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
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.

Identifiants

pubmed: 37890863
doi: 10.1111/aogs.14680
doi:

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).

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Auteurs

Susana Pereira (S)

Fetal Medicine Unit, The Royal London Hospital, Barts Health NHS Trust, London, UK.

Petra Bakker (P)

Department of Obstetrics and Gynecology, Amsterdam UMC, Amsterdam, The Netherlands.

Ahmed Zaima (A)

Department of Obstetrics and Gynaecology, Kingston Hospital NHS Foundation Trust, London, UK.

Tullio Ghi (T)

Department of Medicine and Surgery, University of Parma, Parma, Italy.

Jörg Kessler (J)

Department of Obstetrics and Gynecology, Haukeland University Hospital, Bergen, and Department of Clinical Science, University of Bergen, Bergen, Norway.

Susanna Timonen (S)

Department of Obstetrics and Gynecology, Turku University Hospital, Turku, Finland.

Christoph Vayssière (C)

Department of Obstetrics and Gynecology, Paule de Viguier Hospital, Toulouse III University, Toulouse, France.

Katrin Löser (K)

Department of Obstetrics and Gynecology, South Jutland Hospital Aabenraa Campus, Aabenraa, Denmark.

Kaisa Holmberg (K)

Department of Obstetrics and Gynecology, Turku University Hospital, Turku, Finland.

Yves Jacquemyn (Y)

Department of Obstetrics and Gynecology, Antwerp University Hospital, Edegem, and Global Health Institute, Antwerp University, Antwerp, Belgium.

Edwin Chandraharan (E)

Global Academy of Medical Education & Training, London, UK.

David Wertheim (D)

School of Computing and Information Systems, Faculty of Science, Engineering and Computing, Kingston University, London, UK.

Per Olofsson (P)

Institution of Clinical Sciences Malmö, Lund University, Malmö, Sweden.

Classifications MeSH