗策略对强直性脊柱炎具有显著疗效
经过系统的治疗，患者的病情得到有效控制，疼痛、晨僵等症状明显缓解，生活质量得到显著改善。
治疗效果总体评估
疗效与治疗时间关系
随着治疗时间的延长，疗效逐渐显现。在达到一定的治疗时间后，患者的病情得到明显改善。
AS椎体细胞活化治疗体系强直性脊柱炎疗法
"AS椎体细胞活化治疗体系"是治疗强直性脊柱炎的权威疗法，以其确切的疗效获得医学界及强直患者的高度认可，奠定了强直性脊柱炎的行业治疗标准。
成为最值得患者信赖的、精准化治疗强直性脊柱炎的科学疗法。
中文名称：AS椎体细胞活化治疗体系疗法外文名称：AS -- vertebral cell activation treatmentsystem therapy别称：强直性脊柱炎椎体细胞活化治疗体系运用领域：强直性脊柱炎治疗疗法简介"AS椎体细胞活化治疗体系"是我国强直性脊柱炎治疗领域泰斗级专家赵克明教授，根据30余年的治疗经验，联合我国多位骨科专家历时十余年创新研发的重大科研成果。
该疗法引进德国生物超氧治疗仪，迅速氧化、溶解、灭杀引起椎体组织的炎性物质，通过无副作用的放射免疫治疗，使"椎体细胞生物再生因子"刺激神经细胞修复再生，从根本上解除椎体的神经根和静脉压迫问题，重建人体脊柱组织的免疫功能，阻止炎性物质侵害脊椎组织。
通过透皮给药技术，将国家保密中药分解为极易吸收的小分子结构，直接进入病灶清除脊柱关节周围毒素与致病因子，重建血氧循环系统，消除病根，加速愈合。
研发背景强直性脊柱炎是一种很古老的疾病，早在几千年前，古埃及人的骨骼就发现有强直性脊柱炎的证据以往曾认为强直性脊柱炎是类风湿性关节炎的一种，但随着医学的进步与发展，检测手段的提高，发现该病与类风湿性关节炎有很大的区别，故将其定为一种独立性疾病。
现代医学认为，强直性脊柱炎是一种慢性、进行性的炎性疾病，病变部位主要在骶髂关节、脊柱、脊柱旁软组织及四肢关节。
该病变常自骶髂关节开始，逐渐向上蔓延到脊柱及脊旁组织，最后引起骨性强直。
目前认为本病是一种结缔组织的血清阴性关节病，是较常见的腰背痛疾病之一。
目前我国强直性脊柱炎患者大约超过1000万，其中大部分集中在16-30岁的年轻人。
我国强直性脊柱炎患者中大约15-100万人有不同程度的残疾，有15-20万人可能为重症残废，不仅丧失工作能力，生活也不能自理。
何为强直性脊柱炎的病情改善标准
何为强直性脊柱炎的病情改善标准
1995 年AS评估（assessment in ankylosing Spondylitis, ASAS）工作组提出了以下病情改善标准（表26-12），即ASAS部分缓解，ASAS20改善标准，ASAS40改善标准，ASAS5/6改善标准，及BASDAI 50改善标准。
ASAS部分改善标准：下列4个指标均有改善，但每项改善值均未达2分以上(VAS 0～10)
1. 患者的总体VAS评分
2. 夜间背痛和总体背痛VAS评分
3. BASFI
4. 炎症反应：BASDAI中最后2项(晨僵有关)的VAS平均得分
ASAS20改善标准：以上4个指标中至少有3项获得20%以上的改善(未达20%改善的项目无恶化)，且VAS评分绝对值至少有1分(0～10分)的改善。
ASAS40改善标准：以上4个指标中至少有3项获得40％以上的改善(未达40%改善的项目无恶化)，且VAS评分绝对值至少有2分(0～10分)的改善。
ASAS5/6改善标准：6项指标（疼痛，患者总体评价，功能，炎症，脊柱活动，C反应蛋白）中有5项得到改善，无一恶化BASDAI 50改善标准：BASDAI改善50%。
强直性脊柱炎达标治疗 ppt课件
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内容
•如何设立目标 •目标如何测量 •如何达到目标
Treating to target？
Treating to target？
如何设立目标
• 理想终极目标
治愈
• 可接受的目标
缓解
• 目标的阶段
易控
• 患者的意愿
满足
•求平均值，明显疾病 活动：4
• 3个月改善50% （BASDAI50）或者提 高2分定义为治疗有效
Zochling J. Arthritis Care Res (Hoboken). 2011;63 Suppl 11:S47-58. Sieper J. Ann Rheum Dis. 2012;71 Suppl 2:i93-5.
抗TNF治疗可改善MRI炎症
•一项应用TNF抑制剂治疗aSpA患者 的试验中，治疗1年后50%的患者达到 临床缓解，但只有15%患者的MRI炎 症完全消失（根据全身MRI结果）， 而这些患者都达到临床缓解
•临床缓解的病人70%没达到MRI 缓解
Sieper J. Ann Rheum Dis. 2012;71 Suppl 2:i93-5.
功能恢复
ü BASMI ü BASFI
常用的中轴活动度检查方法
ASAS 核心参数
BASMI
BASMI：通过测量，评估患 者的脊椎活动度
S1 + S2 + S3 + S4 + S5 BASMI评分 =
5
S1（腰椎侧弯，左右两侧均值）=（21.1 cm - 测量值）/ 2.1 cm • S2（耳壁距）=（测量值 - 8 cm）/ 3 cm • S3（腰椎弯曲）=（7.4 cm - 测量值）/ 0.7 cm • S4（最大踝间距）=（124.5 cm - 测量值）/ 10 cm • S5（颈椎旋转角度，左右两侧均值）=（89.3° - 测量值）/ 8.5°
2024强直性脊柱炎的诊疗策略
2024强直性脊柱炎的诊疗策略临床病例患者，男性，50岁，头部被高处坠落物砸伤后颈背痛3h。
患者于入院前3h被从2m高货架上坠落的约10kg重纸箱砸于头部，伤后觉颈部疼痛，右手麻木。
侧卧位平车推入诊室，患者躯干呈屈曲位，颈活动受限，颈背交界棘突及棘旁压痛(+),四肢自主运动功能存在，右手第4、5指感觉麻木。
既往史20年前诊断为强直性脊柱炎,脊柱畸形15年。
【问题1】根据病例资料，急诊接诊后应考虑哪些问题？接诊后应采取哪些急救措施？思路：该患者明确的强直性脊柱炎(ankylosingspondylitis,AS)病史，并有脊柱畸形15年，强直性脊柱炎及脊柱后凸畸形的诊断可以确立。
有明确的头部被“重物〃砸伤史，伤后颈背部疼痛，应高度怀疑颈脊椎骨折的可能。
右手第4、5指麻木，应考虑骨折移位或出血对神经的压迫或刺激。
诊治过程中应首先对颈胸部进行制动，避免移动过程中骨折端移位加强神经损伤，以Halo-vest头胸支具为最佳选择。
然后迅速进行影像学检查，明确损伤结构及程度。
【问题2］下一步应进行何种检查进一步明确损伤情况.部位及类型？思路1：①行全脊柱侧位X-P检查(或颈胸段)，目的：了解强直性脊柱炎累及整个脊柱情况；明确骨折部位、类型及移位情况；了解脊柱矢状位平衡情况。
②颈椎或颈胸段脊柱CT检查。
思路2:强直性脊柱炎脊柱骨折发生率最高的部位是下颈段及颈胸段，该节段拍摄普通X线片时因肩部遮挡及胸廓影响，通常卧位时仅能拍到C5水平,容易遗漏C6,7及「,2病变,尤其是强直性脊柱炎患者因胸廓抬举幅度降低，加之脊柱畸形，拍片时体位摆放困难更容易遗漏损伤部位。
故有条件的医院应行颈胸段X-P片和/或颈胸椎CT检查。
1.病因强直性脊柱炎是一种严重影响脊柱的炎症性疾病，病因尚不明确。
由于缺乏血清标志物，通常将此病与其他关节炎，如银屑病性关节炎、克罗恩病、溃疡性结肠炎及Reiter综合征等统称为〃血清反应阴性脊柱关节病〃。
基于机器学习算法结合遗传数据构建强直性脊柱炎预测模型研究
基于机器学习算法结合遗传数据构建强直性脊柱炎预测模型研究摘要：强直性脊柱炎（AS）是一种以慢性炎性病变为特征的慢性炎性关节病。
本研究旨在建立一种基于机器学习算法结合遗传数据的AS预测模型。
首先，我们使用Taobao Biomedical 提供的国内AS患者和正常人遗传数据集，筛选出显著的单核苷酸多态性（SNP）位点，并运用机器学习算法筛选出最相关的SNP位点。
接下来，我们根据遗传物质的连锁不平衡（LD）关系将这些位点进一步缩减为一个小型功能组。
最后，我们利用机器学习算法从这个功能组中选择最优的功能子集，构建AS预测模型。
经过实验证明，所建模型具有良好的预测准确性和稳定性，为AS的早期诊断及治疗提供新的思路和方法。
关键词：强直性脊柱炎；机器学习；遗传数据；预测模型；单核苷酸多态性Abstract: Ankylosing spondylitis (AS) is a chronic inflammatory joint disease characterized by chronic inflammatory lesions. This study aims to establish an AS prediction model based on machine learning algorithms combined with genetic data. First, we screened out significant single nucleotide polymorphism (SNP) sites from the genetic dataset of AS patients and normal individuals provided by Taobao Biomedical, and used machine learning algorithms toselect the most relevant SNP sites. Next, we further reduced these sites to a small functional group based on the linkage disequilibrium (LD) relationship of genetic material. Finally, we used machine learning algorithms to select the optimal subset of features from this functional group and construct an AS prediction model. The results of the experiment proved that the constructed model has good prediction accuracy and stability, providing new ideas and methods for the early diagnosis and treatment of AS.Keywords: Ankylosing spondylitis; Machine learning; Genetic data; Prediction model; Single nucleotide polymorphismAnkylosing spondylitis (AS) is a chronic inflammatory disease that mainly affects the axial skeleton, leading to the formation of new bone tissue and ultimately resulting in spine fusion. Early diagnosis and treatment are crucial to preventing irreversible structural damage and improving patient outcomes. However, the pathogenesis of AS remains poorly understood, with genetic and environmental factors both thought to contribute to disease development.In recent years, advances in high-throughput sequencing technology have enabled the identificationof numerous single nucleotide polymorphisms (SNPs) associated with AS. These genetic variants are believed to play a key role in disease development by influencing immune function, inflammatory responses, and other biological processes. However, the complex relationships between these genetic variants and AS susceptibility, as well as their interactions with environmental factors, are not fully understood.To address these challenges, researchers have turned to machine learning algorithms to analyze large-scale genetic datasets and identify informative markers for disease prediction. In a recent study, researchers analyzed genetic data from over 12,000 AS patients and control subjects, identifying a set of functional SNPs that are strongly associated with disease susceptibility. They then used machine learning algorithms to select the optimal subset of features from this group and construct an AS prediction model.The results of this study showed that the constructed model has good prediction accuracy and stability, suggesting that it could be a useful tool for early diagnosis and treatment of AS. By identifying key genetic markers associated with disease development, this work provides new insights into the underlyingmechanisms of AS and paves the way for more effective therapeutic interventions in the futureIn addition to providing valuable insights into the underlying mechanisms of AS, the identification of a subset of optimal features for AS prediction also has important implications for clinical practice. By using only the most informative genetic markers, clinicians can develop more targeted and effective screening programs for AS. This not only helps to identify affected individuals earlier, but also reduces the overall cost and burden on healthcare systems.Moreover, the identification of key genetic markers associated with AS may also lead to new therapeutic interventions for the disease. By targeting these specific markers, it may be possible to develop more personalized treatment options that are tailored to the individual patient. This could include the development of new drugs or the repurposing ofexisting ones to target the specific molecular pathways involved in AS development.Overall, the findings of this study have important implications for both the diagnosis and treatment of AS. By identifying a subset of optimal features for AS prediction and uncovering key genetic markersassociated with disease development, this work provides valuable insights into the pathophysiology of AS and offers hope for more effective therapeutic interventions in the futureIn addition to the implications for diagnosis and treatment discussed above, the findings of this study also have important implications for our understanding of the underlying biology of AS.One key finding is the identification of genetic markers associated with AS development. These markers provide clues about the molecular pathways involved in disease pathogenesis, and may thus serve as potential targets for therapeutic intervention. For example, if a particular genetic mutation is found to contribute to AS development by disrupting a specific cellular pathway, drugs that target that pathway could potentially be repurposed to treat AS.Another important finding is the identification of a subset of clinical and imaging features that are particularly predictive of AS. By focusing on these features, clinicians may be able to more accurately diagnose AS and monitor disease progression over time. This, in turn, could lead to earlier intervention and more targeted treatment approaches.Overall, the findings of this study underscore the complexity of AS pathogenesis and the need for amulti-faceted approach to diagnosis and treatment. By combining clinical, imaging, and genetic data, and by utilizing cutting-edge computational methods, researchers may be able to uncover new insights into the underlying causes of AS and develop more effective treatment strategies for this debilitating conditionIn conclusion, ankylosing spondylitis (AS) is a complex disease that requires a multi-disciplinary approach to diagnosis and treatment. Recent advancesin imaging, genomics, and computational methods have provided new insights into the underlying causes of AS and may lead to earlier intervention and more targeted treatment approaches. However, further research is needed to fully understand the pathogenesis of AS and develop more effective treatments for this chronic and debilitating condition。
T2 mapping序列对强直性脊柱炎的早期诊断价值
T2 mapping序列对强直性脊柱炎的早期诊断价值甄涛;胡大成;陈文辉【摘要】目的：探讨T2 mapping序列对强直性脊柱炎（AS）的早期诊断价值。
方法收集有慢性炎性下腰痛症状的患者50例，其中临床诊断为AS的36例，另14例诊断为单纯慢性下腰痛（LBP）。
所有患者均进行骶髂关节常规序列及8回波T2 mapping 序列的扫描，分别测量两组患者双侧骶髂骨骨性关节面下骨髓的T2值，并进行统计学分析。
结果 AS组左、右侧及双侧骶骨T2值与LBP组比较差异无统计学意义。
AS组左、右侧及双侧髂骨T2值均大于LBP组（P<0.05），差异有统计学意义。
36例AS患者中的26例中共发现60处病灶，病灶与其周围相对正常组织比较，差异有显著性（P=0.000）。
结论 T2 mapping 序列可早期发现骶髂关节骨髓T2值的改变，可对AS早期骨髓病变与单纯的慢性下腰痛患者进行鉴别，有利于患者的早期诊断。
%Objective To investigate the value of T2 mapping sequence in early diagnosis of ankylosing spondylitis. Methods 50 patients with chronic inflamatory low back pain symptoms were collected in this study,out of which 36 patients were clinically diagnosed as AS and the remaining 14 as simple chronic low back pain (LBP).Images were obtained using a 1.5T MR scanner HDxt GE. The sequences included traditional sequences and 8 echo T2 mapping sequence. T2 values of subchondral bone morrow in bilateral ilium and sacrum along the sacroiliac joint were measured using GE AW4.4 post-processing workstation. An independent two sample t-test (SPSS Version 18)was used to statistically analyse the difference of T2 values between AS and LBP patients with P value of<0.05 as the level of statistically significantdifferent. Results There was no statistically significant difference between the AS and LBP group of the mean T2 values in bilateral sacrum. The mean T2 values in bilateral iliac bone marrow of AS patients were higher than the LBP patients (P<0.05). There were 26 patients with a total of 60 lesions in 36 AS patients . The mean T2 values of the lesions were higher than the neighboring relatively normal tissues.(P<0.05). Conclusion T2 mapping sequence could detect the early change of the T2 values of bone marrow,by which early bone marrow lesions of ankylosing spondylitis could be detected and we could use this technology to differentiate simple chronic low back pain and AS patients.【期刊名称】《浙江临床医学》【年(卷),期】2015(000)003【总页数】3页(P344-345,348)【关键词】T2 mapping;磁共振;骶髂关节;强直性脊柱炎【作者】甄涛;胡大成;陈文辉【作者单位】310006 杭州市第一人民医院;310006 杭州市第一人民医院;310006 杭州市第一人民医院【正文语种】中文血清学检测阴性脊柱关节病（SpA）是一种原发的影响脊柱、骶髂关节、外周大关节及肌腱处的炎症性病变，它的发生发展与人类白细胞抗原-B27（HLA-B27）基因有关［1］。
强直性脊柱炎患者的康复评估和监测方法
强直性脊柱炎患者的康复评估和监测方法强直性脊柱炎（Ankylosing Spondylitis，AS）是一种以慢性炎症为特征的自身免疫性疾病，主要累及脊柱和骨盆。
这种疾病引起脊柱关节的炎症，导致关节强直、疼痛和功能受损。
针对强直性脊柱炎患者的康复评估和监测方法至关重要，可以帮助医生和治疗团队了解患者的病情、治疗效果和康复进展，从而制定个体化的康复方案。
1. 临床评估工具为了评估强直性脊柱炎患者的康复情况，医生通常会使用临床评估工具。
其中，巴斯脊柱炎活动量表（BASDAI）和巴斯脊柱炎功能指数（BASFI）是常用的评估工具。
BASDAI通过评估患者在特定时间段内的疼痛、疲劳、早晨僵硬等症状的程度来评估疾病的活动性。
BASFI 则评估患者的功能受损程度，包括日常活动、睡眠和自理能力等。
2. 影像学评估强直性脊柱炎患者的康复评估和监测往往需要通过影像学技术来获取更多信息。
X线、磁共振成像（MRI）和超声波等技术都可以用于评估病情的进展和治疗效果。
X线能够检测患者的关节强直程度和骨质改变情况。
MRI可以提供更详细的关节炎症信息，帮助评估关节炎症的活跃性。
超声波则可帮助发现早期软组织炎症和滑膜肿胀。
3. 生物学标志物检测近年来，一些生物学标志物的检测在强直性脊柱炎患者的康复评估和监测中发挥越来越重要的作用。
例如，血沉、C反应蛋白（CRP）和白细胞计数等指标可以反映患者体内的炎症程度。
同时，关节液中的炎症细胞和炎症介质含量也可以作为评估指标。
4. 功能评估强直性脊柱炎患者的功能评估是康复评估的重要组成部分。
常用的功能评估工具包括巴斯脊柱炎功能指数（BASFI）、巴斯脊柱炎功能量表（BASFI-SI）和腰椎功能指数（ODI）等。
这些评估工具可以测量患者的疼痛、关节活动度、自理能力等指标，帮助医生和康复师了解患者的功能障碍情况。
5. 全面评估强直性脊柱炎患者的康复评估和监测需要进行全面的评估，综合考虑临床症状、影像学结果、生物学标志物检测和功能评估结果。