World Journal of Pediatrics最新文献

Background: Screening and pre-symptomatic diagnosis in newborns allows early treatment of thalassemia and abnormal hemoglobin (Hb) disorders in childhood. However, there remains a lack of efficient methods to screen for hemoglobinopathies in newborns. This study aimed to establish a bottom-up mass spectrometry (MS)-based method for efficient screening of hemoglobinopathies in newborns using dried blood spot (DBS) samples.

Methods: We developed LC-MS methodology using high-performance liquid chromatography (HPLC) combined with high-resolution mass spectrometry (HRMS). DBS samples from patients covering the most common types of hemoglobinopathies and normal controls were collected. We extracted Hb from a 3.2 mm disc punched from the DBS sample, which was then digested with trypsin to release a series of Hb-specific peptides. Using HPLC-HRMS, we identified disease-related peptides for biomarker design. Using this methodology, we built a prediction model using binary logistic regression to facilitate efficient screening.

Results: This new method costs less than $1 per test and can process at least 192 samples per batch. Our methodology is fast with a sampling and analysis time of 2.6 minutes and inter- and intra-assay coefficients of variation below 14.67%. Moreover, we report low limits of quantification for the proteo-specific peptides (0.50-60.00 μg/L). No significant matrix effects or carryover were observed. Our method could give reliable results even with DBS samples stored for one month. Prospective application of this method to 2726 newborns identified 87 patients with hemoglobinopathies and achieved high screening sensitivity and specificity for deletional α-thalassemia (--^SEA) (100.00% and 100.00%), β-thalassemia (97.50% and 89.63%) and other abnormal Hb disorders.

Conclusions: We have developed a low-cost, high-throughput method for reliable screening of thalassemia and abnormal Hb disorders in newborns. This could be deployed as a first-line screening test.

Background: Acute kidney injury is associated with a prolonged hospital stay and high mortality for pediatric patients. The previous prediction models are based on a pre-defined time window which may affect its feasibility in clinical practice. This study aimed to develop and validate a real-time acute kidney injury risk prediction model for hospitalized pediatric patients.

Methods: Based on a retrospective cohort composed of eligible pediatric patients hospitalized in Beijing Children's Hospital and Chongqing Children's Hospital, a machine learning model to predict acute kidney injury occurrence was developed and validated. The prediction model was established using a stacking technique to combine three base learners including XGBoost, LightGBM, and CatBoost. Particle swarm optimization algorithm was used to tune hyperparameters. Next, we assessed the performance of the prediction model using the area under the receiver-operating curve and the area under the precision-recall curve.

Results: A total of 26,671 patients were included (20,967 in the derivation set, 5242 in the internal validation set, and 462 in the external validation set) contributing to 36,828 hospitalizations. The new proposed model had excellent performance for predicting any acute kidney injury within 24 hours. Both the internal [area under the receiver-operating curve (AUROC) was 0.851, area under the precision-recall curve (AUPR) was 0.322] and external validation set (AUROC was 0.869, AUPR was 0.270) approved the model's feasibility in predicting the risk of acute kidney injury.

Conclusion: The established risk predicting model can be used for real-time prevention of acute kidney injury in hospitalized pediatric patients.

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