分析巴西重症登革热患者数据，识别并发症高危因素并提出预测工具。
Clinical characteristics, complications and outcomes of critically ill patients with Dengue in Brazil, 2012-2024: a nationwide, multicentre cohort study
- 基于全国253个重症病房数据，用机器学习预测登革热并发症风险。
- 10.1%患者出现并发症，高白细胞、低血小板及老年患者风险显著升高。
- 研究为登革热高发区早期预警和干预提供实用工具，适合临床医生参考。
登革热疫情是重大公共卫生问题，巴西在2024年报告了全球71%的病例。本研究旨在描述2012-2024年巴西重症监护室（ICU）中重症登革热患者的特征，评估时间趋势，描述入院后新发并发症，并确定入院时预测住院期间发生并发症的风险因素。研究纳入来自56家医院253个ICU的11,047例登革热患者。采用描述性统计分析登革热ICU人群特征，使用逻辑回归识别入住期间并发症的风险因素，并构建机器学习框架以预测并发症演变风险。可视化分析使用ISARIC VERTEX工具。结果显示，在11,047例入院患者中，1,117例（10.1%）发展为并发症，包括非侵入性通气（437例）、有创通气（166例）、血管活性药物使用（364例）、输血（353例）和肾替代治疗（103例）。年龄>80岁（OR: 3.10, 95% CI: 2.02–4.92）、慢性肾病（OR: 2.94, 2.22–3.89）、肝硬化（OR: 3.65, 1.82–7.04）、血小板<50,000细胞/mm³（OR: 2.25, 1.89–2.68）和白细胞>7,000细胞/mm³（OR: 2.47, 2.02–3.03）为并发症显著风险因素。研究提出一种机器学习预测工具，具备良好判别力和校准性能。结论：本研究揭示了重症登革热患者的关键并发症风险因素，如高龄、合并症、高白细胞与低血小板水平，并提供了可应用于登革热流行地区早期识别与干预的预测工具。
原文摘要 · Abstract (English)
Background. Dengue outbreaks are a major public health issue, with Brazil reporting 71% of global cases in 2024. Purpose. This study aims to describe the profile of severe dengue patients admitted to Brazilian Intensive Care units (ICUs) (2012-2024), assess trends over time, describe new onset complications while in ICU and determine the risk factors at admission to develop complications during ICU stay. Methods. We performed a prospective study of dengue patients from 253 ICUs across 56 hospitals. We used descriptive statistics to describe the dengue ICU population, logistic regression to identify risk factors for complications during the ICU stay, and a machine learning framework to predict the risk of evolving to complications. Visualisations were generated using ISARIC VERTEX. Results. Of 11,047 admissions, 1,117 admissions (10.1%) evolved to complications, including non-invasive (437 admissions) and invasive ventilation (166), vasopressor (364), blood transfusion (353) and renal replacement therapy (103). Age>80 (OR: 3.10, 95% CI: 2.02-4.92), chronic kidney disease (OR: 2.94, 2.22-3.89), liver cirrhosis (OR: 3.65, 1.82-7.04), low platelets (<50,000 cells/mm3; OR: OR: 2.25, 1.89-2.68), and high leukocytes (>7,000 cells/mm3; OR: 2.47, 2.02-3.03) were significant risk factors for complications. A machine learning tool for predicting complications was proposed, showing accurate discrimination and calibration. Conclusion. We described a large cohort of dengue patients admitted to ICUs and identified key risk factors for severe dengue complications, such as advanced age, presence of comorbidities, higher level of leukocytes and lower level of platelets. The proposed prediction tool can be used for early identification and targeted interventions to improve outcomes in dengue-endemic regions.
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