ArticleThe British journal of general practice : the journal of the Royal College of General Practitioners2022
New clinical prediction model for early recognition of sepsis in adult primary care patients: a prospective diagnostic cohort study of development and external validation.
Article in The British journal of general practice : the journal of the Royal College of General Practitioners, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 13 citations in OpenAlex.
- Diagnosing Sepsis in Primary Care: Navigating Clinical Uncertainty Across Vulnerable Patient Groups.Cureus · 2026Review
- Implementing a sepsis prediction score in out-of-hours primary care: Feasibility and acceptability study.The European journal of general practice · 2025Article
- The LEADING guideline: Reporting standards for expert panel, best-estimate diagnosis, and longitudinal expert all data (LEAD) methods.Comprehensive psychiatry · 2025Article
- Recognition of Serious Infections in the Elderly Visiting the Emergency Department: The Development of a Diagnostic Prediction Model (ROSIE).Geriatrics (Basel, Switzerland) · 2025Article
- A methodological systematic review of validation and performance of sepsis real-time prediction models.NPJ digital medicine · 2025Article
- The LEADING Guideline: Reporting Standards for Expert Panel, Best-Estimate Diagnosis, and Longitudinal Expert All Data (LEAD) Studies.medRxiv : the preprint server for health sciences · 2024Article
- Observational
- Article
- How Extracellular Nano-Vesicles Can Play a Role in Sepsis? An Evidence-Based Review of the Literature.International journal of nanomedicine · 2023Review
- Shaking chills may be better than rigors for sepsis prediction.The British journal of general practice : the journal of the Royal College of General Practitioners · 2022Article
- Development and external validation of a new clinical prediction model for early recognition of sepsis.The British journal of general practice : the journal of the Royal College of General Practitioners · 2022Article
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Authors and funding
25 authors at 16 institutions in 8 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundRecognising patients who need immediate hospital treatment for sepsis while simultaneously limiting unnecessary referrals is challenging for GPs.
aimTo develop and validate a sepsis prediction model for adult patients in primary care. DESIGN AND
settingThis was a prospective cohort study in four out-of-hours primary care services in the Netherlands, conducted between June 2018 and March 2020.
methodAdult patients who were acutely ill and received home visits were included. A total of nine clinical variables were selected as candidate predictors, next to the biomarkers C-reactive protein, procalcitonin, and lactate. The primary endpoint was sepsis within 72 hours of inclusion, as established by an expert panel. Multivariable logistic regression with backwards selection was used to design an optimal model with continuous clinical variables. The added value of the biomarkers was evaluated. Subsequently, a simple model using single cut-off points of continuous variables was developed and externally validated in two emergency department populations.
resultsA total of 357 patients were included with a median age of 80 years (interquartile range 71-86), of which 151 (42%) were diagnosed with sepsis. A model based on a simple count of one point for each of six variables (aged >65 years; temperature >38°C; systolic blood pressure ≤110 mmHg; heart rate >110/min; saturation ≤95%; and altered mental status) had good discrimination and calibration (C-statistic of 0.80 [95% confidence interval = 0.75 to 0.84]; Brier score 0.175). Biomarkers did not improve the performance of the model and were therefore not included. The model was robust during external validation.
conclusionBased on this study's GP out-of-hours population, a simple model can accurately predict sepsis in acutely ill adult patients using readily available clinical parameters.
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