Suppr超能文献

用于预测肺隐球菌病患者隐球菌性脑膜炎的列线图。

A nomogram to predict cryptococcal meningitis in patients with pulmonary cryptococcosis.

作者信息

Tan Xiaoli, Deng Min, Fang Zhixian, Yang Qi, Zhang Ming, Wu Jiasheng, Chen Wenyu

机构信息

Department of Respiration, The Affiliated Hospital of Jiaxing University, Jiaxing, China.

Department of Infectious Diseases, The Affiliated Hospital of Jiaxing University, Jiaxing, China.

出版信息

Heliyon. 2024 Apr 30;10(9):e30281. doi: 10.1016/j.heliyon.2024.e30281. eCollection 2024 May 15.

Abstract

BACKGROUND

The most serious manifestation of pulmonary cryptococcosis is complicated with cryptococcal meningitis, while its clinical manifestations lack specificity with delayed diagnosis and high mortality. The early prediction of this complication can assist doctors to carry out clinical interventions in time, thus improving the cure rate. This study aimed to construct a nomogram to predict the risk of cryptococcal meningitis in patients with pulmonary cryptococcosis through a scoring system.

METHODS

The clinical data of 525 patients with pulmonary cryptococcosis were retrospectively analyzed, including 317 cases (60.38 %) with cryptococcal meningitis and 208 cases (39.62 %) without cryptococcal meningitis. The risk factors of cryptococcal meningitis were screened by univariate analysis, LASSO regression analysis and multivariate logistic regression analysis. Then the risk factors were incorporated into the nomogram scoring system to establish a prediction model. The model was validated by receiver operating characteristic (ROC) curve, decision curve analysis (DCA) and clinical impact curve.

RESULTS

Fourteen risk factors for cryptococcal meningitis in patients with pulmonary cryptococcosis were screened out by statistical method, including 6 clinical manifestations (fever, headache, nausea, psychiatric symptoms, tuberculosis, hematologic malignancy) and 8 clinical indicators (neutrophils, lymphocytes, glutamic oxaloacetic transaminase, T cells, helper T cells, killer T cells, NK cells and B cells). The AUC value was 0.978 (CI 96.2 %∼98.9 %), indicating the nomogram was well verified.

CONCLUSION

The nomogram scoring system constructed in this study can accurately predict the risk of cryptococcal meningitis in patients with pulmonary cryptococcosis, which may provide a reference for clinical diagnosis and treatment of patients with cryptococcal meningitis.

摘要

背景

肺隐球菌病最严重的表现是合并隐球菌性脑膜炎,其临床表现缺乏特异性,诊断延迟且死亡率高。对该并发症的早期预测可协助医生及时进行临床干预,从而提高治愈率。本研究旨在通过评分系统构建一种列线图,以预测肺隐球菌病患者发生隐球菌性脑膜炎的风险。

方法

回顾性分析525例肺隐球菌病患者的临床资料,其中317例(60.38%)合并隐球菌性脑膜炎,208例(39.62%)未合并隐球菌性脑膜炎。通过单因素分析、LASSO回归分析和多因素逻辑回归分析筛选隐球菌性脑膜炎的危险因素。然后将危险因素纳入列线图评分系统,建立预测模型。通过受试者工作特征(ROC)曲线、决策曲线分析(DCA)和临床影响曲线对模型进行验证。

结果

通过统计学方法筛选出肺隐球菌病患者隐球菌性脑膜炎的14个危险因素,包括6种临床表现(发热、头痛、恶心、精神症状、结核病、血液系统恶性肿瘤)和8项临床指标(中性粒细胞、淋巴细胞、谷草转氨酶、T细胞、辅助性T细胞、杀伤性T细胞、自然杀伤细胞和B细胞)。AUC值为0.978(CI 96.2%~98.9%),表明列线图得到了良好验证。

结论

本研究构建的列线图评分系统可准确预测肺隐球菌病患者发生隐球菌性脑膜炎的风险,可为隐球菌性脑膜炎患者的临床诊断和治疗提供参考。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8dc3/11079104/d4ae01c32150/gr1.jpg

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验