ArticleCritical care research and practice2021
Mortality Prediction Using SaO
Article in Critical care research and practice, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
16 citing papers in PubMed.
- Admission SpOTranslational pediatrics · 2026Article
- Artificial intelligence in intensive care units: a scoping review addressing the translational gap to clinical practice.Korean journal of anesthesiology · 2026Article
- Non-invasive SpO2/FiO2 ratio (SFR) as surrogate for PaO2/FiO2 ratio (PFR): A scoping review.Journal of critical care medicine (Universitatea de Medicina si Farmacie din Targu-Mures) · 2025Review
- Triaging acute chest syndrome clinical decision-making using bedside SaO2/FiO2 ratio.Blood advances · 2025Observational
- Unraveling relevant cross-waves pattern drifts in patient-hospital risk factors among hospitalized COVID-19 patients using explainable machine learning methods.BMC infectious diseases · 2025Article
- Revolutionizing Chinese medicine granule placebo with a machine learning four-color model.Chinese medicine · 2025Article
- An interpretable machine learning model for predicting mortality risk in adult ICU patients with acute respiratory distress syndrome.Frontiers in medicine · 2025Article
- Baseline predictors for 28-day COVID-19 severity and mortality among hospitalized patients: results from the IMPACC study.Frontiers in medicine · 2025Article
- ARDS Studies in Critical Care Journals: How Representative Are the Patients Studied?Critical care research and practice · 2025Article
- Utility of Clinical Variables for Deciding Palliative Care in Paraquat Poisoning: A Retrospective Study.Indian journal of critical care medicine : peer-reviewed, official publication of Indian Society of Critical Care Medicine · 2024Article
- Prognostic Accuracy of SpOThe western journal of emergency medicine · 2023Article
- Hemodynamic, Oxygenation and Lymphocyte Parameters Predict COVID-19 Mortality.Pathophysiology : the official journal of the International Society for Pathophysiology · 2023Article
- XGBoost-Based Simple Three-Item Model Accurately Predicts Outcomes of Acute Ischemic Stroke.Diagnostics (Basel, Switzerland) · 2023Article
- Pulse oximetry for the diagnosis and management of acute respiratory distress syndrome.The Lancet. Respiratory medicine · 2022Review
- Outcome Prediction in Critically-Ill Patients with Venous Thromboembolism and/or Cancer Using Machine Learning Algorithms: External Validation and Comparison with Scoring Systems.International journal of molecular sciences · 2022Article
- Comparison of machine learning and logistic regression as predictive models for adverse maternal and neonatal outcomes of preeclampsia: A retrospective study.Frontiers in cardiovascular medicine · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposePaO
methodsWe extracted data from the eICU Collaborative Research Database. The features age, gender, SaO
resultsThe XGBoost hyperparameter optimized model had an AUC score of .85 on the test set. The hyperparameters selected to train the final models were as follows: colsample_bytree of 0.8, gamma of 1, max_depth of 3, subsample of 1, min_child_weight of 10, and scale_pos_weight of 3. The SHAP, ELI5, and XGBoost feature importance analysis demonstrates that the S/F ratio ranks as the strongest predictor for mortality amongst the physiologic variables. The partial dependence plots illustrate that mortality rises significantly above S/F values of 200.
conclusionS/F was a stronger predictor of mortality than P/F based upon feature importance evaluation of our data. Our study is hypothesis-generating and a prospective evaluation is warranted.
Identifiers
What OpenQuestion holds
Registered trials
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.