Improving Newborn Resuscitation by Making Every Birth a Learning Event.
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ABSTRACT:
SUBMITTER: Bettinger K
PROVIDER: S-EPMC8700033 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:The European Resuscitation Council has produced these newborn life support guidelines, which are based on the International Liaison Committee on Resuscitation (ILCOR) 2020 Consensus on Science and Treatment Recommendations (CoSTR) for Neonatal Life Support. The guidelines cover the management of the term and preterm infant. The topics covered include an algorithm to aid a logical approach to resuscitation of the newborn, factors before delivery, training and education, thermal control, management of the umbilical cord after birth, initial assessment and categorisation of the newborn infant, airway and breathing and circulation support, communication with parents, considerations when withholding and discontinuing support.
| S-EPMC8170636 | biostudies-literature
Project description:BackgroundSerious games, and especially digital game based learning (DGBL) methodologies, have the potential to strengthen classic learning methodology in all medical procedures characterized by a flowchart (e.g., neonatal resuscitation algorithm). However, few studies have compared short- and long-term knowledge retention in DGBL methodologies with a control group undergoing specialist training led by experienced operators. In particular, resident doctors' learning still has limited representation in simulation-based education literature.ObjectiveA serious computer game DIANA (DIgital Application in Newborn Assessment) was developed, according to newborn resuscitation algorithm, to train pediatric/neonatology residents in neonatal resuscitation algorithm knowledge and implementation (from procedure knowledge to ventilation/chest compressions rate). We analyzed user learning curves after each session and compared knowledge retention against a classic theoretical teaching session.MethodsPediatric/neonatology residents of the Azienda Ospedaliera Universitaria Pisana (AOUP) were invited to take part in the study and were split into a game group or a control group; both groups were homogeneous in terms of previous training and baseline scores. The control group attended a classic 80 min teaching session with a neonatal trainer, while game group participants played four 20 min sessions over four different days. Three written tests (pre/immediately post-training and at 28 days) were used to evaluate and compare the two groups' performances.ResultsForty-eight pediatric/neonatology residents participated in the study. While classic training by a neonatal trainer demonstrated an excellent effectiveness in short/long-term knowledge retention, DGBL methodology proved to be equivalent or better. Furthermore, after each game session, DGBL score improved for both procedure knowledge and ventilation/chest compressions rate.ConclusionsIn this study, DGBL was as effective as classic specialist training for neonatal resuscitation in terms of both algorithm memorization and knowledge retention. User appreciation for the methodology and ease of administration, including remotely, support the use of DGBL methodologies for pediatric/neonatology residents education.
| S-EPMC9010974 | biostudies-literature
Project description:BACKGROUND:Globally, preterm birth is the leading cause of neonatal death with estimated prevalence and associated mortality highest in low- and middle-income countries (LMICs). Accurate identification of preterm infants is important at the individual level for appropriate clinical intervention as well as at the population level for informed policy decisions and resource allocation. As early prenatal ultrasound is commonly not available in these settings, gestational age (GA) is often estimated using newborn assessment at birth. This approach assumes last menstrual period to be unreliable and birthweight to be unable to distinguish preterm infants from those that are small for gestational age (SGA). We sought to leverage machine learning algorithms incorporating maternal factors associated with SGA to improve accuracy of preterm newborn identification in LMIC settings. METHODS AND FINDINGS:This study uses data from an ongoing obstetrical cohort in Lusaka, Zambia that uses early pregnancy ultrasound to estimate GA. Our intent was to identify the best set of parameters commonly available at delivery to correctly categorize births as either preterm (<37 weeks) or term, compared to GA assigned by early ultrasound as the gold standard. Trained midwives conducted a newborn assessment (<72 hours) and collected maternal and neonatal data at the time of delivery or shortly thereafter. New Ballard Score (NBS), last menstrual period (LMP), and birth weight were used individually to assign GA at delivery and categorize each birth as either preterm or term. Additionally, machine learning techniques incorporated combinations of these measures with several maternal and newborn characteristics associated with prematurity and SGA to develop GA at delivery and preterm birth prediction models. The distribution and accuracy of all models were compared to early ultrasound dating. Within our live-born cohort to date (n = 862), the median GA at delivery by early ultrasound was 39.4 weeks (IQR: 38.3-40.3). Among assessed newborns with complete data included in this analysis (n = 468), the median GA by ultrasound was 39.6 weeks (IQR: 38.4-40.3). Using machine learning, we identified a combination of six accessible parameters (LMP, birth weight, twin delivery, maternal height, hypertension in labor, and HIV serostatus) that can be used by machine learning to outperform current GA prediction methods. For preterm birth prediction, this combination of covariates correctly classified >94% of newborns and achieved an area under the curve (AUC) of 0.9796. CONCLUSIONS:We identified a parsimonious list of variables that can be used by machine learning approaches to improve accuracy of preterm newborn identification. Our best-performing model included LMP, birth weight, twin delivery, HIV serostatus, and maternal factors associated with SGA. These variables are all easily collected at delivery, reducing the skill and time required by the frontline health worker to assess GA. TRIAL REGISTRATION:ClinicalTrials.gov Identifier: NCT02738892.
| S-EPMC6392324 | biostudies-literature
Project description:Purpose: Foxp2 is the first and for now the only gene connected to speech and language in humans. Two aminoacid substitutions took place in this protein during recent human evolution, after our split from the last common ancestor with chimpanzees, and are most likely to have undergone positive selection in human lineage (Enard et al., 2002). Methods: Transgenic mice in which the wild-type (murine) version of Foxp2 was replaced with the one bearing two human-specific amino acid substitutions (i.e. "humanized" Foxp2) - Foxp2hum/hum, have been compared to their wild-type (WT) counterparts in terms of behavior, electrophysiology and striatal gene expression. The latter was analyzed through RNA-sequencing performed on pooled indexed libraries on three flow cells on Illumina GAIIx. The reads were mapped to mouse genome (mm9) by TopHat 1.4.1 and were counted using Bedtools. mRNA profiles were obtained with more than 20 million reads for every sample. Differential gene expression was analyzed with DESeq using multifactor model (Anders and Huber, 2010). Results: Wild-type and Foxp2hum/hum mice did not show any significant differences in expression at individual gene level, neither in dorsomedial nor in dorsolateral striatum. However, when genes were grouped into functional categories and analyzed accordingly, this revealed a significant downregulation of functional categories related to synaptic signalling and plasticity in dorsomedial striatum of Foxp2hum/hum mice. RNA-sequencing was performed on dorsomedial and dorsolateral striatum of wild-type and Foxp2hum/hum mice, on three flow cells Illumina GAIIx. The libraries from each sample were indexed and pooled together.
2014-09-24 | E-GEOD-60659 | biostudies-arrayexpress
Project description:Purpose: Foxp2 is the first and for now the only gene connected to speech and language in humans. Two aminoacid substitutions took place in this protein during recent human evolution, after our split from the last common ancestor with chimpanzees, and are most likely to have undergone positive selection in human lineage (Enard et al., 2002). Methods: Transgenic mice in which the wild-type (murine) version of Foxp2 was replaced with the one bearing two human-specific amino acid substitutions (i.e. "humanized" Foxp2) - Foxp2hum/hum, have been compared to their wild-type (WT) counterparts in terms of behavior, electrophysiology and striatal gene expression. The latter was analyzed through RNA-sequencing performed on pooled indexed libraries on three flow cells on Illumina GAIIx. The reads were mapped to mouse genome (mm9) by TopHat 1.4.1 and were counted using Bedtools. mRNA profiles were obtained with more than 20 million reads for every sample. Differential gene expression was analyzed with DESeq using multifactor model (Anders and Huber, 2010). Results: Wild-type and Foxp2hum/hum mice did not show any significant differences in expression at individual gene level, neither in dorsomedial nor in dorsolateral striatum. However, when genes were grouped into functional categories and analyzed accordingly, this revealed a significant downregulation of functional categories related to synaptic signalling and plasticity in dorsomedial striatum of Foxp2hum/hum mice.
2014-09-24 | GSE60659 | GEO
Project description:The Saving Lives at Birth (SL@B) funding partners joined in 2011 to source, support, and scale maternal and newborn health (MNH) innovations to improve maternal and newborn survival by focusing on the 24 hours around the time of birth. A multi-methods, retrospective portfolio evaluation was conducted to determine SL@B's impact. Forty semi-structured, key informant interviews (KIIs) were conducted with experts in global MNH based in low- and middle-income and in high-income countries to assess the SL@B program. KIIs were conducted with global MNH technical experts, innovators, government officials in low- and middle-income countries, donors, private investors, and implementing partners to include the full spectrum of voices involved in identifying and scaling innovations. Data were analyzed using thematic analysis. Stakeholders believe the SL@B program has been successful in changing the way maternal and newborn health programs are delivered with a focus on doing things differently through innovation. The open approach to sourcing innovation was seen as positive to the extent that it brought more interdisciplinary stakeholders to think about the problem of maternal and newborn survival. However, a demand-driven approach that aims to source innovations that address MNH priority needs and takes into account the needs of end users (e.g. individuals and governments) was suggested as a strategy for ensuring that more innovations go to scale.
| S-EPMC8279342 | biostudies-literature
Project description:BackgroundAnnually, 14 million newborns require stimulation to initiate breathing at birth and 6 million require bag-mask-ventilation (BMV). Many countries have invested in facility-based neonatal resuscitation equipment and training. However, there is no consistent tracking for neonatal resuscitation coverage.MethodsThe EN-BIRTH study, in five hospitals in Bangladesh, Nepal, and Tanzania (2017-2018), collected time-stamped data for care around birth, including neonatal resuscitation. Researchers surveyed women and extracted data from routine labour ward registers. To assess accuracy, we compared gold standard observed coverage to survey-reported and register-recorded coverage, using absolute difference, validity ratios, and individual-level validation metrics (sensitivity, specificity, percent agreement). We analysed two resuscitation numerators (stimulation, BMV) and three denominators (live births and fresh stillbirths, non-crying, non-breathing). We also examined timeliness of BMV. Qualitative data were collected from health workers and data collectors regarding barriers and enablers to routine recording of resuscitation.ResultsAmong 22,752 observed births, 5330 (23.4%) babies did not cry and 3860 (17.0%) did not breathe in the first minute after birth. 16.2% (n = 3688) of babies were stimulated and 4.4% (n = 998) received BMV. Survey-report underestimated coverage of stimulation and BMV. Four of five labour ward registers captured resuscitation numerators. Stimulation had variable accuracy (sensitivity 7.5-40.8%, specificity 66.8-99.5%), BMV accuracy was higher (sensitivity 12.4-48.4%, specificity > 93%), with small absolute differences between observed and recorded BMV. Accuracy did not vary by denominator option. < 1% of BMV was initiated within 1 min of birth. Enablers to register recording included training and data use while barriers included register design, documentation burden, and time pressure.ConclusionsPopulation-based surveys are unlikely to be useful for measuring resuscitation coverage given low validity of exit-survey report. Routine labour ward registers have potential to accurately capture BMV as the numerator. Measuring the true denominator for clinical need is complex; newborns may require BMV if breathing ineffectively or experiencing apnoea after initial drying/stimulation or subsequently at any time. Further denominator research is required to evaluate non-crying as a potential alternative in the context of respectful care. Measuring quality gaps, notably timely provision of resuscitation, is crucial for programme improvement and impact, but unlikely to be feasible in routine systems, requiring audits and special studies.
| S-EPMC7995695 | biostudies-literature
Project description:Fluid bolus therapy (FBT) is fundamental to the management of circulatory shock in critical care but balancing the benefits and toxicities of FBT has proven challenging in individual patients. Improved predictors of the hemodynamic response to a fluid bolus, commonly referred to as a fluid challenge, are needed to limit non-beneficial fluid administration and to enable automated clinical decision support and patient-specific precision critical care management. In this study we retrospectively analyzed data from 394 fluid boluses from 58 pigs subjected to either hemorrhagic or distributive shock. All animals had continuous blood pressure and cardiac output monitored throughout the study. Using this data, we developed a machine learning (ML) model to predict the hemodynamic response to a fluid challenge using only arterial blood pressure waveform data as the input. A Random Forest binary classifier referred to as the ML fluid responsiveness algorithm (MLFRA) was trained to detect fluid responsiveness (FR), defined as a ≥ 15% change in cardiac stroke volume after a fluid challenge. We then compared its performance to pulse pressure variation, a commonly used metric of FR. Model performance was assessed using the area under the receiver operating characteristic curve (AUROC), confusion matrix metrics, and calibration curves plotting predicted probabilities against observed outcomes. Across multiple train/test splits and feature selection methods designed to assess performance in the setting of small sample size conditions typical of large animal experiments, the MLFRA achieved an average AUROC, recall (sensitivity), specificity, and precision of 0.82, 0.86, 0.62. and 0.76, respectively. In the same datasets, pulse pressure variation had an AUROC, recall, specificity, and precision of 0.73, 0.91, 0.49, and 0.71, respectively. The MLFRA was generally well-calibrated across its range of predicted probabilities and appeared to perform equally well across physiologic conditions. These results suggest that ML, using only inputs from arterial blood pressure monitoring, may substantially improve the accuracy of predicting FR compared to the use of pulse pressure variation. If generalizable, these methods may enable more effective, automated precision management of critically ill patients with circulatory shock.
| S-EPMC10817926 | biostudies-literature
Project description:To be able to reliably generate theoretical libraries that can be used in SWATH experiments, we developed a prediction framework, deep-learning for SWATH analysis (dpSWATH), to improve the sensitivity and specificity of data generated by Q-TOF mass spectrometers. The theoretical library built by dpSWATH allowed us to increase the identification rate of proteins and peptides compared to traditional or library-free methods. Especially, the in-silico library built based on the transcriptome scale identified the most proteins while kept a similar FDR as DDA library. Based on our analysis we conclude that dpSWATH is superior in predicting libraries that can be used for SWATH-MS measurements compared to other algorithms that are based on Orbitrap data.
2023-01-16 | PXD038407 | Pride
Project description:Objective: Evaluate whether weekly audits of neonatal resuscitation using video and physiological parameter recordings improved guideline compliance and documentation in medical records. Study design: Neonatal care providers of the Neonatal Intensive Care Unit (NICU) of Leiden University Medical Center reviewed recordings of neonatal resuscitation during weekly plenary audits since 2014. In an observational pre-post cohort study, we studied a cohort of infants born before and after implementation of weekly audits. Video and physiological parameter recordings of infants needing resuscitation were analyzed. These recordings were compared with the prevailing resuscitation guideline and corresponding documentation in the medical record using a pre-set checklist. Results: A total of 212 infants were included, 42 before and 170 after implementation of weekly audits, with a median (IQR) gestational age of 30 (27-35) weeks vs. 30 (29-33) weeks (p = 0.64) and birth weight of 1368 (998-1780) grams vs. 1420 (1097-1871) grams (p = 0.67). After weekly audits were implemented, providers complied more often to the guideline (63 vs. 77%; p < 0.001). Applying the correct respiratory support based on heart rate and respiration, air conditions (dry vs. humidified air), fraction of inspired oxygen (FiO2), timely start of interventions and evaluation of delivered care improved. Total number of correctly documented items in medical records increased from 39 to 65% (p < 0.001). Greatest improvements were achieved in documentation of present providers, mode of respiratory support and details about transport to the NICU. Conclusion: Regular auditing using video and physiological parameter recordings of infants needing resuscitation at birth improved providers' compliance with resuscitation guideline and documentation in medical records.
| S-EPMC6646726 | biostudies-literature