ArticleJournal of clinical laboratory analysis2021
Predictive value of red blood cell distribution width in septic shock patients with thrombocytopenia: A retrospective study using machine learning.
Article in Journal of clinical laboratory analysis, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Diagnostic value of RDW for the prediction of mortality in adult sepsis patients: A systematic review and meta-analysis.Frontiers in immunology · 2022Pooled it
- XGBoost outperforms other machine learning models in diagnosing Sepsis-Associated Thrombocytopenia: a multicenter retrospective study.Frontiers in medicine · 2026Article
- Septic Shock in Hematological Malignancies: Role of Artificial Intelligence in Predicting Outcomes.Current oncology (Toronto, Ont.) · 2025Review
- A machine learning model for robust prediction of sepsis-induced coagulopathy in critically ill patients with sepsis.Frontiers in cellular and infection microbiology · 2025Article
- Association Between the Red Blood Cell Distribution Width and 30-Day Mortality in Intensive Care Patients Undergoing Cardiac Surgery: A Retrospective Observational Study Based on the Medical Information Mart for Intensive Care-IV Database.Annals of laboratory medicine · 2024Observational
- Interpretable machine learning model for early prediction of 28-day mortality in ICU patients with sepsis-induced coagulopathy: development and validation.European journal of medical research · 2024Article
- Deep learning approach for early prediction of COVID-19 mortality using chest X-ray and electronic health records.BMC bioinformatics · 2023Article
- Applications of Artificial Intelligence in Thrombocytopenia.Diagnostics (Basel, Switzerland) · 2023Review
- Predictive value of red blood cell distribution width in septic shock patients with thrombocytopenia: A retrospective study using machine learning.Journal of clinical laboratory analysis · 2021Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSepsis-associated thrombocytopenia (SAT) is common in critical patients and results in the elevation of mortality. Red cell distribution width (RDW) can reflect body response to inflammation and oxidative stress. We try to investigate the relationship between the RDW and the prognosis of patients with SAT through machine learning.
methods809 patients were retrospectively analyzed from the Medical Information Mart for Intensive Care III (MIMIC-III) database. The eXtreme Gradient Boosting (XGBoost) and SHapley Additive exPlanations (SHAP) were used to analyze the impact of each feature. Logistic regression analysis, propensity score matching (PSM), receiver-operating characteristics (ROC) curve analysis, and the Kaplan-Meier method were used for data processing.
resultsThe patients with thrombocytopenia had higher 28-day mortality (48.2%). Machine learning indicated that RDW was the second most important in predicting 28-day mortality. The RDW was significantly increased in non-survivors by logistic regression and PSM. ROC curve shows that RDW has moderate predictive power for 28-day mortality. The patients with RDW>16.05 exhibited higher mortality through Kaplan-Meier analysis.
conclusionsInterpretable machine learning can be applied in clinical research. Elevated RDW is not only common in patients with SAT but is also associated with a poor prognosis.
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