ArticleBMC emergency medicine2022
Prediction of prognosis in elderly patients with sepsis based on machine learning (random survival forest).
Article in BMC emergency medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 58 papers, 6 of them syntheses that pooled it.
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Who cites it
58 citing papers in PubMed, 6 syntheses or guidelines pooled it, 88 citations in OpenAlex.
- AI-Based Sepsis Prediction in Hospitalized Adults: Systematic Review, Subgroup Meta-Analysis, and Contextual Analysis of Clinical Burden.Journal of medical Internet research · 2026Pooled it
- Machine learning-based methods in diagnosing cardiac amyloidosis: a meta-analysis.Frontiers in cardiovascular medicine · 2026Pooled it
- Improving triage performance in emergency departments using machine learning and natural language processing: a systematic review.BMC emergency medicine · 2024Pooled it
- Machine learning for the prediction of sepsis-related death: a systematic review and meta-analysis.BMC medical informatics and decision making · 2023Pooled it
- 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
- Bioinformatics reveals the prognostic potential of manganese metabolism-related genes in lung adenocarcinoma.Functional & integrative genomics · 2026Article
- Hemoglobin glycation index correlates with the prognoses of critically ill patients with hyperlipidemia: a retrospective cohort study from the MIMIC-IV database.BMC endocrine disorders · 2026Article
- Early prognostication of septic shock in Korean adults aged 80 years and over: serum albumin combined with Sequential Organ Failure Assessment Score.Acute and critical care · 2026Article
- The Relation Between the Aggregate Index of Systemic Inflammation and the Mortality Rate of Cardiovascular Disease in the Elderly Population.Anatolian journal of cardiology · 2026Article
- Predicting time-to-event outcomes in critically ill patients with intracerebral hemorrhage using machine learning.The Journal of international medical research · 2026Article
- Molecular subtyping and immune microenvironment heterogeneity in pediatric influenza-associated prolonged multiple organ dysfunction syndrome.Translational pediatrics · 2026Article
- Machine Learning Models for Predicting Liver Metastasis at Diagnosis and Overall Survival in Ovarian Cancer: A SEER-Based Study.International journal of women's health · 2026Article
- Analysis and implications of validation results for a clinical data-based early warning model for geriatric sepsis.Frontiers in public health · 2026Article
- Discovery and validation of a multi-protein panel for predicting non-fatal major adverse cardiovascular events in diabetic kidney disease.Frontiers in endocrinology · 2026Article
- An explainable machine learning model predicts pediatric varicella encephalitis.Frontiers in cellular and infection microbiology · 2026Article
- Association between albumin-corrected anion gap and 60-day all-cause mortality in critically ill shock patients.European journal of medical research · 2025Article
- Establishing and validating models integrated with hematological biomarkers and clinical characteristics for the prognosis of non-esophageal squamous cell carcinoma patients.Annals of medicine · 2025Article
- Admission lactate and short-term mortality in the geriatric ICU: comparison with established severity scores.Aging clinical and experimental research · 2025Article
- Temporal validation of the Ramathibodi Older Sepsis Score (ROSS) for predicting 28-day mortality of older sepsis patients in the emergency department.BMC emergency medicine · 2025Article
Corrections and comments
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundElderly patients with sepsis have many comorbidities, and the clinical reaction is not obvious. Thus, clinical treatment is difficult. We planned to use the laboratory test results and comorbidities of elderly patients with sepsis from a large-scale public database Medical Information Mart for Intensive Care (MIMIC) IV to build a random survival forest (RSF) model and to evaluate the model's predictive value for these patients.
methodsClinical information of elderly patients with sepsis in MIMIC IV database was collected retrospectively. Machine learning (RSF) was used to select the top 30 variables in the training cohort to build the final RSF model. The model was compared with the traditional scoring systems SOFA, SAPSII, and APSIII. The performance of the model was evaluated by C index and calibration curve.
resultsA total of 6,503 patients were enrolled in the study. The top 30 important variables screened by RSF were used to construct the final RSF model. The new model provided a better C-index (0.731 in the validation cohort). The calibration curve described the agreement between the predicted probability of RSF model and the observed 30-day survival.
conclusionsWe constructed a prognostic model to predict a 30-day mortality risk in elderly patients with sepsis based on machine learning (RSF algorithm), and it proved superior to the traditional scoring systems. The risk factors affecting the patients were also ranked. In addition to the common risk factors of vasopressors, ventilator use, and urine output. Newly added factors such as RDW, type of ICU unit, malignant cancer, and metastatic solid tumor also significantly influence prognosis.
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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.