Success or failure of non-invasive positive pressure ventilation in children with acute respiratory failure. Could it be predicted?

Abstract

<b>Introduction: </b> Noninvasive ventilation (NIV) has been developed to reduce complications associated with invasive ventilation (IV). Failure of NIV and delay in endotracheal intubation can increase patients’ morbidity and mortality. Thus early determination of patients who are unlikely to benefit from NIV is crucial for their management. We aimed in this study to identify the early predictors of success of NIV in children with acute respiratory failure (ARF).<br /> <b>Material and methods: </b> Fifty patients with ARF who fulfilled the study selection criteria were ventilated non-invasively and were assessed initially for their severity of critical illness by the Pediatric Logistic Organ Dysfunction (PELOD) score. Clinical, gasometric, respiratory mechanics and oxygenation indices were assessed at 0, 30 and 60 min and 4 and 24 h from the start of NIV. The success group was identified by reduction in respiratory effort, reduction in oxygen demand, improvement in gasometric parameters, and avoidance of intubation.<br /> <b>Results</b>: Sixty-two percent of patients had successful NIV. Neither type of ARF nor patients’ demographics affected the outcome of NIV. The success rate was 80% among patients with mild to moderate acute respiratory distress syndrome (ARDS), 20% with severe ARDS, and 71.8% in patients with bronchopneumonia. Multivariate analysis revealed that baseline PELOD score of less than 14.5 ±2.7, SpO2/FiO2 ratio more than 208 ±57, oxygenation index (OI) 7 ±3.4 and mean airway pressure (MAP) 8.6 ±1.3 are independent predictors for success of NIV.<br /> <b>Conclusions</b>: The NIV is a promising respiratory support modality in pediatric ARF. Baseline degree of critical illness and saturation oxygenation indices together with MAP change after the 1st h from the NIV trial represented the best predictors of success of the trial in the current study.

Predictors of early synchronized non-invasive ventilation failure for infants < 32 weeks of gestational age with respiratory distress syndrome

“…Although we have three statistically significant predictors by multivariate logistic regression, the predictor with the largest OR is grade of RDS, followed by MAP and antenatal steroid use having the smallest effect with an OR of 1.09. A study in older children with respiratory failure also identified two comparable criteria as predictors for NIV success: MAP change and baseline degree of illness [33]. A possible limitation of our study is that the failure group was small, with only 12 patients, and therefore did not allow for extraction of other potential predictors.…”

“…Other predictive factors that have been reported in the literature for pediatric NIV but were not included in this study are a higher PaO 2 /FiO 2 before NIV [33], hemodynamic response to NIV for success and a higher PaCO 2 for failure [34], low pH immediately after initiation of NIV as a predictor of failure [35], respiratory rate prior to NIV (James et al .) and the number of failing organs [36].…”

Risk of tuberculosis in low birth weight children from East Romania

“…Both mother and child were treated favorably. This case report highlighted a prolonged latency period of TB infection, emphasizing the need for urgent treatment of neonates in such situations [21], especially taking into account pediatric acute respiratory failure [22].…”

TERT rs2853669 as predictor for overall survival in patients with acute myeloid leukemia

“…The effect size of association between each of the studied SNPs and OS was quantified by hazard ratio (HR) adjusted according to the above-mentioned factors. Candidate predictors included in multivariable Cox regression models were significant univariate predictors along with all important clinical variables [23], so that the number of predictors (p) was lower than m/15, where m was the number of deaths in the studied sample [24]. Statistical analysis was performed using the advanced environment for statistical computing R version 3.4.4.…”