An advisory system for artificial ventilation of the newborn utilizing a neural network

Automated oxygen delivery for preterm infants with respiratory dysfunction

BACKGROUND

Many preterm infants require respiratory support to maintain an optimal level of oxygenation, as oxygen levels both below and above the optimal range are associated with adverse outcomes. Optimal titration of oxygen therapy for these infants presents a major challenge, especially in neonatal intensive care units (NICUs) with suboptimal staffing. Devices that offer automated oxygen delivery during respiratory support of neonates have been developed since the 1970s, and individual trials have evaluated their effectiveness.

OBJECTIVES

To assess the benefits and harms of automated oxygen delivery systems, embedded within a ventilator or oxygen delivery device, for preterm infants with respiratory dysfunction who require respiratory support or supplemental oxygen therapy.

SEARCH METHODS

We searched CENTRAL, MEDLINE, CINAHL, and clinical trials databases without language or publication date restrictions on 23 January 2023. We also checked the reference lists of retrieved articles for other potentially eligible trials.

SELECTION CRITERIA

We included randomised controlled trials and randomised cross-over trials that compared automated oxygen delivery versus manual oxygen delivery, or that compared different automated oxygen delivery systems head-to-head, in preterm infants (born before 37 weeks' gestation).

DATA COLLECTION AND ANALYSIS

We used standard Cochrane methods. Our main outcomes were time (%) in desired oxygen saturation (SpO2) range, all-cause in-hospital mortality by 36 weeks' postmenstrual age, severe retinopathy of prematurity (ROP), and neurodevelopmental outcomes at approximately two years' corrected age. We expressed our results using mean difference (MD), standardised mean difference (SMD), and risk ratio (RR) with 95% confidence intervals (CIs). We used GRADE to assess the certainty of evidence.

MAIN RESULTS

We included 18 studies (27 reports, 457 infants), of which 13 (339 infants) contributed data to meta-analyses. We identified 13 ongoing studies. We evaluated three comparisons: automated oxygen delivery versus routine manual oxygen delivery (16 studies), automated oxygen delivery versus enhanced manual oxygen delivery with increased staffing (three studies), and one automated system versus another (two studies). Most studies were at low risk of bias for blinding of personnel and outcome assessment, incomplete outcome data, and selective outcome reporting; and half of studies were at low risk of bias for random sequence generation and allocation concealment. However, most were at high risk of bias in an important domain specific to cross-over trials, as only two of 16 cross-over trials provided separate outcome data for each period of the intervention (before and after cross-over). Automated oxygen delivery versus routine manual oxygen delivery Automated delivery compared with routine manual oxygen delivery probably increases time (%) in the desired SpO2 range (MD 13.54%, 95% CI 11.69 to 15.39; I2 = 80%; 11 studies, 284 infants; moderate-certainty evidence). No studies assessed in-hospital mortality. Automated oxygen delivery compared to routine manual oxygen delivery may have little or no effect on risk of severe ROP (RR 0.24, 95% CI 0.03 to 1.94; 1 study, 39 infants; low-certainty evidence). No studies assessed neurodevelopmental outcomes. Automated oxygen delivery versus enhanced manual oxygen delivery There may be no clear difference in time (%) in the desired SpO2 range between infants who receive automated oxygen delivery and infants who receive manual oxygen delivery (MD 7.28%, 95% CI -1.63 to 16.19; I2 = 0%; 2 studies, 19 infants; low-certainty evidence). No studies assessed in-hospital mortality, severe ROP, or neurodevelopmental outcomes. Revised closed-loop automatic control algorithm (CLACfast) versus original closed-loop automatic control algorithm (CLACslow) CLACfast allowed up to 120 automated adjustments per hour, whereas CLACslow allowed up to 20 automated adjustments per hour. CLACfast may result in little or no difference in time (%) in the desired SpO2 range compared to CLACslow (MD 3.00%, 95% CI -3.99 to 9.99; 1 study, 19 infants; low-certainty evidence). No studies assessed in-hospital mortality, severe ROP, or neurodevelopmental outcomes. OxyGenie compared to CLiO2 Data from a single small study were presented as medians and interquartile ranges and were not suitable for meta-analysis.

AUTHORS' CONCLUSIONS

Automated oxygen delivery compared to routine manual oxygen delivery probably increases time in desired SpO2 ranges in preterm infants on respiratory support. However, it is unclear whether this translates into important clinical benefits. The evidence on clinical outcomes such as severe retinopathy of prematurity are of low certainty, with little or no differences between groups. There is insufficient evidence to reach any firm conclusions on the effectiveness of automated oxygen delivery compared to enhanced manual oxygen delivery or CLACfast compared to CLACslow. Future studies should include important short- and long-term clinical outcomes such as mortality, severe ROP, bronchopulmonary dysplasia/chronic lung disease, intraventricular haemorrhage, periventricular leukomalacia, patent ductus arteriosus, necrotising enterocolitis, and long-term neurodevelopmental outcomes. The ideal study design for this evaluation is a parallel-group randomised controlled trial. Studies should clearly describe staffing levels, especially in the manual arm, to enable an assessment of reproducibility according to resources in various settings. The data of the 13 ongoing studies, when made available, may change our conclusions, including the implications for practice and research.

Application and potential of artificial intelligence in neonatal medicine

Neonatal care is becoming increasingly complex with large amounts of rich, routinely recorded physiological, diagnostic and outcome data. Artificial intelligence (AI) has the potential to harness this vast quantity and range of information and become a powerful tool to support clinical decision making, personalised care, precise prognostics, and enhance patient safety. Current AI approaches in neonatal medicine include tools for disease prediction and risk stratification, neurological diagnostic support and novel image recognition technologies. Key to the integration of AI in neonatal medicine is the understanding of its limitations and a standardised critical appraisal of AI tools. Barriers and challenges to this include the quality of datasets used, performance assessment, and appropriate external validation and clinical impact studies. Improving digital literacy amongst healthcare professionals and cross-disciplinary collaborations are needed to harness the full potential of AI to help take the next significant steps in improving neonatal outcomes for high-risk infants.

Power of breathing determined noninvasively with use of an artificial neural network in patients with respiratory failure

OBJECTIVE

To determine work of breathing per minute or power of breathing noninvasively (POB(N)) by using an artificial neural network (ANN) without the need for an esophageal catheter in patients with respiratory failure.

DESIGN

Prospective study comparing the relationship between POB(N) and invasively measured power of breathing (POB(I)).

SETTING

Intensive care unit of a university hospital.

PATIENTS

Forty-five intubated adults (age, 51 +/- 11 yrs; weight, 71 +/- 18 kg; 28 males and 17 females) receiving pressure support ventilation (PSV).

INTERVENTIONS

Data from an esophageal catheter and airway pressure/flow sensor were used to measure POB(I). A pretrained ANN provided real time calculation of POB(N). POB(I) and POB(N) were measured at various levels of PSV, ranging from 5 to 25 cm H(2)O.

MEASUREMENTS AND MAIN RESULTS

POB(N) was highly correlated with POB(I) (r = 0.91; p < .002), and because POB(N) explained or predicted 83% of the variance in POB(I), it was considered a very good predictor (r(2) = 0.83; p < .002). Bias was negligible (0.00) and precision was clinically acceptable (2.2 J/min).

CONCLUSIONS

POB can be calculated noninvasively with reasonable clinical accuracy for patients receiving ventilatory support by using an ANN. This method obviates the need for inserting an esophageal catheter and thus greatly simplifies measurement of POB. POB(N) may be a clinically useful tool for consideration when setting PSV to unload the respiratory muscles. Before considering its use in clinical practice, POB(N) would need to be incorporated within the context of load tolerance and shown to improve outcomes.

Predicting extubation outcome in preterm newborns: a comparison of neural networks with clinical expertise and statistical modeling

Even though ventilator technology and monitoring of premature infants has improved immensely over the past decades, there are still no standards for weaning and determining optimal extubation time for those infants. Approximately 30% of intubated preterm infants will fail attempted extubation, requiring reintubation and resuming of mechanical ventilation. A machine-learning approach using artificial neural networks (ANNs) to aid in extubation decision making is hereby proposed. Using expert opinion, 51 variables were identified as being relevant for the decision of whether to extubate an infant who is on mechanical ventilation. The data on 183 premature infants, born between 1999 and 2002, were collected by review of medical charts. The ANN extubation model was compared with alternative statistical modeling using multivariate logistic regression and also with the clinician's own predictive insight using sensitivity analysis and receiver operating characteristic curves. The optimal ANN model used 13 parameters and achieved an area under the receiver operating characteristic curve of 0.87 (out-of-sample validation), comparing favorably with multivariate logistic regression. It also compared well with the clinician's expertise, which raises the possibility of being useful as an automated alert tool. Because an ANN learns directly from previous data obtained in the institution where it is to be used, this makes it particularly amenable for application to evidence-based medicine. Given the variety of practices and equipment being used in different hospitals, this may be particularly relevant in the context of caring for preterm newborns who are on mechanical ventilation.

Assessment of respiratory system mechanics by artificial neural networks: an exploratory study

We evaluated 1) the performance of an artificial neural network (ANN)-based technology in assessing the respiratory system resistance (Rrs) and compliance (Crs) in a porcine model of acute lung injury and 2) the possibility of using, for ANN training, signals coming from an electrical analog (EA) of the lung. Two differently experienced ANNs were compared. One ANN (ANN(BIO)) was trained on tracings recorded at different time points after the administration of oleic acid in 10 anesthetized and paralyzed pigs during constant-flow mechanical ventilation. A second ANN (ANN(MOD)) was trained on EA simulations. Both ANNs were evaluated prospectively on data coming from four different pigs. Linear regression between ANN output and manually computed mechanics showed a regression coefficient (R) of 0.98 for both ANNs in assessing Crs. On Rrs, ANN(BIO) showed a performance expressed by R = 0.40 and ANN(MOD) by R = 0.61. These results suggest that ANNs can learn to assess the respiratory system mechanics during mechanical ventilation but that the assessment of resistance and compliance by ANNs may require different approaches.