Analytics Joins New Fight Against Cerebral Palsy

Can analyzing fetal heart rate and uterine contraction patterns during labor help detect a fetus in distress and at risk for neurological disorders?


Can analyzing fetal heart rate and uterine contraction patterns during labor help detect a fetus in distress and at risk for neurological disorders?

Finding out is the goal of a new study that the University of California at San Francisco and Kaiser Permanente will conduct, using perinatal technology from vendor PeriGen, which sells a fetal monitoring system and pattern recognition/analytics software.

The study will focus on neonatal encephalopathy, which is characterized by disturbed neurologic function in the earliest days of life and can lead to various lifelong impairments, particularly cerebral palsy.

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Using electronic fetal monitoring tracings captured several years ago, pattern recognition software will analyze the tracings to assess how a fetus is tolerating the birth and to look for different combinations of patterns to correlate if any associations with cerebral palsy may be present.

Cerebral palsy is rather rare, occurring in about one of 1,000 births, but its outcomes are very adverse, says Emily Hamilton, M.D., a veteran OB-GYN practitioner and senior vice president of clinical research at PeriGen. Among other researchers, Hamilton will contribute to study design, data analysis and interpretation.

Using a large birth cohort from Kaiser Permanente in Northern California, researchers expect to analyze 300 to 500 abnormal events distilled from up to 50,000 live births, then will compare findings with about 5,000 normal births to begin to understand potential casual factors. Yvonne Wu, M.D., a professor of clinical neurology and pediatrics at UCSF, who has spent 14 years researching cerebral palsy and neonatal encephalopathy, will lead the project.

The first phase of the project will take about year and depending on results researchers likely will ask additional questions and extend the analysis, Hamilton says. UCSF is funding the project through its Resource Allocation Program.

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