Igor Tona Peres , Otavio T. Ranzani , Leonardo S.L. Bastos , Silvio Hamacher , Tom Edinburgh , Esteban Garcia-Gallo , Fernando Augusto Bozza
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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. Visualizations were generated using ISARIC VERTEX.</div></div><div><h3>Results</h3><div>Of 11,047 admissions, 1117 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 (odds ratio [OR]: 3.10, 95% confidence interval: 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: 2.25, 1.89-2.68), and high leukocytes (>7000 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.</div></div><div><h3>Conclusion</h3><div>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.</div></div>","PeriodicalId":14006,"journal":{"name":"International Journal of Infectious Diseases","volume":"159 ","pages":"Article 108023"},"PeriodicalIF":4.3000,"publicationDate":"2025-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Clinical characteristics, complications and outcomes of critically ill patients with Dengue in Brazil, 2012-2024: A nationwide, multicenter cohort study\",\"authors\":\"Igor Tona Peres , Otavio T. Ranzani , Leonardo S.L. Bastos , Silvio Hamacher , Tom Edinburgh , Esteban Garcia-Gallo , Fernando Augusto Bozza\",\"doi\":\"10.1016/j.ijid.2025.108023\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Background</h3><div>Dengue outbreaks are a major public health issue, with Brazil reporting 71% of global cases in 2024.</div></div><div><h3>Purpose</h3><div>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.</div></div><div><h3>Methods</h3><div>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. Visualizations were generated using ISARIC VERTEX.</div></div><div><h3>Results</h3><div>Of 11,047 admissions, 1117 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 (odds ratio [OR]: 3.10, 95% confidence interval: 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: 2.25, 1.89-2.68), and high leukocytes (>7000 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.</div></div><div><h3>Conclusion</h3><div>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.</div></div>\",\"PeriodicalId\":14006,\"journal\":{\"name\":\"International Journal of Infectious Diseases\",\"volume\":\"159 \",\"pages\":\"Article 108023\"},\"PeriodicalIF\":4.3000,\"publicationDate\":\"2025-08-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Infectious Diseases\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1201971225002474\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"INFECTIOUS DISEASES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Infectious Diseases","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1201971225002474","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"INFECTIOUS DISEASES","Score":null,"Total":0}
Clinical characteristics, complications and outcomes of critically ill patients with Dengue in Brazil, 2012-2024: A nationwide, multicenter cohort study
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. Visualizations were generated using ISARIC VERTEX.
Results
Of 11,047 admissions, 1117 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 (odds ratio [OR]: 3.10, 95% confidence interval: 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: 2.25, 1.89-2.68), and high leukocytes (>7000 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.
期刊介绍:
International Journal of Infectious Diseases (IJID)
Publisher: International Society for Infectious Diseases
Publication Frequency: Monthly
Type: Peer-reviewed, Open Access
Scope:
Publishes original clinical and laboratory-based research.
Reports clinical trials, reviews, and some case reports.
Focuses on epidemiology, clinical diagnosis, treatment, and control of infectious diseases.
Emphasizes diseases common in under-resourced countries.