Apgar scores and mathematics

Summary: The Apgar score is a simple prognostic device for neonatal care.

Throughout the developed world and in many other countries, every newborn baby is assessed according to various factors, each of which is assigned a score that is aggregated to quantify the baby’s condition and prognosis. The system was introduced in the 1950s by Dr Virginia Apgar, whose last name has come to serve as a mnemonic for the assessed categories: activity, pulse, grimace (reflex), appearance (skin color), and respiration. The Apgar score indicates the health of the newborn and the likelihood that medical treatment or special intervention will be necessary much more quickly and more accurately than any system that had previously been in use.

In medicine, there are many scoring systems designed to predict and identify clinical situations in which the potential value of intensive care is low, while the burden of therapy is high, providing a numerical prediction of mortality. In gastroenterology, the Child-Pugh score is a scoring system to assess the prognosis of chronic liver disease; for vascular patients, the Eagle score allows estimation of a patient’s risk of dying during heart surgery; the probability of pulmonary embolism is estimated by the Geneva score; the Gleason Grading system is used to help evaluate the prognosis of men with prostate cancer; and for pediatric end-stage liver disease, a scoring system exists for prioritizing allocation of liver transplants for children under 12 years of age.

Development and Effectiveness

Dr. Virginia Apgar was the first woman at Columbia University College of Physicians and Surgeons to be named a full professor. She developed a practical method for measuring the status of probable survival of newborn infants. Initially, she listed all objective signs that pertained in any way to the condition of the infant birth. Then she observed that five of these signs could be easily determined one minute after the complete birth of the baby. Depending on if the sign was absent, weak, or present, a rating of 0, 1, or 2 was given for each signal. The signs are heart rate (slow, normal, fast, or irregular), respiratory effort (from normal to distressed), reflex response (from over- to under-reactive), muscle tone, and color (pale, normal, or blue). In this system, infants in poor condition scored 0–2, infants in fair condition scored 3–7, and a score of 10 indicated a baby in the best possible condition.

In 1953, she observed the mortality rates of 2096 newborn infants with low, moderate, and high Apgar scores within 60 seconds after complete birth. This evaluation was rapidly adopted in delivery rooms throughout the United States and elsewhere. In 1959, a study with 15,348 infants established the predictive value of the Apgar score. The death rate among infants scoring 2, 1, or 0 was about 15%, while the rate for infants scoring 10 was about 0.13%. This prediction is especially useful in judging the urgency for resuscitative measures, such as respiratory assistance. It can be used to guide care, including intensive care. The score is generally determined by doctors and nurses at one minute and at five minutes after delivery. The five-minute score is generally accepted as the best predictor for newborn infant survival. A low score on the one-minute test may show that the neonate requires medical attention but is not necessarily an indication that long-term problems will occur, particularly if there is an improvement for the five-minute test.

Prediction

Probability is used to express knowledge or belief that an event will occur or it has occurred. A prediction is a statement that tells what might happen in the future based upon the given information. Prediction methods are important in various fields, including medicine, physics, and finance. Mathematics can be used to develop predictions, which are based on a careful analysis of patterns and collected data. Apgar recognized the patterns related to a baby’s health signs and used them as a basis to make subsequent predictions. This example provides a clear idea of a credible prediction that was based on some form of empirical evidence. Thanks to this predictor approach, thousands of babies with special needs get the care they need immediately. Although it is not possible to make a 100% accurate prediction, predictions based on solid data and statistical analysis can increase the likelihood of accuracy.

Before 1952, the way to judge the condition of a newborn baby quickly and accurately shortly after birth was based on “breathing time” and “crying time.” Apgar’s accurate observations between 1949 and 1952 allowed the development of the automatic method of one-minute observation covering several signs easily. Thus, using some mathematical tools it is possible to transform qualitative values, such as physiological signs of babies, into quantity values—Apgar scores. By making predictions using Apgar scores it is also possible to perform the reverse: using the quantitative values (scores) to predict future qualitative values (health of babies).

Bibliography

Apel, M. A. Virginia Apgar: Innovative Female Physician and Inventor of the Apgar Score (Women Hall of Famers in Mathematics and Science). New York: Rosen Publishing Group, 2004.

Infarom Publishing. “Probability Theory Guide and Applications.” http://probability.infarom.ro.

National Institutes of Health: National Library of Medicine. “Changing the Face of Medicine, Dr. Virginia Apgar.” http://www.nlm.nih.gov/changingthefaceofmedicine/physicians/biography‗12.html.