Observational studyPloS one2024
Establishment and validation of a prognostic nomogram for severe fever with thrombocytopenia syndrome: A retrospective observational study.
Observational study in PloS one, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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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.
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Who cites it
2 citing papers in PubMed.
- Early mortality risk prediction in severe fever with thrombocytopenia syndrome using an interpretable machine learning model based on routine clinical parameters.Frontiers in public health · 2026Article
- Prediction of Mortality by Clinical Laboratory Parameters in Severe Fever with Thrombocytopenia Syndrome: A Meta-Analysis.Tropical medicine and infectious disease · 2025Review
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSeveral scoring systems have been proposed to predict the risk of death due to severe fever with thrombocytopenia syndrome (STFS), but they have limitations. We developed a new prognostic nomogram for STFS-related death and compared its performance with previous scoring systems and the Acute Physiology and Chronic Health Evaluation score (APACHE II Score).
methodsA total of 292 STFS patients were retrospectively enrolled between January 2016 and March 2023. Boruta's algorithm and backward stepwise regression were used to select variables for constructing the nomogram. Time-dependent receiver operating characteristic (ROC) curves and clinical decision curves were generated to compare the strengths of the nomogram with others.
resultsAge, Sequential Organ Failure Assessment Score (SOFA score), state of consciousness, continuous renal replacement therapy (CRRT), and D-dimer were significantly correlated with mortality in both univariate and multivariate analyses (P<0.05). We developed a nomogram using these variables to predict mortality risk, which outperformed the SFTS and APACHE II scores (Training ROC: 0.929 vs. 0.848 vs. 0.792; Validation ROC: 0.938 vs. 0.839 vs. 0.851; P<0.001). In the validation set, the SFTS model achieved an accuracy of 76.14%, a sensitivity of 95.31%, a specificity of 25.00%, a precision of 77.22%, and an F1 score of 85.32%. The nomogram showed a superior performance with an accuracy of 86.36%, a precision of 88.24%, a recall of 93.75%, and an F1 score of 90.91%.
conclusionAge, consciousness, SOFA Score, CRRT, and D-Dimer are independent risk factors for STFS-related death. The nomogram based on these factors has an excellent performance in predicting STFS-related death and is recommended for clinical practice.
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