A structured-data algorithm for semiautomated surveillance of surgical site infection after colorectal surgery: A diagnostic accuracy study

IF 4.8 3区医学 Q1 INFECTIOUS DISEASES

Journal of Infection and Public Health Pub Date : 2026-04-01 Epub Date: 2026-01-15 DOI:10.1016/j.jiph.2026.103151

Daniel Casanova-Portoles , Josep M. Badia , Carlos G. Forero , Néstor Sánchez-Martínez , Manel Romero , Toni Alonso-Solís , Enric Limón , Miquel Pujol , Joan Sancho

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引用次数: 0

Abstract

Background

Manual surveillance of surgical site infections (SSIs) after colorectal surgery is resource-intensive, limiting scalability. Semiautomated algorithms based on structured electronic health record (EHR) data may maintain high case-finding sensitivity while reducing workload.

Methods

A retrospective diagnostic-accuracy study was conducted in a teaching hospital participating in a nationwide SSI surveillance programme. All elective colorectal procedures performed between January 2010 and December 2023 were included. SSIs were classified according to CDC-NHSN/ECDC criteria. Eight binary EHR-derived “alerts” were combined into a composite rule (any alert positive). Manual surveillance served as the reference standard. Performance was assessed overall, by SSI depth (superficial, deep, organ/space), and by procedure type (colon vs rectal). Discrimination (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated with 95 % confidence intervals (CIs).

Results

A total of 1213 patients (1085 colon; 128 rectal) were included. The overall SSI incidence was 11.2 % (3.1 % superficial, 1.2 % deep, 6.8 % organ/space). The composite alert achieved an AUC of 0.859 (95 % CI 0.838–0.878) for any SSI, with sensitivity 0.721, specificity 0.876, PPV 0.424, and NPV 0.961. At this operating point, 19 % of procedures would be flagged for manual verification, corresponding to an estimated 81 % reduction in full chart reviews. Discrimination was highest for organ/space infections (AUC 0.919; sensitivity 0.831; specificity 0.911). Performance for deep SSI was intermediate (AUC 0.805), and for superficial SSI, more limited (AUC 0.571). Sensitivity was higher for colon surgery (AUC 0.853) and specificity higher for rectal surgery (AUC 0.881).

Conclusions

The structured-data algorithm demonstrated strong overall discrimination and excellent performance for organ/space infections, supporting the feasibility of semiautomated surveillance without compromising detection quality. External and prospective validation, definition of diagnostic safety thresholds, and workload-reduction analyses are required to optimise implementation. Exploration of NLP add-ons may be considered where resources permit. ClinicalTrials.gov: NCT07130656.

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结直肠术后手术部位感染半自动监测的结构化数据算法：诊断准确性研究

背景：人工监测结直肠手术后手术部位感染（ssi）是资源密集型的，限制了可扩展性。基于结构化电子健康记录（EHR）数据的半自动算法可以在减少工作量的同时保持较高的病例查找灵敏度。方法在参与全国SSI监测项目的某教学医院进行回顾性诊断准确性研究。包括2010年1月至2023年12月期间进行的所有择期结直肠手术。根据CDC-NHSN/ECDC标准对ssi进行分类。8个由ehr衍生的二元“警报”被合并成一个复合规则（任何阳性警报）。人工监测作为参考标准。通过SSI深度（浅表、深部、器官/空间）和手术类型（结肠和直肠）对性能进行总体评估。以95% %置信区间（ci）计算辨别力（AUC）、敏感性、特异性、阳性预测值（PPV）和阴性预测值（NPV）。结果共纳入1213例患者，其中结肠1085例，直肠128例。SSI的总发生率为11.2 %（3.1 %浅表，1.2 %深部，6.8 %器官/间隙）。对于任何SSI，复合预警的AUC为0.859(95 % CI 0.838-0.878)，敏感性0.721，特异性0.876,PPV 0.424, NPV 0.961。在这个操作点，19% %的程序将被标记为手动验证，对应于完整图表审查的估计减少81% %。脏器/空间感染的鉴别率最高（AUC 0.919，敏感性0.831，特异性0.911）。深度SSI的性能为中等（AUC为0.805），浅表SSI的性能更有限（AUC为0.571）。结肠手术的敏感性较高（AUC 0.853），直肠手术的特异性较高（AUC 0.881）。结论结构化数据算法对器官/空间感染具有较强的整体判别能力和优异的检测性能，支持在不影响检测质量的情况下实现半自动监测的可行性。需要外部和前瞻性验证、诊断安全阈值的定义以及工作量减少分析来优化实施。在资源允许的情况下，可以考虑对NLP附加组件进行勘探。ClinicalTrials.gov: NCT07130656。

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来源期刊

Journal of Infection and Public Health PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH -INFECTIOUS DISEASES

CiteScore

13.10

自引率

1.50%

发文量

203

审稿时长

96 days

期刊介绍： The Journal of Infection and Public Health, first official journal of the Saudi Arabian Ministry of National Guard Health Affairs, King Saud Bin Abdulaziz University for Health Sciences and the Saudi Association for Public Health, aims to be the foremost scientific, peer-reviewed journal encompassing infection prevention and control, microbiology, infectious diseases, public health and the application of healthcare epidemiology to the evaluation of health outcomes. The point of view of the journal is that infection and public health are closely intertwined and that advances in one area will have positive consequences on the other. The journal will be useful to all health professionals who are partners in the management of patients with communicable diseases, keeping them up to date. The journal is proud to have an international and diverse editorial board that will assist and facilitate the publication of articles that reflect a global view on infection control and public health, as well as emphasizing our focus on supporting the needs of public health practitioners. It is our aim to improve healthcare by reducing risk of infection and related adverse outcomes by critical review, selection, and dissemination of new and relevant information in the field of infection control, public health and infectious diseases in all healthcare settings and the community.