Improving manual oxygen titration in preterm infants by training and guideline implementation

To study oxygen saturation (SpO2) targeting before and after training and guideline implementation of manual oxygen titration, two cohorts of preterm infants 21%. ABCs where oxygen therapy was given were identified and analyzed. After training and guideline implementation the %SpO2-wtr increased (median interquartile range (IQR)) 48.0 (19.6-63.9) % vs 61.9 (48.5-72.3) %; p 95% (44.0 (27.8-66.2) % vs 30.8 (22.6-44.5) %; p 95% did not decrease (73% vs 64%; ns) but lasted shorter (2 (0-7) vs 1 (1-3) minute; p < 0.004). CONCLUSION: Training and guideline implementation in manual oxygen titration improved SpO2 targeting in preterm infants with more time spent within the target range and less frequent hyperoxaemia. The durations of hypoxaemia and hyperoxaemia during ABCs were shorter. What is Known: • Oxygen saturation targeting in preterm infants can be challenging and the compliance is low when oxygen is titrated manually. • Hyperoxaemia often occurs after oxygen therapy for oxygen desaturation during apnoeas. What is New: • Training and implementing guidelines improved oxygen saturation targeting and reduced hyperoxaemia. • Training and implementing guidelines improved manual oxygen titration during ABC

Correlation and Interchangeability of Venous and Capillary Blood Gases in Non-Critically Ill Neonates

Background: Venous blood gas (VBG) is frequently used in the neonatal unit as alternative for capillary blood gas (CBG). However, studies reporting correlation are conflicting and data on interchangeability in neonates are lacking. Objective: We investigated the correlation and interchangeability of the components between VBG and CBG in infants admitted to the neonatal intensive care unit. Methods: In a prospective study in the neonatal unit in Leiden University Medical Center (Netherlands), simultaneously VBG and CBG were withdrawn in neonates when both venous puncture and intravenous access as blood gas monitoring was indicated. From each blood gas analysis, a Pearson correlation, intraclass correlation, and Bland-Altman analysis was performed. Clinically acceptable difference for each blood gas value was defined up-front by means of an absolute difference: pH ± 0.05; partial pressure of carbon dioxide (pCO2) (±0.67 kPa = 5 mmHg); partial pressure of oxygen (pO2) (±0.67 kPa = 5 mmHg); base excess ± 3 mmol/l; and bicarbonate (HCO3-) ± 3 mmol/l. Results: In 93 patients [median gestational age 31 (IQR 29-34) weeks], 193 paired samples of VBG and CBG were collected. The Pearson correlation between VBG and CBG was very strong for pH (r = 0.79; P < 0.001), BE (r = 0.90; P < 0.001) and bicarbonate (r = 0.87; P < 0.001); strong for pCO2 (r = 0.68; P < 0.001); and moderate for pO2 (r = 0.31; P < 0.001). The percentage of the interchangeability within our acceptable absolute difference for pH was 88%, pCO2 72%, pO2 55%, BE 90%, and bicarbonate 94%. Conclusion: VBG and CBG in neonates are well correlated and mostly interchangeable, except for pO2

Insightful stress detection from physiology modalities using Learning Vector Quantization

Abstract Stress in daily life can lead to severe conditions as burn-out and depression and has a major impact on society. Being able to measure mental stress reliably opens up the ability to intervene in an early stage. We performed a large-scale study in which skin conductance, respiration and electrocardiogram were measured in semi-controlled conditions. Using Learning Vector Quantization techniques, we obtained up to 88% accuracy in the classification task to separate stress from relaxation. Relevance learning was used to identify the most informative features, indicating that most information is embedded in the cardiac signals. In addition to commonly used features, we also explored various novel features, of which the very-high frequency band of the power spectrum was found to be a very relevant addition

Comparison of two automated oxygen controllers in oxygen targeting in preterm infants during admission:: an observational study

ObjectiveTo compare the effect of two different automated oxygen control devices on time preterm infants spent in different oxygen saturation (SpO(2)) ranges during their entire stay in the neonatal intensive care unit (NICU). DesignRetrospective cohort study of prospectively collected data. SettingTertiary level neonatal unit in the Netherlands. PatientsPreterm infants (OxyGenie 75 infants, CLiO2 111 infants) born at 24-29 weeks' gestation receiving at least 72 hours of respiratory support between October 2015 and November 2020. InterventionsInspired oxygen concentration was titrated by the OxyGenie controller (SLE6000 ventilator) between February 2019 and November 2020 and the CLiO2 controller (AVEA ventilator) between October 2015 and December 2018 as standard of care. Main outcome measuresTime spent within SpO(2) target range (TR, 91-95% for either epoch) and other SpO(2) ranges. ResultsTime spent within the SpO(2) TR when receiving supplemental oxygen was higher during OxyGenie control (median 71.5 [IQR 64.6-77.0]% vs 51.3 [47.3-58.5]%,

Predicting Success of a Digital Self-Help Intervention for Alcohol and Substance Use With Machine Learning

BACKGROUND: Digital self-help interventions for reducing the use of alcohol tobacco and other drugs (ATOD) have generally shown positive but small effects in controlling substance use and improving the quality of life of participants. Nonetheless, low adherence rates remain a major drawback of these digital interventions, with mixed results in (prolonged) participation and outcome. To prevent non-adherence, we developed models to predict success in the early stages of an ATOD digital self-help intervention and explore the predictors associated with participant’s goal achievement. METHODS: We included previous and current participants from a widely used, evidence-based ATOD intervention from the Netherlands (Jellinek Digital Self-help). Participants were considered successful if they completed all intervention modules and reached their substance use goals (i.e., stop/reduce). Early dropout was defined as finishing only the first module. During model development, participants were split per substance (alcohol, tobacco, cannabis) and features were computed based on the log data of the first 3 days of intervention participation. Machine learning models were trained, validated and tested using a nested k-fold cross-validation strategy. RESULTS: From the 32,398 participants enrolled in the study, 80% of participants did not complete the first module of the intervention and were excluded from further analysis. From the remaining participants, the percentage of success for each substance was 30% for alcohol, 22% for cannabis and 24% for tobacco. The area under the Receiver Operating Characteristic curve was the highest for the Random Forest model trained on data from the alcohol and tobacco programs (0.71 95%CI 0.69–0.73) and (0.71 95%CI 0.67–0.76), respectively, followed by cannabis (0.67 95%CI 0.59–0.75). Quitting substance use instead of moderation as an intervention goal, initial daily consumption, no substance use on the weekends as a target goal and intervention engagement were strong predictors of success. DISCUSSION: Using log data from the first 3 days of intervention use, machine learning models showed positive results in identifying successful participants. Our results suggest the models were especially able to identify participants at risk of early dropout. Multiple variables were found to have high predictive value, which can be used to further improve the intervention

Predicting Success of a Digital Self-Help Intervention for Alcohol and Substance Use With Machine Learning

Background: Digital self-help interventions for reducing the use of alcohol tobacco and other drugs (ATOD) have generally shown positive but small effects in controlling substance use and improving the quality of life of participants. Nonetheless, low adherence rates remain a major drawback of these digital interventions, with mixed results in (prolonged) participation and outcome. To prevent non-adherence, we developed models to predict success in the early stages of an ATOD digital self-help intervention and explore the predictors associated with participant’s goal achievement. Methods: We included previous and current participants from a widely used, evidence-based ATOD intervention from the Netherlands (Jellinek Digital Self-help). Participants were considered successful if they completed all intervention modules and reached their substance use goals (i.e., stop/reduce). Early dropout was defined as finishing only the first module. During model development, participants were split per substance (alcohol, tobacco, cannabis) and features were computed based on the log data of the first 3 days of intervention participation. Machine learning models were trained, validated and tested using a nested k-fold cross-validation strategy. Results: From the 32,398 participants enrolled in the study, 80% of participants did not complete the first module of the intervention and were excluded from further analysis. From the remaining participants, the percentage of success for each substance was 30% for alcohol, 22% for cannabis and 24% for tobacco. The area under the Receiver Operating Characteristic curve was the highest for the Random Forest model trained on data from the alcohol and tobacco programs (0.71 95%CI 0.69–0.73) and (0.71 95%CI 0.67–0.76), respectively, followed by cannabis (0.67 95%CI 0.59–0.75). Quitting substance use instead of moderation as an intervention goal, initial daily consumption, no substance use on the weekends as a target goal and intervention engagement were strong predictors of success. Discussion: Using log data from the first 3 days of intervention use, machine learning models showed positive results in identifying successful participants. Our results suggest the models were especially able to identify participants at risk of early dropout. Multiple variables were found to have high predictive value, which can be used to further improve the intervention

Tidal volumes at birth as predictor for adverse outcome in congenital diaphragmatic hernia

Objective To assess the predictive value of tidal volume (Vt) of spontaneous breaths at birth in infants with congenital diaphragmatic hernia (CDH).Design Prospective study.Setting Tertiary neonatal intensive care unit.Patients Thirty infants with antenatally diagnosed CDH born at Hospital Sant Joan de Deu in Barcelona from September 2013 to September 2015.Interventions Spontaneous breaths and inflations given in the first 10 min after intubation at birth were recorded using respiratory function monitor. Only expired Vt of uninterrupted spontaneous breaths was included for analysis. Receiver operating characteristics (ROC) analysis was performed and the area under the curve (AUC) was estimated to assess the predictive accuracy of Vt.Main outcome measures Mortality before hospital discharge and chronic lung disease (CLD) at day 28 of life.Results There were 1.233 uninterrupted spontaneous breaths measured, and the overall mean Vt was 2.8 +/- 2.1 mL/kg. A lower Vt was found in infants who died (n=14) compared with survivors (n=16) (1.7 +/- 1.6 vs 3.7 +/- 2.1 mL/kg; p=0.008). Vt was lower in infants who died during admission or had CLD (n=20) compared with survivors without CLD (n=10) (2.0 +/- 1.7 vs 4.3 +/- 2.2 mL/kg; p=0.004). ROC analysis showed that Vt <= 2.2 mL/kg predicted mortality with 79% sensitivity and 81% specificity (AUC=0.77, p=0.013). Vt <= 3.4 mL/kg was a good predictor of death or CLD (AUC=0.80, p=0.008) with 85% sensitivity and 70% specificity.Conclusion Vt of spontaneous breaths measured immediately after birth is associated with mortality and CLD. Vt seems to be a reliable predictor but is not an independent predictor after adjustment for observed/expected lung to head ratio and liver position.Developmen

Effect of Tactile Stimulation on Termination and Prevention of Apnea of Prematurity: A Systematic Review

Apnea of prematurity (AOP) is one of the most common diagnoses in preterm infants. Severe and recurrent apneas are associated with cerebral injury and adverse neurodevelopmental outcome. Despite pharmacotherapy and respiratory support to prevent apneas, a proportion of infants continue to have apneas and often need tactile stimulation, mask, and bag ventilation and/or extra oxygen. The duration of the apnea and the concomitant hypoxia and bradycardia depends on the response time of the nurse. We systematically reviewed the literature with the aim of providing an overview of what is known about the effect of manual and mechanical tactile stimulation on AOP. Tactile stimulation, manual or mechanical, has been shown to shorten the duration of apnea, hypoxia, and or bradycardia or even prevent an apnea. Automated stimulation, using closed-loop pulsating or vibrating systems, has been shown to be effective in terminating apneas, but data are scarce. Several studies used continuous mechanical stimulation, with pulsating, vibrating, or oscillating stimuli, to prevent apneas, but the reported effect varied. More studies are needed to confirm whether automated stimulation using a closed loop is more effective than manual stimulation, how and where the automated stimulation should be performed and the potential side effects

Clinical outcomes of preterm infants while using automated controllers during standard care: comparison of cohorts with different automated titration strategies

Objective To compare short-term clinical outcome after using two different automated oxygen controllers (OxyGenie and CLiO 2). Design Propensity score-matched retrospective observational study. Setting Tertiary-level neonatal unit in the Netherlands. Patients Preterm infants (OxyGenie n=121, CLiO 2 n=121) born between 24+0-29+6 weeks of gestation. Median (IQR) gestational age in the OxyGenie cohort was 28+3 (26+3.5-29+0) vs 27+5 (26+5-28+3) in the CLiO 2 cohort, respectively 42% and 46% of infants were male and mean (SD) birth weight was 1034 (266) g vs 1022 (242) g. Interventions Inspired oxygen was titrated by OxyGenie (SLE6000) or CLiO 2 (AVEA) during respiratory support. Main outcome measures Mortality, retinopathy of prematurity (ROP), bronchopulmonary dysplasia and necrotising enterocolitis. Results Fewer infants in the OxyGenie group received laser coagulation for ROP (1 infant vs 10; risk ratio 0.1 (95% CI 0.0 to 0.7); p=0.008), and infants stayed shorter in the neonatal intensive care unit (NICU) (28 (95% CI 15 to 42) vs 40 (95% CI 25 to 61) days; median difference 13.5 days (95% CI 8.5 to 19.5); p<0.001). Infants in the OxyGenie group had fewer days on continuous positive airway pressure (8.4 (95% CI 4.8 to 19.8) days vs 16.7 (95% CI 6.3 to 31.1); p<0.001) and a significantly shorter days on invasive ventilation (0 (95% CI 0 to 4.2) days vs 2.1 (95% CI 0 to 8.4); p=0.012). There were no statistically significant differences in all other morbidities. Conclusions In this propensity score-matched retrospective study, the OxyGenie epoch was associated with less morbidity when compared with the CLiO 2 epoch. There were significantly fewer infants that received treatment for ROP, received less intensive respiratory support and, although there were more supplemental oxygen days, the duration of stay in the NICU was shorter. A larger study will have to replicate these findings