Fluid management in patients with acute kidney injury – A post-hoc analysis of the FINNAKI study

Inkinen, Nina; Jukarainen, Sakari; Wiersema, Renske; Poukkanen, Meri; Pettilä, Ville; Vaara, Suvi T.

doi:10.1016/j.jcrc.2021.05.002

Cited by 3 publications

(1 citation statement)

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“…Similar findings have been reported in earlier studies ( 27 – 29 ). Furthermore, Inkinen et al ( 30 ) demonstrated that this phenomenon is linked to a lack of recovery from AKI. Excessive fluid intake may result in interstitial renal edema, which can raise renal perfusion pressure and impair kidney function.…”

Section: Discussionmentioning

confidence: 99%

Predicting the risk factors of diabetic ketoacidosis-associated acute kidney injury: A machine learning approach using XGBoost

Fan

Wang

et al. 2023

Front. Public Health

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ObjectiveThe purpose of this study was to develop and validate a predictive model based on a machine learning (ML) approach to identify patients with DKA at increased risk of AKI within 1 week of hospitalization in the intensive care unit (ICU).MethodsPatients diagnosed with DKA from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database according to the International Classification of Diseases (ICD)-9/10 code were included. The patient’s medical history is extracted, along with data on their demographics, vital signs, clinical characteristics, laboratory results, and therapeutic measures. The best-performing model is chosen by contrasting the 8 Ml models. The area under the receiver operating characteristic curve (AUC), sensitivity, accuracy, and specificity were calculated to select the best-performing ML model.ResultsThe final study enrolled 1,322 patients with DKA in total, randomly split into training (1,124, 85%) and validation sets (198, 15%). 497 (37.5%) of them experienced AKI within a week of being admitted to the ICU. The eXtreme Gradient Boosting (XGBoost) model performed best of the 8 Ml models, and the AUC of the training and validation sets were 0.835 and 0.800, respectively. According to the result of feature importance, the top 5 main features contributing to the XGBoost model were blood urea nitrogen (BUN), urine output, weight, age, and platelet count (PLT).ConclusionAn ML-based individual prediction model for DKA-associated AKI (DKA-AKI) was developed and validated. The model performs robustly, identifies high-risk patients early, can assist in clinical decision-making, and can improve the prognosis of DKA patients to some extent.

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Section: Discussionmentioning

confidence: 99%