Clinical predictors of malignancy in lymphadenopathy: A multivariable analysis from a quick diagnosis unit

淋巴结肿大恶性肿瘤的临床预测因素:来自快速诊断单元的多变量分析

阅读:1

Abstract

BACKGROUND: Peripheral lymphadenopathy (LA) has diverse causes and may indicate malignancy, particularly in referred patients. AIM: To characterise patients referred for unexplained LA to a quick diagnosis unit, and identify independent predictors of malignancy. DESIGN AND METHODS: We conducted a retrospective study of 485 consecutive adults evaluated for unexplained LA between 2017 and 2023. The primary outcome was malignancy. Secondary outcomes included diagnostic delay and time to oncology referral. Demographic, clinical and laboratory variables were compared across aetiological groups. A parsimonious multivariable logistic regression model included five clinically relevant predictors identified in univariable analyses and supported by biological plausibility. RESULTS: Median age was 46 years, and time to first visit was 11 days. Cervical nodes were most frequent (51.9%), followed by supraclavicular (18.6%). Malignancy was diagnosed in 20.8% of patients, with diagnostic delay of 26.5 days (15.5-42). Other specific diagnoses were established in 35.5% of cases, while 43.7% were reactive. Malignant cases were older (60.8 vs 42 years), predominantly male (68.3% vs 44.5%), had higher drug exposure (50.0% vs 29.8%), and shorter symptom duration (45 vs 90 days). In multivariable analysis, independent predictors of malignancy were: age (odds ratio (OR) 1.71 per 10-year increase), male sex (OR 3.25), lymph node size (OR 1.36 per 5 mm increase), indurated consistency (OR 3.42), and supraclavicular location (OR 4.96). Median time to oncology evaluation was 47 days. CONCLUSION: The QDU model enables timely diagnosis and detects malignancy in over 20% of cases. Recognising clinical predictors may help prioritise high-risk patients and streamline diagnostic pathways.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。