Xiaofeng Xu, Xiaohong Su, Weipeng Li, Li Xu, Dongcheng Li, Kai Dai, Junyu Liu, Jia Liu, Fuhua Peng, Ying Jiang
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Least Absolute Shrinkage and Selection Operator regression identified prognostic factors, and multivariate logistic regression was used to construct a nomogram for predicting mortality. The model's discrimination, calibration, and decision curve analysis (DCA) were evaluated.</p><p><strong>Results: </strong>Elderly patients accounted for 23.5% of the study population, exhibited distinct clinical characteristics, and had a significantly higher one-year all-cause mortality rate (31.2% [95% confidence interval (CI) 23.61-38.71] vs. 13.8% [95% CI 10.77-16.81], P < 0.001). Four prognostic factors for elderly patients were identified, and a predictive nomogram was developed. The predictive model achieved an area under the curve (AUC) of 0.81 (95% CI 0.71-0.91), and the AUC was 0.79 (95% CI 0.70-0.87) in the internal validation. The model was well-calibrated, and DCA indicated a net benefit.</p><p><strong>Conclusion: </strong>Non-HIV elderly CM patients present distinct clinical characteristics and have a higher mortality risk. 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引用次数: 0
摘要
背景:隐球菌性脑膜炎(CM)是一种危及生命的真菌感染,在非hiv(人类免疫缺陷病毒)老年人群中发病率越来越高。然而,关于非hiv老年患者CM的数据有限。本研究旨在使用迄今为止最大的数据集分析非hiv老年CM患者的临床特征、结局和预后因素。方法:对2013年至2022年期间接受治疗的667例非hiv CM患者的数据进行回顾性队列研究。患者分为老年组(≥60岁)和非老年组。分析临床特征、实验室结果和神经影像学结果。最小绝对收缩和选择算子回归确定预后因素,并使用多变量逻辑回归构建预测死亡率的nomogram。对模型的判别性、校正性和决策曲线分析(DCA)进行了评价。结果:老年患者占研究人群的23.5%,临床特征明显,1年全因死亡率明显高于老年患者(31.2%[95%置信区间(CI) 23.61 ~ 38.71] vs. 13.8% [95% CI 10.77 ~ 16.81], P结论:非hiv老年CM患者临床特征明显,死亡风险较高。该预测模型有助于早期识别高危患者,指导及时干预。
Clinical features and mortality risk factors in non-HIV elderly patients with cryptococcal meningitis: A retrospective cohort study from 2013 to 2022.
Background: Cryptococcal meningitis (CM) is a life-threatening fungal infection with increasing incidence among non-HIV (human immunodeficiency virus) elderly populations. However, data on CM in non-HIV elderly patients are limited. This study aimed to analyze the clinical features, outcomes, and prognostic factors in non-HIV elderly CM patients using the largest dataset to date.
Methods: A retrospective cohort study was conducted using data from 667 non-HIV CM patients treated between 2013 and 2022. Patients were categorized into elderly (≥60 years) and non-elderly groups. Clinical features, laboratory findings, and neuroimaging results were analyzed. Least Absolute Shrinkage and Selection Operator regression identified prognostic factors, and multivariate logistic regression was used to construct a nomogram for predicting mortality. The model's discrimination, calibration, and decision curve analysis (DCA) were evaluated.
Results: Elderly patients accounted for 23.5% of the study population, exhibited distinct clinical characteristics, and had a significantly higher one-year all-cause mortality rate (31.2% [95% confidence interval (CI) 23.61-38.71] vs. 13.8% [95% CI 10.77-16.81], P < 0.001). Four prognostic factors for elderly patients were identified, and a predictive nomogram was developed. The predictive model achieved an area under the curve (AUC) of 0.81 (95% CI 0.71-0.91), and the AUC was 0.79 (95% CI 0.70-0.87) in the internal validation. The model was well-calibrated, and DCA indicated a net benefit.
Conclusion: Non-HIV elderly CM patients present distinct clinical characteristics and have a higher mortality risk. The predictive model may facilitate the early identification of high-risk patients and guide timely interventions.
期刊介绍:
PLOS Neglected Tropical Diseases publishes research devoted to the pathology, epidemiology, prevention, treatment and control of the neglected tropical diseases (NTDs), as well as relevant public policy.
The NTDs are defined as a group of poverty-promoting chronic infectious diseases, which primarily occur in rural areas and poor urban areas of low-income and middle-income countries. Their impact on child health and development, pregnancy, and worker productivity, as well as their stigmatizing features limit economic stability.
All aspects of these diseases are considered, including:
Pathogenesis
Clinical features
Pharmacology and treatment
Diagnosis
Epidemiology
Vector biology
Vaccinology and prevention
Demographic, ecological and social determinants
Public health and policy aspects (including cost-effectiveness analyses).