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

Abstract

Introduction

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. Material and methods

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). Results

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). Conclusions

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.