Validation of Two Scoring Tools to Predict Risk of Augmented Renal Clearance in Trauma Patients.

INTRODUCTION: Augmented renal clearance (ARC) is prevalent in trauma populations. Identification is underrecognized by calculated creatinine clearance or estimated glomerular filtration rate equations. Predictive scores may assist with ARC identification. The goal of this study was to evaluate validity of the ARCTIC score and ARC Predictor to predict ARC in critically ill trauma patients. METHODS: This single center, retrospective study was performed at an academic level 1 trauma center. Critically ill adult trauma patients undergoing 24-h urine-collection were included. Patients with serum creatinine >1.5 mg/dL, kidney replacement therapy, suspected rhabdomyolysis, chronic kidney disease, or inaccurate urine collection were excluded. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) for ARCTIC Score and ARC Predictor were calculated. Receiver operating characteristic curves were created for ARCTIC score and ARC Predictor models. RESULTS: One-hundred and twenty-two patients with ARC and 78 patients without ARC were included. The ARCTIC score sensitivity, specificity, PPV, and NPV were 89%, 54%, 75%, and 75%, respectively. The ARC Predictor demonstrated sensitivity, specificity, PPV, and NPV of 77%, 88%, 91%, and 71%, respectively. Regression analyses revealed both ARCTIC score ≥6 and ARC Predictor threshold >0.5 as significant risk factors for ARC in presence of traumatic brain injury, obesity, injury severity score, and negative nitrogen balance (ARCTIC ≥6: odds ratio 8.59 [95% confidence interval 3.90-18.92], P < 0.001; ARC Predictor >0.5: odds ratio 20.07 [95% confidence interval 8.53-47.19], P < 0.001). CONCLUSIONS: These findings corroborate validity of two pragmatic prediction tools to identify patients at high risk of ARC. Future studies evaluating correlations between ARCTIC score, ARC Predictor, and clinical outcomes are warranted.

NLRexpress-A bundle of machine learning motif predictors-Reveals motif stability underlying plant Nod-like receptors diversity.

Examination of a collection of over 80,000 Plant Nod-like receptors (NLRs) revealed an overwhelming sequence diversity underlying functional specificity of pathogen detection, signaling and cooperativity. The NLR canonical building blocks-CC/TIR/RPW8, NBS and LRR-contain, however, a number of conserved sequence motifs showing a significant degree of invariance amongst different NLR groups. To identify these motifs we developed NLRexpress-a bundle of 17 machine learning (ML)-based predictors, able to swiftly and precisely detect CC, TIR, NBS, and LRR motifs while minimizing computing time without accuracy losses-aimed as an instrument scalable for screening overall proteomes, transcriptomes or genomes for identifying integral NLRs and discriminating them against incomplete sequences lacking key motifs. These predictors were further used to screen a subset of ∼34,000 regular plant NLR sequences. Motifs were analyzed using unsupervised ML techniques to assess the structural correlations hidden underneath pattern variabilities. Both the NB-ARC switch domain which admittedly is the most conserved region of NLRs and the highly diverse LRR domain with its vastly variable lengths and repeat irregularities-show well-defined relations between motif subclasses, highlighting the importance of structural invariance in shaping NLR sequence diversity. The online NLRexpress webserver can be accessed at https://nlrexpress.biochim.ro.