Pediatric Research最新文献

Background: Dravet syndrome (DS) is a developmental and epileptic encephalopathy caused mainly by SCN1A variants, several other genes have been implicated in DS-like phenotype.

Methods: DS and DS-like patients were collected from February 2005 to December 2023. Clinical data and genetic results were collected and analyzed.

Results: 1215 patients were enrolled. SCN1A variants were identified in 1061 patients (87.3%), Thirty-one DS-like patients(2.6%) harbored variants in nine genes: PCDH19 (9), GABRA1 (6), GABRB2 (4), GABRG2 (3), GABRB3 (1), HCN1 (3), SCN2A (1), TBC1D24 (2). ALDH7A1 (2). DS-like patients with PCDH19 variants often exhibited clustered seizures with less frequent status epilepticus. Variants in GABA_A receptor genes were associated with a relatively better response to anti-seizure medications. Oxcarbazepine exacerbated seizures in patients GABRG2, GABRB2 or GABRA1 variants. ALDH7A1 patients achieved seizure control with pyridoxine, while TBC1D24-related cases exhibited distinct focal myoclonic features. SCN2A gain-of-function variants responded favorably to oxcarbazepine.

Conclusions: This study confirms SCN1A as the predominant genetic cause of DS, while identifying nine additional genes (PCDH19, GABRA1, GABRB2, GABRG2, GABRB3, HCN1, SCN2A, TBC1D24, and ALDH7A1) associated with DS-like phenotypes. This study highlights the significance of identifying the underlying genetic cause in guiding appropriate treatment strategies in DS or DS-like patients.

Impact: SCN1A variants were detected in 87% of 1,215 Chinese patients with Dravet syndrome. Nine genes including PCDH19, GABRA1, GABRB2, GABRG2, GABRB3, HCN1, SCN2A, TBC1D24 and ALDH7A1 were linked to DS-like phenotype in 31 patients. Sodium channel blockers may worsen seizures in patients with GABA_A receptor gene variants. Genetic testing improves etiological diagnosis, enabling targeted and individualized patient care in Dravet or Dravet-like syndrome.

Impact: This study demonstrates that young children with weaknesses in objectively-measured delay of gratification and inhibitory control are more likely to be provided media for emotion regulation purposes. This may increase their risk of developing problematic media use and weaker emotion regulation skills later in childhood.

Background: Congenital heart disease (CHD) is a significant congenital anomaly, with ventricular septal defect (VSD) and transposition of the great arteries (TGA) being commonly encountered. Despite advancements in prenatal diagnosis and postnatal care, predicting neonatal outcomes remains difficult due to fetal compensatory mechanisms. This study utilizes and adapts existing mathematical models, without patient-specific parameters, to simulate the hemodynamic effects of VSD and TGA during the fetal-to-neonatal transition, validated against literature data.

Method: The model uses a closed-loop 0D-1D cardiovascular system and an oxygen-carbon dioxide exchange model to retrieve clinically relevant information.

Results: Results show realistic replication of flow, pressure profiles, oxygen saturation, and carbon dioxide levels for both normal and CHD-compromised fetuses.

Conclusion: The mathematical model offers valuable insights into hemodynamic alterations during the fetal-to-neonatal transition, also under two pathophysiological conditions. It provides a non-invasive means of predicting clinically relevant parameters, which is essential for postnatal treatment planning in the prenatal period, especially given the potential need for immediate postnatal hemodynamic management. Overall, this model presents a promising tool for predicting the impact of CHD postnatally based on prenatal information and for guiding timely and effective treatment strategies. As such, the model may be considered as a potential basis for other applications in perinatal hemodynamics including fetal-to-neonatal transition.

Impact: This study introduces a mathematical model that simulates the hemodynamic impact of congenital heart disease (CHD) during the fetal-to-neonatal transition, focusing on ventricular septal defect (VSD) and transposition of the great arteries (TGA). By integrating a one-fiber heart model and oxygen and carbon dioxide dynamics, the model gives a prediction of critical clinical parameters like flow, pressure, and oxygen saturation. This mathematical model has a potential to serve as non-invasive tool, providing valuable insights for prenatal planning and postnatal management, helping guide timely interventions for newborns affected by CHD.

Background: This study aimed to address the critical gap in the limited application of machine learning (ML) for identifying developmental delays in low-resource settings by developing models to predict off-track development in infants aged 0 to 6 months and identify key predictors.

Methods: A cross-sectional study involving 1,995 singleton infants aged 0 to 6 months was conducted in Kaloleni and Rabai sub-counties, Kilifi, Kenya, between March 2023 and March 2024. Development was assessed using the World Health Organization's Indicators of Infant and Young Child Development tool, with Development-for-Age Z-scores used to classify infants as on- or off-track. Ridge logistic regression (LR), random forest (RF), and extreme gradient boosting (XGBoost) models were trained using sociodemographic, psychosocial, clinical/biological, nutritional, and health-related predictors. Performance was evaluated using area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity. SHapley Additive exPlanations enhanced model interpretability.

Results: Approximately 10.4% of infants were developmentally off-track. Ridge LR, RF, and XGBoost showed similar performance, with AUCs of 76.6%, 75.8%, and 76.1%, respectively. Limited psychosocial stimulation and increasing infant age were the strongest predictors.

Conclusions: This study highlights the burden of developmental delays in low-resource settings. ML models show promise for early risk prediction and targeted intervention, though further validation is recommended.

Impact: Early intervention programs are proven to enhance optimal childhood development, yet the vital step of early identification of developmental delays is often overlooked. While machine learning is increasingly used to predict or identify health outcomes, its application in identifying developmental outcomes, particularly in low-resource settings, remains limited. This study contributes to the literature by applying machine learning to identify infants who are developmentally off-track and highlights key predictors. Limited psychosocial stimulation and increasing infant age were the strongest predictors, alongside low socioeconomic status, maternal mental health challenges, limited healthcare access, and nutritional and biological risks.

Background: Inflammation is central to acute kidney injury (AKI) pathogenesis. The systemic immune-inflammation index (SII), a novel marker of immune-inflammatory balance, has been shown to predict outcomes in adult critically ill AKI patients, with limited pediatric evidence. This study aimed to assess the prognostic value of SII for mortality in critically ill children with AKI.

Methods: This single-center study included 619 PICU AKI patients (January 2019-June 2023,KDIGO criteria). Patients were categorized into survivors (n = 450) and non-survivors (n = 169), with demographic and clinical differences compared. SII was calculated using initial blood counts within 24 hours of PICU admission. Analyses included restricted cubic splines (RCS), logistic regression, and Kaplan-Meier curves.

Results: Non-survivors had lower SII (P < 0.001). RCS revealed a threshold-dependent non-linear SII-mortality association (P < 0.001): mortality positively linked to SII < 470.95 × 10⁹/L (low group) and negatively to 470.95 ≤ SII < 1019.99 × 10⁹/L (middle group). After adjusting for confounders, the low group had higher mortality risk vs. the middle group (AOR = 2.531, 95% CI = 2.011-6.013, P = 0.008).

Conclusions: In critically ill children with AKI, SII exhibited a threshold-dependent non-linear association with mortality, where lower SII levels were independently associated with an increased risk of mortality.

Impact statement: In adult acute kidney injury (AKI) patients, both low and high systemic immune-inflammation index (SII) levels are significantly associated with increased in-hospital and intensive care unit (ICU) mortality. However, the association between SII and pediatric intensive care unit (PICU) mortality in pediatric AKI patients remains unclear. This study found a non-linear association between SII and mortality in critically ill children with AKI, where lower SII levels were independently associated with an increased risk of mortality. SII may be an early marker for predicting PICU mortality in critically ill children with AKI.

Background: Apnea in newborns causes the glottis to adduct and prevents gas from entering the lungs. As hypoxia is an inhibitor of fetal breathing, we have investigated the effect of hypoxia on glottis patency and breathing in preterm newborn rabbits.

Methods: Rabbit kittens (29 d gestation, term ~32 d; n = 12) were delivered by c-section, fitted with a custom face mask and oesophageal catheter to measure breathing efforts. After birth, kittens underwent phase contrast X-ray imaging while receiving CPAP using the following sequence of gases: (i) room-air, (ii) 100% oxygen, (iii) 100% nitrogen and (iv) 100% oxygen again. Glottis patency was visually assessed to determine %time that the glottis was open in each gas.

Results: The glottis remained open for longer (44.7 ± 1.8% vs. 15.5 ± 1.8%, p < 0.0001) and breathing rates were higher (18.1 ± 0.5 breaths/min vs. 11.3 ± 1.1 breaths/min, p = 0.0001) when kittens were exposed to 100% oxygen compared to room-air. When exposed to 100% nitrogen, breathing became unstable and resulted in apnea and a fully closed glottis in all kittens. Glottis patency and breathing were restored by resuscitating with 100% oxygen.

Conclusion: These results highlight the importance of avoiding hypoxia and promoting stable breathing to ensure the glottis is open when giving CPAP.

Impact: While preterm newborns commonly receive non-invasive respiratory support (CPAP) in the delivery room, this can be ineffective, particularly if the infant is apneic. We have demonstrated that hypoxia induces unstable breathing patterns in preterm newborn kittens, eventually causing apnea. When breathing is unstable, the glottis opens but only during inspiration. Between breaths and during apnea, the glottis remains closed, which prevents effective delivery of CPAP via face mask. This study highlights that adequate oxygenation is critically important for maintaining breathing activity and the use of oxygen in the delivery room can enhance the effectiveness of non-invasive respiratory support.