Cardiorespiratory behavior of preterm infants receiving continuous positive airway pressure and high flow nasal cannula post extubation: randomized crossover study

Noninvasive Positive Airway Pressure Management for Post-extubation Support in Preterm Infants: Observational Cohort Study with Overlap Weighting Analysis

BACKGROUND

Nasal continuous positive airway pressure (NCPAP), nasal intermittent positive pressure ventilation (NIPPV), and high-flow nasal cannula (HFNC) are often used after initial extubation in preterm infants. However, data regarding the choice between NCPAP/NIPPV and HFNC are limited. This study examined which therapy was more effective as post-extubation support.

METHODS

This is a retrospective, cohort study that used the Diagnosis Procedure Combination database in Japan, 2011-2021. Propensity score overlap weighting analyses were performed to compare the composite outcomes of in-hospital death and reintubation in preterm infants who received NCPAP/NIPPV and HFNC. We identified infants born at gestational age 22-36 weeks who were intubated within 1 day of birth. We included patients who underwent NCPAP/NIPPV or HFNC after initial extubation. Patients with airway obstruction or congenital airway abnormalities were excluded.

RESULTS

We identified 1,203 preterm infants treated with NCPAP/NIPPV (n = 525) or HFNC (n = 678). The median (interquartile range) gestational age at delivery was 30 (27-33) weeks, and birth weight was 1296 (884-1,802) g. Compared with the HFNC group, the NCPAP/NIPPV group had a significantly lower proportion of the composite outcome after the overlap weighting analysis (risk ratio, 0.62; 95% confidence interval, 0.47 to 0.83; p = 0.001). This significant difference was also observed in infants born at gestational age 22-31 weeks, whereas no significant difference was observed in infants born at gestational age 32-36 weeks.

CONCLUSIONS

NCPAP/NIPPV may be a superior post-extubation support than HFNC in preterm infants, especially in those born at gestational age of 22-31 weeks.

Automated prediction of extubation success in extremely preterm infants: the APEX multicenter study

BACKGROUND

Extremely preterm infants are frequently subjected to mechanical ventilation. Current prediction tools of extubation success lacks accuracy.

METHODS

Multicenter study including infants with birth weight ≤1250 g undergoing their first extubation attempt. Clinical data and cardiorespiratory signals were acquired before extubation. Primary outcome was prediction of extubation success. Automated analysis of cardiorespiratory signals, development of clinical and cardiorespiratory features, and a 2-stage Clinical Decision-Balanced Random Forest classifier were used. A leave-one-out cross-validation was done. Performance was analyzed by ROC curves and determined by balanced accuracy. An exploratory analysis was performed for extubations before 7 days of age.

RESULTS

A total of 241 infants were included and 44 failed (18%) extubation. The classifier had a balanced accuracy of 73% (sensitivity 70% [95% CI: 63%, 76%], specificity 75% [95% CI: 62%, 88%]). As an additional clinical-decision tool, the classifier would have led to an increase in extubation success from 82% to 93% but misclassified 60 infants who would have been successfully extubated. In infants extubated before 7 days of age, the classifier identified 16/18 failures (specificity 89%) and 73/105 infants with success (sensitivity 70%).

CONCLUSIONS

Machine learning algorithms may improve a balanced prediction of extubation outcomes, but further refinement and validation is required.

IMPACT

A machine learning-derived predictive model combining clinical data with automated analyses of individual cardiorespiratory signals may improve the prediction of successful extubation and identify infants at higher risk of failure with a good balanced accuracy. Such multidisciplinary approach including medicine, biomedical engineering and computer science is a step forward as current tools investigated to predict extubation outcomes lack sufficient balanced accuracy to justify their use in future trials or clinical practice. Thus, this individualized assessment can optimize patient selection for future trials of extubation readiness by decreasing exposure of low-risk infants to interventions and maximize the benefits of those at high risk.

Diagnostic Features and Potential Applications of PPG Signal in Healthcare: A Systematic Review

Recent research indicates that Photoplethysmography (PPG) signals carry more information than oxygen saturation level (SpO2) and can be utilized for affordable, fast, and noninvasive healthcare applications. All these encourage the researchers to estimate its feasibility as an alternative to many expansive, time-wasting, and invasive methods. This systematic review discusses the current literature on diagnostic features of PPG signal and their applications that might present a potential venue to be adapted into many health and fitness aspects of human life. The research methodology is based on the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines 2020. To this aim, papers from 1981 to date are reviewed and categorized in terms of the healthcare application domain. Along with consolidated research areas, recent topics that are growing in popularity are also discovered. We also highlight the potential impact of using PPG signals on an individual’s quality of life and public health. The state-of-the-art studies suggest that in the years to come PPG wearables will become pervasive in many fields of medical practices, and the main domains include cardiology, respiratory, neurology, and fitness. Main operation challenges, including performance and robustness obstacles, are identified.

Adjustment of high flow nasal cannula rates using real-time work of breathing indices in premature infants with respiratory insufficiency

OBJECTIVE

To assess the feasibility of real-time monitoring of work of breathing (WOB) indices and the impact of adjusting HFNC flow on breathing synchrony and oxygen stability in premature infants.

STUDY DESIGN

A prospective, observational study of infants stable on HFNC. The flow adjusted per predetermined algorithm. Respiratory inductive plethysmography (RIP) noninvasively measured WOB. A high-resolution pulse oximeter collected oxygen saturation and heart rate data. Summary statistics and mixed linear models were used.

RESULTS

Baseline data for 32 infants, final analysis of 21 infants. Eighty-one percent with abnormal WOB. Sixty-two percent demonstrated 20% improvement in WOB. For infants with gestational age <28 weeks, an incremental increase in HFNC flow rate decreased WOB (p < 0.001) and improved oxygen saturation and stability (p < 0.01).

CONCLUSIONS

Premature infants do not receive optimal support on HFNC. The use of a real-time feedback system to adjust HFNC is feasible and improves WOB, oxygen saturation, and oxygen stability. This technology may improve the utility of HFNC in premature infants.

Automatic unsupervised respiratory analysis of infant respiratory inductance plethysmography signals

Infants are at risk for potentially life-threatening postoperative apnea (POA). We developed an Automated Unsupervised Respiratory Event Analysis (AUREA) to classify breathing patterns obtained with dual belt respiratory inductance plethysmography and a reference using Expectation Maximization (EM). This work describes AUREA and evaluates its performance. AUREA computes six metrics and inputs them into a series of four binary k-means classifiers. Breathing patterns were characterized by normalized variance, nonperiodic power, instantaneous frequency and phase. Signals were classified sample by sample into one of 5 patterns: pause (PAU), movement (MVT), synchronous (SYB) and asynchronous (ASB) breathing, and unknown (UNK). MVT and UNK were combined as UNKNOWN. Twenty-one preprocessed records obtained from infants at risk for POA were analyzed. Performance was evaluated with a confusion matrix, overall accuracy, and pattern specific precision, recall, and F-score. Segments of identical patterns were evaluated for fragmentation and pattern matching with the EM reference. PAU exhibited very low normalized variance. MVT had high normalized nonperiodic power and low frequency. SYB and ASB had a median frequency of respectively, 0.76Hz and 0.71Hz, and a mode for phase of 4^o and 100^o. Overall accuracy was 0.80. AUREA confused patterns most often with UNKNOWN (25.5%). The pattern specific F-score was highest for SYB (0.88) and lowest for PAU (0.60). PAU had high precision (0.78) and low recall (0.49). Fragmentation was evident in pattern events <2s. In 75% of the EM pattern events >2s, 50% of the samples classified by AUREA had identical patterns. Frequency and phase for SYB and ASB were consistent with published values for synchronous and asynchronous breathing in infants. The low normalized variance in PAU, was consistent with published scoring rules for pediatric apnea. These findings support the use of AUREA to classify breathing patterns and warrant a future evaluation of clinically relevant respiratory events.