Literature DB >> 28591756

The "Fetal Reserve Index": Re-Engineering the Interpretation and Responses to Fetal Heart Rate Patterns.

Robert D Eden1, Mark I Evans, Shara M Evans, Barry S Schifrin.   

Abstract

OBJECTIVE: Electronic fetal monitoring (EFM) correlates poorly with neonatal outcome. We present a new metric: the "Fetal Reserve Index" (FRI), formally incorporating EFM with maternal, obstetrical, fetal risk factors, and excessive uterine activity for assessment of risk for cerebral palsy (CP).
METHODS: We performed a retrospective, case-control series of 50 term CP cases with apparent intrapartum neurological injury and 200 controls. All were deemed neurologically normal on admission. We compared the FRI against ACOG Category (I-III) system and long-term outcome parameters against ACOG monograph (NEACP) requirements for labor-induced fetal neurological injury.
RESULTS: Abnormal FRI's identified 100% of CP cases and did so hours before injury. ACOG Category III identified only 44% and much later. Retrospective ACOG monograph criteria were found in at most 30% of intrapartum-acquired CP patients; only 27% had umbilical or neonatal pH <7.0.
CONCLUSIONS: In this initial, retrospective trial, an abnormal FRI identified all cases of labor-related neurological injury more reliably and earlier than Category III, which may allow fetal therapy by intrauterine resuscitation. The combination of traditional EFM with maternal, obstetrical, and fetal risk factors creating the FRI performed much better as a screening test than EFM alone. Our quantified screening system needs further evaluation in prospective trials.
© 2017 S. Karger AG, Basel.

Entities:  

Keywords:  ACOG monitoring classification system; Cerebral palsy; Electronic fetal monitoring; Fetal Reserve Index; Intrauterine resuscitation; Neonatal encephalopathy

Mesh:

Year:  2017        PMID: 28591756     DOI: 10.1159/000475927

Source DB:  PubMed          Journal:  Fetal Diagn Ther        ISSN: 1015-3837            Impact factor:   2.587


  8 in total

1.  The Fetal Reserve Index Significantly Outperforms ACOG Category System in Predicting Cord Blood Base Excess and pH: A Methodological Failure of the Category System.

Authors:  Mark I Evans; David W Britt; Robert D Eden; Paula Gallagher; Shara M Evans; Barry S Schifrin
Journal:  Reprod Sci       Date:  2019-03-04       Impact factor: 3.060

Review 2.  Resistance to Change.

Authors:  Mark I Evans; David W Britt
Journal:  Reprod Sci       Date:  2022-07-07       Impact factor: 2.924

3.  Comparison of the predictive ability for perinatal acidemia in neonates between the NICHD 3-tier FHR system combined with clinical risk factors and the fetal reserve index.

Authors:  Ninlapa Pruksanusak; Natthicha Chainarong; Siriwan Boripan; Alan Geater
Journal:  PLoS One       Date:  2022-10-20       Impact factor: 3.752

Review 4.  Changing Perspectives of Electronic Fetal Monitoring.

Authors:  Mark I Evans; David W Britt; Shara M Evans; Lawrence D Devoe
Journal:  Reprod Sci       Date:  2021-10-18       Impact factor: 2.924

5.  Relationship Between Deceleration Morphology and Phase Rectified Signal Averaging-Based Parameters During Labor.

Authors:  Massimo W Rivolta; Moira Barbieri; Tamara Stampalija; Roberto Sassi; Martin G Frasch
Journal:  Front Med (Lausanne)       Date:  2021-11-25

6.  Fetal heart rate evolution patterns in cerebral palsy associated with umbilical cord complications: a nationwide study.

Authors:  Junichi Hasegawa; Masahiro Nakao; Tomoaki Ikeda; Satoshi Toyokawa; Emi Jojima; Shoji Satoh; Kiyotake Ichizuka; Nanako Tamiya; Akihito Nakai; Keiya Fujimori; Tsugio Maeda; Satoru Takeda; Hideaki Suzuki; Shigeru Ueda; Mitsutoshi Iwashita; Tsuyomu Ikenoue
Journal:  BMC Pregnancy Childbirth       Date:  2022-03-03       Impact factor: 3.007

7.  Detection of Preventable Fetal Distress During Labor From Scanned Cardiotocogram Tracings Using Deep Learning.

Authors:  Martin G Frasch; Shadrian B Strong; David Nilosek; Joshua Leaverton; Barry S Schifrin
Journal:  Front Pediatr       Date:  2021-12-03       Impact factor: 3.418

8.  Machine Learning Algorithm to Predict Acidemia Using Electronic Fetal Monitoring Recording Parameters.

Authors:  Javier Esteban-Escaño; Berta Castán; Sergio Castán; Marta Chóliz-Ezquerro; César Asensio; Antonio R Laliena; Gerardo Sanz-Enguita; Gerardo Sanz; Luis Mariano Esteban; Ricardo Savirón
Journal:  Entropy (Basel)       Date:  2021-12-30       Impact factor: 2.524

  8 in total

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