ArticleBMC medicine2026
A validated model for early prediction of group A streptococcal aetiology in necrotising soft tissue infections using minimal patient data.
Article in BMC medicine, 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
15 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundNecrotising soft tissue infections (NSTI) are life-threatening conditions caused by diverse bacteria. Treatment strategies have remained largely universal and unchanged, and only modest improvements in patient outcomes have been observed. Emerging insights into NSTI pathogenesis may enable more targeted approaches. Because microbial aetiology is central to guiding appropriate therapy, we aimed to develop and externally validate machine learning models capable of predicting microbial aetiology using only data available at an early stage. In parallel, we explored whether similar models could predict selected clinical endpoints related to surgical management, patient handling, and organ support.
methodsWe used data from the INFECT study, an international multicentre prospective cohort investigating NSTI characteristics and pathogenesis. A total of 409 adults with surgically confirmed NSTI were enrolled between February 2013 and June 2017 from five Scandinavian hospitals. More than 700 clinical variables were collected from hospital admission to intensive care unit entry. Machine learning models were developed to predict the presence of Streptococcus pyogenes (GAS, Group A streptococcus) and five clinical endpoints: risk of amputation, size of skin defect, maximum skin defect size, length of intensive care (ICU) stay, and need for renal replacement therapy. Unsupervised variable selection was implemented, and Shapley Additive explanations were used for model interpretability. External validation employed a retrospective multicentre cohort of 216 NSTI patients treated in 11 Dutch hospitals between January 2013 and December 2017.
resultsEight presurgical variables (age, diabetes, affected area, prior surgical intervention, and blood creatinine and haemoglobin concentrations) were sufficient for predicting GAS aetiology with high discriminatory power. Performance was good in both the development cohort (ROC-AUC 0.828; 95% CI 0.763-0.883) and the external validation cohort (ROC-AUC 0.758; 95% CI 0.696-0.821). Prediction of clinical endpoints related to surgical management, ICU stay, and organ support was unsuccessful.
conclusionsWe developed and externally validated a model predicting GAS aetiology in NSTI using presurgical data alone. Early identification of GAS may improve clinical handling and support tailored decisions on treatment and infection control, including management of close contacts and reduction of hospital transmission risk.
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.