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Digital ulcers in scleroderma: staging, characteristics and sub-setting through observation of 1614 digital lesions.

机译:硬皮病中的数字溃疡:通过观察1614个数字病变来分期,特征和亚环境。

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OBJECTIVE: To evaluate in SSc, the frequency of digital lesions and the morphology, characteristics, natural course and time to healing of 1614 digital ulcers (DUs). METHODS: One hundred SSc patients were followed up for 4 years. In the first step, the digital lesions were observed and classified at the time of presentation [digital pitting scar (DPS); DU; calcinosis; gangrene]. In the second step, DUs were divided into subsets according to their origin and main features. In the third step, the time to healing was recorded for each DU and the influence of DU main characteristics on time to healing was also evaluated. RESULTS: In the first step, 1614 digital lesions were observed: DPS, 712 (44.1%) lesions; DU, 785 (48.6%); calcinosis, 110 (6.8%); and gangrene, 7 (0.8%). In the second step, DUs were subsetted as follows: DU developed on DPS (8.8%), pure DU; DU developed on calcinosis (60%); DU derived from gangrene. In the third step, the mean time to healing was 25.6 (15.6) days in DPS, 76.2 (64) days in pure DU, 93.6 (59.2) days in calcinosis ulcers and 281.1 (263.3) in gangrene. CONCLUSIONS: In SSc, digital lesions are represented by DPS, DU, calcinosis and gangrene, and provide an evidence-based DU subsetting according to their origin and main characteristics. Subsetting may be helpful for a precise DU evaluation and staging, and in randomized controlled trials for a precise identification of those DUs that are to be included in therapeutic studies.
机译:目的:在SSc中评估数字病变的频率以及1614例数字溃疡(DUs)的形态,特征,自然病程和治愈时间。方法:对100例SSc患者进行了4年的随访。第一步,在出现时观察数字病变并进行分类[数字凹痕(DPS);杜;煅烧坏疽]。第二步,根据DU的来源和主要特征将其分为子集。在第三步中,记录每个DU的愈合时间,并评估DU主要特性对愈合时间的影响。结果:第一步,观察到1614个数字病变:DPS,712个病变(44.1%);杜,785(48.6%);煅烧110(6.8%);和坏疽,7(0.8%)。在第二步中,将DU分为以下子集:DU在DPS上显影(8.8%),纯DU; DU因钙化而发展(60%); DU来自坏疽。在第三步中,DPS的平均治愈时间为25.6(15.6)天,纯DU为76.2(64)天,煅烧溃疡为93.6(59.2)天,而坏疽为281.1(263.3)。结论:在SSc中,数字病变以DPS,DU,钙化病和坏疽为代表,并根据其起源和主要特征提供了基于证据的DU亚型。子集可能有助于精确的DU评估和分期,并且在随机对照试验中有助于精确识别要纳入治疗研究的DU。

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