Surgery – Diagnostic testing
Reexamination Certificate
1999-09-17
2001-07-03
Nasser, Robert L. (Department: 3736)
Surgery
Diagnostic testing
C600S304000, C128S920000
Reexamination Certificate
active
06254537
ABSTRACT:
The present invention relates to health care, and more particularly, to a method and system for monitoring fetal health and for predicting fetal outcomes based on data about the mother and the fetus.
BACKGROUND OF THE INVENTION
With the continuously escalating cost of health care, one significant issue is balancing the cost of health care versus the quality of health care. While some percentage of health care dollars are wasted on dubious or inappropriate care, it is also true that, even in the United States, there is still preventable disease and death that occurs. The question is how to assure the highest possible quality of care without incurring excessive costs.
One area of health care where the cost-quality debate rages is in the care of pregnant women, where the intent is to deliver healthy babies while preserving the health of the mother. Current national health objectives have targeted reductions in the following areas: infant deaths (before, during, and after birth); low birth weight; severe complications of pregnancy; and instances of severe mental retardation. Current obstetrical practices include: identifying risk factors; monitoring fetal heart rate and maternal uterine contractions (with a Doppler ultrasound device and a tocodynamometer, respectively); ultrasound measurements; amniocentesis; and other laboratory tests which were virtually unknown twenty years ago. Unfortunately, the use of these various monitoring and measurement methods have not significantly improved infant health. Currently-available methods for categorizing pregnancies as healthy or potentially at risk do not appear to be sufficiently accurate and reliable. In addition, since these various monitoring and measurement methods have been implemented, it is believed that the cesarean delivery rate has dramatically increased. While the cesarean delivery rate is now on a slight decline, national health objectives also include a targeted reduction in the cesarean delivery rate.
Much of antepartum care (occurring in physicians' offices from conception until labor) and all of intrapartum care (occurring in the hospital while the patient is in labor) is concerned with the early identification of potentially adverse situations and subsequent early intervention to avoid or minimize adverse events. Yet, there is no highly sensitive and specific technology to assess fetal well-being. A method that accurately and reliably categorizes pregnancies as healthy or potentially at risk is needed to improve perinatal outcomes while managing health care costs.
Typical intrapartum monitoring includes electronic fetal monitoring (EFM) which is often made up of a dual-track strip chart or display of both fetal heart rate (FHR) and uterine contractions (UC). Obstetricians currently determine when it is advisable to intervene (delivering the child by cesarean section as opposed to vaginally) by viewing the EFM data and relying upon their past training and experience. Because of the great uncertainty inherent in this approach, it is natural for obstetricians to seek to err on the side of a cesarean section, which is more likely to result in a safe delivery for a fetus, even though the cesarean section may not have been necessary; is much more costly; and has a potentially greater health impact on the mother. Thus, a much larger number of cesarean sections end up being performed than are necessary. For all of these reasons, a reliable predictor of fetal outcomes would be very useful in assisting obstetricians in determining when it is appropriate to intervene in the delivery process.
Others have proposed systems for automatically assessing fetal health based upon the fetal heart rate. An example of this is disclosed by Dawes, et al.,
Int. J. Biomed. Comput
., 25 (1990) pp. 287-294, “Criteria for the Design of Fetal Heart Rate Analysis Systems,” and Dawes, et al.,
Obstetrics & Gynecology
, Vol. 80, No. 4, October 1992, pp. 673-678, “Short-term Fetal Heart Rate Variation, Decelerations, and Umbilical Flow Velocity Waveforms Before Labor.” The Dawes articles suggest measuring the short-term time variations in the fetal heart rate and determining that the fetal health is in jeopardy when the variations fall below a predetermined threshold. It is believed that such approaches are not sufficiently robust. Furthermore they do not reflect and predict fetal outcomes with sufficient accuracy. It is believed that this lack of robustness is due in part to a lack of any type of artificial intelligence that would allow a system to adapt to and learn about new situations and an inability to accurately assess fetal health from noisy data.
It is against this background, and the desire to solve the problems of and improve on the prior art, that the above invention has been developed.
SUMMARY OF THE INVENTION
The present invention is based upon the belief that EFM data has previously been improperly or incompletely analyzed and interpreted in determining fetal outcomes. The present invention relies upon determining hidden relationships between known EFM data in different scales and known fetal outcomes in past cases for which adequate data are available, and then recognizing characteristics in new EFM data in a current patient that will allow the system to predict the fetal outcome based thereon.
Particularly, the present invention is related to a method of automatically providing an output representative of the health of a fetus carried by a mother, based on fetal and maternal data, with a computer-based predictor. The method includes receiving biographical data about the fetus and the mother and categorizing the fetal and maternal data combination as one of a plurality of types of cases based on the biographical data. The method also includes receiving physical data about the current condition of the fetus and the mother and processing the physical data to extract characteristic features. The method further includes selecting a subset of the characteristic features based on the type of case that the fetal and maternal data combination has been categorized as, and providing an output representative of the health of the fetus from the subset of characteristic features based on previously-developed fuzzy relationships between fetal and maternal data and fetal health.
The method may further include communicating the output to a user. The reported output may be representative of the current status of the fetus. The reported output may be representative of the health of the fetus at a birth that could occur at a predetermined time in the future. The predetermined time may be approximately twenty minutes after the output is reported. The method may further include developing fuzzy relationships between fetal and maternal data and fetal health based on data and known fetal health from past pregnancies and births. The method may further include using the reported fetal health to adjust and optimize the selecting operation. The method may further include using the reported fetal health to adjust and optimize the providing operation.
The present invention is also directed to a method of automatically reporting fetal health for a fetus carried by a mother, based on fetal and maternal data, with a computer-based assessor. The method includes receiving fetal and maternal data and processing the fetal and maternal data to generate and select an optimal set of fetal and maternal features. The method also includes applying the optimal selected set of fetal and maternal features to the assessor to assess and predict fetal health based on identified fuzzy relationships between fetal and maternal data and fetal health.
The method may further include a case-based reasoning processor that receives biographical data about the fetus and the mother and, using case-based reasoning, determines the optimal set of data features to be applied to the assessor. The optimal set of features may be selected based on a learning process that allows the assessor to categorize the fetal and maternal data as belonging to one of a plurality of types of cases and to select
Astorino Michael
Gibson Dunn & Crutcher, LLP
Nasser Robert L.
Rose Biomedical Development Corporation
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