ArticleBioMed research international2021
Prediction of 90-Day Mortality among Sepsis Patients Based on a Nomogram Integrating Diverse Clinical Indices.
Article in BioMed research international, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 2 of them syntheses 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
17 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Prediction models for mortality in patients with sepsis: a systematic review and meta-analysis.Frontiers in medicine · 2026Pooled it
- Machine learning for the prediction of sepsis-related death: a systematic review and meta-analysis.BMC medical informatics and decision making · 2023Pooled it
- TM and t-PAIC may be possible biomarkers to predict postoperative infection after cardiac surgery with cardiopulmonary bypass.Journal of cardiothoracic surgery · 2026Observational
- The diagnostic and prognostic value of sTM, t-PAIC, and NLRP3 in ICU patients with sepsis.BMC infectious diseases · 2026Article
- Explainable machine learning model for predicting septic shock in critically sepsis patients based on coagulation indexes: A multicenter cohort study.Chinese journal of traumatology = Zhonghua chuang shang za zhi · 2025Article
- Non-invasive urinary proteomic biomarkers for prognostic assessment in sepsis.Scientific reports · 2025Article
- Development of a novel clinical prediction model for sepsis related mortality by combining NEWS, PIRO and lactate.Biomolecules & biomedicine · 2025Article
- Performance Verification of High Sensitivity Analyzer for TAT, PIC, TM, and t-PAIC.TH open : companion journal to thrombosis and haemostasis · 2025Article
- Article
- Hierarchical Capability in Distinguishing Severities of Sepsis via Serum Lactate: A Network Meta-Analysis.Biomedicines · 2024Review
- Article
- Prognostic significance of blood immune cells in children with sepsis and establishment of a predictive model for PICU mortality: a retrospective study.Frontiers in pediatrics · 2024Article
- Risk factors for sepsis in patients with colorectal cancer complicated with gastrointestinal perforation and its impact on prognosis.Journal of gastrointestinal oncology · 2023Article
- Development and validation of a predictive model for in-hospital mortality in patients with sepsis-associated liver injury.Annals of translational medicine · 2022Article
- LncRNA GAS5 relates to Th17 cells and serves as a potential biomarker for sepsis inflammation, organ dysfunctions and mortality risk.Journal of clinical laboratory analysis · 2022Article
- Tissue-Type Plasminogen Activator-Inhibitor Complex as an Early Predictor of Septic Shock: A Retrospective, Single-Center Study.Disease markers · 2022Article
- Analysis of the prevalence and risk factors of post-intensive-care syndrome and post-sepsis syndrome in survivors of sepsis.Science progressArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSepsis is prevalent among intensive care units and is a frequent cause of death. Several studies have identified individual risk factors or potential predictors of sepsis-associated mortality, without defining an integrated predictive model. The present work was aimed at defining a nomogram for reliably predicting mortality.
methodsWe carried out a retrospective, single-center study based on 231 patients with sepsis who were admitted to our intensive care unit between May 2018 and October 2020. Patients were randomly split into training and validation cohorts. In the training cohort, multivariate logistic regression and a stepwise algorithm were performed to identify risk factors, which were then integrated into a predictive nomogram. Nomogram performance was assessed against the training and validation cohorts based on the area under receiver operating characteristic curves (AUC), calibration plots, and decision curve analysis.
resultsAmong the 161 patients in the training cohort and 70 patients in the validation cohort, 90-day mortality was 31.6%. Older age and higher values for the international normalized ratio, lactate level, and thrombomodulin level were associated with greater risk of 90-day mortality. The nomogram showed an AUC of 0.810 (95% CI 0.739 to 0.881) in the training cohort and 0.813 (95% CI 0.708 to 0.917) in the validation cohort. The nomogram also performed well based on the calibration curve and decision curve analysis.
conclusionThis nomogram may help identify sepsis patients at elevated risk of 90-day mortality, which may help clinicians allocate resources appropriately to improve patient outcomes.
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.