ArticleBMC medical research methodology2022
Machine learning approach for the prediction of 30-day mortality in patients with sepsis-associated encephalopathy.
Article in BMC medical research methodology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
26 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Predicting sepsis onset in ICU using machine learning models: a systematic review and meta-analysis.BMC infectious diseases · 2023Pooled it
- Liraglutide alleviates sepsis-associated encephalopathy via attenuating neuronal damage, glial cell activation and mitochondrial dysfunction in a mouse model of sepsis.European journal of medical research · 2026Article
- A 24-hour landmark machine learning model for predicting new-onset sepsis-associated encephalopathy.Frontiers in medicine · 2026Article
- Development and validation of an explainable machine learning model for mortality prediction in ICU patients with lung cancer.Frontiers in cell and developmental biology · 2026Article
- Online Clinical Calculator for Predicting 28-Day Mortality in Older Adult Patients With Sepsis-Associated Encephalopathy: Retrospective Study Using MIMIC-IV.JMIR medical informatics · 2025Article
- Trajectories of blood urea nitrogen and 28-day mortality in patients with sepsis-associated delirium: a retrospective cohort analysis of the MIMIC-IV database.BMC infectious diseases · 2025Article
- Development and validation of an explainable machine learning model for predicting prognosis in sepsis patients with a history of cancer who were admitted to the intensive care unit.The Journal of international medical research · 2025Article
- Relationship between antibiotic use and short-term risk of mortality in patients with sepsis-associated encephalopathy: a study based on the medical information mart for intensive care database.BMC infectious diseases · 2025Observational
- Machine learning approach for the prediction of 30-day mortality in patients with sepsis-associated delirium.PloS one · 2025Article
- Early prediction of sepsis associated encephalopathy in elderly ICU patients using machine learning models: a retrospective study based on the MIMIC-IV database.Frontiers in cellular and infection microbiology · 2025Article
- Triglyceride-glucose index: a novel prognostic marker for sepsis-associated encephalopathy severity and outcomes.Frontiers in neurology · 2025Article
- Comprehensive risk factor-based nomogram for predicting one-year mortality in patients with sepsis-associated encephalopathy.Scientific reports · 2024Article
- Prediction of sepsis mortality in ICU patients using machine learning methods.BMC medical informatics and decision making · 2024Article
- Machine Learning and Clinical Predictors of Mortality in Cardiac Arrest Patients: A Comprehensive Analysis.Medical science monitor : international medical journal of experimental and clinical research · 2024Article
- Review
- Enhancing predictions with a stacking ensemble model for ICU mortality risk in patients with sepsis-associated encephalopathy.The Journal of international medical research · 2024Article
- Development and validation of a nomogram to predict the risk of sepsis-associated encephalopathy for septic patients in PICU: a multicenter retrospective cohort study.Journal of intensive care · 2024Article
- A simple nomogram for predicting the mortality of PICU patients with sepsis-associated encephalopathy: a multicenter retrospective study.Frontiers in neurology · 2024Article
- Prognostic analysis of elderly patients with pathogenic microorganisms positive for sepsis-associated encephalopathy.Frontiers in microbiology · 2024Article
- Explainable machine learning for early prediction of sepsis in traumatic brain injury: A discovery and validation study.PloS one · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveOur study aimed to identify predictors as well as develop machine learning (ML) models to predict the risk of 30-day mortality in patients with sepsis-associated encephalopathy (SAE). MATERIALS AND
methodsML models were developed and validated based on a public database named Medical Information Mart for Intensive Care (MIMIC)-IV. Models were compared by the area under the curve (AUC), accuracy, sensitivity, specificity, positive and negative predictive values, and Hosmer-Lemeshow good of fit test.
resultsOf 6994 patients in MIMIC-IV included in the final cohort, a total of 1232 (17.62%) patients died following SAE. Recursive feature elimination (RFE) selected 15 variables, including acute physiology score III (APSIII), Glasgow coma score (GCS), sepsis related organ failure assessment (SOFA), Charlson comorbidity index (CCI), red blood cell volume distribution width (RDW), blood urea nitrogen (BUN), age, respiratory rate, PaO
conclusionsThe ML models, as demonstrated by our study, can be used to evaluate the prognosis of SAE patients in the intensive care unit (ICU). Online calculator could facilitate the sharing of predictive models.
Indexed as
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.