Predicting extubation outcomes using the Heart Rate Characteristics index in preterm infants: a cohort study

Artificial intelligence in the NICU to predict extubation success in prematurely born infants

OBJECTIVES

Mechanical ventilation in prematurely born infants, particularly if prolonged, can cause long term complications including bronchopulmonary dysplasia. Timely extubation then is essential, yet predicting its success remains challenging. Artificial intelligence (AI) may provide a potential solution.

CONTENT

A narrative review was undertaken to explore AI's role in predicting extubation success in prematurely born infants. Across the 11 studies analysed, the range of reported area under the receiver operator characteristic curve (AUC) for the selected prediction models was between 0.7 and 0.87. Only two studies implemented an external validation procedure. Comparison to the results of clinical predictors was made in two studies. One group reported a logistic regression model that outperformed clinical predictors on decision tree analysis, while another group reported clinical predictors outperformed their artificial neural network model (AUCs: ANN 0.68 vs. clinical predictors 0.86). Amongst the studies there was an heterogenous selection of variables for inclusion in prediction models, as well as variations in definitions of extubation failure.

SUMMARY

Although there is potential for AI to enhance extubation success, no model's performance has yet surpassed that of clinical predictors.

OUTLOOK

Future studies should incorporate external validation to increase the applicability of the models to clinical settings.

A Nomogram for Predicting Extubation Failure in Preterm Infants with Gestational Age Less than 29 Weeks

INTRODUCTION

How to avoid reintubations in prematurity remains a hard nut. This study aimed to develop and validate a nomogram for predicting extubation failure in preterm infants who received different modes of noninvasive ventilation as post-extubation support.

METHODS

This was a secondary analysis of pre-existing data from a large multicenter RCT combined with a multicenter retrospective investigation in three tertiary referral NICUs in China. The training cohort consisted of extubated infants from the RCT and the validation cohort included neonates admitted to the three NICUs in the last 5 years. The nomogram was developed through univariate and multivariate logistic regression analyses of peri-extubation clinical variables.

RESULTS

A total of 432 and 183 preterm infants (25 weeks ≤ gestational age [GA] <29 weeks) were, respectively, included in the training cohort and the validation cohort. Lower birth weight, lower Apgar 5-min score, lower postmenstrual age at extubation, lower PO2 and higher PCO2 before extubation, and continuous positive airway pressure rather than nasal intermittent positive pressure ventilation or noninvasive high-frequency oscillatory ventilation after extubation were associated with higher risks of extubation failure (p < 0.05), on which the nomogram was established. In both the training cohort and the validation cohort, the nomogram demonstrated good predictive accuracy (area under the receiver operating characteristic curve = 0.744 and 0.826); the Hosmer-Lemeshow test (p = 0.192 and 0.401) and the calibration curve (R2 = 0.195 and 0.307) proved a good fitness and conformity; and the decision curve analysis showed significant net benefit at the best threshold (p = 0.201).

CONCLUSION

This nomogram could serve as a good decision-support tool when predicting extubation failure in preterm infants with GA less than 29 weeks.

Autonomic markers of extubation readiness in premature infants

BACKGROUND

In premature infants, extubation failure is common and difficult to predict. Heart rate variability (HRV) is a marker of autonomic tone. Our aim is to test the hypothesis that autonomic impairment is associated with extubation readiness.

METHODS

Retrospective study of 89 infants <28 weeks. HRV metrics 24 h prior to extubation were compared for those with and without extubation success within 72 h. Receiver-operating curve analysis was conducted to determine the predictive ability of each metric, and a predictive model was created.

RESULTS

Seventy-three percent were successfully extubated. The success group had significantly lower oxygen requirement, higher sympathetic HRV metrics, and a lower parasympathetic HRV metric. α₁ (measure of autocorrelation, related to sympathetic tone) was the best predictor of success-area under the curve (AUC) of .73 (p = 0.001), and incorporated into a predictive model had an AUC of 0.81 (p < 0.0001)-sensitivity of 81% and specificity of 78%.

CONCLUSIONS

Extubation success is associated with HRV. We show an autonomic imbalance with low sympathetic and elevated parasympathetic tone in those who failed. α₁, a marker of sympathetic tone, was noted to be the best predictor of extubation success especially when incorporated into a clinical model.

IMPACT

This article depicts autonomic markers predictive of extubation success. We depict an autonomic imbalance in those who fail extubation with heightened parasympathetic and blunted sympathetic signal. We describe a predictive model for extubation success with a sensitivity of 81% and specificity of 78%.

Oxygen saturation histogram classification system to evaluate response to doxapram treatment in preterm infants

BACKGROUND

An oxygen saturation (SpO₂) histogram classification system has been shown to enable quantification of SpO₂ instability into five types, based on histogram distribution and time spent at SpO₂ ≤ 80%. We aimed to investigate this classification system as a tool to describe response to doxapram treatment in infants with severe apnea of prematurity.

METHODS

This retrospective study included 61 very-low-birth-weight infants who received doxapram. SpO₂ histograms were generated over the 24-h before and after doxapram start. Therapy response was defined as a decrease of ≥1 histogram types after therapy start.

RESULTS

The median (IQR) histogram type decreased from 4 (3-4) before to 3 (2-3) after therapy start (p < 0.001). The median (IQR) FiO₂ remained constant before (27% [24-35%]) and after (26% [22-35%]) therapy. Thirty-six infants (59%) responded to therapy within 24 h. In 34/36 (94%) of the responders, invasive mechanical ventilation (IMV) was not required during the first 72 h of therapy, compared to 15/25 (60%) of non-responders (p = 0.002). Positive and negative predictive values of the 24-h response for no IMV requirement within 72 h were 0.46 and 0.94, respectively.

CONCLUSIONS

Classification of SpO₂ histograms provides an objective bedside measure to assess response to doxapram therapy and can serve as a tool to detect changes in oxygenation status around respiratory interventions.

IMPACT

The SpO₂ histogram classification system provides a tool for quantifying response to doxapram therapy. The classification system allowed estimation of the probability of invasive mechanical ventilation requirement, already within a few hours of treatment. The SpO₂ histogram classification system allows an objective bedside assessment of the oxygenation status of the preterm infant, making it possible to assess the changes in oxygenation status in response to respiratory interventions.

Neonatal heart rate variability: a contemporary scoping review of analysis methods and clinical applications

BACKGROUND

Neonatal heart rate variability (HRV) is widely used as a research tool. However, HRV calculation methods are highly variable making it difficult for comparisons between studies.

OBJECTIVES

To describe the different types of investigations where neonatal HRV was used, study characteristics, and types of analyses performed.

ELIGIBILITY CRITERIA

Human neonates ≤1 month of corrected age.

SOURCES OF EVIDENCE

A protocol and search strategy of the literature was developed in collaboration with the McGill University Health Center's librarians and articles were obtained from searches in the Biosis, Cochrane, Embase, Medline and Web of Science databases published between 1 January 2000 and 1 July 2020.

CHARTING METHODS

A single reviewer screened for eligibility and data were extracted from the included articles. Information collected included the study characteristics and population, type of HRV analysis used (time domain, frequency domain, non-linear, heart rate characteristics (HRC) parameters) and clinical applications (physiological and pathological conditions, responses to various stimuli and outcome prediction).

RESULTS

Of the 286 articles included, 171 (60%) were small single centre studies (sample size <50) performed on term infants (n=136). There were 138 different types of investigations reported: physiological investigations (n=162), responses to various stimuli (n=136), pathological conditions (n=109) and outcome predictor (n=30). Frequency domain analyses were used in 210 articles (73%), followed by time domain (n=139), non-linear methods (n=74) or HRC analyses (n=25). Additionally, over 60 different measures of HRV were reported; in the frequency domain analyses alone there were 29 different ranges used for the low frequency band and 46 for the high frequency band.

CONCLUSIONS

Neonatal HRV has been used in diverse types of investigations with significant lack of consistency in analysis methods applied. Specific guidelines for HRV analyses in neonates are needed to allow for comparisons between studies.

The Intertemporal Role of Respiratory Support in Improving Neonatal Outcomes: A Narrative Review

Defining improvements in healthcare can be challenging due to the need to assess multiple outcomes and measures. In neonates, although progress in respiratory support has been a key factor in improving survival, the same degree of improvement has not been documented in certain outcomes, such as bronchopulmonary dysplasia. By exploring the evolution of neonatal respiratory care over the last 60 years, this review highlights not only the scientific advances that occurred with the application of invasive mechanical ventilation but also the weakness of the existing knowledge. The contributing role of non-invasive ventilation and less-invasive surfactant administration methods as well as of certain pharmacological therapies is also discussed. Moreover, we analyze the cost-benefit of neonatal care-respiratory support and present future challenges and perspectives.

Biosignal-Based Digital Biomarkers for Prediction of Ventilator Weaning Success

We evaluated new features from biosignals comprising diverse physiological response information to predict the outcome of weaning from mechanical ventilation (MV). We enrolled 89 patients who were candidates for weaning from MV in the intensive care unit and collected continuous biosignal data: electrocardiogram (ECG), respiratory impedance, photoplethysmogram (PPG), arterial blood pressure, and ventilator parameters during a spontaneous breathing trial (SBT). We compared the collected biosignal data's variability between patients who successfully discontinued MV (n = 67) and patients who did not (n = 22). To evaluate the usefulness of the identified factors for predicting weaning success, we developed a machine learning model and evaluated its performance by bootstrapping. The following markers were different between the weaning success and failure groups: the ratio of standard deviations between the short-term and long-term heart rate variability in a Poincaré plot, sample entropy of ECG and PPG, α values of ECG, and respiratory impedance in the detrended fluctuation analysis. The area under the receiver operating characteristic curve of the model was 0.81 (95% confidence interval: 0.70-0.92). This combination of the biosignal data-based markers obtained during SBTs provides a promising tool to assist clinicians in determining the optimal extubation time.