ArticleJournal of clinical medicine2022
Machine Learning Model Development and Validation for Predicting Outcome in Stage 4 Solid Cancer Patients with Septic Shock Visiting the Emergency Department: A Multi-Center, Prospective Cohort Study.
Article in Journal of clinical medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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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.
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Who cites it
7 citing papers in PubMed.
- Artificial intelligence for early sepsis detection and dynamic prognostication in onco-critical care.World journal of critical care medicine · 2026Review
- Development and multi-center validation of a machine learning‑based prediction model for mortality in tumor-related sepsis.BMC infectious diseases · 2026Article
- Development of a machine learning-based sepsis prediction model for real-world clinical settings in South Korea: a single-center retrospective study.Journal of Korean biological nursing science · 2026Article
- The role of artificial intelligence in sepsis in the Emergency Department: a narrative review.Annals of translational medicine · 2025Review
- Comparison of Predictive Models for Keloid Recurrence Based on Machine Learning.Journal of cosmetic dermatology · 2025Article
- Septic shock in the immunocompromised cancer patient: a narrative review.Critical care (London, England) · 2024Review
- Review
Corrections and comments
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Authors and funding
14 authors.
Funding
Abstract
A reliable prognostic score for minimizing futile treatments in advanced cancer patients with septic shock is rare. A machine learning (ML) model to classify the risk of advanced cancer patients with septic shock is proposed and compared with the existing scoring systems. A multi-center, retrospective, observational study of the septic shock registry in patients with stage 4 cancer was divided into a training set and a test set in a 7:3 ratio. The primary outcome was 28-day mortality. The best ML model was determined using a stratified 10-fold cross-validation in the training set. A total of 897 patients were included, and the 28-day mortality was 26.4%. The best ML model in the training set was balanced random forest (BRF), with an area under the curve (AUC) of 0.821 to predict 28-day mortality. The AUC of the BRF to predict the 28-day mortality in the test set was 0.859. The AUC of the BRF was significantly higher than those of the Sequential Organ Failure Assessment score and the Acute Physiology and Chronic Health Evaluation II score (both p < 0.001). The ML model outperformed the existing scores for predicting 28-day mortality in stage 4 cancer patients with septic shock. However, further studies are needed to improve the prediction algorithm and to validate it in various countries. This model might support clinicians in real-time to adopt appropriate levels of care.
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Registered trials
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