Sensitivity Analysis of a Cardio-respiratory Model in Preterm Newborns for the Study of Patent Ductus Arteriosus

International audienceThis paper proposes an integrated model of cardio-respiratory interactions in preterm newborns, focused on the study of the patent ductus arteriosus (PDA). A formal model parameter sensitivity analysis on blood flow through the PDA is performed. Results show that the proposed model is capable of simulating hemodynamics in right-to-left and left-to-right shunts. For both configurations, the most significant parameters are associated with mechanical ventricular properties and circulatory parameters related to left ventricle loading conditions. These results highlight important physiological mechanisms involved in PDA and provide key information towards the definition of patient-specific parameters. © 2021 IEEE

Model-Based Analysis of Apnea-Bradycardia events in Newborns

International audienceIn preterm infants, recurrent episodes of apnea, bradycardia and severe intermittent hypoxia are mainly related to cardiorespiratory immaturity. These episodes are associated with major risks during the first weeks of life. Cardiorespiratory data consisting of a continuous 12 hours recording of transthoracic impedance and ECG signals were acquired in 18 preterm neonates. 106 isolated apnea events (>10 sec) were manually annotated from the database, of which 19 apneas with bradycardia. A system-level physiological model of cardio-respiratory interactions in the newborn is proposed and used to reproduce simulations of mixed apneas with and without bradycardia, by modifying the functional residual capacity. A first qualitative comparison between the simulations and the clinical data shows a close match between the experimental and simulated heart rate series during apnea with bradycardia (RMSE 4.96 bpm) and without (RMSE 2.02 bpm). © 2022 Creative Commons

Early Detection of Late Onset Sepsis in Premature Infants Using Visibility Graph Analysis of Heart Rate Variability

International audienceObjective: This study was designed to test the diagnostic value of visibility graph features derived from the heart rate time series to predict late onset sepsis (LOS) in preterm infants using machine learning. Methods: The heart rate variability (HRV) data was acquired from 49 premature newborns hospitalized in neonatal intensive care units (NICU). The LOS group consisted of patients who received more than five days of antibiotics, at least 72 hours after birth. The control group consisted of infants who did not receive antibiotics. HRV features in the days prior to the start of antibiotics (LOS group) or in a randomly selected period (control group) were compared against a baseline value calculated during a calibration period. After automatic feature selection, four machine learning algorithms were trained. All the tests were done using two variants of the feature set: one only included traditional HRV features, and the other additionally included visibility graph features. Performance was studied using area under the receiver operating characteristics curve (AUROC). Results: The best performance for detecting LOS was obtained with logistic regression, using the feature set including visibility graph features, with AUROC of 87.7% during the six hours preceding the start of antibiotics, and with predictive potential (AUROC above 70%) as early as 42 h before start of antibiotics. Conclusion: These results demonstrate the usefulness of introducing visibility graph indexes in HRV analysis for sepsis prediction in newborns. Significance: The method proposed the possibility of non-invasive, real-Time monitoring of risk of LOS in a NICU setting

Haematocrit and red blood cell transport in preterm infants: an observational study

AIMS—To test whether cardiac output acts as a compensatory response to changes in haematocrit.
METHODS—A cohort of 38 preterm infants (27-31 weeks' gestation) was studied with repeated Doppler measurements of left ventricular output during the 1st month of life. Red blood cell transport was calculated when the duct was closed.
RESULTS—Multiple regression analysis showed that left ventricular output correlated negatively with haematocrit when the duct was closed (n = 84) and when it was open (n = 59). The influence of an increase of 10% in haematocrit absolute value on mean (SD) left ventricular output was estimated at −55 (11) ml/kg/min. Mean (SD) red blood cell transport was 132 (30) ml/kg/min with a mean (SD) intra-individual variability of 20% (8.8%). Red blood cell transport was increased more frequently by left ventricular output than by haematocrit. Haematocrit and left ventricular output but not red blood cell transport were dependent on postnatal age.
CONCLUSION—These results suggest that in preterm infants cardiac output adaptation is effective in attenuating the effects of red blood cell mass variations on systemic oxygen carrying capacity.

Evaluation of maturation in preterm infants through an ensemble machine learning algorithm using physiological signals

International audienceThis study was designed to test if heart rate variability (HRV) data from preterm and full-term infants could be used to estimate their functional maturational age (FMA), using a machine learning model. We propose that the FMA, and its deviation from the postmenstrual age (PMA) of the infants could inform physicians about the progress of the maturation of the infants. The HRV data was acquired from 50 healthy infants, born between 25 and 41 weeks of gestational age, who did not present any signs of abnormal maturation relative to their age group during the period of observation. The HRV features were used as input for a machine learning model that uses filtering and genetic algorithms for feature selection, and an ensemble machine learning (EML) algorithm, which combines linear and random forest regressions, to produce as output a FMA. Using HRV data, the FMA had a mean absolute error of 0.93 weeks, 95% CI [0.78, 1.08], compared to the PMA. These results demonstrate that HRV features of newborn infants can be used by an EML model to estimate their FMA. This method was also generalized using respiration rate variability (RRV) and bradycardia data, obtaining similar results. The FMA, predicted either by HRV, RRV or bradycardia, and its deviation from the true PMA of the infants, could be used as a surrogate measure of the maturational age of the infants, which could potentially be monitored non-invasively and in real-time in the setting of neonatal intensive care units