Predicting Failure of Non-Invasive Ventilation With RAM Cannula in Bronchiolitis

Respiratory Support Practices for Bronchiolitis in the Pediatric Intensive Care Unit

Importance

Admissions to the pediatric intensive care unit (PICU) due to bronchiolitis are increasing. Whether this increase is associated with changes in noninvasive respiratory support practices is unknown.

Objective

To assess whether the number of PICU admissions for bronchiolitis between 2013 and 2022 was associated with changes in the use of high-flow nasal cannula (HFNC), noninvasive ventilation (NIV), and invasive mechanical ventilation (IMV) and to identify factors associated with HFNC and NIV success and failure.

Design, Setting, and Participants

This cross-sectional study examined encounter data from the Virtual Pediatric Systems database on annual PICU admissions for bronchiolitis and ventilation practices among patients aged younger than 2 years admitted to 27 PICUs between January 1, 2013, and December 31, 2022. Use of HFNC and NIV was defined as successful if patients were weaned to less invasive support (room air or low-flow nasal cannula for HFNC; room air, low-flow nasal cannula, or HFNC for NIV).

Main Outcomes and Measures

The main outcome was the number of PICU admissions for bronchiolitis requiring the use of HFNC, NIV, or IMV. Linear regression was used to analyze the association between admission year and absolute numbers of encounters stratified by the maximum level of respiratory support required. Multivariable logistic regression was used to analyze factors associated with HFNC and NIV success and failure (defined as not meeting the criteria for success).

Results

Included in the analysis were 33 816 encounters for patients with bronchiolitis (20 186 males [59.7%]; 1910 patients [5.6%] aged ≤28 days and 31 906 patients [94.4%] aged 29 days to <2 years) treated at 27 PICUs from 2013 to 2022. A total of 7615 of 15 518 patients (49.1%) had respiratory syncytial virus infection and 1522 of 33 816 (4.5%) had preexisting cardiac disease. Admissions to the PICU increased by 350 (95% CI, 170-531) encounters annually. When data were grouped by the maximum level of respiratory support required, HFNC use increased by 242 (95% CI, 139-345) encounters per year and NIV use increased by 126 (95% CI, 64-189) encounters per year. The use of IMV did not significantly change (10 [95% CI, -11 to 31] encounters per year). In all, 22 381 patients (81.8%) were successfully weaned from HFNC to low-flow oxygen therapy or room air, 431 (1.6%) were restarted on HFNC, 3057 (11.2%) were escalated to NIV, and 1476 (5.4%) were escalated to IMV or extracorporeal membrane oxygenation (ECMO). Successful use of HFNC increased from 820 of 1027 encounters (79.8%) in 2013 to 3693 of 4399 encounters (84.0%) in 2022 (P = .002). In all, 8476 patients (81.5%) were successfully weaned from NIV, 787 (7.6%) were restarted on NIV, and 1135 (10.9%) were escalated to IMV or ECMO. Success with NIV increased from 224 of 306 encounters (73.2%) in 2013 to 1335 of 1589 encounters (84.0%) in 2022 (P < .001). In multivariable logistic regression, lower weight, higher Pediatric Risk of Mortality III score, cardiac disease, and PICU admission from outside the emergency department were associated with greater odds of HFNC and NIV failure.

Conclusions and Relevance

Findings of this cross-sectional study of patients aged younger than 2 years admitted for bronchiolitis suggest there was a 3-fold increase in PICU admissions between 2013 and 2022 associated with a 4.8-fold increase in HFNC use and a 5.8-fold increase in NIV use. Further research is needed to standardize approaches to HFNC and NIV support in bronchiolitis to reduce resource strain.

A data-driven model for early prediction of need for invasive mechanical ventilation in pediatric intensive care unit patients

RATIONALE

Acute respiratory failure is a life-threatening clinical outcome in critically ill pediatric patients. In severe cases, patients can require mechanical ventilation (MV) for survival. Early recognition of these patients can potentially help clinicians alter the clinical course and lead to improved outcomes.

OBJECTIVES

To build a data-driven model for early prediction of the need for mechanical ventilation in pediatric intensive care unit (PICU) patients.

METHODS

The study consists of a single-center retrospective observational study on a cohort of 13,651 PICU patients admitted between 1/01/2010 and 5/15/2018 with a prevalence of 8.06% for MV due to respiratory failure. XGBoost (extreme gradient boosting) and a convolutional neural network (CNN) using medication history were used to develop a prediction model that could yield a time-varying "risk-score"-a continuous probability of whether a patient will receive MV-and an ideal global threshold was calculated from the receiver operating characteristics (ROC) curve. The early prediction point (EPP) was the first time the risk-score surpassed the optimal threshold, and the interval between the EPP and the start of the MV was the early warning period (EWT). Spectral clustering identified patient groups based on risk-score trajectories after EPP.

RESULTS

A clinical and medication history-based model achieved a 0.89 area under the ROC curve (AUROC), 0.6 sensitivity, 0.95 specificity, 0.55 positive predictive value (PPV), and 0.95 negative predictive value (NPV). Early warning time (EWT) median [inter-quartile range] of this model was 9.9[4.2-69.2] hours. Clustering risk-score trajectories within a six-hour window after the early prediction point (EPP) established three patient groups, with the highest risk group's PPV being 0.92.

CONCLUSIONS

This study uses a unique method to extract and apply medication history information, such as time-varying variables, to identify patients who may need mechanical ventilation for respiratory failure and provide an early warning period to avert it.

Clinical practice guidelines: management of severe bronchiolitis in infants under 12 months old admitted to a pediatric critical care unit

PURPOSE

We present guidelines for the management of infants under 12 months of age with severe bronchiolitis with the aim of creating a series of pragmatic recommendations for a patient subgroup that is poorly individualized in national and international guidelines.

METHODS

Twenty-five French-speaking experts, all members of the Groupe Francophone de Réanimation et Urgence Pédiatriques (French-speaking group of paediatric intensive and emergency care; GFRUP) (Algeria, Belgium, Canada, France, Switzerland), collaborated from 2021 to 2022 through teleconferences and face-to-face meetings. The guidelines cover five areas: (1) criteria for admission to a pediatric critical care unit, (2) environment and monitoring, (3) feeding and hydration, (4) ventilatory support and (5) adjuvant therapies. The questions were written in the Patient-Intervention-Comparison-Outcome (PICO) format. An extensive Anglophone and Francophone literature search indexed in the MEDLINE database via PubMed, Web of Science, Cochrane and Embase was performed using pre-established keywords. The texts were analyzed and classified according to the Grading of Recommendations Assessment, Development and Evaluation (GRADE) methodology. When this method did not apply, an expert opinion was given. Each of these recommendations was voted on by all the experts according to the Delphi methodology.

RESULTS

This group proposes 40 recommendations. The GRADE methodology could be applied for 17 of them (3 strong, 14 conditional) and an expert opinion was given for the remaining 23. All received strong approval during the first round of voting.

CONCLUSION

These guidelines cover the different aspects in the management of severe bronchiolitis in infants admitted to pediatric critical care units. Compared to the different ways to manage patients with severe bronchiolitis described in the literature, our original work proposes an overall less invasive approach in terms of monitoring and treatment.

Predicting Flow Rate Escalation for Pediatric Patients on High Flow Nasal Cannula Using Machine Learning

Background: High flow nasal cannula (HFNC) is commonly used as non-invasive respiratory support in critically ill children. There are limited data to inform consensus on optimal device parameters, determinants of successful patient response, and indications for escalation of support. Clinical scores, such as the respiratory rate-oxygenation (ROX) index, have been described as a means to predict HFNC non-response, but are limited to evaluating for escalations to invasive mechanical ventilation (MV). In the presence of apparent HFNC non-response, a clinician may choose to increase the HFNC flow rate to hypothetically prevent further respiratory deterioration, transition to an alternative non-invasive interface, or intubation for MV. To date, no models have been assessed to predict subsequent escalations of HFNC flow rates after HFNC initiation. Objective: To evaluate the abilities of tree-based machine learning algorithms to predict HFNC flow rate escalations. Methods: We performed a retrospective, cohort study assessing children admitted for acute respiratory failure under 24 months of age placed on HFNC in the Johns Hopkins Children's Center pediatric intensive care unit from January 2019 through January 2020. We excluded encounters with gaps in recorded clinical data, encounters in which MV treatment occurred prior to HFNC, and cases electively intubated in the operating room. The primary study outcome was discriminatory capacity of generated machine learning algorithms to predict HFNC flow rate escalations as compared to each other and ROX indices using area under the receiver operating characteristic (AUROC) analyses. In an exploratory fashion, model feature importance rankings were assessed by comparing Shapley values. Results: Our gradient boosting model with a time window of 8 h and lead time of 1 h before HFNC flow rate escalation achieved an AUROC with a 95% confidence interval of 0.810 ± 0.003. In comparison, the ROX index achieved an AUROC of 0.525 ± 0.000. Conclusion: In this single-center, retrospective cohort study assessing children under 24 months of age receiving HFNC for acute respiratory failure, tree-based machine learning models outperformed the ROX index in predicting subsequent flow rate escalations. Further validation studies are needed to ensure generalizability for bedside application.

A Retrospective Analysis of Feeding Practices and Complications in Patients with Critical Bronchiolitis on Non-Invasive Respiratory Support

Limited data exist regarding feeding pediatric patients managed on non-invasive respiratory support (NRS) modes that augment oxygenation and ventilation in the setting of acute respiratory failure. We conducted a retrospective cohort study to explore the safety of feeding patients managed on NRS with acute respiratory failure secondary to bronchiolitis. Children up to two years old with critical bronchiolitis managed on continuous positive airway pressure, bilevel positive airway pressure, or RAM cannula were included. Of the 178 eligible patients, 64 were reportedly nil per os (NPO), while 114 received enteral nutrition (EN). Overall equivalent in severity of illness, younger patients populated the EN group, while the NPO group experienced a higher incidence of intubation. Duration of stay in the pediatric intensive care unit and non-invasive respiratory support were shorter in the NPO group, though intubation eliminated the former difference. Within the EN group, ninety percent had feeds initiated within 48 h and 94% reached full feeds within 7 days of NRS initiation, with an 8% complication and <1% aspiration rate. Reported complications did not result in escalation of respiratory support. Notably, a significant improvement in heart rate and respiratory rate was noted after feeds initiation. Taken together, our study supports the practice of early enteral nutrition in patients with critical bronchiolitis requiring NRS.