Development and external validation of a new clinical prediction model for early recognition of sepsis in adult patients in primary care: a diagnostic study

Background: Recognising patients who need immediate hospital treatment for sepsis while simultaneously limiting unnecessary referrals is challenging for GPs. Aim: Develop and validate a sepsis prediction model for adult patients in primary care. Design and setting: Prospective cohort study in four out-of-hours primary care services in the Netherlands, between June 2018 and March 2020. Method: Acutely ill adult patients who received home visits were included. Nine clinical variables were 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 cutoff points of continuous variables was developed and externally validated in two emergency department populations. Results: 357 patients were included with a median age of 80 years, of which 151 (42%) were diagnosed with sepsis. A model based on a simple count of one point for each of six variables (age>65 years; temperature>38°C; systolic blood pressure≤110 mmHg; heart rate>110/min; saturation≤95%; altered mental status) had good discrimination and calibration (C statistic of 0.80 [95% CI 0.75-0.84]; 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. Conclusion: Based on this GP out-of-ours population, a simple model can accurately predict sepsis in acutely ill adult patients using readily available clinical parameters.