#4479 Kim-1, Il-18 and Ngal in Machine Learning Prediction of Kidney Injury Among Children Undergoing Hematopoietic Stem Cell Transplantation

Abstract

Background and Aims Children undergoing allogeneic hematopoietic stem cell transplantation (alloHSCT) are particularly vulnerable to acute kidney injury (AKI), especially in the early post-transplantation period. The major risk factors of AKI development are aggressive immunosuppression and infectious complications. In the meantime, malnutrition and hypermetabolic state of the patient, together with the routine intensive hydration during first 3 weeks after HSCT and subsequent forced diuresis, alter the serum creatinine concentration, modifying the estimated glomerular filtration rate (eGFR) value too. Therefore, the risk of underrating serum creatinine and overrating eGFR values is high, making the assessment of the degree of kidney damage during the first month after HSCT a challenge. Therefore, markers of tubular dysfunction and damage, like kidney injury molecule (KIM)-1, neutrophil gelatinase-associated lipocalin (NGAL) or interleukin (IL)-18, may be of added value while assessing renal function and analyzing the risk of AKI in this population. The aim of study was to assess the serum concentrations of damage biomarkers (KIM-1, NGAL, IL-18) in children undergoing alloHSCT, in relation to another surrogate marker of renal dysfunction, hyperfiltration. Another aim was to analyze the potential value of KIM-1, NGAL, and IL-18 as predictors of kidney damage in children after alloHSCT, with the use of artificial intelligence tools. Method The study group contained 22 children undergoing alloHSCT, followed up for 4 weeks after transplantation. Serum concentrations of KIM-1, NGAL, and IL-18 were assessed by ELISA in fixed time points (before HSCT, 1 day after HSCT, 1, 2 3, 4 weeks after transplantation). eGFR values (counted based on Schwartz formula) and the rate of hyperfiltration (eGFR > 140ml/min/1.73sq.m.) were evaluated at the beginning (before HSCT) and at the end (4 weeks after HSCT) of observation, when neither hydration nor diuretics were used. Statistical analysis was performed with the use of package Statistica, the comparisons between paired data were evaluated by using nonparametric tests (Friedman, Wilcoxon). Additionally, the patients within the database were randomly divided into two groups. The training group allowed to build a Random Forest Classifier (RFC) with the highest possible predictive power, while the testing group allowed to assess the effectiveness of prediction on new data and the clinical utility. Moreover, the contribution of individual variables was evaluated by GINI importance. Results KIM-1, NGAL, and IL-18 serum concentrations increased systematically until the 3rd week after HSCT, with statistically significant differences between subsequent observation points, then remained elevated until the 4th week after HSCT. Median eGFR values before transplantation and 4 weeks after HSCT were comparable, although the rate of patients with hyperfiltration increased. The RFC model built on the basis of 3 input variables, KIM-1, NGAL, and IL-18 concentrations in serum of children before HSCT, was able to effectively assess the rate of patients with hyperfiltration 4 weeks after the procedure. RF Classifier achieved AUROC of 0.8333, accuracy of 80.00%, positive predictive value of 0.8667, and sensitivity of 0.8000. The contribution of KIM-1, IL-18 and NGAL to the prediction in this model was comparable (33.73%, 32.77%, and 33.5%, respectively). Conclusion KIM-1, NGAL, and IL-18 are useful in assessing acute tubular damage in children after HSCT. Their values before HSCT may also serve as markers of incipient renal dysfunction 4 weeks after alloHSCT. The developed model seems a clinically useful tool to target patients who are at risk of kidney injury after HSCT. The Random Forest Classifier seems a promising tool for such analysis, that should be tested on a larger group of patients.