A clinical informatics approach to bronchopulmonary dysplasia: current barriers and future possibilities.

Bronchopulmonary dysplasia (BPD) is a complex, multifactorial lung disease affecting preterm neonates that can result in long-term pulmonary and non-pulmonary complications. Current therapies mainly focus on symptom management after the development of BPD, indicating a need for innovative approaches to predict and identify neonates who would benefit most from targeted or earlier interventions. Clinical informatics, a subfield of biomedical informatics, is transforming healthcare by integrating computational methods with patient data to improve patient outcomes. The application of clinical informatics to develop and enhance clinical therapies for BPD presents opportunities by leveraging electronic health record data, applying machine learning algorithms, and implementing clinical decision support systems. This review highlights the current barriers and the future potential of clinical informatics in identifying clinically relevant BPD phenotypes and developing clinical decision support tools to improve the management of extremely preterm neonates developing or with established BPD. However, the full potential of clinical informatics in advancing our understanding of BPD with the goal of improving patient outcomes cannot be achieved unless we address current challenges such as data collection, storage, privacy, and inherent data bias.

Novel vanadyl complexes synthesis, characterization and interactions with bovine serum albumin-effects on STZ- diabetes rats.

Drug-protein interactions are essential since most administered drugs bind abundantly and reversibly to serum albumin and are delivered mainly as a complex with protein. The nature and strength of drug-protein interactions have a big impact on how a drug works biologically. The binding parameters are useful in studying the pharmacological response of drugs and the designing of dosage forms. Serum albumin is regarded as optimal model for in vitro research on drug-protein interaction since it is the main protein that binds medicines and other physiological components. In this perspective, binary complex have been synthesized and characterized, from vanadium metal and acetylacetone(4,4,4-trifluoro-1-(2-theonyl)-1,3-butanedione). Imidazole, 2-Methyl-imidazole, and 2-Ethyl-imidazole auxiliary ligands were employed for the synthesis of ternary complexes. Additionally, UV absorption and fluorescence emission spectroscopy were used to examine the binding interactions between vanadium complexes and Bovine Serum Albumin. The outcomes of the binding studies and spectral approaches were in strong agreement with one another. These complexes upon inoculation into diabetes-induced Wistar rats stabilized their serum glucose levels within 3 days. From various studies, it was discovered that the ordering of glucose-lowering actions of these metal complexes were equivalent. The vanadium ternary metal complex derived from (4,4,4-trifluoro-1-(2-theonyl)-1,3-butanedione) and imidazole as ligands is the best among the other metal vanadium complexes.

Transforming neonatal care with artificial intelligence: challenges, ethical consideration, and opportunities.

Artificial intelligence (AI) offers tremendous potential to transform neonatology through improved diagnostics, personalized treatments, and earlier prevention of complications. However, there are many challenges to address before AI is ready for clinical practice. This review defines key AI concepts and discusses ethical considerations and implicit biases associated with AI. Next we will review literature examples of AI already being explored in neonatology research and we will suggest future potentials for AI work. Examples discussed in this article include predicting outcomes such as sepsis, optimizing oxygen therapy, and image analysis to detect brain injury and retinopathy of prematurity. Realizing AI's potential necessitates collaboration between diverse stakeholders across the entire process of incorporating AI tools in the NICU to address testability, usability, bias, and transparency. With multi-center and multi-disciplinary collaboration, AI holds tremendous potential to transform the future of neonatology.

Neighborhood Deprivation and Association With Neonatal Intensive Care Unit Mortality and Morbidity for Extremely Premature Infants.

Importance: Socioeconomic status affects pregnancy and neurodevelopment, but its association with hospital outcomes among premature infants is unknown. The Area Deprivation Index (ADI) is a validated measure of neighborhood disadvantage that uses US Census Bureau data on income, educational level, employment, and housing quality. Objective: To determine whether ADI is associated with neonatal intensive care unit (NICU) mortality and morbidity in extremely premature infants. Design, Setting, and Participants: This retrospective cohort study was performed at 4 level IV NICUs in the US Northeast, Mid-Atlantic, Midwest, and South regions. Non-Hispanic White and Black infants with gestational age of less than 29 weeks and born between January 1, 2012, and December 31, 2020, were included in the analysis. Addresses were converted to census blocks, identified by Federal Information Processing Series codes, to link residences to national ADI percentiles. Exposures: ADI, race, birth weight, sex, and outborn status. Main Outcomes and Measures: In the primary outcome, the association between ADI and NICU mortality was analyzed using bayesian logistic regression adjusted for race, birth weight, outborn status, and sex. Risk factors were considered significant if the 95% credible intervals excluded zero. In the secondary outcome, the association between ADI and NICU morbidities, including late-onset sepsis, necrotizing enterocolitis (NEC), and severe intraventricular hemorrhage (IVH), were also analyzed. Results: A total of 2765 infants with a mean (SD) gestational age of 25.6 (1.7) weeks and mean (SD) birth weight of 805 (241) g were included in the analysis. Of these, 1391 (50.3%) were boys, 1325 (47.9%) reported Black maternal race, 498 (18.0%) died before NICU discharge, 692 (25.0%) developed sepsis or NEC, and 353 (12.8%) had severe IVH. In univariate analysis, higher median ADI was found among Black compared with White infants (77 [IQR, 45-93] vs 57 [IQR, 32-77]; P < .001), those who died before NICU discharge vs survived (71 [IQR, 45-89] vs 64 [IQR, 36-86]), those with late-onset sepsis or NEC vs those without (68 [IQR, 41-88] vs 64 [IQR, 35-86]), and those with severe IVH vs those without (69 [IQR, 44-90] vs 64 [IQR, 36-86]). In a multivariable bayesian logistic regression model, lower birth weight, higher ADI, and male sex were risk factors for mortality (95% credible intervals excluded zero), while Black race and outborn status were not. The ADI was also identified as a risk factor for sepsis or NEC and severe IVH. Conclusions and Relevance: The findings of this cohort study of extremely preterm infants admitted to 4 NICUs in different US geographic regions suggest that ADI was a risk factor for mortality and morbidity after adjusting for multiple covariates.

Enzyme PTP-1B Inhibition Studies by Vanadium Metal Complexes: a Kinetic Approach.

The medical field now needs more novel drugs to treat obesity and type-2 diabetes mellitus (T2D) than ever before. Obesity and T2D are both characterized by resistance to the hormones leptin and insulin. PTP-1B is a promising target for drug growth, as strong genetic, pharmacological, and biochemical evidence points to the possibility of treating diabetes and obesity by blocking the PTP-1B enzyme. Studies have also found that PTP-1B is overexpressed in patients with diabetes and obesity, suggesting that inhibiting PTP-1B may be a useful technique in their care. There are no clinically used PTP-1B inhibitors, despite the fact that numerous naturally occurring PTP-1B inhibitors have demonstrated great therapeutic promise. This is most likely due to their low activity or lack of selectivity. It is still important to look for more effective and focused PTP-1B inhibitors. A few organovanadium metal complexes were synthesized and characterized, and binding studies on vanadium complexes with PTP-B were also performed using fluorescence emission spectroscopy. Additionally, we theoretically (molecular modeling) and experimentally (enzyme kinetics) examined the PTP-1B inhibitory effects of these vanadium metal complexes and found that they have excellent PTP-1B inhibitory properties.

Use of an electronic medical record to optimize a neonatal sepsis score for mortality prediction.

OBJECTIVE: Late-onset sepsis (LOS) is a significant cause of mortality in preterm infants. The neonatal sequential organ failure assessment (nSOFA) provides an objective assessment of sepsis risk but requires manual calculation. We developed an EMR pipeline to automate nSOFA calculation for more granular analysis of score performance and to identify optimal alerting thresholds. METHODS: Infants born <33 weeks of gestation with LOS were included. A SQL-based pipeline calculated hourly nSOFA scores 48 h before/after sepsis evaluation. Sensitivity analysis identified the optimal timing and threshold of nSOFA for LOS mortality. RESULTS: Eighty episodes of LOS were identified (67 survivors, 13 non-survivor). Non-survivors had persistently elevated nSOFA scores, markedly increasing 12 h prior to culture. At sepsis evaluation, the AUC for nSOFA >2 was 0.744 (p = 0.0047); thresholds of >3 and >4 produced lower AUCs. CONCLUSIONS: nSOFA is persistently elevated for infants with LOS mortality compared to survivors with an optimal alert threshold >2.

Improving child health through Big Data and data science.

Child health is defined by a complex, dynamic network of genetic, cultural, nutritional, infectious, and environmental determinants at distinct, developmentally determined epochs from preconception to adolescence. This network shapes the future of children, susceptibilities to adult diseases, and individual child health outcomes. Evolution selects characteristics during fetal life, infancy, childhood, and adolescence that adapt to predictable and unpredictable exposures/stresses by creating alternative developmental phenotype trajectories. While child health has improved in the United States and globally over the past 30 years, continued improvement requires access to data that fully represent the complexity of these interactions and to new analytic methods. Big Data and innovative data science methods provide tools to integrate multiple data dimensions for description of best clinical, predictive, and preventive practices, for reducing racial disparities in child health outcomes, for inclusion of patient and family input in medical assessments, and for defining individual disease risk, mechanisms, and therapies. However, leveraging these resources will require new strategies that intentionally address institutional, ethical, regulatory, cultural, technical, and systemic barriers as well as developing partnerships with children and families from diverse backgrounds that acknowledge historical sources of mistrust. We highlight existing pediatric Big Data initiatives and identify areas of future research. IMPACT: Big Data and data science can improve child health. This review highlights the importance for child health of child-specific and life course-based Big Data and data science strategies. This review provides recommendations for future pediatric-specific Big Data and data science research.

Evaluation of the Neonatal Sequential Organ Failure Assessment and Mortality Risk in Preterm Infants with Necrotizing Enterocolitis.

INTRODUCTION: The neonatal sequential organ failure assessment (nSOFA) score is a tool for calculating mortality risk of infants in the neonatal intensive care unit. The utility of the nSOFA in determining the risk of mortality or the association with surgical intervention among infants with necrotizing enterocolitis (NEC) has not been investigated. METHODS: We performed a retrospective, cohort study of preterm (<37 weeks) infants with NEC Bell's stage ≥ IIA at six hospitals from 2008 to 2020. An nSOFA score (range 0-15) was assigned to each patient at nine time points from 48 h before or after clinical illness was suspected. RESULTS: Of the 259 infants, nSOFA scores for infants who died (n = 39) or had the composite outcome of surgery or death (n = 114) were significantly higher (p < 0.05) early in the NEC course compared to nSOFA scores for infants who survived medical NEC. Twelve hours after evaluation, the area under the receiver operating characteristic curve was 0.87 (95% confidence interval [CI], 0.80-0.93) to discriminate for mortality and 0.84 (95% CI, 0.79-0.90) for surgery or death (p < 0.001). A maximum nSOFA score of ≥4 at -6, 0, 6, or 12 h following evaluation was associated with a 20-fold increase in mortality and 19-fold increase in surgery or death compared with a score of <4 (p < 0.001). CONCLUSION: In this multicenter cohort, the nSOFA score was able to discriminate well for death as well as surgery or death among infants with NEC. The nSOFA is a clinical research tool that may be used in infants with NEC to improve classification by objective quantification of organ dysfunction.

Evaluation of the Neonatal Sequential Organ Failure Assessment and Mortality Risk in Preterm Infants With Late-Onset Infection.

Importance: Infection in neonates remains a substantial problem. Advances for this population are hindered by the absence of a consensus definition for sepsis. In adults, the Sequential Organ Failure Assessment (SOFA) operationalizes mortality risk with infection and defines sepsis. The generalizability of the neonatal SOFA (nSOFA) for neonatal late-onset infection-related mortality remains unknown. Objective: To determine the generalizability of the nSOFA for neonatal late-onset infection-related mortality across multiple sites. Design, Setting, and Participants: A multicenter retrospective cohort study was conducted at 7 academic neonatal intensive care units between January 1, 2010, and December 31, 2019. Participants included 653 preterm (<33 weeks) very low-birth-weight infants. Exposures: Late-onset (>72 hours of life) infection including bacteremia, fungemia, or surgical peritonitis. Main Outcomes and Measures: The primary outcome was late-onset infection episode mortality. The nSOFA scores from survivors and nonsurvivors with confirmed late-onset infection were compared at 9 time points (T) preceding and following event onset. Results: In the 653 infants who met inclusion criteria, median gestational age was 25.5 weeks (interquartile range, 24-27 weeks) and median birth weight was 780 g (interquartile range, 638-960 g). A total of 366 infants (56%) were male. Late-onset infection episode mortality occurred in 97 infants (15%). Area under the receiver operating characteristic curves for mortality in the total cohort ranged across study centers from 0.71 to 0.95 (T0 hours), 0.77 to 0.96 (T6 hours), and 0.78 to 0.96 (T12 hours), with utility noted at all centers and in aggregate. Using the maximum nSOFA score at T0 or T6, the area under the receiver operating characteristic curve for mortality was 0.88 (95% CI, 0.84-0.91). Analyses stratified by sex or Gram-stain identification of pathogen class or restricted to infants born at less than 25 weeks' completed gestation did not reduce the association of the nSOFA score with infection-related mortality. Conclusions and Relevance: The nSOFA score was associated with late-onset infection mortality in preterm infants at the time of evaluation both in aggregate and in each center. These findings suggest that the nSOFA may serve as the foundation for a consensus definition of sepsis in this population.