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Journal of Pediatric Nursing 2023The aim of this study was to evaluate the risks of self-feeding, transition to early solid food and family meals, choking risk, anemia risk and obesity risk in... (Randomized Controlled Trial)
Randomized Controlled Trial
PURPOSE
The aim of this study was to evaluate the risks of self-feeding, transition to early solid food and family meals, choking risk, anemia risk and obesity risk in 6-12-month-old infants who were introduced to complementary feeding using the traditional complementary feeding (TCF) and baby-led weaning (BLW) methods/training.
DESIGN AND METHODS
Mothers of infants who had not yet transitioned to complementary feeding were included in this randomized study. The mothers of 62 infants included in the study were randomized into the intervention groups as TCF and BLW, classified according to the number of children and education level. The research was carried out according to the CONSORT-2010 guidelines after randomization and was concluded with 52 infants and their mothers.
RESULTS
It was found in the study that self-feeding and transition to solid foods in infants fed with the BLW method was higher than the infants fed with the TCF method (p < 0.05). A significant increase was observed in the hemoglobin level of infants fed with the BLW method over time (p < 0.001).
CONCLUSIONS
It was concluded that the BLW method did not lead to risks of obesity, anemia and iron deficiency in transition to complementary feeding. Secondary results indicated that feeding with the BLW method promoted self-feeding and early transition to solid foods and did not lead to the risk of choking.
PRACTICE IMPLICATIONS
Complementary feeding with the BLW method can be safely used by both mothers, healthcare professionals and researchers.
TRIAL REGISTRATION
register.
CLINICALTRIALS
gov; Identifier: NCT05771324.
Topics: Infant; Female; Child; Humans; Weaning; Feeding Behavior; Infant Food; Infant Nutritional Physiological Phenomena; Obesity; Airway Obstruction; Anemia; Breast Feeding
PubMed: 37714048
DOI: 10.1016/j.pedn.2023.09.006 -
International Journal of Nursing Studies Oct 2023Accurately identifying patients at high risk of delirium is vital for timely preventive intervention measures. Approaches for identifying the risk of developing delirium...
BACKGROUND
Accurately identifying patients at high risk of delirium is vital for timely preventive intervention measures. Approaches for identifying the risk of developing delirium among critically ill children are not well researched.
OBJECTIVE
To develop and validate machine learning-based models for predicting delirium among critically ill children 24 h after pediatric intensive care unit (PICU) admission.
DESIGN
A prospective cohort study.
SETTING
A large academic medical center with a 57-bed PICU in southwestern China from November 2019 to February 2022.
PARTICIPANTS
One thousand five hundred and seventy-six critically ill children requiring PICU stay over 24 h.
METHODS
Five machine learning algorithms were employed. Delirium was screened by bedside nurses twice a day using the Cornell Assessment of Pediatric Delirium. Twenty-four clinical features from medical and nursing records during hospitalization were used to inform the models. Model performance was assessed according to numerous learning metrics, including the area under the receiver operating characteristic curve (AUC).
RESULTS
Of the 1576 enrolled patients, 929 (58.9 %) were boys, and the age ranged from 28 days to 15 years with a median age of 12 months (IQR 3 to 60 months). Among them, 1126 patients were assigned to the training cohort, and 450 were assigned to the validation cohort. The AUCs ranged from 0.763 to 0.805 for the five models, among which the eXtreme Gradient Boosting (XGB) model performed best, achieving an AUC of 0.805 (95 % CI, 0.759-0.851), with 0.798 (95 % CI, 0.758-0.834) accuracy, 0.902 sensitivity, 0.839 positive predictive value, 0.640 F1-score and a Brier score of 0.144. Almost all models showed lower predictive performance in children younger than 24 months than in older children. The logistic regression model also performed well, with an AUC of 0.789 (95 % CI, 0.739, 0.838), just under that of the XGB model, and was subsequently transformed into a nomogram.
CONCLUSIONS
Machine learning-based models can be established and potentially help identify critically ill children who are at high risk of delirium 24 h after PICU admission. The nomogram may be a beneficial management tool for delirium for PICU practitioners at present.
Topics: Male; Humans; Child; Infant, Newborn; Female; Prospective Studies; Critical Illness; Delirium; Intensive Care Units, Pediatric; Hospitalization; Machine Learning
PubMed: 37542959
DOI: 10.1016/j.ijnurstu.2023.104565 -
Neonatal Network : NN May 2024Neonatal hypoglycemia (NH) is broadly defined as a low plasma glucose concentration that elicits hypoglycemia-induced impaired brain function. To date, no universally... (Review)
Review
Neonatal hypoglycemia (NH) is broadly defined as a low plasma glucose concentration that elicits hypoglycemia-induced impaired brain function. To date, no universally accepted threshold (reference range) for plasma glucose levels in newborns has been published, as data consistently indicate that neurologic responses to hypoglycemia differ at various plasma glucose concentrations. Infants at risk for NH include infants of diabetic mothers, small or large for gestational age, and premature infants. Common manifestations include jitteriness, poor feeding, irritability, and encephalopathy. Neurodevelopmental morbidities associated with NH include cognitive and motor delays, cerebral palsy, vision and hearing impairment, and poor school performance. This article offers a timely discussion of the state of the science of NH and recommendations for neonatal providers focused on early identification and disease prevention.
Topics: Humans; Hypoglycemia; Infant, Newborn; Blood Glucose; Neonatal Nursing; Infant, Newborn, Diseases
PubMed: 38816219
DOI: 10.1891/NN-2023-0068 -
Critical Care Nursing Clinics of North... Sep 2023Pediatric critical care nursing is a key pillar in patient care and outcomes for children who are ill and injured. Tremendous advances have occurred in pediatric... (Review)
Review
Pediatric critical care nursing is a key pillar in patient care and outcomes for children who are ill and injured. Tremendous advances have occurred in pediatric critical care and nursing. This article provides an overview of the key advances in pediatric critical care nursing through the decades.
Topics: Child; Humans; Intensive Care Units, Pediatric; Critical Care Nursing; Critical Care; Critical Illness; Pediatric Nursing
PubMed: 37532380
DOI: 10.1016/j.cnc.2023.04.001 -
MCN. the American Journal of Maternal...
Topics: Female; Humans; Breast Feeding; Milk, Human
PubMed: 37840206
DOI: 10.1097/NMC.0000000000000958 -
Critical Care Nursing Clinics of North... Jun 2024
Topics: Humans; Neonatal Nursing; Infant, Newborn
PubMed: 38705696
DOI: 10.1016/j.cnc.2023.12.004 -
Critical Care Nursing Clinics of North... Sep 2023
Topics: Child; Humans; Critical Care Nursing; Forecasting; Critical Care; Pediatric Nursing; Intensive Care Units, Pediatric
PubMed: 37532389
DOI: 10.1016/j.cnc.2023.05.004 -
Critical Care Nursing Clinics of North... Mar 2024
Topics: Infant, Newborn; Humans; Neonatal Nursing; Intensive Care Units, Neonatal; Evidence-Based Medicine
PubMed: 38296379
DOI: 10.1016/j.cnc.2023.11.009 -
Anales de Pediatria Aug 2023
Topics: Child; Humans; Pediatric Nursing; Workforce
PubMed: 37474416
DOI: 10.1016/j.anpede.2023.06.014 -
Journal of Pediatric... 2023Cancer is a significant health problem in Turkey with pediatric cancer being the fourth leading cause of death among children. Pediatric oncology has been acknowledged...
Cancer is a significant health problem in Turkey with pediatric cancer being the fourth leading cause of death among children. Pediatric oncology has been acknowledged as a pediatric subspecialty since 1983, and 3,000 new cases of childhood cancer are expected every year. We describe our country's geography, the number and distribution of pediatric oncology centers, the profile of clinical and academic nurses, and our clinical practice. We present two nurse-led research projects. Although nursing practice differs according to centers, treatment and care are usually evidence-based, especially in university and public hospitals in big cities. Nurses with an undergraduate education work in pediatric oncology units; however, few nurses with graduate degrees work in clinical settings. The Turkish Oncology Nurses Association supports the development and implementation of guidelines for evidence-based nursing care. Nurses' clinical responsibilities include patient admission to the clinic, patient and family education, care coordination, patient care, symptom management, palliative, and intensive care services. Results of two recent nurse-led studies highlight challenges to meet the needs of patients and families from surrounding countries, including refugees, and opportunities for nurses to provide holistic support to parents of hospitalized children. Increasing the number of nurses is a priority to improve pediatric oncology nursing care. Actions to advance pediatric oncology nursing include developing advanced clinical roles for nurses with graduate degrees; supporting nurses caring for children and families from outside Turkey, including language support services; and resources to conduct national and international studies related to professional and holistic care delivery.
Topics: Humans; Child; Turkey; Nurses; Neoplasms; Delivery of Health Care; Palliative Care
PubMed: 37908072
DOI: 10.1177/27527530231197221