Predicting pediatric cardiac surgery-associated acute kidney injury using machine learning.

BACKGROUND: Prediction of cardiac surgery-associated acute kidney injury (CS-AKI) in pediatric patients is crucial to improve outcomes and guide clinical decision-making. This study aimed to develop a supervised machine learning (ML) model for predicting moderate to severe CS-AKI at postoperative day 2 (POD2). METHODS: This retrospective cohort study analyzed data from 402 pediatric patients who underwent cardiac surgery at a university-affiliated children's hospital, who were separated into an 80%-20% train-test split. The ML model utilized demographic, preoperative, intraoperative, and POD0 clinical and laboratory data to predict moderate to severe AKI categorized by Kidney Disease: Improving Global Outcomes (KDIGO) stage 2 or 3 at POD2. Input feature importance was assessed by SHapley Additive exPlanations (SHAP) values. Model performance was evaluated using accuracy, area under the receiver operating curve (AUROC), precision, recall, area under the precision-recall curve (AUPRC), F1-score, and Brier score. RESULTS: Overall, 13.7% of children in the test set experienced moderate to severe AKI. The ML model achieved promising performance, with accuracy of 0.91 (95% CI: 0.82-1.00), AUROC of 0.88 (95% CI: 0.72-1.00), precision of 0.92 (95% CI: 0.70-1.00), recall of 0.63 (95% CI: 0.32-0.96), AUPRC of 0.81 (95% CI: 0.61-1.00), F1-score of 0.73 (95% CI: 0.46-0.99), and Brier score loss of 0.09 (95% CI: 0.00-0.17). The top ten most important features assessed by SHAP analyses in this model were preoperative serum creatinine, surgery duration, POD0 serum pH, POD0 lactate, cardiopulmonary bypass duration, POD0 vasoactive inotropic score, sex, POD0 hematocrit, preoperative weight, and POD0 serum creatinine. CONCLUSIONS: A supervised ML model utilizing demographic, preoperative, intraoperative, and immediate postoperative clinical and laboratory data showed promising performance in predicting moderate to severe CS-AKI at POD2 in pediatric patients.

Identification of Fusarium andiyazi Associated with the Bakanae Disease of Rice in Turkey.

Rice is an important nutrient staple for much of the Turkish population. In this study, field surveys were carried out to collect isolates of Fusarium andiyazi, a member of the Fusarium fujikuroi species complex (FFSC) associated with Bakanae disease. As part of the study, morphological and microscopic features of Fusarium andiyazi isolates recovered from infected rice plants were determined. The molecular detection of F. andiyazi was carried out through PCR analysis of partial translation elongation factor 1-alpha (TEF-1α) gene and the internal transcribed spacer (ITS) region. Gib2 and Fgc primers were used for specific detection of strain of FFSC. In the phylogenetic analysis, using the sequences of the partial TEF-1α gene, F. andiyazi were clustered in the same branch with the reference isolates. The pathogenicity tests revealed that the isolates of F. andiyazi were pathogen on susceptible Baldo cultivar under room climate conditions. The disease severity ranged from 3.27% to 86.30%. The pathogen causes typical disease symptoms associated with Bakanae and mostly inhibited seed germination, while most of the isolates caused abnormal elongation compared to the control plants. This is the first report of presence of F. andiyazi causing Bakanae disease on rice in Turkey.