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

Abstract 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 s were identified 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 s were associated with cardiorespiratory instability, while 61.2% of apnoeas ≥20 s were not. Multiple clinical/demographic (postmenstrual and gestational age, sex, weight z-score, ventilation mode) and dynamic (baseline heart rate, oxygen saturation, 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, and 66.0% using only clinical/demographic features. Conclusions Multiple factors influence cardiorespiratory responses to apnoea. Predictive modelling may enable personalised apnoea definitions, improving individualised care. Impact We investigated variability in cardiorespiratory instability following apnoea in preterm infants. 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. Prediction of cardiorespiratory instability could enable personalised apnoea alarms and inform discharge and treatment decision making.