Demographics and clinical characteristics.

Background
Measuring sepsis incidence and associated mortality at scale using administrative data is hampered by variation in diagnostic coding. This study aimed first to compare how well bedside severity scores predict 30-day mortality in hospitalised patients with infection, then to assess the ability of combinations of administrative data items to identify patients with sepsis.
Methods
This retrospective case note review examined 958 adult hospital admissions between October 2015 and March 2016. Admissions with blood culture sampling were matched 1:1 to admissions without a blood culture. Case note review data were linked to discharge coding and mortality. For patients with infection the performance characteristics of Sequential Organ Failure Assessment (SOFA), National Early Warning System (NEWS), quick SOFA (qSOFA), and Systemic Inflammatory Response Syndrome (SIRS) were calculated for predicting 30-day mortality. Next, the performance characteristics of administrative data (blood cultures and discharge codes) for identifying patients with sepsis, defined as SOFA ≥2 because of infection, were calculated.
Results
Infection was documented in 630 (65.8%) admissions and 347 (55.1%) patients with infection had sepsis. NEWS (Area Under the Receiver Operating Characteristic, AUROC 0.78 95%CI 0.72–0.83) and SOFA (AUROC 0.77, 95%CI 0.72–0.83), performed similarly well for prediction of 30-day mortality. Having an infection and/or sepsis International Classification of Diseases, Tenth Revision (ICD-10) code (AUROC 0.68, 95%CI 0.64–0.71) performed as well in identifying patients with sepsis as having at least one of: an infection code; sepsis code, or; blood culture (AUROC 0.68, 95%CI 0.65–0.71), Sepsis codes (AUROC 0.53, 95%CI 0.49–0.57) and positive blood cultures (AUROC 0.52, 95%CI 0.49–0.56) performed least well.
Conclusions
SOFA and NEWS best predicted 30-day mortality in patients with infection. Sepsis ICD-10 codes lack sensitivity. For health systems without suitable electronic health records, blood culture sampling has potential utility as a clinical component of a proxy marker for sepsis surveillance.

研究背景
利用行政数据规模化评估脓毒症（sepsis）发病率及其相关死亡率的研究，常因诊断编码的差异而受阻。本研究首先旨在比较各类床旁严重程度评分对感染住院患者30天死亡率的预测效能，其次评估行政数据项组合识别脓毒症患者的能力。

研究方法
本回顾性病历审查研究纳入了2015年10月至2016年3月期间的958例成人住院病例。将进行血培养采样的住院病例与未进行血培养的住院病例按1:1比例进行匹配。将病历回顾数据与出院编码及死亡率数据进行关联。针对感染患者，本研究计算了序贯器官衰竭评估（Sequential Organ Failure Assessment, SOFA）、国家早期预警系统（National Early Warning System, NEWS）、快速SOFA（quick SOFA, qSOFA）以及全身炎症反应综合征（Systemic Inflammatory Response Syndrome, SIRS）这几项指标在预测30天死亡率中的效能特征。随后，本研究计算了行政数据（血培养结果与出院编码）在识别脓毒症患者中的效能特征，其中脓毒症的定义为因感染导致SOFA评分≥2分。

研究结果
630例（65.8%）住院病例记录存在感染，其中347例（55.1%）感染患者合并脓毒症。国家早期预警系统（NEWS，受试者工作特征曲线下面积（Area Under the Receiver Operating Characteristic, AUROC）0.78，95%置信区间CI 0.72–0.83）与序贯器官衰竭评估（SOFA，AUROC 0.77，95%CI 0.72–0.83）在预测30天死亡率方面表现相当。同时携带感染和/或脓毒症国际疾病分类第十次修订版（International Classification of Diseases, Tenth Revision, ICD-10）编码的病例（AUROC 0.68，95%CI 0.64–0.71）在识别脓毒症患者方面的效能，与至少满足以下任一条件的病例相当：存在感染编码、脓毒症编码，或进行过血培养采样（AUROC 0.68，95%CI 0.65–0.71）；仅携带脓毒症编码（AUROC 0.53，95%CI 0.49–0.57）或血培养结果阳性（AUROC 0.52，95%CI 0.49–0.56）的病例识别效能最差。

研究结论
SOFA与NEWS是感染患者30天死亡率的最佳预测指标。脓毒症ICD-10编码的敏感性不足。对于缺乏适配电子健康记录的医疗系统而言，血培养采样可作为脓毒症监测替代标志物的临床组成部分，具备应用潜力。