Abstract
BackgroundTraditionally, patient risk scoring is done by evaluating vital signs and clinical severity scores with clinical intuition. Urinary biomarkers can add objectivity to these models to make risk prediction more accurate. We used metabolomics to identify prognostic urinary biomarkers of mortality or need for renal replacement therapy (RRT). Additionally, we assessed acute kidney injury (AKI) diagnosis, injury severity score (ISS), and AKI stage.MethodsUrine samples (n = 82) from a previous study of combat casualties were evaluated using proton nuclear magnetic resonance (1H-NMR) spectroscopy. Chenomx software was used to identify and quantify urinary metabolites. Metabolite concentrations were normalized by urine output, autoscaled, and log-transformed. Partial least squares discriminant analysis (PLS-DA) and statistical analysis were performed. Receiver operating characteristic (ROC) curves were used to assess prognostic utility of biomarkers for mortality and RRT.ResultsEighty-four (84) metabolites were identified and quantified in each urine sample. Of these, 11 were identified as drugs or drug metabolites and excluded. The PLS-DA models for ISS and AKI diagnosis did not have acceptable model statistics. Therefore, only mortality/RRT and AKI stage were analyzed further. Of 73 analyzed metabolites, 9 were significantly associated with mortality/RRT (p < 0.05) and 11 were significantly associated with AKI stage (p < 0.05). 1-Methylnicotinamide was the only metabolite to be significantly associated (p < 0.05) with all outcomes and was significantly higher (p < 0.05) in patients with adverse outcomes. Elevated lactate and 1-methylnicotinamide levels were associated with higher AKI stage and mortality and RRT, whereas elevated glycine levels were associated with patients who survived and did not require RRT, or had less severe AKI. ROC curves for each of these metabolites and the combined panel had good predictive value (lactate AUC = 0.901, 1-methylnicotinamide AUC = 0.864, glycine AUC = 0.735, panel AUC = 0.858).ConclusionsWe identified urinary metabolites associated with AKI stage and the primary outcome of mortality or need for RRT. Lactate, 1-methylnicotinamide, and glycine may be used as a panel of predictive biomarkers for mortality and RRT. 1-Methylnicotinamide is a novel biomarker associated with adverse outcomes. Additional studies are necessary to determine how these metabolites can be utilized in clinically-relevant risk prediction models.
Highlights
Patient risk scoring is done by evaluating vital signs and clinical severity scores with clinical intuition
Among intensive care unit (ICU) patients with acute kidney injury (AKI), mortality is around 50%, and in patients that require renal replacement therapy (RRT) it can be as high as 80% [3]
Sample collection and 1H‐nuclear magnetic resonance (NMR) data The study population was comprised of 82 patients (US military personnel) admitted to a combat hospital in Afghanistan with traumatic injury requiring ICU-level care [1]
Summary
Patient risk scoring is done by evaluating vital signs and clinical severity scores with clinical intuition. Risk prediction is especially important in Gisewhite et al Crit Care (2021) 25:119 hospitals caring for combat injured patients. This patient population may have delayed access to care, limited access to resources, multiple mechanisms of injury, more severe injury, and higher risk of a penetrating injury [2]. Accurate prediction models may allow for better resource allocation, more efficient triage of patients, and identify patients at higher risk for an adverse outcome, which will improve patient survival and recovery [1]. Important barriers to improving AKI treatment and outcomes are identifying patients at risk for non-recovery or developing severe AKI [5]
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