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Background General practitioners (GPs) often assess patients with acute infections. It is challenging for GPs to recognize patients needing immediate hospital referral for sepsis while avoiding unnecessary referrals. This study aimed to predict adverse sepsis-related outcomes from telephone triage information of patients presenting to out-of-hours GP cooperatives. Methods A retrospective cohort study using linked routine care databases from out-of-hours GP cooperatives, general practices, hospitals and mortality registration. We included adult patients with complaints possibly related to an acute infection, who were assessed (clinic consultation or home visit) by a GP from a GP cooperative between 2017-2019. We used telephone triage information to derive a risk prediction model for sepsis-related adverse outcome (infection-related ICU admission within seven days or infection-related death within 30 days) using logistic regression, random forest, and neural network machine learning techniques. Data from 2017 and 2018 were used for derivation and from 2019 for validation. Results We included 155, 486 patients (median age of 51 years; 59% females) in the analyses. The strongest predictors for sepsis-related adverse outcome were age, type of contact (home visit or clinic consultation), patients considered ABCD unstable during triage, and the entry complaints”general malaise”, “shortness of breath” and “fever”. The multivariable logistic regression model resulted in a C-statistic of 0.89 (95% CI 0.88-0.90) with good calibration. Machine learning models performed similarly to the logistic regression model. A “sepsis alert” based on a predicted probability >1% resulted in a sensitivity of 82% and a positive predictive value of 4.5%. However, most events occurred in patients receiving home visits, and model performance was substantially worse in this subgroup (C-statistic 0.70). Conclusion Several patient characteristics identified during telephone triage of patients presen

Background: Tachypnoea in acutely ill patients can be an early sign of a life-threatening condition such as sepsis. Routine measurement of the respiratory rate by GPs might improve the recognition of sepsis. Aim: To assess the accuracy and feasibility of respiratory rate measurements by GPs. Design & setting: Observational cross-sectional mixed-methods study in the setting of out-of-hours (OOH) home visits at three GP cooperatives in The Netherlands. Method: GPs were observed during the assessment of acutely ill patients, and semi-structured interviews were performed. The GP-assessed respiratory rate was compared with a reference measurement. In the event that the respiratory rate was not counted, GPs were asked to estimate the rate (dichotomised as ≥22 breaths per minute or <22 breaths per minute). Results: Observations of 130 acutely ill patients were included, and 14 GPs were interviewed. In 33 patients (25%), the GP counted the respiratory rate. A mean difference of 0.27 breaths per minute (95% confidence interval [CI] = -5.7 to 6.3) with the reference measurement was found. At a cut-off point of ≥22 breaths per minute, a sensitivity of 86% (95% CI = 57% to 98%) was found when the GP counted the rate, and a sensitivity of 43% (95% CI = 22% to 66%) when GPs estimated respiratory rates. GPs reported both medical and practical reasons for not routinely measuring the respiratory rate. Conclusion: GPs are aware of the importance of assessing the respiratory rate of acutely ill adult patients, and counted measurements are accurate. However, in most patients the respiratory rate was not counted, and the rate was often underestimated when estimated.

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.