App may improve identification of women’s risk factors and remind women of aspirin recommendation
FRIDAY, Nov. 5, 2021 (HealthDay News) — There is a need to improve identification of women at risk for preeclampsia, as well as in communication between patients and practitioners about the use of low-dose aspirin (LDASA) treatment, according to a study published online Oct. 29 in JAMA Network Open.
Tamar Krishnamurti, Ph.D., from University of Pittsburgh, and colleagues assessed the potential underuse of LDASA and reasons for underuse. The analysis included data from the MyHealthyPregnancy app and patient health records from 2,567 pregnancies among 2,563 women in a single health care system.
The researchers found that 12.3 percent of pregnancies had at least one factor associated with a high risk for preeclampsia, while 40.9 pregnancies had two or more factors associated with moderate risk. Among 1,015 pregnancies in which women answered voluntary questions about aspirin use, 12.2 percent met at least one criterion for highest risk for preeclampsia. For nearly half of these pregnancies (46 percent), the patient indicated that their practitioner recommended LDASA. Examination of medical records showed that nearly three-quarters of these pregnancies (72.6 percent) had evidence of an LDASA recommendation, but 36.7 percent of women were unaware of it. Of 441 participants with two or more moderate-risk criteria with data related to aspirin use, only 3 to 6 percent reported that they had been advised to take low-dose aspirin.
“The finding that many patients didn’t recall an aspirin recommendation highlights that there is huge opportunity to improve information-sharing,” Krishnamurti said in a statement. “We had incredibly high engagement with participant responses to voluntary questions through the MyHealthyPregnancy app, so there is real opportunity to leverage this engagement to bridge communication gaps between providers and patients.”
Several authors disclosed financial ties to Naima Health, which has a patent pending for a structured medical data classification system for monitoring and remediating treatment risks.
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