Jannot Anne-Sophie, Burgun Anita, Thervet Eric, Pallet Nicolas
Departments of Medical Informatics, Biostatistics and Public Health.
Assistance Publique Hôpitaux de Paris, Paris, France.
Clin J Am Soc Nephrol. 2017 Jun 7;12(6):874-884. doi: 10.2215/CJN.10981016. Epub 2017 May 11.
The exploration of electronic hospital records offers a unique opportunity to describe in-depth the prevalence of conditions associated with diagnoses at an unprecedented level of comprehensiveness. We used a diagnosis-wide approach, adapted from phenome-wide association studies (PheWAS), to perform an exhaustive analysis of all diagnoses associated with hospital-acquired AKI (HA-AKI) in a French urban tertiary academic hospital over a period of 10 years.
DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: We retrospectively extracted all diagnoses from an i2b2 (Informatics for Integrating Biology and the Bedside) clinical data warehouse for patients who stayed in this hospital between 2006 and 2015 and had at least two plasma creatinine measurements performed during the first week of their stay. We then analyzed the association between HA-AKI and each International Classification of Diseases (ICD)-10 diagnostic category to draw a comprehensive picture of diagnoses associated with AKI. Hospital stays for 126,736 unique individuals were extracted.
Hemodynamic impairment and surgical procedures are the main factors associated with HA-AKI and five clusters of diagnoses were identified: sepsis, heart diseases, polytrauma, liver disease, and cardiovascular surgery. The ICD-10 code corresponding to AKI (N17) was recorded in 30% of the cases with HA-AKI identified, and in this situation, 20% of the diagnoses associated with HA-AKI corresponded to kidney diseases such as tubulointerstitial nephritis, necrotizing vasculitis, or myeloma cast nephropathy. Codes associated with HA-AKI that demonstrated the greatest increase in prevalence with time were related to influenza, polytrauma, and surgery of neoplasms of the genitourinary system.
Our approach, derived from PheWAS, is a valuable way to comprehensively identify and classify all of the diagnoses and clusters of diagnoses associated with HA-AKI. Our analysis delivers insights into how diagnoses associated with HA-AKI evolved over time. On the basis of ICD-10 codes, HA-AKI appears largely underestimated in this academic hospital.
对电子医院记录的探索提供了一个独特的机会,以前所未有的全面程度深入描述与诊断相关疾病的患病率。我们采用了一种从全表型关联研究(PheWAS)改编而来的全诊断方法,对一家法国城市三级学术医院10年间与医院获得性急性肾损伤(HA-AKI)相关的所有诊断进行详尽分析。
设计、设置、参与者及测量:我们回顾性地从i2b2(整合生物学与床边信息学)临床数据仓库中提取了2006年至2015年期间入住该医院且在住院第一周至少进行过两次血肌酐测量的患者的所有诊断信息。然后,我们分析了HA-AKI与每个国际疾病分类(ICD)-10诊断类别之间的关联,以全面了解与急性肾损伤相关的诊断情况。共提取了126,736名个体的住院记录。
血流动力学损害和外科手术是与HA-AKI相关的主要因素,并且识别出了五组诊断类别:脓毒症、心脏病、多发伤、肝病和心血管手术。在确定为HA-AKI的病例中,30%记录了与急性肾损伤对应的ICD-10编码(N17),在这种情况下,与HA-AKI相关的诊断中有20%对应于肾病,如肾小管间质性肾炎、坏死性血管炎或骨髓瘤管型肾病。随时间患病率增加幅度最大的与HA-AKI相关的编码与流感、多发伤以及泌尿生殖系统肿瘤手术有关。
我们源自PheWAS的方法是全面识别和分类与HA-AKI相关的所有诊断及诊断类别组的一种有价值的方式。我们的分析揭示了与HA-AKI相关的诊断随时间的演变情况。基于ICD-10编码,在这家学术医院中HA-AKI似乎被严重低估。