摘要

BACKGROUND:Acute kidney injury (AKI) is an adverse event that carries significant morbidity. Given that interventions after AKI occurrence have poor performance, there is substantial interest in prediction of AKI prior to its diagnosis. However, integration of real-time prognostic modeling into the electronic health record (EHR) has been challenging, as complex models increase the risk of error and complicate deployment. Our goal in this study was to create an implementable predictive model to accurately predict AKI in hospitalized patients and could be easily integrated within an existing EHR system. METHODS AND FINDINGS:We performed a retrospective analysis looking at data of 169,859 hospitalized adults admitted to one of three study hospitals in the United States (in New Haven and Bridgeport, Connecticut) from December 2012 to February 2016. Demographics, medical comorbidities, hospital procedures, medications, and laboratory data were used to develop a model to predict AKI within 24 hours of a given observation. Outcomes of AKI severity, requirement for renal replacement therapy, and mortality were also measured and predicted. Models were trained using discrete-time logistic regression in a subset of Hospital 1, internally validated in the remainder of Hospital 1, and externally validated in Hospital 2 and Hospital 3. Model performance was assessed via the area under the receiver-operator characteristic (ROC) curve (AUC). The training set cohort contained 60,701 patients, and the internal validation set contained 30,599 patients. External validation data sets contained 43,534 and 35,025 patients. Patients in the overall cohort were generally older (median age ranging from 61 to 68 across hospitals); 44%-49% were male, 16%-20% were black, and 23%-29% were admitted to surgical wards. In the training set and external validation set, 19.1% and 18.9% of patients, respectively, developed AKI. The full model, including all covariates, had good ability to predict imminent AKI for the validation set, sustained AKI, dialysis, and death with AUCs of 0.74 (95% CI 0.73-0.74), 0.77 (95% CI 0.76-0.78), 0.79 (95% CI 0.73-0.85), and 0.69 (95% CI 0.67-0.72), respectively. A simple model using only readily available, time-updated laboratory values had very similar predictive performance to the complete model. The main limitation of this study is that it is observational in nature; thus, we are unable to conclude a causal relationship between covariates and AKI and do not provide an optimal treatment strategy for those predicted to develop AKI. CONCLUSIONS:In this study, we observed that a simple model using readily available laboratory data could be developed to predict imminent AKI with good discrimination. This model may lend itself well to integration into the EHR without sacrificing the performance seen in more complex models.

译文

背景: 急性肾损伤 (AKI) 是一种不良事件,具有显著的发病率。鉴于 AKI 发生后的干预表现不佳,在诊断 AKI 之前对 AKI 的预测有很大的兴趣。然而,将实时预后建模集成到电子健康记录 (EHR) 中一直具有挑战性,因为复杂的模型增加了错误风险并使部署复杂化。我们在这项研究中的目标是创建一个可实施的预测模型来准确预测住院患者的 AKI,并且可以很容易地集成到现有的 EHR 系统中。方法和结果: 我们对美国三家研究医院之一 (康涅狄格州纽黑文和布里奇波特) 的 169,859 名住院成年人的数据进行了回顾性分析从 2012年12月到 2016年2月。人口统计学、医学合并症、医院程序、药物和实验室数据被用来开发一个模型来预测给定观察后 24 小时内的 AKI。还测量和预测了 AKI 严重程度、肾脏替代治疗需求和死亡率的结果。模型在医院 1 的一个子集中使用离散时间逻辑回归进行训练,在医院 1 的剩余部分中进行内部验证,在医院 2 和医院 3 中进行外部验证。通过受试者-操作者特征曲线 (AUC) 下的面积评估模型性能。训练集队列包含 60,701 名患者,内部验证集包含 30,599 名患者。外部验证数据集包含 43,534 和 35,025 名患者。整个队列中的患者通常年龄较大 (医院的平均年龄从 61 岁到 68 岁不等); 44%-49% 是男性,16%-20% 是黑人, 23%-29% 人被送入外科病房。在训练集和外部验证集中,分别有 19.1% 和 18.9% 的患者发生 AKI。完整的模型,包括所有的协变量,有很好的能力预测即将发生的 AKI 的验证集,持续的 AKI,透析和死亡的 auc 为 0.74 (95% CI 0.73-0.74), 分别为 0.77 (95% CI 0.76-0.78) 、 0.79 (95% CI 0.73-0.85) 和 0.69 (95% CI 0.67-0.72)。一个仅使用现成的、时间更新的实验室值的简单模型与完整模型具有非常相似的预测性能。这项研究的主要局限在于它本质上是观察性的; 因此, 我们无法得出协变量和 AKI 之间的因果关系,也无法为那些预测发展为 AKI 的人提供最佳治疗策略。结论: 在这项研究中,我们观察到,使用现成的实验室数据可以开发一个简单的模型来预测即将发生的 AKI,并具有良好的区分度。该模型可以很好地集成到 EHR 中,而不会牺牲在更复杂模型中看到的性能。

Acute kidney injury (AKI)

肾内泌尿 肾损伤 疾病
概述  :  

疾病概述急性肾损伤(Acute kidney injury, AKI)是由多种病因引起的肾功能快速下降而出现的临床综合征。可发生于既往无肾脏病者,也可发生在原有慢性肾脏病的基础上。与急性肾衰竭相比,AKI的提出更强调对这一综合征早期诊断、早期治疗的重要性。 病理机制AKI的发病机制多样,根据病理发生的解剖部位不同可分为三大类:肾前性、肾性和肾后性。肾前性AKI的常见病因包括血容量减少、有效动脉血容量减少和肾内血流动力学改变等。肾后性AKI源于急性尿路梗阻,从肾盂到尿道任一水平

Acute   英/əˈkjuːt/   美 /əˈkjuːt/

释    义   adj. 严重的,[医] 急性的;敏锐的;激烈的;尖声的

同根词   acutely adv. 尖锐地;剧烈地

例    句   They think his illness is acute rather than chronic. 他们认为他的病是急性的,不是慢性的。

 

Kidney   /ˈkɪdni/

释    义   n. [解剖] 肾脏;腰子;个性

               n. (Kidney)人名;(英)基德尼

例    句   Can you live with only one kidney?   只有一个肾你能活下来吗?

 

Injury   /ˈɪndʒəri/

释    义   n. 伤害,损害;受伤处

同根词   injured adj. 受伤的;受损害的; 

               injurious n. 有害的;诽谤的

               injuriously adv. 有害地;伤害地

例    句   If my presence is an injury to someone, how can I do ?    如果我的存在,对某人是一种伤害,我该怎么做?

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