Potential impact of a new sepsis prediction model for the primary care setting: early health economic evaluation using an observational cohort.

OBJECTIVES: To estimate the potential referral rate and cost impact at different cut-off points of a recently developed sepsis prediction model for general practitioners (GPs). DESIGN: Prospective observational study with decision tree modelling. SETTING: Four out-of-hours GP services in the Netherlands. PARTICIPANTS: 357 acutely ill adult patients assessed during home visits. PRIMARY AND SECONDARY OUTCOME MEASURES: The primary outcome is the cost per patient from a healthcare perspective in four scenarios based on different cut-off points for referral of the sepsis prediction model. Second, the number of hospital referrals for the different scenarios is estimated. The potential impact of referral of patients with sepsis on mortality and hospital admission was estimated by an expert panel. Using these study data, a decision tree with a time horizon of 1 month was built to estimate the referral rate and cost impact in case the model would be implemented. RESULTS: Referral rates at a low cut-off (score 2 or 3 on a scale from 0 to 6) of the prediction model were higher than observed for patients with sepsis (99% and 91%, respectively, compared with 88% observed). However, referral was also substantially higher for patients who did not need hospital assessment. As a consequence, cost-savings due to referral of patients with sepsis were offset by increased costs due to unnecessary referral for all cut-offs of the prediction model. CONCLUSIONS: Guidance for referral of adult patients with suspected sepsis in the primary care setting using any cut-off point of the sepsis prediction model is not likely to save costs. The model should only be incorporated in sepsis guidelines for GPs if improvement of care can be demonstrated in an implementation study. TRIAL REGISTRATION NUMBER: Dutch Trial Register (NTR 7026).

New clinical prediction model for early recognition of sepsis in adult primary care patients: a prospective diagnostic cohort study of development and external validation.

BACKGROUND: Recognising patients who need immediate hospital treatment for sepsis while simultaneously limiting unnecessary referrals is challenging for GPs. AIM: To develop and validate a sepsis prediction model for adult patients in primary care. DESIGN AND SETTING: This was a prospective cohort study in four out-of-hours primary care services in the Netherlands, conducted between June 2018 and March 2020. METHOD: Adult 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. RESULTS: A 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. CONCLUSION: Based 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.