Trial reportFrontiers in cellular and infection microbiology2022
Clinical characteristics and risk factors associated with ICU-acquired infections in sepsis: A retrospective cohort study.
Trial report in Frontiers in cellular and infection microbiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.
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Who cites it
38 citing papers in PubMed, 47 citations in OpenAlex.
- Association between systemic immune-inflammation index and ICU-acquired infection in critically Ill patients without infection: a competing risks analysis from a retrospective cohort.BMC infectious diseases · 2026Article
- The role of red blood cell distribution width in prognosis prediction among critically ill patients with obstructive sleep apnea: Insights from the MIMIC-IV database.Scientific reports · 2026Article
- Development of a nomogram to predict in-hospital mortality of trauma patients in the ICU: an analysis of the MIMIC-IV database.Scientific reports · 2026Article
- Article
- Development and validation of a prognostic nomogram model for 28-day mortality based on IP-10 and clinical parameters in septic patients in the emergency department.Frontiers in cellular and infection microbiology · 2026Article
- SNHG5 Exacerbates Sepsis-Induced Inflammatory Injury in Coronary Artery Endothelial Cells by Regulating METAP2-Mediated IL-8 Secretion.Mediators of inflammation · 2026Article
- The role of acupuncture as a complementary therapy for sepsis: a narrative review of the evidence and future trial directions.Frontiers in medicine · 2026Review
- ICU-acquired infections and mortality in community-acquired pneumonia-induced sepsis: Insights from a transcriptomic analysis.PloS one · 2026Article
- Factors Associated with Candidemia After Living Donor Liver Transplantation: A Case-Control Study.Journal of clinical medicine · 2025Article
- Development and validation of a diagnostic prediction model for acute liver injury in sepsis based on serum ferritin level.European journal of medical research · 2025Article
- Interpretable Machine Learning Model Integrating Electrocardiographic and Acute Physiology Metrics for Mortality Prediction in Critical Ill Patients.Journal of clinical medicine · 2025Article
- Article
- Red cell distribution width and clinical outcomes in sepsis patients infected with Escherichia coli using data from MIMIC-IV.European journal of medical research · 2025Article
- The association between ionized calcium level and 28-day mortality in patients with sepsis: a cohort study.Scientific reports · 2025Article
- Central line-associated bloodstream infections in children: a systematic review and meta-analysis.Translational pediatrics · 2025Article
- Critical Management of Septic Orthopedic Patients: The Impact of Intensive Care on Survival and Recovery.Life (Basel, Switzerland) · 2025Article
- Optimizing Antibiotic Treatment Duration for ESBL-Producing Enterobacteriaceae Bacteremia in ICU: A Multicentric Retrospective Cohort Study.Antibiotics (Basel, Switzerland) · 2025Article
- The frequency of sepsis-associated delirium in intensive care unit and its effect on nurse workload.Journal of clinical nursing · 2025Article
- Development and validation of a nomogram to predict survival in septic patients with heart failure in the intensive care unit.Scientific reports · 2025Article
- A Web-Based Dynamic Nomogram for Early Diagnosis in Sepsis: Development and Validation with Real-World Clinical Utility.Infection and drug resistance · 2025Article
Corrections and comments
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Authors and funding
13 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Intensive care unit (ICU)-acquired infection is a common cause of poor prognosis of sepsis in the ICU. However, sepsis-associated ICU-acquired infections have not been fully characterized. The study aims to assess the risk factors and develop a model that predicts the risk of ICU-acquired infections in patients with sepsis. Methods: We retrieved data from the Medical Information Mart for Intensive Care (MIMIC) IV database. Patients were randomly divided into training and validation cohorts at a 7:3 ratio. A multivariable logistic regression model was used to identify independent risk factors that could predict ICU-acquired infection. We also assessed its discrimination and calibration abilities and compared them with classical score systems. Results: Of 16,808 included septic patients, 2,871 (17.1%) developed ICU-acquired infection. These patients with ICU-acquired infection had a 17.7% ICU mortality and 31.8% in-hospital mortality and showed a continued rise in mortality from 28 to 100 days after ICU admission. The classical Systemic Inflammatory Response Syndrome Score (SIRS), Sequential Organ Failure Assessment (SOFA), Oxford Acute Severity of Illness Score (OASIS), Simplified Acute Physiology Score II (SAPS II), Logistic Organ Dysfunction Score (LODS), Charlson Comorbidity Index (CCI), and Acute Physiology Score III (APS III) scores were associated with ICU-acquired infection, and cerebrovascular insufficiency, Gram-negative bacteria, surgical ICU, tracheostomy, central venous catheter, urinary catheter, mechanical ventilation, red blood cell (RBC) transfusion, LODS score and anticoagulant therapy were independent predictors of developing ICU-acquired infection in septic patients. The nomogram on the basis of these independent predictors showed good calibration and discrimination in both the derivation (AUROC = 0.737; 95% CI, 0.725-0.749) and validation (AUROC = 0.751; 95% CI, 0.734-0.769) populations and was superior to that of SIRS, SOFA, OASIS, SAPS II, LODS, CCI, and APS III models. Conclusions: ICU-acquired infections increase the likelihood of septic mortality. The individualized prognostic model on the basis of the nomogram could accurately predict ICU-acquired infection and optimize management or tailored therapy.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.