ArticleFrontiers in cellular and infection microbiology2020
Prediction of Sepsis in COVID-19 Using Laboratory Indicators.
Article in Frontiers in cellular and infection microbiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it, 20 citations in OpenAlex.
- Early detection of sepsis using machine learning algorithms: a systematic review and network meta-analysis.Frontiers in medicine · 2024Pooled it
- Untargeted and semi-targeted metabolomics approach for profiling small intestinal and fecal metabolome using high-resolution mass spectrometry.Metabolomics : Official journal of the Metabolomic Society · 2025Trial
- Algal metabolites as emerging anti-virulence therapeutics against Pseudomonas aeruginosa.Archives of microbiology · 2025Review
- Leveraging near-real-time patient and population data to incorporate fluctuating risk of severe COVID-19: development and prospective validation of a personalised risk prediction tool.EClinicalMedicine · 2025Article
- Advances and Challenges in the Diagnosis of Leishmaniasis.Molecular diagnosis & therapy · 2025Review
- Validation and interpretation of machine-learning models for rapid identification of active tuberculosis infection using routine laboratory indicators.Frontiers in cellular and infection microbiology · 2025Article
- Machine learning for the prediction of in-hospital mortality in patients with spontaneous intracerebral hemorrhage in intensive care unit.Scientific reports · 2024Article
- Conventional and unconventional T-cell responses contribute to the prediction of clinical outcome and causative bacterial pathogen in sepsis patients.Clinical and experimental immunology · 2024Article
- The unfolded protein response pathway as a possible link in the pathogenesis of COVID-19 and sepsis.Archives of virology · 2024Article
- Tumor Cell-Derived Exosomal Hybrid Nanosystems Loaded with Rhubarbic Acid and Tanshinone IIA for Sepsis Treatment.Journal of inflammation research · 2024Article
- Explainable Machine-Learning Model for Prediction of In-Hospital Mortality in Septic Patients Requiring Intensive Care Unit Readmission.Infectious diseases and therapy · 2022Article
- Clinical prediction system of complications among patients with COVID-19: A development and validation retrospective multicentre study during first wave of the pandemic.Intelligence-based medicine · 2022Article
- Risk Factors for Radiological Progression Within Admissive One Week in the Hospitalized COVID-19 Omicron Variant-Infected Patients.Infection and drug resistance · 2022Article
- The application of explainable artificial intelligence (XAI) in electronic health record research: A scoping review.Digital healthArticle
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Authors and funding
12 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: The outbreak of coronavirus disease 2019 (COVID-19) has become a global public health concern. Many inpatients with COVID-19 have shown clinical symptoms related to sepsis, which will aggravate the deterioration of patients' condition. We aim to diagnose Viral Sepsis Caused by SARS-CoV-2 by analyzing laboratory test data of patients with COVID-19 and establish an early predictive model for sepsis risk among patients with COVID-19. Methods: This study retrospectively investigated laboratory test data of 2,453 patients with COVID-19 from electronic health records. Extreme gradient boosting (XGBoost) was employed to build four models with different feature subsets of a total of 69 collected indicators. Meanwhile, the explainable Shapley Additive ePlanation (SHAP) method was adopted to interpret predictive results and to analyze the feature importance of risk factors. Findings: The model for classifying COVID-19 viral sepsis with seven coagulation function indicators achieved the area under the receiver operating characteristic curve (AUC) 0.9213 (95% CI, 89.94-94.31%), sensitivity 97.17% (95% CI, 94.97-98.46%), and specificity 82.05% (95% CI, 77.24-86.06%). The model for identifying COVID-19 coagulation disorders with eight features provided an average of 3.68 (±) 4.60 days in advance for early warning prediction with 0.9298 AUC (95% CI, 86.91-99.04%), 82.22% sensitivity (95% CI, 67.41-91.49%), and 84.00% specificity (95% CI, 63.08-94.75%). Interpretation: We found that an abnormality of the coagulation function was related to the occurrence of sepsis and the other routine laboratory test represented by inflammatory factors had a moderate predictive value on coagulopathy, which indicated that early warning of sepsis in COVID-19 patients could be achieved by our established model to improve the patient's prognosis and to reduce mortality.
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