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利用电子临床数据识别儿科脓毒症进行流行病学监测。

Identification of Pediatric Sepsis for Epidemiologic Surveillance Using Electronic Clinical Data.

机构信息

Pediatric Sepsis Program at the Children's Hospital of Philadelphia, Philadelphia, PA.

Department of Pediatrics, Children's Hospital of Philadelphia, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA.

出版信息

Pediatr Crit Care Med. 2020 Feb;21(2):113-121. doi: 10.1097/PCC.0000000000002170.

Abstract

OBJECTIVES

A method to identify pediatric sepsis episodes that is not affected by changing diagnosis and claims-based coding practices does not exist. We derived and validated a surveillance algorithm to identify pediatric sepsis using routine clinical data and applied the algorithm to study longitudinal trends in sepsis epidemiology.

DESIGN

Retrospective observational study.

SETTING

Single academic children's hospital.

PATIENTS

All emergency and hospital encounters from January 2011 to January 2019, excluding neonatal ICU and cardiac center.

EXPOSURE

Sepsis episodes identified by a surveillance algorithm using clinical data to identify infection and concurrent organ dysfunction.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

A surveillance algorithm was derived and validated in separate cohorts with suspected sepsis after clinician-adjudication of final sepsis diagnosis. We then applied the surveillance algorithm to determine longitudinal trends in incidence and mortality of pediatric sepsis over 8 years. Among 93,987 hospital encounters and 1,065 episodes of suspected sepsis in the derivation period, the surveillance algorithm yielded sensitivity 78% (95% CI, 72-84%), specificity 76% (95% CI, 74-79%), positive predictive value 41% (95% CI, 36-46%), and negative predictive value 94% (95% CI, 92-96%). In the validation period, the surveillance algorithm yielded sensitivity 84% (95% CI, 77-92%), specificity of 65% (95% CI, 59-70%), positive predictive value 43% (95% CI, 35-50%), and negative predictive value 93% (95% CI, 90-97%). Notably, most "false-positives" were deemed clinically relevant sepsis cases after manual review. The hospital-wide incidence of sepsis was 0.69% (95% CI, 0.67-0.71%), and the inpatient incidence was 2.8% (95% CI, 2.7-2.9%). Risk-adjusted sepsis incidence, without bias from changing diagnosis or coding practices, increased over time (adjusted incidence rate ratio per year 1.07; 95% CI, 1.06-1.08; p < 0.001). Mortality was 6.7% and did not change over time (adjusted odds ratio per year 0.98; 95% CI, 0.93-1.03; p = 0.38).

CONCLUSIONS

An algorithm using routine clinical data provided an objective, efficient, and reliable method for pediatric sepsis surveillance. An increased sepsis incidence and stable mortality, free from influence of changes in diagnosis or billing practices, were evident.

摘要

目的

目前尚无一种不受诊断变化和基于索赔的编码实践影响的儿科脓毒症发作识别方法。我们开发并验证了一种使用常规临床数据识别小儿脓毒症的监测算法,并应用该算法研究了脓毒症流行病学的纵向趋势。

设计

回顾性观察性研究。

地点

单家学术儿童医院。

患者

2011 年 1 月至 2019 年 1 月期间所有急诊和住院就诊,不包括新生儿 ICU 和心脏中心。

暴露

使用临床数据识别感染和并发器官功能障碍的监测算法识别的脓毒症发作。

干预措施

无。

测量和主要结果

在经过临床医生最终脓毒症诊断裁决的疑似脓毒症患者的两个独立队列中,开发和验证了一种监测算法。然后,我们应用监测算法来确定 8 年内儿科脓毒症的发病率和死亡率的纵向趋势。在 93987 次住院就诊和 1065 例疑似脓毒症发作的推导期内,监测算法的敏感性为 78%(95%CI,72-84%),特异性为 76%(95%CI,74-79%),阳性预测值为 41%(95%CI,36-46%),阴性预测值为 94%(95%CI,92-96%)。在验证期内,监测算法的敏感性为 84%(95%CI,77-92%),特异性为 65%(95%CI,59-70%),阳性预测值为 43%(95%CI,35-50%),阴性预测值为 93%(95%CI,90-97%)。值得注意的是,大多数“假阳性”在手动审查后被认为是临床上相关的脓毒症病例。医院范围内脓毒症的发病率为 0.69%(95%CI,0.67-0.71%),住院患者的发病率为 2.8%(95%CI,2.7-2.9%)。没有因诊断或编码实践变化而产生偏差的风险调整脓毒症发病率随时间增加(每年调整发病率比为 1.07;95%CI,1.06-1.08;p<0.001)。死亡率为 6.7%,且随时间无变化(每年调整比值比为 0.98;95%CI,0.93-1.03;p=0.38)。

结论

使用常规临床数据的算法为儿科脓毒症监测提供了一种客观、高效和可靠的方法。发病率增加而死亡率稳定,不受诊断或计费实践变化的影响。

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