Faculty of Health Studies (Faisal, Mohammed), University of Bradford, Bradford, UK; Bradford Institute for Health Research (Faisal), Bradford, UK; Department of Renal Medicine (Richardson), York Teaching Hospital NHS Foundation Trust Hospital, York, UK; School of Clinical Therapies (Scally), University College Cork, Cork, Ireland; Department of Strategy & Planning (Howes), Northern Lincolnshire and Goole Hospitals, Scunthorpe, UK; York Teaching Hospital NHS Foundation Trust (Beatson), York, UK; Northern Lincolnshire and Goole Hospitals (Speed), Scunthorpe, UK; The Strategy Unit (Mohammed), NHS Midlands and Lancashire Commissioning Support Unit, West Bromwich, UK.
Faculty of Health Studies (Faisal, Mohammed), University of Bradford, Bradford, UK; Bradford Institute for Health Research (Faisal), Bradford, UK; Department of Renal Medicine (Richardson), York Teaching Hospital NHS Foundation Trust Hospital, York, UK; School of Clinical Therapies (Scally), University College Cork, Cork, Ireland; Department of Strategy & Planning (Howes), Northern Lincolnshire and Goole Hospitals, Scunthorpe, UK; York Teaching Hospital NHS Foundation Trust (Beatson), York, UK; Northern Lincolnshire and Goole Hospitals (Speed), Scunthorpe, UK; The Strategy Unit (Mohammed), NHS Midlands and Lancashire Commissioning Support Unit, West Bromwich, UK
CMAJ. 2019 Apr 8;191(14):E382-E389. doi: 10.1503/cmaj.181418.
In hospitals in England, patients' vital signs are monitored and summarized into the National Early Warning Score (NEWS); this score is more accurate than the Quick Sepsis-related Organ Failure Assessment (qSOFA) score at identifying patients with sepsis. We investigated the extent to which the accuracy of the NEWS is enhanced by developing and comparing 3 computer-aided NEWS (cNEWS) models (M0 = NEWS alone, M1 = M0 + age + sex, M2 = M1 + subcomponents of NEWS + diastolic blood pressure) to predict the risk of sepsis.
We included all emergency medical admissions of patients 16 years of age and older discharged over 24 months from 2 acute care hospital centres (York Hospital [YH] for model development and a combined data set from 2 hospitals [Diana, Princess of Wales Hospital and Scunthorpe General Hospital] in the Northern Lincolnshire and Goole National Health Service Foundation Trust [NH] for external model validation). We used a validated Canadian method for defining sepsis from administrative hospital data.
The prevalence of sepsis was lower in YH (4.5%, 1596/35 807) than in NH (8.5%, 2983/35 161). The C statistic increased across models (YH: M0 0.705, M1 0.763, M2 0.777; NH: M0 0.708, M1 0.777, M2 0.791). For NEWS of 5 or higher, sensitivity increased (YH: 47.24% v. 50.56% v. 52.69%; NH: 37.91% v. 43.35% v. 48.07%), the positive likelihood ratio increased (YH: 2.77 v. 2.99 v. 3.06; NH: 3.18 v. 3.32 v. 3.45) and the positive predictive value increased (YH: 11.44% v. 12.24% v. 12.49%; NH: 22.75% v. 23.55% v. 24.21%).
From the 3 cNEWS models, model M2 is the most accurate. Given that it places no additional burden of data collection on clinicians and can be automated, it may now be carefully introduced and evaluated in hospitals with sufficient informatics infrastructure.
在英国的医院中，患者的生命体征被监测并汇总为国家早期预警评分（NEWS）；与快速脓毒症相关器官衰竭评估（qSOFA）评分相比，该评分更能准确识别脓毒症患者。我们研究了通过开发和比较 3 种计算机辅助 NEWS（cNEWS）模型（M0 = 仅 NEWS，M1 = M0 + 年龄+性别，M2 = M1 + NEWS 的亚组分+舒张压）来提高 NEWS 准确性的程度，以预测脓毒症的风险。
我们纳入了 2 个急性护理中心（约克医院[YH]用于模型开发和来自北林肯郡和古尔国家卫生服务信托基金会[NH]的 2 家医院[戴安娜公主医院和斯肯索普综合医院]的合并数据集）在 24 个月内出院的 16 岁及以上的所有急诊医疗入院患者。我们使用一种经过验证的加拿大方法从医院管理数据中定义脓毒症。
YH（4.5%，1596/35807）的脓毒症患病率低于 NH（8.5%，2983/35161）。模型的 C 统计量均增加（YH：M0 0.705，M1 0.763，M2 0.777；NH：M0 0.708，M1 0.777，M2 0.791）。对于 NEWS 为 5 或更高，敏感性增加（YH：47.24% v. 50.56% v. 52.69%；NH：37.91% v. 43.35% v. 48.07%），阳性似然比增加（YH：2.77 v. 2.99 v. 3.06；NH：3.18 v. 3.32 v. 3.45），阳性预测值增加（YH：11.44% v. 12.24% v. 12.49%；NH：22.75% v. 23.55% v. 24.21%）。
在 3 种 cNEWS 模型中，模型 M2 最准确。鉴于它不会给临床医生带来额外的数据收集负担，并且可以实现自动化，因此现在可以在具有足够信息学基础设施的医院中仔细引入和评估该模型。
