Comparison of NEWS2 and a Machine Learning Model for Early Sepsis Warning: A Prospective Observational Study
试验速览
- 阶段
- 不适用
- 状态
- 进行中（未招募）
- 发起方
- 入组人数
- 100
- 试验地点
- 2
- 主要终点
- Lead-Time of NEWS2 KDS in Sepsis
研究概览
简要总结
This prospective observational study aims to objectively measure the lead-time (the time from the first KDS alert to sepsis diagnosis) of the NEWS2-based clinical decision support system (KDS) and compare its early warning performance with a machine learning model trained on 2000 patients and externally validated. The study seeks to answer the following main questions:
How early does the NEWS2-based KDS provide an alert before sepsis diagnosis?
Does a machine learning model, developed using logistic regression and externally validated in a prospective cohort, offer superior specificity and comparable sensitivity to KDS?
Participants who are already receiving routine clinical care at Kocaeli City Hospital will have their vital signs and laboratory data monitored as part of standard practice. NEWS2 scores will be calculated automatically and the time of the first alert (T0) will be recorded. Sepsis diagnosis will be confirmed by an increase in SOFA score ≥ 2 (T1), evaluated by two independent and blinded physicians. Lead-time will be calculated as the difference between T1 (hours×60) and T0 (minutes). The machine learning model will be tested prospectively on this cohort, and its performance will be compared with KDS using sensitivity, specificity, F1 score, ROC-AUC, and accuracy.
详细描述
Sepsis is a life-threatening organ dysfunction caused by a dysregulated host response to infection. Early recognition and treatment are critical for improving outcomes. The National Early Warning Score 2 (NEWS2) is widely used as an early warning system, but its lead-time (the time from alert to diagnosis) has not been objectively measured in prospective studies. This study aims to fill this gap by prospectively evaluating the lead-time of NEWS2-based KDS and comparing its performance with a machine learning model. The machine learning model was developed using 2000 patients from the PhysioNet Sepsis Prediction Challenge 2019 database and externally validated on a prospective cohort of 100 patients from Kocaeli City Hospital. The study will provide evidence on the comparative utility of traditional warning systems and machine learning approaches for early sepsis detection.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Prospective
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Patients aged 18 years and older
- •Admitted to Kocaeli City Hospital Anesthesiology and Reanimation Clinic
- •Suspected infection at the time of hospital admission
- •Complete vital signs recorded
- •Informed consent obtained from the patient or legal representative
排除标准
- •Patients under 18 years of age
- •Pregnant patients
- •Patients with chronic kidney disease requiring dialysis
- •Patients with a history of organ transplantation
- •Patients with incomplete data
结局指标
主要结局
Lead-Time of NEWS2 KDS in Sepsis
时间窗: From hospital admission to sepsis diagnosis, death, or discharge, whichever occurs first, assessed up to 14 days
Lead-Time Calculated from NEWS2 KDS Alert and SOFA Score Change
时间窗: From hospital admission to sepsis diagnosis, death, or discharge, whichever occurs first, assessed up to 14 days
Lead-time is defined as the time from the first KDS alert (NEWS2 ≥ 5) to the diagnosis of sepsis, confirmed by an increase in SOFA score ≥ 2 points. It is calculated using the formula: Lead-Time (minutes) = \[T1 (hours × 60)\] - T0 (minutes), where T0 is the time of the first NEWS2 measurement and T1 is the time of first SOFA increase ≥ 2.
次要结局
- Predictive Performance of Machine Learning Model vs KDS(Within 14 days of hospital admission)
研究者
feyza özkan
Anestesiology and reanimation Doctor Principal Investigator
Kocaeli Derince Education and Research Hospital