背景:拇指腕掌(CMC)骨关节炎(OA)进展患者的可测量变化包括关节间隙狭窄,骨赘形成,半脱位,和邻近组织的变化。半脱位,机械不稳定的迹象,被假定为CMCOA进展的早期生物力学指标。已经提出了各种射线照相视图和手部姿势来最好地评估CMC脱位。但是从CT图像中获得的3D测量作为最佳度量。然而,我们不知道哪个拇指姿势会导致最表明OA进展的半脱位。
目的:使用骨赘体积作为OA进展的定量指标,我们问:(1)背脱位是否因拇指姿势而异,时间,拇指CMCOA患者的疾病严重程度(2)在哪种拇指姿势下,背侧半脱位最能区分稳定性CMCOA患者和进行性CMCOA患者?(3)在这些姿势中,背骨半脱位的值表明CMCOA进展的可能性很高?
方法:2011年至2014年期间,743例患者在我们的机构接受了治疗。我们考虑了年龄在45到75岁之间的人,触诊有压痛或研磨试验结果阳性,并将Eaton第0期或第1期放射学拇指CMCOA修改为可能符合入选条件。基于这些标准,109名患者符合条件。在符合条件的病人中,19人因为对研究参与缺乏兴趣而被排除在外,另外四个在最低研究随访之前丢失或数据集不完整,留下86名(43名女性患者,平均年龄为53±6岁,43名男性患者,平均年龄为60±7岁)患者进行分析。25名年龄在45至75岁之间的无症状参与者(对照)也被前瞻性招募参加本研究。对照的纳入标准包括在临床检查期间没有拇指疼痛和没有CMCOA的证据。在25个招募的控制中,三个人失去了随访,剩下22个用于分析(13名女性患者,平均年龄55±7岁,9名男性患者,平均年龄58±9岁)。在6年的研究期间,以11个拇指姿势获取患者和对照组的CT图像:中立,内收,绑架,屈曲,扩展,抓,jar,捏,抓住装载,jar已加载,和捏加载。患者在登记时(0年)和1.5年、3年、4.5年和6年以及对照组在0年和6年采集CT图像。从CT图像来看,第一掌骨(MC1)和梯形的骨模型被分割,并根据其CMC关节面计算坐标系。计算MC1相对于梯形的掌侧-背侧位置,并针对骨骼大小进行归一化。根据梯形骨赘体积将患者分为稳定性OA和进行性OA亚组。通过拇指姿势分析MC1掌侧-背侧位置,时间,使用线性混合效应模型和疾病严重程度。数据报告为平均值和95%置信区间。各组分析了每个拇指姿势的入组时掌侧-背侧位置的差异和研究过程中的迁移率(对照,稳定的OA,并正在进行OA)。MC1位置的受试者工作特征曲线分析用于识别将OA稳定的患者与OA进展的患者区分开的拇指姿势。YoudenJ统计量用于确定要测试的那些姿势的半脱位的优化截止值,作为OA进展的指标。灵敏度,特异性,负预测值,计算阳性预测值以评估MC1位置的姿态特异性截止值作为进展性OA指标的表现.
结果:在屈曲中,在患有稳定性OA(平均-6.2%[95%CI-8.8%至-3.6%])和对照(平均-6.1%[95%CI-8.9%至-3.2%])的患者中,MC1位置位于关节中心,进展性OA患者出现背侧半脱位(平均5.0%[95%CI1.3%to8.6%];p<0.001)。进展性OA组中与MC1背侧半脱位最快相关的姿势是拇指屈曲(平均每年增加3.2%[95%CI2.5%至3.9%])。相比之下,在稳定OA组中,MC1背向迁移的速度要慢得多(p<0.001),平均每年仅为0.1%(95%CI-0.4%至0.6%)。招募时屈曲期间,掌侧MC1位置的临界值为1.5%(C统计量:0.70)是OA进展的中度指标,阳性预测值高(0.80),阴性预测值低(0.54)。屈曲半脱位率的阳性和阴性预测值(每年2.1%)很高(分别为0.81和0.81)。大多数指标表明OA进展的可能性很高(敏感性0.96,阴性预测值0.89)是双重截止值,将屈曲中的半脱位率(每年2.1%)与有负荷的捏(每年1.2%)相结合。
结论:在拇指屈曲姿势中,只有进展性OA组出现MC1背侧半脱位。屈曲进展的MC1位置截断值为梯形的1.5%,这表明该姿势中任何程度的背侧半脱位都表明拇指CMCOA进展的可能性很高。然而,仅屈指的掌侧MC1位置不足以排除进展.纵向数据的可用性提高了我们识别疾病可能保持稳定的患者的能力。在屈曲过程中MC1位置每年变化<2.1%和在捏负荷过程中MC1位置每年变化<1.2%的患者中,在整个6年的研究期间,他们的疾病将保持稳定的信心非常高.这些截止率是一个下限,以及在各自的手部姿势中,背侧半脱位每年超过2%至1%的患者,极有可能经历进行性疾病。
结论:我们的研究结果表明,在有CMCOA早期症状的患者中,旨在进一步减少背侧半脱位的非手术干预措施或保留梯形和限制半脱位的手术治疗可能是有效的。我们的半脱位指标是否可以从更广泛可用的技术中严格计算,还有待确定。如普通射线照相术或超声波。
Measurable changes in patients with progression of thumb carpometacarpal (CMC) osteoarthritis (OA) include joint space narrowing, osteophyte formation, subluxation, and adjacent-tissue changes. Subluxation, an indication of mechanical instability, is postulated as an early biomechanical indicator of progressing CMC OA. Various radiographic views and hand postures have been proposed to best assess CMC subluxation, but 3D measurements derived from CT images serve as the optimal metric. However, we do not know which thumb pose yields subluxation that most indicates OA progression.
Using osteophyte volume as a quantitative measure of OA progression, we asked: (1) Does dorsal subluxation vary by thumb pose, time, and disease severity in patients with thumb CMC OA? (2) In which thumb pose(s) does dorsal subluxation most differentiate patients with stable CMC OA from those with progressing CMC OA? (3) In those poses, what values of dorsal subluxation indicate a high likelihood of CMC OA progression?
Between 2011 and 2014, 743 patients were seen at our institutions for trapeziometacarpal pain. We considered individuals who were between the ages of 45 and 75 years, had tenderness to palpation or a positive grind test result, and had modified Eaton Stage 0 or 1 radiographic thumb CMC OA as potentially eligible for enrollment. Based on these criteria, 109 patients were eligible. Of the eligible patients, 19 were excluded because of a lack of interest in study participation, and another four were lost before the minimum study follow-up or had incomplete datasets, leaving 86 (43 female patients with a mean age of 53 ± 6 years and 43 male patients with a mean age of 60 ± 7 years) patients for analysis. Twenty-five asymptomatic participants (controls) aged 45 to 75 years were also prospectively recruited to participate in this study. Inclusion criteria for controls included an absence of thumb pain and no evidence of CMC OA during clinical examination. Of the 25 recruited controls, three were lost to follow-up, leaving 22 for analysis (13 female patients with a mean age of 55 ± 7 years and nine male patients with a mean age of 58 ± 9 years). Over the 6-year study period, CT images were acquired of patients and controls in 11 thumb poses: neutral, adduction, abduction, flexion, extension, grasp, jar, pinch, grasp loaded, jar loaded, and pinch loaded. CT images were acquired at enrollment (Year 0) and Years 1.5, 3, 4.5, and 6 for patients and at Years 0 and 6 for controls. From the CT images, bone models of the first metacarpal (MC1) and trapezium were segmented, and coordinate systems were calculated from their CMC articular surfaces. The volar-dorsal location of the MC1 relative to the trapezium was computed and normalized for bone size. Patients were categorized into stable OA and progressing OA subgroups based on trapezial osteophyte volume. MC1 volar-dorsal location was analyzed by thumb pose, time, and disease severity using linear mixed-effects models. Data are reported as the mean and 95% confidence interval. Differences in volar-dorsal location at enrollment and rate of migration during the study were analyzed for each thumb pose by group (control, stable OA, and progressing OA). A receiver operating characteristic curve analysis of MC1 location was used to identify thumb poses that differentiated patients whose OA was stable from those whose OA was progressing. The Youden J statistic was used to determine optimized cutoff values of subluxation from those poses to be tested as indicators of OA progression. Sensitivity, specificity, negative predictive values, and positive predictive values were calculated to assess the performance of pose-specific cutoff values of MC1 locations as indicators of progressing OA.
In flexion, the MC1 locations were volar to the joint center in patients with stable OA (mean -6.2% [95% CI -8.8% to -3.6%]) and controls (mean -6.1% [95% CI -8.9% to -3.2%]), while patients with progressing OA exhibited dorsal subluxation (mean 5.0% [95% CI 1.3% to 8.6%]; p < 0.001). The pose associated with the most rapid MC1 dorsal subluxation in the progressing OA group was thumb flexion (mean 3.2% [95% CI 2.5% to 3.9%] increase per year). In contrast, the MC1 migrated dorsally much slower in the stable OA group (p < 0.001), at only a mean of 0.1% (95% CI -0.4% to 0.6%) per year. A cutoff value of 1.5% for the volar MC1 position during flexion at enrollment (C-statistic: 0.70) was a moderate indicator of OA progression, with a high positive predictive value (0.80) but low negative predictive value (0.54). Positive and negative predictive values of subluxation rate in flexion (2.1% per year) were high (0.81 and 0.81, respectively). The metric that most indicated a high likelihood of OA progression (sensitivity 0.96, negative predictive value 0.89) was a dual cutoff that combined the subluxation rate in flexion (2.1% per year) with that of loaded pinch (1.2% per year).
In the thumb flexion pose, only the progressing OA group exhibited MC1 dorsal subluxation. The MC1 location cutoff value for progression in flexion was 1.5% volar to the trapezium , which suggests that dorsal subluxation of any amount in this pose indicates a high likelihood of thumb CMC OA progression. However, volar MC1 location in flexion alone was not sufficient to rule out progression. The availability of longitudinal data improved our ability to identify patients whose disease will likely remain stable. In patients whose MC1 location during flexion changed < 2.1% per year and whose MC1 location during pinch loading changed < 1.2% per year, the confidence that their disease would remain stable throughout the 6-year study period was very high. These cutoff rates were a lower limit, and any patients whose dorsal subluxation advanced faster than 2% to 1% per year in their respective hand poses, were highly likely to experience progressive disease.
Our findings suggest that in patients with early signs of CMC OA, nonoperative interventions aimed to reduce further dorsal subluxation or operative treatments that spare the trapezium and limit subluxation may be effective. It remains to be determined whether our subluxation metrics can be rigorously computed from more widely available technologies, such as plain radiography or ultrasound.