Literature DB >> 32735438

Intrapartum Fetal Monitoring.

James J Arnold1, Breanna L Gawrys2.   

Abstract

Continuous electronic fetal monitoring was developed to screen for signs of hypoxic-ischemic encephalopathy, cerebral palsy, and impending fetal death during labor. Because these events have a low prevalence, continuous electronic fetal monitoring has a false-positive rate of 99%. The widespread use of continuous electronic fetal monitoring has increased operative and cesarean delivery rates without improved neonatal outcomes, but its use is appropriate in high-risk labor. Structured intermittent auscultation is an underused form of fetal monitoring; when employed during low-risk labor, it can lower rates of operative and cesarean deliveries with neonatal outcomes similar to those of continuous electronic fetal monitoring. However, structured intermittent auscultation remains difficult to implement because of barriers in nurse staffing and physician oversight. The National Institute of Child Health and Human Development terminology is used when reviewing continuous electronic fetal monitoring and delineates fetal risk by three categories. Category I tracings reflect a lack of fetal acidosis and do not require intervention. Category II tracings are indeterminate, are present in the majority of laboring patients, and can encompass monitoring predictive of clinically normal to rapidly developing acidosis. Presence of moderate fetal heart rate variability and accelerations with absence of recurrent pathologic decelerations provides reassurance that acidosis is not present. Category II tracing abnormalities can be addressed by treating reversible causes and providing intrauterine resuscitation, which includes stopping uterine-stimulating agents, fetal scalp stimulation and/or maternal repositioning, intravenous fluids, or oxygen. Recurrent deep variable decelerations can be corrected with amnioinfusion. Category III tracings are highly concerning for fetal acidosis, and delivery should be expedited if immediate interventions do not improve the tracing.

Entities:  

Year:  2020        PMID: 32735438

Source DB:  PubMed          Journal:  Am Fam Physician        ISSN: 0002-838X            Impact factor:   3.292


  1 in total

1.  Using Decision Tree Classification and AdaBoost Classification to Build the Abnormal Data Monitoring System of Financial Accounting in Colleges and Universities.

Authors:  Yan Jiang
Journal:  Comput Intell Neurosci       Date:  2022-09-16
  1 in total

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