Centre for Health Informatics & Health Data Research UK North, Division of Informatics, Imaging and Data Science, School of Health Sciences, Faculty of Biology, Medicine and Health, Vaughan House, The University of Manchester, Manchester Academic Health Science Centre, Manchester, M13 9PL, UK.
Chadderton South Health Centre, Eaves Lane, Chadderton, Oldham, OL9 8RG, UK.
Infection. 2024 Aug;52(4):1469-1479. doi: 10.1007/s15010-024-02235-8. Epub 2024 Apr 16.
Sepsis is a life-threatening organ dysfunction caused by dysregulated host response to infection. The purpose of the study was to measure the associations of specific exposures (deprivation, ethnicity, and clinical characteristics) with incident sepsis and case fatality.
Two research databases in England were used including anonymized patient-level records from primary care linked to hospital admission, death certificate, and small-area deprivation. Sepsis cases aged 65-100 years were matched to up to six controls. Predictors for sepsis (including 60 clinical conditions) were evaluated using logistic and random forest models; case fatality rates were analyzed using logistic models.
108,317 community-acquired sepsis cases were analyzed. Severe frailty was strongly associated with the risk of developing sepsis (crude odds ratio [OR] 14.93; 95% confidence interval [CI] 14.37-15.52). The quintile with most deprived patients showed an increased sepsis risk (crude OR 1.48; 95% CI 1.45-1.51) compared to least deprived quintile. Strong predictors for sepsis included antibiotic exposure in prior 2 months, being house bound, having cancer, learning disability, and diabetes mellitus. Severely frail patients had a case fatality rate of 42.0% compared to 24.0% in non-frail patients (adjusted OR 1.53; 95% CI 1.41-1.65). Sepsis cases with recent prior antibiotic exposure died less frequently compared to non-users (adjusted OR 0.7; 95% CI 0.72-0.76). Case fatality strongly decreased over calendar time.
Given the variety of predictors and their level of associations for developing sepsis, there is a need for prediction models for risk of developing sepsis that can help to target preventative antibiotic therapy.
败血症是一种危及生命的器官功能障碍,由宿主对感染的调节反应失调引起。本研究的目的是测量特定暴露(贫困、种族和临床特征)与败血症发病和病死率的关联。
使用英格兰的两个研究数据库,包括来自初级保健的匿名患者水平记录,这些记录与医院入院、死亡证明和小区域贫困情况相关联。对年龄在 65-100 岁的败血症病例与多达 6 名对照进行匹配。使用逻辑和随机森林模型评估败血症的预测因素(包括 60 种临床疾病);使用逻辑模型分析病死率。
分析了 108317 例社区获得性败血症病例。严重衰弱与发生败血症的风险密切相关(粗比值比[OR] 14.93;95%置信区间[CI] 14.37-15.52)。与最不贫困五分位数相比,最贫困患者的五分位数显示败血症风险增加(粗 OR 1.48;95%CI 1.45-1.51)。败血症的强预测因素包括在过去 2 个月内使用抗生素、行动不便、患有癌症、学习障碍和糖尿病。严重衰弱患者的病死率为 42.0%,而非衰弱患者为 24.0%(调整后的 OR 1.53;95%CI 1.41-1.65)。与非使用者相比,近期使用抗生素的败血症病例死亡频率较低(调整后的 OR 0.7;95%CI 0.72-0.76)。病死率随着时间的推移而大幅下降。
鉴于预测败血症发病的各种预测因素及其关联水平,需要开发用于预测败血症发病风险的模型,以帮助针对预防性抗生素治疗。