ArticleBMC infectious diseases2026
Development and multi-center validation of a machine learning‑based prediction model for mortality in tumor-related sepsis.
Article in BMC infectious diseases, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
Abstract
backgroundTumor-Related Sepsis requires a novel, straightforward model for early and precise prognosis prediction due to inadequate current assessments.
methodsThis retrospective study utilized data from the MIMIC-IV 3.0 database for model development and internal validation. External validation was performed using datasets from different centers to enhance the model's generalizability. Three machine learning techniques were employed for variable selection. After comparing multiple models, the best-performing one was selected and used to develop a clinically applicable nomogram.
resultsThis study included 3777 cases for the development of the model. Four distinct prediction models were developed by integrating various machine learning techniques and clinical characterization methods. These models incorporated 8, 14, 8, and 9 variables, respectively. During internal validation, Model 4 demonstrated acceptable discriminative performance, with AUC values of 0.771 (95% CI: 0.750-0.791) for the training set and 0.769 (95% CI: 0.738-0.799) for the test set. Compared to other models, Model 4 exhibited comparable calibration accuracy and higher clinical utility. It also yielded higher AUC values than the APACHE II, SOFA, and LODS scoring systems. In external validation, Model 4 maintained consistent performance, achieving AUC values of 0.703 (95% CI: 0.676-0.730) for the EICU-CARD database and 0.714 (95% CI: 0.633-0.796) for the Guangxi tertiary hospital dataset. A nomogram was developed to facilitate clinical interpretation and decision-making.
conclusionsDespite the retrospective design, this study developed a concise nomogram prediction model using multiple machine learning approaches and multi-database validation. The model demonstrated moderate discriminative ability in both internal and external validation, suggesting potential clinical utility that requires further prospective evaluation. CLINICAL
trial registrationThis study was registered with the Chinese Clinical Trial Registry on April 22, 2025 (registration number ChiCTRPID270259).
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.