Risk of apnoea-related cardiorespiratory instability in preterm infants is modulated by clinical, demographic and dynamic indicators

Background

Apnoea of prematurity is common and may cause desaturation and/or bradycardia. There is marked variability in infants’ cardiorespiratory responses to apnoea, despite standardised clinical thresholds. Factors influencing apnoea-related cardiorespiratory instability and whether instability can be predicted warrant investigation.

Methods

181,511 apnoeas >5 seconds were identified from continuous physiological recordings from 146 preterm infants <37 weeks’ postmenstrual age. Cardiorespiratory instability was defined as bradycardia (>30% heart rate reduction) and/or oxygen desaturation (<85%). Mixed-effects models assessed clinical, demographic and dynamic modulators of the relationship between apnoea duration and cardiorespiratory instability. Machine learning (XGBoost) was used to train models to predict apnoea-related cardiorespiratory instability.

Results

Longer duration apnoeas were associated with increased instability, although variability was substantial and 3.6% of apnoeas <10 seconds were associated with cardiorespiratory instability, while 61.2% of apnoeas ≥20 seconds were not. Multiple clinical/demographic (postmenstrual and gestational age, sex, weight z-score, and ventilation mode) and dynamic (baseline heart rate, oxygen saturation, and recent apnoea clustering) factors were associated with increased instability risk. Apnoea-related cardiorespiratory instability could be predicted with a balanced test accuracy of 75.8% when incorporating all features, while a model using only clinical/demographic features achieved 66.0%.

Conclusions

Multiple factors influence cardiorespiratory responses to apnoea. Predictive modelling may enable personalised apnoea definitions, improving individualised care.

Impact statement

Impact statement

What is the key message of your article?

• We investigated variability in cardiorespiratory instability following apnoea in preterm infants.

What does it add to the existing literature?

• We demonstrate multiple factors which influence the cardiorespiratory changes following apnoea and develop a machine learning model which can accurately predict apnoea-related cardiorespiratory instability.

What is the impact?

• Prediction of cardiorespiratory instability could enable personalised apnoea alarms and treatment.