ArticleNursing in critical care2025
Development and validation of a prediction model for in-hospital mortality in patients with sepsis.
Article in Nursing in critical care, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Prediction models for mortality in patients with sepsis: a systematic review and meta-analysis.Frontiers in medicine · 2026Pooled it
- Long-Term Recovery After Severe Sepsis: A Case Study of Rhabdomyolysis and Suspected Remifentanil Withdrawal During ICU Recovery.Nursing in critical care · 2026Article
- Development and validation of an interpretable machine learning model for predicting sepsis risk in pleural effusion patients.Journal of thoracic disease · 2026Article
- Development and validation of a prediction model for in-hospital mortality in patients with sepsis.Nursing in critical care · 2025Article
- Diagnostic value of the combined detection of serum tumor necrosis factor-α and fecal calprotectin in early sepsis-related encephalopathy.Frontiers in medicine · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
backgroundSepsis, a life-threatening condition marked by organ dysfunction due to a dysregulated host response to infection, involves complex physiological and biochemical abnormalities.
aimTo develop a multivariate model to predict 4-, 6-, and 8-week mortality risks in intensive care units (ICUs). STUDY
designA retrospective cohort of 2389 sepsis patients was analysed using data captured by a clinical decision support system. Patients were randomly allocated into training (n = 1673) and validation (n = 716) sets at a 7:3 ratio. Least Absolute Shrinkage and Selection Operator (LASSO) regression identified variables incorporated into a multivariate Cox proportional hazards regression model to construct a prognostic nomogram. The area under the receiver operating characteristic curve (AUROC) assessed model accuracy, while performance was evaluated for discrimination, calibration and clinical utility.
resultsA risk score was developed based on 11 independent predictors from 35 initial factors. Key predictors included minimum Acute Physiology and Chronic Health Evaluation II (APACHE II) score as having the greatest impact on prognosis, followed by days of mechanical ventilation, number of vasopressors, maximum and minimum Sequential Organ Failure Assessment (SOFA) scores, infection sources, Gram-positive or Gram-negative bacteria and malignancy. The nomogram demonstrated superior discriminative ability, with AUROC values of 0.882 (95% confidence interval [CI], 0.855-0.909) and 0.851 (95% CI, 0.804-0.899) at 4 weeks; 0.836 (95% CI, 0.798-0.874) and 0.820 (95% CI, 0.761-0.878) at 6 weeks; and 0.843 (95% CI, 0.800-0.887) and 0.794 (95% CI, 0.720-0.867) at 8 weeks for training and validation sets, respectively.
conclusionA validated nomogram and web-based calculator were developed to predict in-hospital mortality in ICU sepsis patients. Targeting identified risk factors may improve outcomes for critically ill patients. RELEVANCE TO CLINICAL PRACTICE: The developed prediction model and nomogram offer a tool for assessing in-hospital mortality risk in ICU patients with sepsis, potentially aiding in nursing decisions and resource allocation.
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.