Risk factors for acute kidney injury in vancomycin and piperacillin/tazobactam combination therapy: A retrospective study

Yamashita, Yuma; Kawaguchi, Hiroshi; Yano, Tsubasa; Sakurai, Norihiro; Shibata, Wataru; Oshima, Kazuhiro; Imai, Takumi; Yamada, Koichi; Nakamura, Yasutaka; Nagayama, Katsuya; Kakeya, Hiroshi

doi:10.1016/j.jiac.2021.07.017

Cited by 1 publication

(1 citation statement)

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“…It is well established that AKI usually occurs in susceptible populations with high-risk factors or following certain specific medical procedures, and several previous studies have obtained more consistent conclusions on the evaluation of risk factors by analyzing the static and dynamic characteristics of a large number of AKI patients (3)(4)(5)(6)(7)(8). Based on the preventability of AKI and the ease of diagnosis, increasing research is focused on the early diagnosis of AKI, with the aim of early assessment of inpatients to determine their risk stratification, dynamic adjustment of treatment protocols, replacement of potentially dangerous medical orders, and interventions to avoid or reduce potential kidney injury and ultimately to achieve inpatient renal protection by reducing the incidence of AKI, avoiding a series of subsequent adverse events.…”

Section: Introductionmentioning

confidence: 99%

Machine learning for acute kidney injury: Changing the traditional disease prediction mode

Xia

Huang

et al. 2023

Front. Med.

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Acute kidney injury (AKI) is a serious clinical comorbidity with clear short-term and long-term prognostic implications for inpatients. The diversity of risk factors for AKI has been recognized in previous studies, and a series of predictive models have been developed using traditional statistical methods in conjunction with its preventability, but they have failed to meet the expectations in limited clinical applications, the rapid spread of electronic health records and artificial intelligence machine learning technology has brought new hope for the construction of AKI prediction models. In this article, we systematically review the definition and classification of machine learning methods, modeling ideas and evaluation methods, and the characteristics and current status of modeling studies. According to the modeling objectives, we subdivided them into critical care medical setting models, all medical environment models, special surgery models, special disease models, and special nephrotoxin exposure models. As the first review article to comprehensively summarize and analyze machine learning prediction models for AKI, we aim to objectively describe the advantages and disadvantages of machine learning approaches to modeling, and help other researchers more quickly and intuitively understand the current status of modeling research, inspire ideas and learn from experience, so as to guide and stimulate more research and more in-depth exploration in the future, which will ultimately provide greater help to improve the overall status of AKI diagnosis and treatment.

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