ArticleBMJ open2025
Developing and validating a prognostic model to predict ICU mortality in patients with sepsis-associated thrombocytopenia: a retrospective cohort study based on MIMIC-IV.
Article in BMJ open, 2025. 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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2 citing papers in PubMed.
- Fibrinogen-to-red blood cell ratio is associated with disease burden and short-term outcomes in hospitalized patients with gout: comparison with conventional inflammatory indices.Frontiers in medicine · 2026Article
- From vital sign trajectories to data-driven targets: defining exploratory blood gas ranges in sepsis-associated thrombocytopenia.Frontiers in medicine · 2026Article
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7 authors.
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Abstract
objectiveGiven the high morbidity and mortality of patients with sepsis-associated thrombocytopenia (SATP) in the intensive care unit, this study retrospectively analysed the influencing factors for poor prognosis in patients with SATP using the MIMIC database, constructed a nomogram model and verified the predictive performance of the model.
designA retrospective cohort study.
settingThe data from MIMIC-IV, V.2.2. CLINICAL CHARACTERISTICS: The clinical features of SATP, including demographics, comorbidities, vital signs, laboratory parameters, treatments and clinical management, were extracted from the MIMIC-IV database.
methods1409 patients with SATP were included in this study and randomly divided into a training set and a validation set in a ratio of 7:3. The least absolute shrinkage and selection operator and multivariable Cox regression analysis were used to determine the optimal predictors and establish a prediction model. The receiver operating characteristic curve, calibration curve and decision curve analysis (DCA) were used to verify the accuracy and application value of the model.
resultsThe nomogram model incorporates nine factors, including clinical characteristics, laboratory test indicators, comorbidities and treatment methods, which were identified as predictors of SATP and used to construct the model. The area under the curve of the model was 0.868 (95% CI: 0.794 to 0.942) in the training set and 0.836 (95% CI: 0.681 to 0.991) in the validation set. The calibration curve and DCA confirmed the clinical application value of the nomogram.
conclusionsThe constructed nomogram for predicting patients with SATP has favourable predictive ability and is helpful to further optimise clinical management strategies.
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