Machine learning model for early prediction of acute kidney injury (AKI) in pediatric critical care.

BACKGROUND: Acute kidney injury (AKI) in pediatric critical care patients is diagnosed using elevated serum creatinine, which occurs only after kidney impairment. There are no treatments other than supportive care for AKI once it has developed, so it is important to identify patients at risk to prevent injury. This study develops a machine learning model to learn pre-disease patterns of physiological measurements and predict pediatric AKI up to 48 h earlier than the currently established diagnostic guidelines. METHODS: EHR data from 16,863 pediatric critical care patients between 1 month to 21 years of age from three independent institutions were used to develop a single machine learning model for early prediction of creatinine-based AKI using intelligently engineered predictors, such as creatinine rate of change, to automatically assess real-time AKI risk. The primary outcome is prediction of moderate to severe AKI (Stage 2/3), and secondary outcomes are prediction of any AKI (Stage 1/2/3) and requirement of renal replacement therapy (RRT). Predictions generate alerts allowing fast assessment and reduction of AKI risk, such as: "patient has 90% risk of developing AKI in the next 48 h" along with contextual information and suggested response such as "patient on aminoglycosides, suggest check level and review dose and indication". RESULTS: The model was successful in predicting Stage 2/3 AKI prior to detection by conventional criteria with a median lead-time of 30 h at AUROC of 0.89. The model predicted 70% of subsequent RRT episodes, 58% of Stage 2/3 episodes, and 41% of any AKI episodes. The ratio of false to true alerts of any AKI episodes was approximately one-to-one (PPV 47%). Among patients predicted, 79% received potentially nephrotoxic medication after being identified by the model but before development of AKI. CONCLUSIONS: As the first multi-center validated AKI prediction model for all pediatric critical care patients, the machine learning model described in this study accurately predicts moderate to severe AKI up to 48 h in advance of AKI onset. The model may improve outcome of pediatric AKI by providing early alerting and actionable feedback, potentially preventing or reducing AKI by implementing early measures such as medication adjustment.

Characterization of Korean Red Ginseng (Panax ginseng Meyer): History, preparation method, and chemical composition.

It has been reported that Korean Red Ginseng has been manufactured for 1,123 y as described in the GoRyeoDoGyeong record. The Korean Red Ginseng manufactured by the traditional preparation method has its own chemical component characteristics. The ginsenoside content of the red ginseng is shown as Rg1: 3.3 mg/g, Re: 2.0 mg/g, Rb1: 5.8 mg/g, Rc:1.7 mg/g, Rb2: 2.3 mg/g, and Rd: 0.4 mg/g, respectively. It is known that Korean ginseng generally consists of the main root and the lateral or fine roots at a ratio of about 75:25. Therefore, the red ginseng extract is prepared by using this same ratio of the main root and lateral or fine roots and processed by the historical traditional medicine prescription. The red ginseng extract is prepared through a water extraction (90(°)C for 14-16 h) and concentration process (until its final concentration is 70-73 Brix at 50-60(°)C). The ginsenoside contents of the red ginseng extract are shown as Rg1: 1.3 mg/g, Re: 1.3 mg/g, Rb1: 6.4 mg/g, Rc:2.5 mg/g, Rb2: 2.3 mg/g, and Rd: 0.9 mg/g, respectively. Arginine-fructose-glucose (AFG) is a specific amino-sugar that can be produced by chemical reaction of the process when the fresh ginseng is converted to red ginseng. The content of AFG is 1.0-1.5% in red ginseng. Acidic polysaccharide, which has been known as an immune activator, is at levels of 4.5-7.5% in red ginseng. Therefore, we recommended that the chemical profiles of Korean Red Ginseng made through the defined traditional method should be well preserved and it has had its own chemical characteristics since its traditional development.