FIRST-line support for Assistance in Breathing in Children (FIRST-ABC): protocol for a multicentre randomised feasibility trial of non-invasive respiratory support in critically ill children.

INTRODUCTION: Over 18 000 children are admitted annually to UK paediatric intensive care units (PICUs), of whom nearly 75% receive respiratory support (invasive and/or non-invasive). Continuous positive airway pressure (CPAP) has traditionally been used to provide first-line non-invasive respiratory support (NRS) in PICUs; however, high-flow nasal cannula therapy (HFNC), a novel mode of NRS, has recently gained popularity despite the lack of high-quality trial evidence to support its effectiveness. This feasibility study aims to inform the design and conduct of a future definitive randomised clinical trial (RCT) comparing the two modes of respiratory support. METHODS AND ANALYSIS: We will conduct a three-centre randomised feasibility study over 12 months. Patients admitted to participating PICUs who satisfy eligibility criteria will be recruited to either group A (primary respiratory failure) or group B (postextubation). Consent will be obtained from parents/guardians prior to randomisation in 'planned' group B, and deferred in emergency situations (group A and 'rescue' group B). Participants will be randomised (1:1) to either CPAP or HFNC using sealed, opaque envelopes, from a computer-generated randomisation sequence with variable block sizes. The study protocol specifies algorithms for the initiation, maintenance and weaning of HFNC and CPAP. The primary outcomes are related to feasibility, including the number of eligible patients in each group, feasibility of randomising >50% of eligible patients and measures of adherence to the treatment protocols. Data will also be collected on patient outcomes (eg, mortality and length of PICU stay) to inform the selection of an appropriate outcome measure in a future RCT. We aim to recruit 120 patients to the study. ETHICS AND DISSEMINATION: Ethical approval was granted by the National Research Ethics Service Committee North East-Tyne&Wear South (15/NE/0296). Study findings will be disseminated through peer-reviewed journals, national and international conferences. TRIALS REGISTRATION NUMBER: NCT02612415; pre-results

Outcomes for Children Receiving Noninvasive Ventilation as the First-Line Mode of Mechanical Ventilation at Intensive Care Admission: A Propensity Score-Matched Cohort Study.

OBJECTIVES: To compare outcomes of children receiving noninvasive ventilation with those receiving invasive ventilation as first-line mode of mechanical ventilation following unplanned intensive care admission. DESIGN: Propensity score-matched cohort study analyzing data prospectively collected by the Pediatric Intensive Care Audit Network over 8 years (2007-2014). SETTING: Thirty-one PICUs in the United Kingdom and Ireland; twenty-one of whom submitted Pediatric Critical Care Minimum Dataset data for the entire study period. PATIENTS: Children consecutively admitted to study PICUs. Planned admissions following surgery, unplanned admissions from other hospitals, those on chronic ventilation, and those who did not receive mechanical ventilation on the day of PICU admission were excluded. INTERVENTIONS: Use of noninvasive ventilation, rather than invasive ventilation, as the first-line mode of mechanical ventilation. MEASUREMENTS AND MAIN RESULTS: PICU mortality, length of ventilation, length of PICU stay, and ventilator-free days at day 28. During the study period, there were 151,128 PICU admissions. A total of 15,144 admissions (10%) were eligible for analysis once predefined exclusion criteria were applied: 4,804 (31.7%) received "noninvasive ventilation first," whereas 10,221 (67.5%) received "invasive ventilation first"; 119 (0.8%) admissions could not be classified. Admitting PICU site explained 6.5% of the variation in first-line mechanical ventilation group (95% CI, 2.0-19.0%). In propensity score-matched analyses, receiving noninvasive ventilation first was associated with a significant reduction in mortality by 3.1% (95% CI, 1.7-4.6%), length of ventilation by 1.6 days (95% CI, 1.0-2.3), and length of PICU stay by 2.1 days (95% CI, 1.3-3.0), as well as an increase in ventilator-free days at day 28 by 3.7 days (95% CI, 3.1-4.3). CONCLUSIONS: Use of noninvasive ventilation as first-line mode of mechanical ventilation in critically ill children admitted to PICU in an unplanned fashion may be associated with significant clinical benefits. Further high-quality evidence regarding optimal patient selection and timing of initiation of noninvasive ventilation could lead to less variability in clinical care between institutions and improved patient outcomes

Shock Index Values and Trends in Pediatric Sepsis: Predictors or Therapeutic Targets? A retrospective observational study

BACKGROUND: Shock index (SI) (heart rate/systolic blood pressure) has been used to predict outcome in both adult and pediatric sepsis within the intensive care unit (ICU). We aimed to evaluate the utility of SI prior to pediatric ICU (PICU) admission. METHODS: We conducted a retrospective observational study of children referred to a pediatric intensive care transport service (PICTS) between 2005 and 2011. The predictive value of SI, heart rate and blood pressure at three pre-specified time points (at referral to PICTS, at PICTS arrival at the referring hospital, and at PICU admission), and changes in SI between the time points, were evaluated. Death within the first 48 hours of ICU admission (early death) was the primary outcome variable. RESULTS: Over the seven-year period, 572 children with sepsis were referred to the PICTS. Thirty-nine children died prior to transport to a PICU, while 474 were transported alive. Adjusting for age, time-points and time duration in a multi-level regression analysis, SI was significantly higher in those who died early. There was a significant improvement in SI with the transport team in survivors but not in non-survivors. However, the predictive value of a change in SI for mortality was no better than either a change in heart rate or blood pressure. CONCLUSIONS: The absolute or change in SI does not predict early death any more than heart rate and systolic blood pressure individually in children with sepsis

Evidence-based decision support for pediatric rheumatology reduces diagnostic errors.

BACKGROUND: The number of trained specialists world-wide is insufficient to serve all children with pediatric rheumatologic disorders, even in the countries with robust medical resources. We evaluated the potential of diagnostic decision support software (DDSS) to alleviate this shortage by assessing the ability of such software to improve the diagnostic accuracy of non-specialists. METHODS: Using vignettes of actual clinical cases, clinician testers generated a differential diagnosis before and after using diagnostic decision support software. The evaluation used the SimulConsult® DDSS tool, based on Bayesian pattern matching with temporal onset of each finding in each disease. The tool covered 5405 diseases (averaging 22 findings per disease). Rheumatology content in the database was developed using both primary references and textbooks. The frequency, timing, age of onset and age of disappearance of findings, as well as their incidence, treatability, and heritability were taken into account in order to guide diagnostic decision making. These capabilities allowed key information such as pertinent negatives and evolution over time to be used in the computations. Efficacy was measured by comparing whether the correct condition was included in the differential diagnosis generated by clinicians before using the software ( unaided ), versus after use of the DDSS ( aided ). RESULTS: The 26 clinicians demonstrated a significant reduction in diagnostic errors following introduction of the software, from 28% errors while unaided to 15% using decision support (p \u3c 0.0001). Improvement was greatest for emergency medicine physicians (p = 0.013) and clinicians in practice for less than 10 years (p = 0.012). This error reduction occurred despite the fact that testers employed an open book approach to generate their initial lists of potential diagnoses, spending an average of 8.6 min using printed and electronic sources of medical information before using the diagnostic software. CONCLUSIONS: These findings suggest that decision support can reduce diagnostic errors and improve use of relevant information by generalists. Such assistance could potentially help relieve the shortage of experts in pediatric rheumatology and similarly underserved specialties by improving generalists\u27 ability to evaluate and diagnose patients presenting with musculoskeletal complaints. TRIAL REGISTRATION: ClinicalTrials.gov ID: NCT02205086

Students' perceptions of learning environment in an Indian medical school

<p>Abstract</p> <p>Background</p> <p>Learning environment in any medical school is found to be important in determining students' academic success. This study was undertaken to compare the perceptions of first year and clinical phase students regarding the learning environment at Melaka Manipal Medical College (MMMC) (Manipal Campus) and also to identify the gender wise differences in their perceptions.</p> <p>Methods</p> <p>In the present study, the Dundee Ready Education Environment Measure (DREEM) inventory was used. DREEM was originally developed at Dundee and has been validated as a universal diagnostic inventory for assessing the quality of educational environment. In the present study, DREEM was administered to undergraduate medical students of first year (n = 118) and clinical phase (n = 108) and the scores were compared using a nonparametric test.</p> <p>Results</p> <p>Among the two batches, first year students were found to be more satisfied with the learning environment at MMMC (as indicated by their higher DREEM score) compared to the clinical batch students. Gender wise, there was not much difference in the students' perceptions.</p> <p>Conclusion</p> <p>The present study revealed that both groups of students perceived the learning environment positively. Nevertheless, the study also revealed problematic areas of learning environment in our medical school which enabled us to adopt some remedial measures.</p

Cyclic peptides- small and big and their conformational aspects

Cyclic peptides form an interesting class of compounds for study by conformational analysis, by virtue of their unique conformational features and biological properties. The small cyclic peptides having 3-6 peptide units in their ring, show a variety of conformational characteristics such as occurrence ofcis peptide units, flexibility of peptide dimension and variety in hydrogen bonding. The different possible conformations of cyclic tri- and hexa-peptides are given and certain specific conformational features are discussed for cyclic tetra and pentapeptides. For higher cyclic peptides, the hydrogen bonding requirement for stability of the backbone of the ring, is seen to be kept to a minimum. These various features and their significance are examined and discussed in the light of energy minimization studies and analysis of available experimental data

The effect of care provided by paediatric critical care transport teams on mortality of children transported to paediatric intensive care units in England and Wales: a retrospective cohort study.

BACKGROUND: Centralisation of paediatric intensive care units (PICUs) has the increased the need for specialist paediatric critical care transport teams (PCCT) to transport critically ill children to PICU. We investigated the impact of care provided by PCCTs for children on mortality and other clinically important outcomes. METHODS: We analysed linked national data from the Paediatric Intensive Care Audit Network (PICANet) from children admitted to PICUs in England and Wales (2014-2016) to assess the impact of who led the child's transport, whether prolonged stabilisation by the PCCT was detrimental and the impact of critical incidents during transport on patient outcome. We used logistic regression models to estimate the adjusted odds and probability of mortality within 30 days of admission to PICU (primary outcome) and negative binomial models to investigate length of stay (LOS) and length of invasive ventilation (LOV). RESULTS: The study included 9112 children transported to PICU. The most common diagnosis was respiratory problems; junior doctors led the PCCT in just over half of all transports; and the 30-day mortality was 7.1%. Transports led by Advanced Nurse Practitioners and Junior Doctors had similar outcomes (adjusted mortality ANP: 0.035 versus Junior Doctor: 0.038). Prolonged stabilisation by the PCCT was possibly associated with increased mortality (0.059, 95% CI: 0.040 to 0.079 versus short stabilisation 0.044, 95% CI: 0.039 to 0.048). Critical incidents involving the child increased the adjusted odds of mortality within 30 days (odds ratio: 3.07). CONCLUSIONS: Variations in team composition between PCCTs appear to have little effect on patient outcomes. We believe differences in stabilisation approaches are due to residual confounding. Our finding that critical incidents were associated with worse outcomes indicates that safety during critical care transport is an important area for future quality improvement work

Development and implementation of a real time statistical control method to identify the start and end of the winter surge in demand for paediatric intensive care

Winter surge management in intensive care is hampered by the annual variability in the winter surge. We aimed to develop a real-time monitoring system that could promptly identify the start, and accurately predict the end, of the winter surge in a paediatric intensive care (PIC) setting. We adapted a statistical process control method from the stock market called “Bollinger bands” that compares current levels of demand for PIC services to thresholds based on the medium term average demand. Algorithms to identify the start and end of the surge were developed for a specific PIC service: the North Thames Children's Acute Transport Service (CATS) using eight winters of data (2005–12) to tune the algorithms and one winter to test the final method (2013/14). The optimal Bollinger band thresholds were 1.2 and 1 standard deviations above and below a 41-day moving average of demand respectively. A simple linear model was found to predict the end of the surge and overall demand volume as soon as the start had been identified. Applying the method to the validation winter of 2013/14 showed excellent performance, with the surge identified from 18th November 2013 to 4th January 2014. An Excel tool running the algorithms has been in use within CATS since September 2014. There were three factors which facilitated the successful implementation of this tool: the perceived problem was pressing and identified by the clinical team; there was close clinical engagement throughout and substantial effort was made to develop an easy-to-use Excel tool for sustainable use

A Novel Method to Identify the Start and End of the Winter Surge in Demand for Pediatric Intensive Care in Real Time

OBJECTIVE: Implementation of winter surge management in intensive care is hampered by the annual variability in the start and duration of the winter surge. We aimed to develop a real-time monitoring system that could identify the start promptly and accurately predict the end of the winter surge in a pediatric intensive care setting. DESIGN: We adapted a method from the stock market called "Bollinger bands" to compare current levels of demand for pediatric intensive care services to thresholds based on medium-term average demand. Algorithms to identify the start and end of the surge were developed using Bollinger bands and pragmatic considerations. The method was applied to a specific pediatric intensive care service: the North Thames Children's Acute Transport Service using eight winters of data (2005-2012) to tune the algorithms and one winter to test the final method (2013/2014). SETTING: A regional specialized pediatric retrieval service based in London, United Kingdom. MEASUREMENTS AND MAIN RESULTS: The optimal Bollinger band thresholds were 1.2 and 1 SDs above and below a 41-day moving average of demand, respectively. A simple linear model was found to predict the end of the surge and overall surge demand volume as soon as the start had been identified. Applying the method to the validation winter of 2013/2014 showed excellent performance, with the surge identified from November 18, 2013, to January 4, 2014. CONCLUSIONS: We have developed and tested a novel method to identify the start and predict the end of the winter surge in emergency demand for pediatric intensive care