Is the fetus fit for labor? Introducing fast-and-frugal trees (FFTrees) to simplify triage of women for STAN monitoring: An interobserver agreement comparison with traditional classification.
STAN
cardiotocography
classification
clinical guidelines
fast-and-frugal tree
fetal monitoring
interobserver agreement
labor
midwifery
obstetrics
Journal
Acta obstetricia et gynecologica Scandinavica
ISSN: 1600-0412
Titre abrégé: Acta Obstet Gynecol Scand
Pays: United States
ID NLM: 0370343
Informations de publication
Date de publication:
27 Oct 2023
27 Oct 2023
Historique:
revised:
20
08
2023
received:
17
02
2023
accepted:
03
09
2023
medline:
28
10
2023
pubmed:
28
10
2023
entrez:
27
10
2023
Statut:
aheadofprint
Résumé
It is a shortcoming of traditional cardiotocography (CTG) classification table formats that CTG traces are frequently classified differently by different users, resulting in poor interobserver agreements. A fast-and-frugal tree (FFTree) flow chart may help provide better concordance because it is straightforward and has clearly structured binary questions with understandable "yes" or "no" responses. The initial triage to determine whether a fetus is suitable for labor when utilizing fetal ECG ST analysis (STAN) is very important, since a fetus with restricted capacity to respond to hypoxic stress may not generate STAN events and therefore may become falsely negative. This study aimed to compare physiology-focused FFTree CTG interpretation with FIGO classification for assessing the suitability for STAN monitoring. A retrospective study of 36 CTG traces with a high proportion of adverse outcomes (17/36) selected from a European multicenter study database. Eight experienced European obstetricians evaluated the initial 40 minutes of the CTG recordings and judged whether STAN was a suitable fetal surveillance method and whether intervention was indicated. The experts rated the CTGs using the FFTree and FIGO classifications at least 6 weeks apart. Interobserver agreements were calculated using proportions of agreement and Fleiss' kappa (κ). The proportions of agreement for "not suitable for STAN" were for FIGO 47% (95% confidence interval [CI] 42%-52%) and for FFTree 60% (95% CI 56-64), ie a significant difference; the corresponding figures for "yes, suitable" were 74% (95% CI 71-77) and 70% (95% CI 67-74). For "intervention needed" the figures were 52% (95% CI 47-56) vs 58% (95% CI 54-62) and for "expectant management" 74% (95% CI 71-77) vs 72% (95% CI 69-75). Fleiss' κ agreement on "suitability for STAN" was 0.50 (95% CI 0.44-0.56) for the FIGO classification and 0.57 (95% CI 0.51-0.63) for the FFTree classification; the corresponding figures for "intervention or expectancy" were 0.53 (95% CI 0.47-0.59) and 0.57 (95% CI 0.51-0.63). The proportion of agreement among expert obstetricians using the FFTree physiological approach was significantly higher compared with the traditional FIGO classification system in rejecting cases not suitable for STAN monitoring. That might be of importance to avoid false negative STAN recordings. Other agreement figures were similar. It remains to be shown whether the FFTree simplicity will benefit less experienced users and how it will work in real-world clinical scenarios.
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Informations de copyright
© 2023 The Authors. Acta Obstetricia et Gynecologica Scandinavica published by John Wiley & Sons Ltd on behalf of Nordic Federation of Societies of Obstetrics and Gynecology (NFOG).
Références
Amer-Wåhlin I, Arulkumaran S, Hagberg H, Marsál K, Visser G. Fetal electrocardiogram: ST waveform analysis in intrapartum surveillance. BJOG. 2007;114:1191-1193.
Murray DM, O’ Riordan MN, Horgan R, Boylan G, Higgins JR, Ryan CA. Fetal heart rate patterns in neonatal hypoxic-ischemic encephalopathy: relationship with early cerebral activity and neurodevelopmental outcome. Am J Perinatol. 2009;26:605-612.
Pereira S, Patel R, Zaima A, et al. Physiological CTG categorization in types of hypoxia compared with MRI and neurodevelopmental outcome in infants with HIE. J Matern Fetal Neonatal Med. 2022;13:1-9.
Ayres-de-Campos D, Spong CY, Chandraharan E. FIGO intrapartum fetal monitoring expert consensus panel. FIGO consensus guidelines on intrapartum fetal monitoring: cardiotocography. Int J Gynecol Obstet. 2015;131:13-24.
Ekengård F, Cardell M, Herbst A. Impaired validity of the new FIGO and Swedish CTG classification templates to identify fetal acidosis in the first stage of labor. J Matern Fetal Neonatal Med. 2022;35:4853-4860.
Olofsson P, Ekengård F, Herbst A. Time to reconsider: have the 2015 FIGO and 2017 Swedish intrapartum cardiotocogram classifications led us from Charybdis to Scylla? Acta Obstet Gynecol Scand. 2021;100:1549-1556.
Jonsson M, Söderling J, Ladfors L, et al. Implementation of a revised classification for intrapartum fetal heart rate monitoring and association to birth outcome: a national cohort study. Acta Obstet Gynecol Scand. 2022;101:183-192.
FIGO Subcommittee on Standards in Perinatal Medicine. Guidelines for the use of fetal monitoring. Int J Gynecol Obstet. 1987;25:159-167.
Intrapartum care for healthy women and babies. Clinical guideline [CG190]. Published: 03 December 2014. Last updated: 14 December 2022. nice.org.uk
Pinas A, Chandraharan E. Continuous cardiotocography during labour: analysis, classification and management. Best Pract Res Clin Obstet Gynaecol. 2016;30:33-47.
Rosén KG, Håkan Norén H, Carlsson A. FHR patterns that become significant in connection with ST waveform changes and metabolic acidosis at birth. J Matern Fetal Neonatal Med. 2019;32:3288-3293.
Wang Y, Luan S, Gigerenzer G. Modeling fast-and-frugal heuristics. Psych J. 2022;11:600-611.
Katsikopoulos KV, Şimşek Ö, Buckmann M, Gigerenzer G. Classification in the Wild. The Science and Art of Transparent Decision Making. The MIT Press; 2020.
Luttkus AK, Norén H, Stupin JH, et al. Fetal scalp pH and ST analysis of the fetal ECG as an adjunct to CTG. A multi-center, observational study. J Perinat Med. 2004;32:486-494.
Gracia-Perez-Bonfils A, Vigneswaran K, Cuadras D, Chandraharan E. Does the saltatory pattern on cardiotocograph (CTG) trace really exist? The ZigZag pattern as an alternative definition and its correlation with perinatal outcomes. J Matern Fetal Neonatal Med. 2021;34:3537-3545.
Tarvonen M, Hovi P, Sainio S, Vuorela P, Anderson S, Teramo K. Factors associated with intrapartum ZigZag pattern of fetal heart rate: a retrospective one-year cohort study of 5150 singleton childbirths. Eur J Obstet Gynecol Reprod Biol. 2021;258:118-125.
Grant JM. The fetal heart rate trace is normal, isn't it? Observer Agreement of Categorical Assessments. Lancet. 1991;26(337):215-218.
Landis JR, Koch GG. The measurement of observer agreement for categorical data. Biometrics. 1977;33:159-174.
Sim J, Wright CC. The kappa statistics in reliability studies: use, interpretation, and sample size requirements. Phys Ther. 2005;85:257-268.
Ayres-de-Campos D, Bernardes J. Twenty-five years after the FIGO guidelines for the use of fetal monitoring: time for a simplified approach? Int J Gynaecol Obstet. 2010;110:1-6.
Trimbos JB, Keirse MJ. Observer variability in assessment of antepartum cardiotocograms. BJOG. 1978;85:900-906.
Ekengård F, Cardell M, Herbst A. Low sensitivity of the new FIGO classification system for electronic fetal monitoring to identify fetal acidosis in the second stage of labor. Eur J Obstet Gynecol Reprod Biol X. 2020;9:100120.
Spilka J, Chudáček V, Janků P, et al. Analysis of obstetricians’ decision making on CTG recordings. J Biomed Inform. 2014;51:72-79.
Neumann N, Böckenholt U, Sinha A. A meta-analysis of extremeness aversion. J Consumer Psychol. 2016;26:193-212.