Colson Philippe, Rolain Jean-Marc, Abat Cédric, Charrel Rémi, Fournier Pierre-Edouard, Raoult Didier
Institut Hospitalo-Universitaire (IHU) Méditerranée Infection, Pôle des Maladies Infectieuses et Tropicales Clinique et Biologique, Fédération de Bactériologie-Hygiène-Virologie, Centre Hospitalo-Universitaire Timone, Assistance publique-hôpitaux de Marseille, 264 rue Saint-Pierre, 13385, Marseille, cedex 05, France.
Aix-Marseille Univ., Unité de Recherche sur les Maladies Infectieuses et Tropicales Emergentes (URMITE) UM 63 CNRS 7278 IRD 3R198 INSERM U1095, 27 boulevard Jean Moulin, 13385, Marseille, cedex 05, France.
PLoS One. 2015 Dec 14;10(12):e0144178. doi: 10.1371/journal.pone.0144178. eCollection 2015.
Infectious diseases (IDs) are major causes of morbidity and mortality and their surveillance is critical. In 2002, we implemented a simple and versatile homemade tool, named EPIMIC, for the real-time systematic automated surveillance of IDs at Marseille university hospitals, based on the data from our clinical microbiology laboratory, including clinical samples, tests and diagnoses.
This tool was specifically designed to detect abnormal events as IDs are rarely predicted and modeled. EPIMIC operates using Microsoft Excel software and requires no particular computer skills or resources. An abnormal event corresponds to an increase above, or a decrease below threshold values calculated based on the mean of historical data plus or minus 2 standard deviations, respectively.
Between November 2002 and October 2013 (11 years), 293 items were surveyed weekly, including 38 clinical samples, 86 pathogens, 79 diagnosis tests, and 39 antibacterial resistance patterns. The mean duration of surveillance was 7.6 years (range, 1 month-10.9 years). A total of 108,427 Microsoft Excel file cells were filled with counts of clinical samples, and 110,017 cells were filled with counts of diagnoses. A total of 1,390,689 samples were analyzed. Among them, 172,180 were found to be positive for a pathogen. EPIMIC generated a mean number of 0.5 alert/week on abnormal events.
EPIMIC proved to be efficient for real-time automated laboratory-based surveillance and alerting at our university hospital clinical microbiology laboratory-scale. It is freely downloadable from the following URL: http://www.mediterranee-infection.com/article.php?larub=157&titre=bulletin-epidemiologique (last accessed: 20/11/2015).
传染病是发病和死亡的主要原因,对其进行监测至关重要。2002年,我们基于临床微生物实验室的数据,包括临床样本、检测和诊断结果,在马赛大学医院实施了一种名为EPIMIC的简单通用的自制工具,用于传染病的实时系统自动监测。
该工具专为检测异常事件而设计,因为传染病很少能被预测和建模。EPIMIC使用微软Excel软件运行,无需特殊的计算机技能或资源。异常事件分别对应高于或低于基于历史数据均值加减2个标准差计算得出的阈值的增加或减少。
在2002年11月至2013年10月(11年)期间,每周对293项进行监测,包括38种临床样本、86种病原体、79种诊断检测和39种抗菌药物耐药模式。监测的平均时长为7.6年(范围为1个月至10.9年)。共有108,427个微软Excel文件单元格填入了临床样本计数,110,017个单元格填入了诊断计数。共分析了1,390,689个样本。其中,172,180个样本被检测出病原体呈阳性。EPIMIC每周针对异常事件产生的警报平均数为0.5次。
在我们大学医院临床微生物实验室规模下,EPIMIC被证明对于基于实验室的实时自动监测和警报是有效的。可从以下网址免费下载:http://www.mediterranee-infection.com/article.php?larub=157&titre=bulletin-epidemiologique(最后访问时间:2015年11月20日)。