Title: Deceleration Area and Deceleration Capacity: Poor
predictors of fetal acidaemia in human labour! The strengths of visual
versus computerised cardiotocography interpretation.
Re: Georgieva A, Lear CA, Westgate
JA, Kasai M, Miyagi E, Ikeda T, Gunn AJ, Bennet L. Deceleration area and
capacity during labour-like umbilical cord occlusions identify evolving
hypotension: a controlled study in fetal sheep. BJOG 2021;
https://doi.org/10.1111/1471-0528.16638.
Dear Editor,
It seems intuitive to birth-attendants that the bigger/frequent fetal
heart rate (FHR) decelerations over a longer period (bigger deceleration
area - DA) would lead to increasing fetal
acidaemia/hypotension/hypoxaemic injury. The animal study by Geogieva et
al 1 confirms this known correlation. Furthermore,
there is already much information available from well-designed studies
in human labour examining DA.2,3 These show that
correlation (even statistically-significant) does not entail clinically
useful positive/negative predictive values (PPV/NPV). These studies show
that the DA is unsatisfactory in clinical practice with further multiple
unresolved logistical difficulties.2,3 The
quoted1 study by Cahill et al mistakenly states that
their ‘DA cut-off’ requires five caesareans to prevent one case of
acidaemia; but with its PPV of 4%, the correct calculation is 25 to 1.
The “deceleration capacity (DC)” measures no capacity, hence a
misnomer. The cohort study of DC on 22,000 women1lacked statistically-significant improved acidaemia detection and the
important receiver-operating-characteristic (ROC) curves were missing.
Another large cohort study on 11,980 showed the area under curve (AUC)
for DC to be 0.66.4 This AUC reveals that if we want
to detect 90%, 80% or 50% of acidaemic babies, the same DC was shared
by 80%, 60%, 25% of normal babies respectively.4The AUCs for DA in human studies are very similar and disappointing for
clinical application.2,3
A lesson from wider experience in artificial intelligence (AI) implies
that a constricted-single-parameter approach (e.g. DC/DA) would be
insufficient for the complex intrapartum fetal monitoring. Computers
have been beating chess-grandmasters for 25 years; because chess offers
a “kind learning environment” with fixed rules, patterns repeating
exactingly, feedback extremely accurate and very rapid. In the “unkind
learning environments” devoid of rigid rules, single domains and reams
of perfect historical data; the AI and machine learning have been
disastrous. Cardiotocography (CTG)
requires integration of multiple
FHR parameters with
mother-fetus-labour-condition permutations. Intervention changes
outcome; hence, the feedback can be inaccurate/unreliable. Human
cognition assimilates these paradoxes. The greatest human strength is
the exact opposite of narrow specialisation of AI. It is the ability to
integrate broadly.
Research in computerised non-visual parameters is important but without
subverting the visual CTG interpretation which is indispensable in
foreseeable future. Computerised parameters/assistance could offer
helpful real-time warnings if programmed to emulate/complement the
visual pattern-recognition. Recently, some articles are promoting a
concept/philosophy that all FHR decelerations are due to
hypoxaemia1 which contradicts clinicians’ observations
and all international CTG guidelines. Clinicians are urged that
chemoreflex is an indefatigable guardian of hypoxaemic fetus,
consequently hypoxaemia per se does not matter – await fetal
decompensation - all decelerations are due to hypoxaemia anyway – hence
timing of decelerations is irrelevant (red herring) – DA is the
future.5 Obstetricians can contemplate whether optimal
thresholds of DA/DC relative to clinical-risk-factors, variable
labour/CTG durations and FHR-matrix can be derived
realistically/reliably from retrospective data1(cord-gases available on skewed smaller subgroups). Should
birth-attendants place all FHR decelerations into a single category,
relinquishing to computerisation/DA/DC? Notwithstanding, it seems
important to protect/improve the scientific visual CTG
pattern-recognition given the limitations, changeability and
“back-box” nature of AI.
Statement of interest: The author has no conflict of interest
to declare. Comments on limitations of AI are acknowledged to David
Epstein’s 2019 book “Range”.