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SWIFT 模型在青春期前女孩中硬化性苔藓的应用。

The SWIFT Model for Lichen Sclerosus Among Premenarchal Girls.

机构信息

Division of Pediatric and Adolescent Gynecology, Department of Obstetrics, Gynecology and Reproductive Sciences, Yale School of Medicine, New Haven, CT.

出版信息

J Low Genit Tract Dis. 2022 Jan 1;26(1):46-52. doi: 10.1097/LGT.0000000000000634.

Abstract

OBJECTIVE/PURPOSE: Delay in diagnosis of childhood lichen sclerosus (LS) can be ameliorated with an efficient evaluation tool. We sought to create a useful prognostic tool for rapid and accurate risk stratification for LS in premenarchal girls.

METHOD

We conducted a retrospective chart review at a single institution of premenarchal girls presenting with vulvovaginal complaints at a specialty pediatric and adolescent gynecology clinic at a major academic center. Sixty-nine patients seen between July 2019 and September 2020 were used as a pilot study to create a model for LS based on 18 signs and symptoms. Accuracy of the pilot model was confirmed in a larger data set (additional 105 patients, seen between January 2017 and December 2020), and model parameters were refined through cluster-based analytics.

RESULTS

Pilot study yielded 5 predictors for LS: soreness (S), whitening (W), urinary incontinence (I), fissures (F), and thickening of the clitoral hood (T)-SWIFT. The final refined model is given as log odds (LS) = -7 + 3·S + 17·W + 3·I + 3·F + 18·T. This model yielded a >97% accuracy in predicting LS among 174 unique patients (LS prevalence = 18%).

CONCLUSIONS

The SWIFT model accurately predicts clinical diagnosis of LS in premenarchal girls. Replication in other patient populations is highly encouraged. Awareness of LS is paramount, and an efficient, accurate evaluation tool will prove invaluable in assuring timely diagnosis and treatment for premenarchal patients.

摘要

目的/目的:延迟儿童硬化性苔藓(LS)的诊断可以通过有效的评估工具得到改善。我们旨在为青春期前女孩 LS 快速准确的风险分层创建一个有用的预测工具。

方法

我们在一家主要学术中心的专业儿科和青少年妇科诊所对一名患有外阴阴道疾病的青春期前女孩进行了单中心回顾性图表审查。使用 2019 年 7 月至 2020 年 9 月期间就诊的 69 例患者作为试点研究,根据 18 个体征和症状为 LS 创建模型。在更大的数据集(2017 年 1 月至 2020 年 12 月期间就诊的另外 105 例患者)中确认了试点模型的准确性,并通过基于聚类的分析对模型参数进行了优化。

结果

试点研究得出了 5 个 LS 的预测因素:疼痛(S)、变白(W)、尿失禁(I)、皲裂(F)和阴蒂包皮增厚(T)-SWIFT。最终的精炼模型为对数几率(LS)=-7+3·S+17·W+3·I+3·F+18·T。该模型在 174 例独特患者(LS 患病率=18%)中预测 LS 的准确率>97%。

结论

SWIFT 模型可准确预测青春期前女孩的 LS 临床诊断。强烈鼓励在其他患者人群中进行复制。对 LS 的认识至关重要,有效的、准确的评估工具对于确保青春期前患者的及时诊断和治疗将是非常宝贵的。

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