Predicting sepsis-related mortality and ICU admissions from telephone triage information of patients presenting to out-of-hours GP cooperatives with acute infections:A cohort study of linked routine care databases

BackgroundGeneral 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.MethodsA 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.ResultsWe 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).ConclusionSeveral patient characteristics identified during telephone triage of patients presenting to out-of-hours GP cooperatives were associated with sepsis-related adverse outcomes. Still, on a patient level, predictions were not sufficiently accurate for clinical purposes

[Early recognition of sepsis; a diagnostic challenge for the general practitioner]

Item does not contain fulltextEarly recognition and treatment of sepsis is essential to prevent morbidity and mortality. Many sepsis patients are initially assessed by a general practitioner (GP). Delay can be prevented if patients are referred to the hospital as soon as possible. However, signs and symptoms of sepsis can be subtle or aspecific, complicating the distinction between patients who need urgent care and patients who can be safely treated at home. We describe three patients who were admitted to the intensive care after repeated assessment by GPs in an out-of-hours setting: a 76-year-old man who was diagnosed with urosepsis, an 86-year-old man who was diagnosed with pneumosepsis and a 49-year-old man who was admitted after cardiopulmonary resuscitation due to sepsis. In all cases risk factors and signs of sepsis were present, but the sepsis had not been recognized until presentation to the hospital

Management of sepsis in out-of-hours primary care: a retrospective study of patients admitted to the intensive care unit

Contains fulltext : 196412.pdf (publisher's version ) (Open Access)OBJECTIVES: Timely recognition and treatment of sepsis is essential to reduce mortality and morbidity. Acutely ill patients often consult a general practitioner (GP) as the first healthcare provider. During out-of-hours, GP cooperatives deliver this care in the Netherlands. The aim of this study is to explore the role of these GP cooperatives in the care for patients with sepsis. DESIGN: Retrospective study of patient records from both the hospital and the GP cooperative. SETTING: An intensive care unit (ICU) of a general hospital in the Netherlands, and the colocated GP cooperative serving 260 000 inhabitants. PARTICIPANTS: We used data from 263 patients who were admitted to the ICU due to community-acquired sepsis between January 2011 and December 2015. MAIN OUTCOME MEASURES: Contact with the GP cooperative within 72 hours prior to hospital admission, type of contact, delay from the contact until hospital arrival, GP diagnosis, initial vital signs and laboratory values, and hospital mortality. RESULTS: Of 263 patients admitted to the ICU, 127 (48.3%) had prior GP cooperative contacts. These contacts concerned home visits (59.1%), clinic consultations (18.1%), direct ambulance deployment (12.6%) or telephone advice (10.2%). Patients assessed by a GP were referred in 64% after the first contact. The median delay to hospital arrival was 1.7 hours. The GP had not suspected an infection in 43% of the patients. In this group, the in-hospital mortality rate was significantly higher compared with patients with suspected infections (41.9% vs 17.6%). Mortality difference remained significant after correction for confounders. CONCLUSION: GP cooperatives play an important role in prehospital management of sepsis and recognition of sepsis in this setting proved difficult. Efforts to improve management of sepsis in out-of-hours primary care should not be limited to patients with a suspected infection, but also include severely ill patients without clear signs of infection

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

The ASOS Surgical Risk Calculator: development and validation of a tool for identifying African surgical patients at risk of severe postoperative complications

Background: The African Surgical Outcomes Study (ASOS) showed that surgical patients in Africa have a mortality twice the global average. Existing risk assessment tools are not valid for use in this population because the pattern of risk for poor outcomes differs from high-income countries. The objective of this study was to derive and validate a simple, preoperative risk stratification tool to identify African surgical patients at risk for in-hospital postoperative mortality and severe complications. Methods: ASOS was a 7-day prospective cohort study of adult patients undergoing surgery in Africa. The ASOS Surgical Risk Calculator was constructed with a multivariable logistic regression model for the outcome of in-hospital mortality and severe postoperative complications. The following preoperative risk factors were entered into the model; age, sex, smoking status, ASA physical status, preoperative chronic comorbid conditions, indication for surgery, urgency, severity, and type of surgery. Results: The model was derived from 8799 patients from 168 African hospitals. The composite outcome of severe postoperative complications and death occurred in 423/8799 (4.8%) patients. The ASOS Surgical Risk Calculator includes the following risk factors: age, ASA physical status, indication for surgery, urgency, severity, and type of surgery. The model showed good discrimination with an area under the receiver operating characteristic curve of 0.805 and good calibration with c-statistic corrected for optimism of 0.784. Conclusions: This simple preoperative risk calculator could be used to identify high-risk surgical patients in African hospitals and facilitate increased postoperative surveillance. © 2018 British Journal of Anaesthesia. Published by Elsevier Ltd. All rights reserved.Medical Research Council of South Africa gran