Hardware-software approach for neonatal cardiovascular risk estimation.
Genetic risk is a primary contributing factor to the predisposition of a newborn child to elevated blood pressure later in life. To determine whether there is a correlation between potential genetic risk as established by family history and measured physiologic variables in the neonate, the systolic and diastolic blood pressures and heart rates of 150 newborn babies were automatically monitored at about 30-minute intervals for 48 hours with a Nippon Colin device, starting early after birth. Circadian parameters (obtained by the linear least-squares fit of a 24-hour cosine curve to each individual series) and descriptive statistics for the three circulatory variables were used in a multiple-regression analysis to compute a linear prediction function for a neonatal cardiovascular risk score. This score was obtained for each neonate on the basis of the presence or absence of overt cardiovascular disease, elevated blood pressure, or obesity across two generations, those of the newborn's parents and grandparents. Results from multiple regression indicate that the best model for prediction of the risk score includes the circadian amplitudes of systolic and diastolic blood pressure, the circadian range of heart rate, and the 90% range and standard deviation of diastolic blood pressure. The multiple correlation coefficient between the predicted and the computed risk scales is 0.666, a value that, although statistically significant (p < 0.001), is still low for a generalized practical use of the model in predicting risk.(ABSTRACT TRUNCATED AT 250 WORDS)
Hermida
R C
RC
Bioengineering and Chronobiology Laboratories, E.T.S.I. Telecomunicación, University of Vigo, Spain.
Ayala
D E
DE
Fernández
J R
JR
Fraga
J M
JM
D002318:Cardiovascular Diseases / Q000175:diagnosis / Q000503:physiopathology*; D002940:Circadian Rhythm; D005190:Family; D006801:Humans; D007231:Infant, Newborn; D016014:Linear Models; D008432:Mathematical Computing; D008955:Models, Cardiovascular; D015233:Models, Statistical; D008991:Monitoring, Physiologic; D012044:Regression Analysis; D012307:Risk Factors; D012815:Signal Processing, Computer-Assisted; D012984:Software