Summary points
What was already known on the topic
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Acute kidney injury (AKI) is an adverse event associated with significant short- and long-term
Acute Kidney Injury (AKI) is considered one of the most common complications of acute illness, affecting 11–12 % of all hospitalized patients worldwide with a mortality rate of ∼10 % [1]. AKI is associated with significant short- and long-term morbidity and mortality [2]. Early prediction or prevention of AKI has profound clinical implications but remains a major challenge [3] because AKI is not a disease per se, but rather a loose collection of syndromes [4]. However, the timing of AKI and its clinical manifestations are not random, and they relate to both the type and severity of injury. Data-driven knowledge mining approaches that incorporate “big” electronic medical records (EMR) have presented a unique analytic opportunity for AKI [5].
AKI is related to multiple risk factors including intrinsic situations, exposure to nephrotoxins (e.g. non-steroidal anti-inflammatory drugs [6]), acute illnesses (e.g. sepsis [7]), and major surgeries (such as cardiopulmonary bypass and coronary angiography [[8], [9], [10]]). Intrinsic risk situations include susceptibilities of each individual patient (e.g. age [11]) and those associated with reduced kidney reserve or failure of other organs with known cross-talk with the kidneys (such as heart, liver and respiratory system) [12]. In recent years, there have been several reports regarding novel and previously unknown risk factors for AKI, such as hyperuricemia [13], obstructive sleep apnea [14], hypochloremia and hyperchloremia [15].
Existing studies mainly focused on forecasting tools for the early identification of patients at risk. Many researchers [11,16,17] used a small number of highly correlated risk factors based on existing knowledge to build prediction models, which may miss potential unknown risk factors. Especially in case of medications, which are modifiable risk factors for AKI, most of the past researches [18,19] only collected data on known nephrotoxic drugs. In particular, the recent AKI prediction work by Google published in Nature [20] utilized the whole EMR data from the U.S. Department of Veterans Affairs, but one of the problems with these deep learning models [21,22] may be the lack of interpretability. To the best of our knowledge, the differentiation study of AKI stages focuses only on the comparison of prediction performance, for example, some researchers found that the forecasting models displayed a stepwise increase in the area under the receiver operating characteristic (AUC) across all AKI stages, performing significantly stronger for stages 2/3 than “mild” stage 1 [5,18,23]. However, there is few studies on knowledge mining between AKI stages.
In view of the differences in the etiology and pathophysiology of AKI patients [24], this study used a knowledge mining model to analyze the clinical risk factor differences between AKI stages from the perspective of EMR, to improve prevention, early detection and clinical management.
All adult patients (age at visit≥18) admitted to the University of Kansas Health System (a tertiary academic hospital) for two days or more from November 2007 to December 2016 were included in this retrospective observational cohort study, which included adult patients from all ICU, surgical, and general wards. Given that a patient may have multiple eligible hospital admissions or encounters and develop AKI during one but not another, this study was conducted at the encounter level with a total
Among the final analysis cohort of 76,957 hospital admissions, AKI occurred in 7,259 (9.43 %) encounters, with 6,396 (8.31 %) at stage 1, 678 (0.88 %) at stage 2, and 185 (0.24 %) at stage 3. Distribution of patient demographic variables among AKI stages 1–3 and non-AKI encounters is listed in Table 3. Most demographic variables (expect Asian and other race) were statistically different between AKI and non-AKI encounters (p< = 0.05). And the incidence of AKI in male patients is slightly higher
Current international guidelines recommend risk assessment for AKI for the purpose of preventing kidney injury progression and severity [34]. In order to prevent the occurrence of severe AKI or reduce the duration and severity of AKI as early as possible, in this study, we established a knowledge mining method to screen those risk predictors that may cause severe AKI in hospitalized patients from the perspective of high-dimensional EMR characteristics.
Although some researchers focused solely on
Early prediction or prevention of AKI has profound clinical implications, and data-driven approaches that incorporate “big” EMR has presented a unique analytic opportunity for AKI. We used 9 years of EMR data, including 76,957 eligible hospital encounters, and established a knowledge mining model to screen the risk predictors that are more likely to imply severe AKI. The results of this study provide potential and opportunities to enhance the prevention of severe AKI in clinical care.
LW designed the overall study, ran the experiments, analyzed the results, created the tables and figures, and wrote the manuscript. YH and ML assisted with the EMR data extraction and revised the manuscript. BY helped visualized the results. XZ, WQ and KL contributed in data processing. All authors gave final approval of the manuscript for publication. Summary points What was already known on the topic Acute kidney injury (AKI) is an adverse event associated with significant short- and long-term
The authors report no declarations of interest.
This research was partially supported by the Youth Science Fund of the National Natural Science Foundation of China (Grant No. 61802149), the Major Research Plan of the National Natural Science Foundation of China (Key Program, Grant No. 91746204), the Fundamental Research Funds for the Central Universities (Grant No. 21618315), the Science and Technology Development in Guangdong Province (Major Projects of Advanced and Key Techniques Innovation, Grant No. 2017B030308008), and Guangdong
Then, we used MV-XGB to identify important modifiable features at different times. To analyze effect of a factor, sum of absolute SHAP value [45–47] (equation 3 in Supplement text S1) is typically used but not clinically meaningful, thus, we constructed two new indicators based on SHAP: inter-class-score-difference and exposed-score-difference (Supplement text S3). Inter-class-score-difference calculates how many predicted log-odds-ratio differences between AKI and non-AKI patients can be explained by a feature/view.