Loots Feike J, Hopstaken Rogier, Jenniskens Kevin, Frederix Geert W J, van de Pol Alma C, Van den Bruel Ann, Oosterheert Jan Jelrik, van Zanten Arthur R H, Smits Marleen, Verheij Theo J M
Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht, The Netherlands.
Emergency Department, Gelderse Vallei Hospital, Ede, The Netherlands.
Diagn Progn Res. 2020 Aug 6;4:12. doi: 10.1186/s41512-020-00080-5. eCollection 2020.
Early recognition and treatment of sepsis is crucial to prevent detrimental outcomes. General practitioners (GPs) are often the first healthcare providers to encounter seriously ill patients. The aim of this study is to assess the value of clinical information and additional tests to develop a clinical prediction rule to support early diagnosis and management of sepsis by GPs.
We will perform a diagnostic study in the setting of out-of-hours home visits in four GP cooperatives in the Netherlands. Acutely ill adult patients suspected of a serious infection will be screened for eligibility by the GP. The following candidate predictors will be prospectively recorded: (1) age, (2) body temperature, (3) systolic blood pressure, (4) heart rate, (5) respiratory rate, (6) peripheral oxygen saturation, (7) mental status, (8) history of rigors, and (9) rate of progression. After clinical assessment by the GP, blood samples will be collected in all patients to measure C-reactive protein, lactate, and procalcitonin. All patients will receive care as usual. The primary outcome is the presence or absence of sepsis within 72 h after inclusion, according to an expert panel. The need for hospital treatment for any indication will be assessed by the expert panel as a secondary outcome. Multivariable logistic regression will be used to design an optimal prediction model first and subsequently derive a simplified clinical prediction rule that enhances feasibility of using the model in daily clinical practice. Bootstrapping will be performed for internal validation of both the optimal model and simplified prediction rule. Performance of both models will be compared to existing clinical prediction rules for sepsis.
This study will enable us to develop a clinical prediction rule for the recognition of sepsis in a high-risk primary care setting to aid in the decision which patients have to be immediately referred to a hospital and who can be safely treated at home. As clinical signs and blood samples will be obtained prospectively, near-complete data will be available for analyses. External validation will be needed before implementation in routine care and to determine in which pre-hospital settings care can be improved using the prediction rule.
The study is registered in the Netherlands Trial Registry (registration number NTR7026).
早期识别和治疗脓毒症对于预防不良后果至关重要。全科医生（GPs）常常是首批接触重症患者的医疗服务提供者。本研究的目的是评估临床信息和额外检查的价值，以制定一项临床预测规则，支持全科医生对脓毒症进行早期诊断和管理。
我们将在荷兰的四个全科医生合作社进行一项非工作时间家访环境下的诊断性研究。怀疑有严重感染的急性成年患者将由全科医生筛查是否符合入选条件。以下候选预测指标将被前瞻性记录：（1）年龄，（2）体温，（3）收缩压，（4）心率，（5）呼吸频率，（6）外周血氧饱和度，（7）精神状态，（8）寒战病史，以及（9）病情进展速度。在全科医生进行临床评估后，将采集所有患者的血样以检测C反应蛋白、乳酸和降钙素原。所有患者将接受常规护理。主要结局是根据专家小组判断，入选后72小时内是否存在脓毒症。专家小组将评估因任何指征而需要住院治疗的情况作为次要结局。首先将使用多变量逻辑回归设计一个最佳预测模型，随后推导出一个简化的临床预测规则，以提高该模型在日常临床实践中的可行性。将进行自抽样法对最佳模型和简化预测规则进行内部验证。将这两个模型的性能与现有的脓毒症临床预测规则进行比较。
本研究将使我们能够制定一项用于在高风险初级保健环境中识别脓毒症的临床预测规则，以帮助决定哪些患者必须立即转诊至医院，哪些患者可以安全地在家中接受治疗。由于将前瞻性地获取临床体征和血样，将可获得近乎完整的数据用于分析。在将其应用于常规护理之前，需要进行外部验证，并确定在哪些院前环境中可以使用该预测规则改善护理。
该研究已在荷兰试验注册中心注册（注册号NTR7026）。
