"Prediction" of the one-minute Apgar score from fetal heart rate data.

ABSTRACT

The value of any fetal monitoring technic is in its ability to predict infant outcome. In the present study, the ability of fetal heart rate (FHR) monitoring data to "predict" a measure of short-term infant outcome, the 1-minute Apgar score, was evaluated using univariate and multivariate statistical analyses. Of 61 monitored high-risk infants, 46 had high (7 to 10) and 15 had low (1 to 6) 1-minute Apgar scores. Computer analysis of FHR/intrauterine pressure (IUP) data for these 61 infants revealed that the infants with low Apgar scores had more than the expected number of late decelerations (LD). Using a threshold of ten LD and univariate analysis, 74% of the infants could be properly classified for high or low Apgar scores, but 60% of the infants with low Apgar scores were not identified. Using discriminant function (multivariate) analysis for the numbers of LD and uterine contractions, 47% of the depressed infants were appropriately identified and simple risk scoring equations were devised. Using additional observation vectors, including the number of accelerations and early decelerations, 67% of the depressed infants could be identified. The results of this study suggest that using multiple observation vectors improves the predictive capacity and, thus, the value of fetal monitoring data. Clinical experience suggests that the value of monitoring data can be further enhanced by simultaneous evaluation of other observation vectors from additional perinatal data sets using the technics of this study.