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Muestreo intraparto del lactato del cuero cabelludo fetal para la evaluación fetal en presencia de un trazado no tranquilizador de la frecuencia cardíaca fetal

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Antecedentes

Se puede considerar realizar el muestreo sanguíneo fetal para la estimación del lactato después de la identificación de un patrón de frecuencia cardíaca fetal anormal o no tranquilizador. El volumen más pequeño de sangre requerido para esta prueba, en comparación con la estimación más tradicional del pH, puede mejorar las tasas de muestreo. Para el uso apropiado de esta práctica es necesario realizar una revisión sistemática de su seguridad y efectividad clínicas antes de la introducción generalizada.

Objetivos

Evaluar la efectividad y los riesgos del muestreo del lactato del cuero cabelludo fetal en la evaluación del bienestar fetal durante el trabajo de parto, en comparación con ninguna prueba o una prueba alternativa.

Métodos de búsqueda

Se hicieron búsquedas en el registro de ensayos del Grupo Cochrane de Embarazo y Parto (Cochrane Pregnancy and Childbirth Group) (31 de enero 2015).

Criterios de selección

Todos los ensayos aleatorizados y cuasialeatorizados publicados y no publicados que compararon la prueba del lactato del cuero cabelludo fetal con ninguna prueba o una prueba alternativa para evaluar el estado fetal en presencia de una cardiotocografía no tranquilizadora durante el trabajo de parto.

Obtención y análisis de los datos

Se utilizaron los procedimientos metodológicos estándar del Grupo Cochrane de Embarazo y Parto (Cochrane Pregnancy and Childbirth Group). Dos autores de la revisión de forma independiente evaluaron los resultados.

Resultados principales

La búsqueda identificó dos ensayos controlados aleatorizados (ECA) completados y dos ensayos en curso. Los dos ECA publicados consideraron los resultados de 3348 pares madre‐recién nacidos asignados a la estimación del lactato o del pH de las muestras de sangre fetal cuando estaban clínicamente indicados en el trabajo de parto. En general, los ECA publicados tuvieron riesgo de sesgo bajo o incierto. Hubo alto riesgo de sesgo de realización, ya que no habría sido factible el cegamiento de los médicos o las participantes.

No se encontraron diferencias estadísticamente significativas entre los grupos para la encefalopatía neonatal (riesgo relativo [RR] 1,00; intervalo de confianza [IC] del 95%: 0,32 a 3,09; un estudio, 2992 recién nacidos), ni para la muerte. Ningún estudio informó sobre convulsiones neonatales. Se había planificado informar la muerte junto con otras morbilidades, por ejemplo, la encefalopatía neonatal; sin embargo, no hubo datos disponibles en un formato adecuado, por lo que la muerte por anomalías congénitas se consideró por separado. Las tres muertes neonatales informadas ocurrieron en recién nacidos con hernias diafragmáticas (n = 2) o fibrosis cardíaca congénita (n = 1). Los tres recién nacidos se habían asignado al azar al grupo de pH y no presentaban acidemia al nacer.

No hubo diferencias estadísticamente significativas para cualquiera de los resultados secundarios fetales, neonatales o infantiles preespecificados con datos disponibles. Lo anterior incluyó la puntuación de Apgar baja a los cinco minutos (RR 1,13; IC del 95%: 0,76 a 1,68; dos estudios, 3319 recién nacidos) y el ingreso en las unidades de cuidados intensivos neonatales (RR 1,02; IC del 95%: 0,83 a 1,25; un estudio, 2992 recién nacidos), o la acidemia metabólica (RR 0,91; IC del 95%: 0,60 a 1,36; un estudio, 2675 recién nacidos) considerada en los estudios, ya sea en general o cuando hubo datos disponibles para los recién nacidos a los que se les había tomado una muestra de sangre fetal dentro de los 60 minutos del parto.

A proporciones similares de fetos se les realizaron otras pruebas para evaluar de forma adicional el bienestar durante el trabajo de parto, incluido el pH del cuero cabelludo en el grupo de lactato o lactato del cuero cabelludo en el grupo de pH (RR 0,22; IC del 95%: 0,04 a 1,30; dos estudios 3333 recién nacidos; Tau² 1,00; I² = 58%). Los intentos de tomar una muestra de sangre fetal para calcular el lactato y el pH tuvieron éxito en el 98,7% y el 79,4% de los procedimientos, respectivamente, en el único estudio que informó de este resultado.

No hubo diferencias significativas entre los grupos en cuanto a la modalidad de parto o el parto quirúrgico para un estado fetal no tranquilizador, ya sea para todas las mujeres, o dentro del grupo en el que la muestra de sangre fetal se había tomado dentro de los 60 minutos del parto (por ejemplo, cesárea, todas las participantes, RR 1,09, IC del 95%: 0,97 a 1,22, dos estudios, 3319 mujeres; parto quirúrgico por un estado fetal no tranquilizador, todas las participantes, RR 1,02; IC del 95%: 0,93 a 1,11; un estudio, 2992 mujeres).

Ninguno de los dos estudios informó sobre los efectos adversos de las laceraciones del cuero cabelludo del feto o la ansiedad de la madre.

Conclusiones de los autores

Cuando se indica una prueba adicional para evaluar el bienestar fetal durante el trabajo de parto, es más probable que la estimación del lactato sanguíneo del cuero cabelludo fetal se realice con éxito, comparada con la estimación del pH. Los estudios adicionales pueden considerar un análisis de subgrupos por edad gestacional, estadio del trabajo de parto y muestreo durante el período expulsivo del trabajo de parto. Además, se esperan los resultados de los estudios en curso que comparan la asignación a ninguna muestra de sangre fetal con la toma de muestras de lactato y abordan resultados neonatales a más largo plazo, la satisfacción materna con la monitorización fetal intraparto y un análisis económico.

PICO

Population
Intervention
Comparison
Outcome

El uso y la enseñanza del modelo PICO están muy extendidos en el ámbito de la atención sanitaria basada en la evidencia para formular preguntas y estrategias de búsqueda y para caracterizar estudios o metanálisis clínicos. PICO son las siglas en inglés de cuatro posibles componentes de una pregunta de investigación: paciente, población o problema; intervención; comparación; desenlace (outcome).

Para saber más sobre el uso del modelo PICO, puede consultar el Manual Cochrane.

Resumen en términos sencillos

Uso del lactato sanguíneo del cuero cabelludo fetal para la evaluación del bienestar fetal durante el trabajo de parto

Una frecuencia cardíaca fetal anormal o no tranquilizadora durante el trabajo de parto puede ser causada por la incapacidad del recién nacido de adaptarse a la disminución del suministro de oxígeno durante el parto. El suministro de oxígeno inadecuado puede provocar la aparición de acidosis (niveles bajos del pH) y un aumento del lactato en la sangre. Después de la rotura de las membranas amnióticas y de la dilatación del cuello hasta alrededor de 3 cm, es posible medir los niveles del lactato (o del pH) en una muestra de sangre tomada del cuero cabelludo del feto. Para la prueba del lactato se necesita una cantidad mucho más pequeña de sangre que para medir el pH. Esta revisión identificó dos estudios con 3348 pares madre‐recién nacido, que compararon la prueba del lactato y del pH durante el trabajo de parto. La prueba del lactato tuvo mayores probabilidades de ser exitosa que la prueba del pH, pero no hubo diferencias en cuanto a los resultados del recién nacido, incluido el número de recién nacidos con puntuaciones de Apgar bajas, pH bajo de la sangre del cordón o ingresos en la unidad de cuidados intensivos neonatales. No hubo diferencias en cuanto al número de mujeres a las que se les realizó cesárea o tuvieron un parto con fórceps o ventosa entre los dos grupos. Se concluye que es más probable que la prueba del lactato durante el trabajo de parto se realice con éxito que la prueba del pH.

Authors' conclusions

Implications for practice

When it is clinically determined that further testing is warranted to assess fetal well‐being in labour, fetal scalp blood lactate estimation is more likely to be successfully undertaken, with fewer scalp incisions and results more readily available than for pH estimation. The action cut‐off values for lactate levels need to be made with consideration of the lactate meter being used. There is no available evidence to determine the effectiveness of fetal scalp blood lactate estimation compared with no sampling, on clinical outcomes.

Implications for research

Future studies may consider the outcomes within the subgroups of gestational age, stage of labour, serial fetal scalp lactate measurements and in the presence of other tests of fetal well‐being. Large, well‐conducted studies with sufficient examples of clearly defined intrapartum asphyxia may provide confidence with which fetal scalp lactate estimation can predict intrapartum fetal well‐being and inform a systematic review of diagnostic accuracy. Such trials would also compare the effectiveness of fetal scalp blood lactate estimation versus no blood sampling, to reduce clinically important outcomes such as those presented in this review.

Additionally, future studies may address longer‐term neonatal outcomes and maternal satisfaction or anxiety with fetal monitoring during labour, as well as conduct of an economic analysis. Outcome assessor blinding would be appropriate in future trials.

Background

Description of the condition

Cardiotocography (CTG), often referred to as electronic fetal monitoring (EFM), records the fetal heart rate and uterine contractions to paper or computer, or both. It was introduced in the 1960s with the aim of improving neonatal outcomes by improving intrapartum fetal surveillance. External monitoring of the fetal heart is achieved using an ultrasound transducer placed on the mother's abdomen, over the region of the baby's heart. Once the amniotic membranes have ruptured, it is also possible to monitor the fetal heart rate by attaching an electrode to the baby's scalp. Fetal heart rate patterns can be classified in a number of ways. These include: (i) normal/reassuring; and (ii) when meeting the criteria for normal or reassuring, a range of terms including non‐reassuring, suspicious, atypical, abnormal, pathological or ominous. These classifications are based on the fetal heart rate, its variability and the presence of accelerations or decelerations, compared with the occurrence of uterine contractions.

There is not universal agreement on the definition of these patterns (Di Tommaso 2013). Several groups have published guidelines in an attempt to improve uniformity of interpretation. Examples include the Royal Australian and New Zealand College of Obstetricians and Gynaecologists (RANZCOG 2014), Royal College of Obstetricians and Gynaecologists (NCCWCH 2007), Society of Obstetricians and Gynaecologists of Canada (Liston 2007) and the American College of Obstetricians and Gynecologists (ACOG 2009). Consistent with some of these guidelines and with the clinical practice of the review team, we will generally refer to 'non‐reassuring fetal status/CTG/patterns' in this review, rather than the term 'fetal distress' which is sometimes used inappropriately to refer to CTGs that do not meet normal/reassuring/abnormal status (ACOG 2005). Reassuring patterns require no specific action. Non‐reassuring patterns occur in approximately 15% to 19% of labours (East 2006a; Umstad 1993) and may prompt clinical actions ranging from simple manoeuvres, such as a change of maternal position, improved maternal hydration, through to expedited birth of the baby (by caesarean section, forceps or vacuum), with the aim of preventing or minimising hypoxia in the fetus.

Non‐reassuring CTG patterns may reflect the ability of the individual fetus to adapt to decreases in oxygen supply. Inadequate oxygen supply results in anaerobic metabolism of glucose, which leads to metabolic acidosis. Anaerobic glycolysis results in acidosis through the production of pyruvate, with some conversion to lactate. Low pH is a combined measure of both metabolic acidosis (including base deficit) and the more labile component, respiratory acidosis. The differences in individual fetal responses to a decrease in oxygen (and therefore differences in heart rate changes) mean that the positive predictive value of CTG for adverse outcome is low and the negative predictive value high (Bogdanovic 2014; Holzmann 2015; Nonnenmacher 2010), although this has the potential for improvement with computerised interpretation of CTGs (Georgieva 2014; Strachan 2001). This means that a normal CTG usually indicates reassuring fetal status, while a non‐reassuring CTG does not necessarily equate with 'fetal distress'. These features, combined with marked inter‐observer variation in CTG interpretation by midwives (Devane 2005) and doctors (Palomaki 2006), result in variable but inappropriately high operative birth rates for non‐reassuring fetal status in many hospitals.

Once a non‐reassuring fetal heart rate pattern is identified during labour, a number of additional assessments of fetal well‐being may be considered. Some clinicians may also consider these in the presence of an ominous pattern, rather than proceeding with immediate delivery. These tests aim to assist the clinician to determine which baby may benefit from expedited birth and for which baby labour may be continued safely. Answers are rarely clear cut and the pros and cons of further tests and potential consequences need to be discussed with the mother. These tests do not replace the CTG, but complement it. Cochrane reviews have examined the safety and efficacy of several additional testing options, including fetal pulse oximetry (East 2014), fetal electrocardiography (ECG) for changes in the ST‐segment or PR interval patterns (Neilson 2013), vibroacoustic stimulation (sound and vibration) (East 2013) and near‐infrared spectroscopy (a measure of blood oxygen levels) (Mozurkewich 2000). The addition of fetal scalp blood sampling (FBS) for pH estimation to standard EFM may reduce the caesarean section rate, although the odds of having a caesarean birth are still increased compared to intermittent auscultation of the fetal heart (Alfirevic 2013).

Description of the intervention

Following identification of one abnormal or two non‐reassuring features on the CTG, the Royal College of Obstetricians and Gynaecologists recommends FBS for pH estimation, with a published action algorithm based on the result (NCCWCH 2007). Collection of a small blood sample from the fetal scalp (a fetal blood sample) for blood gas analysis has been practiced since the 1960s (Saling 1967). It is contra‐indicated when the mother is known to have HIV or hepatitis, or where there is suspicion of a bleeding tendency in the fetus (Maiques 1999; Pachydakis 2006). Following rupture of the amniotic membranes and at cervical dilatation greater than or equal to approximately 3 cm, an amnioscope is placed vaginally to allow adequate visualisation of the fetal head. A small sample of blood is then taken from the fetal scalp.

This procedure may be uncomfortable and intrusive for the mother, is invasive to the baby and expensive. Rare complications include infection and haemorrhage (Jaiyesimi 1990; Maiques 1999; Schaap 2011). Traditionally, such testing has required approximately 30 to 50 microlitres of blood, which is often difficult to obtain. Even when the clinician is able to collect this quantity of blood, samples are frequently rejected by the testing equipment due to contamination with air or amniotic fluid. Some equipment can also analyse for other components, such as lactate. Fetal lactate testing equipment requiring a much smaller blood volume (as little as 5 microlitres) is now available (Wiberg‐Itzel 2008).

How the intervention might work

Fetal scalp blood samples of lactate taken within 60 minutes of birth correlate well with umbilical arterial and venous lactate measured following delivery (Bowler 2014; Kruger 1998). Umbilical arterial (UA) lactate values correlate well with UA pH and base deficit values (Kruger 1998; Ramanah 2005). Fetal scalp lactate values correlate significantly with cardiotocography patterns, scalp pH, UA pH, lactate and base deficit, but not with Apgar scores (Holzmann 2015; Ramanah 2010).

Kruger 1999 retrospectively examined the predictive values of simultaneous fetal scalp blood estimation of lactate and pH (n = 326) for Apgar scores, UA pH, UA base deficit and neonatal encephalopathy. Cut‐off values were the 75th percentile for lactate (4.8 mmol/L) and the 25th percentile for pH (7.20). The area under the receiver operator curve was significantly larger for lactate than for pH in predicting neonatal encephalopathy and Apgar score less than four at five minutes (Kruger 1999). Ramanah 2010 prospectively examined the predictive value and feasibility of fetal scalp lactate microsampling in the management of non‐reassuring fetal status during labour (n = 7617). Using receiver operating characteristic curves, a scalp lactate cut‐off value of 5 mmol/L was the most predictive for neonatal acidosis (Ramanah 2010). A retrospective study of 229 scalp lactate measurements reported a positive predictive value of 5% and negative predictive value of 98% of the prediction of an umbilical arterial pH measurement of ≤ 7.10 (Bowler 2014). These findings and evidence from animal studies of the effects of lactate on brain tissue (Engidawork 1997; Myers 1981) suggest that lactate estimation may be a better predictor of severe neonatal morbidity than pH.

Why it is important to do this review

The emerging use of fetal scalp blood sampling for lactate estimation requires systematic evaluation prior to becoming widespread, to ensure the appropriate use of this test in clinical practice (Mulrow 1994). Although many published guidelines of fetal monitoring do not include a recommendation for lactate estimation (including those published by ACOG 2009; Liston 2007; NCCWCH 2007), this trend is changing in some local and national guidelines (for example, Monash Health 2014; RANZCOG 2014; The Women's Hospital 2014).

About this review and its update

The original review (East 2010) and this 2015 update are primarily concerned with the use of fetal scalp lactate sampling in clinical practice. Several of the outcomes of interest would also benefit from a formal review of diagnostic accuracy. However, that is well beyond the scope of this clinical review. Such outcomes include: (i) those that demonstrate the ability of fetal scalp lactate sampling to predict which fetuses are hypoxic or acidaemic, measured after the birth from umbilical arterial cord blood, including pH less than 7.00 (Sehdev 1997) or less than 7.10 (Arikan 2000), lactate (White 2010) and base deficit greater than or equal to 12 mmol/L; (ii) clinical outcomes, including Apgar scores less than seven at five minutes (MacLennan 1999; Sehdev 1997), abnormal neurological status of the baby, possibly caused by inadequate supply of oxygen or blood (neonatal encephalopathy) or long‐term infant disability, or both. Other outcomes of interest may include the success rate of fetal blood sampling for lactate measurement, the volume of blood required for lactate evaluation and the number of fetal scalp lacerations, including those which became infected or haemorrhaged.

Interventions resulting from additional tests of fetal well‐being during labour are also important. For example, overall modes of birth following different forms of monitoring would usually be included in any analysis of this nature. However, it is important to also record specific interventions, such as operative birth (vacuum, forceps and caesarean section) performed for the indication of non‐reassuring fetal status, since assessment of fetal well‐being is the purpose of fetal lactate measurement.

Operative birth for non‐reassuring fetal status has immediate resource implications and long‐term effects. For example, the healthcare facility needs to make provision for an urgent caesarean section. The mother may have an improved likelihood of a vaginal birth in a subsequent pregnancy if the index caesarean was performed for non‐reassuring fetal status rather than for another reason such as dystocia (Shipp 2000). Women's satisfaction with differing forms of fetal monitoring is important in determining the provision of appropriate maternity services (East 2006b). A cost‐effectiveness analysis of the additional costs of fetal scalp lactate sampling, compared with the potential effect of reducing operative birth rates, is also an important consideration (East 2006c).

Objectives

To evaluate the effectiveness and risks of fetal scalp lactate sampling in the assessment of fetal well‐being during labour, compared with no testing or alternative additional testing (pH, fetal pulse oximetry, etc) for women exhibiting a non‐reassuring cardiotocograph trace.

A secondary objective of the review was to determine whether the effectiveness and risks of intrapartum fetal scalp lactate sampling were influenced by the following factors:

  • stage of labour;

  • gestation less than 37 completed weeks, greater than or equal to 37 completed weeks;

  • additional tests performed to confirm the presence or absence of fetal acidaemia during labour.

Methods

Criteria for considering studies for this review

Types of studies

All published and unpublished randomised and quasi‐randomised trials that compared fetal scalp lactate testing with no testing or alternative additional tests (for example, pH, fetal pulse oximetry) to evaluate fetal status in the presence of a non‐reassuring cardiotocograph (CTG) during labour.

Types of participants

Women in labour with a non‐reassuring fetal heart rate trace who would qualify for fetal scalp blood testing by standard birthing suite protocols.

Types of interventions

Fetal scalp blood sampling for lactate estimation versus no sampling or alternative additional tests of fetal well‐being (for example, pH, fetal pulse oximetry).

Types of outcome measures

Primary outcomes

(1) Neonatal seizures
(2) Neonatal encephalopathy
(3) Death or neonatal encephalopathy, or both
(4) Death or neonatal seizures, or both
(5) Death or long‐term infant disability, or both

Secondary outcomes
Secondary outcomes: fetal/neonatal/infant

(6) Apgar scores less than seven at five minutes
(7) Apgar scores less than four at five minutes
(8) Umbilical arterial pH less than 7.00
(9) Umbilical arterial pH less than 7.10
(10) Umbilical arterial base deficit greater than 12
(11) Umbilical arterial lactate
(12) Admission to neonatal intensive care unit
(13) Meconium‐stained amniotic fluid
(14) Neonatal length of hospital stay
(15) Long‐term infant disability
(16) Composite outcome: number of additional tests performed per fetus to evaluate fetal well‐being (for example, fetal oxygen saturation monitoring, fetal electrocardiogram waveform analysis, fetal pH estimation)
(17) Fetal oxygen saturation values less than 30%
(18) Fetal electrocardiography (ECG) ST‐segment elevation
(19) Fetal ECG PR‐interval shortening
(20) Fetal scalp laceration
(21) Fetal scalp laceration infection requiring treatment

Secondary outcomes: maternal

(22) Caesarean section
(23) Assisted vaginal birth (forceps or vacuum)
(24) Caesarean section for non‐reassuring fetal status
(25) Assisted vaginal birth (forceps or vacuum) for non‐reassuring fetal status
(26) Maternal satisfaction with fetal monitoring in labour
(27) Maternal anxiety
(28) Maternal length of hospital stay

Secondary outcomes: economic

(29) Cost‐effectiveness of fetal monitoring

Other outcomes/considerations

We recorded the system used for lactate measurement and whether or not the maternity unit acted on prespecified cut‐off values for lactate.

We also considered measures of practical consideration for tests to assess fetal well‐being, including the success rate of fetal scalp blood sampling for fetal lactate and the volume of the fetal blood sample(s).

The following methods section of this review is based on a standard template used by the Cochrane Pregnancy and Childbirth Group.

Search methods for identification of studies

The following methods section of this review is based on a standard template used by the Cochrane Pregnancy and Childbirth Group.

Electronic searches

We searched the Cochrane Pregnancy and Childbirth Group’s Trials Register by contacting the Trials Search Co‐ordinator (31 January 2015).

The Cochrane Pregnancy and Childbirth Group’s Trials Register is maintained by the Trials Search Co‐ordinator and contains trials identified from:

  1. monthly searches of the Cochrane Central Register of Controlled Trials (CENTRAL);

  2. weekly searches of MEDLINE (Ovid);

  3. weekly searches of Embase (Ovid);

  4. monthly searches of CINAHL (EBSCO);

  5. handsearches of 30 journals and the proceedings of major conferences;

  6. weekly current awareness alerts for a further 44 journals plus monthly BioMed Central email alerts.

Details of the search strategies for CENTRAL, MEDLINE, Embase and CINAHL, the list of handsearched journals and conference proceedings, and the list of journals reviewed via the current awareness service can be found in the ‘Specialized Register’ section within the editorial information about the Cochrane Pregnancy and Childbirth Group.

Trials identified through the searching activities described above are each assigned to a review topic (or topics). The Trials Search Co‐ordinator searches the register for each review using the topic list rather than keywords.

We did not apply any language or date restrictions.

Data collection and analysis

For methods used in the previous version of this review, seeEast 2010.

For this update, the following methods were used for assessing the reports that were identified as a result of the updated search.

The following methods section of this review is based on a standard template used by the Cochrane Pregnancy and Childbirth Group.

Selection of studies

At least two review authors (C East (CE), L Leader (LL), P Sheehan (PS) independently assessed for inclusion all the potential studies identified as a result of the search strategy. We planned to resolve any disagreement through discussion or, if required, consult another person.

Data extraction and management

We designed a form to extract data. For eligible studies, two review authors (CE, LL) extracted the data using the agreed form. We planned to resolve discrepancies through discussion or, if required, consult another person. We entered data into Review Manager software (RevMan 2014) and checked for accuracy (CE, LL, N Henshall (NH)). If information regarding any of the above was unclear, we attempted to contact authors of the original reports to provide further details.

At least two review authors independently assessed risk of bias for each study using the criteria outlined in the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2011). We planned to resolve any disagreement by discussion or involving another assessor.

Assessment of risk of bias in included studies

(1) Random sequence generation (checking for possible selection bias)

We considered for each included study the method used to generate the allocation sequence in sufficient detail to allow an assessment of whether it should produce comparable groups.

We assessed the method as:

  • low risk of bias (any truly random process, e.g. random number table; computer random number generator);

  • high risk of bias (any non‐random process, e.g. odd or even date of birth; hospital or clinic record number);

  • unclear risk of bias.

(2) Allocation concealment (checking for possible selection bias)

We considered for each included study the method used to conceal allocation to interventions prior to assignment and will assess whether intervention allocation could have been foreseen in advance of, or during recruitment, or changed after assignment.

We assessed these methods as:

  • low risk of bias (e.g. telephone or central randomisation; consecutively numbered sealed opaque envelopes);

  • high risk of bias (open random allocation; unsealed or non‐opaque envelopes, alternation; date of birth);

  • unclear risk of bias.

(3.1) Blinding of participants and personnel (checking for possible performance bias)

We considered for each included study the methods used, if any, to blind study participants and personnel from knowledge of which intervention a participant received. We considered studies at low risk of bias if they were blinded, or if we judged that the lack of blinding would be unlikely to have affected the results. We assessed blinding separately for different outcomes or classes of outcomes.

We assessed the methods as:

  • low, high or unclear risk of bias for participants;

  • low, high or unclear risk of bias for personnel.

(3.2) Blinding of outcome assessment (checking for possible detection bias)

We described for each included study the methods used, if any, to blind outcome assessors from knowledge of which intervention a participant received. We assessed blinding separately for different outcomes or classes of outcomes. We assessed methods used to blind outcome assessment as:

  • low, high or unclear risk of bias.

(4) Incomplete outcome data (checking for possible attrition bias due to the amount, nature and handling of incomplete outcome data)

We considered for each included study, and for each outcome or class of outcomes, the completeness of data including attrition and exclusions from the analysis. We stated whether attrition and exclusions were reported, the numbers included in the analysis at each stage (compared with the total randomised participants), reasons for attrition or exclusion where reported, and whether missing data were balanced across groups or were related to outcomes.Where sufficient information was reported, or could be supplied by the trial authors, we planned to re‐include missing data in the analyses which we undertake.

We assessed methods as:

  • low risk of bias (e.g. no missing outcome data; missing outcome data balanced across groups);

  • high risk of bias (e.g. numbers or reasons for missing data imbalanced across groups; ‘as treated’ analysis done with substantial departure of intervention received from that assigned at randomisation);

  • unclear risk of bias.

We planned to consider excluding trials where there was more than 20% missing data.

(5) Selective reporting (checking for reporting bias)

We considered for each included study how we investigated the possibility of selective outcome reporting bias and what we found.

We assessed methods as:

  • low risk of bias (where it was clear that all of the study’s pre‐specified outcomes and all expected outcomes of interest to the review had been reported);

  • high risk of bias (where not all the study’s pre‐specified outcomes had been reported; one or more reported primary outcomes were not pre‐specified; outcomes of interest were reported incompletely and so cannot be used; study failed to include results of a key outcome that would have been expected to have been reported);

  • unclear risk of bias.

(6) Other bias (checking for (checking for bias due to problems not covered by (1) to (5) above)

We considered for each included study any important concerns we had about other possible sources of bias.

We assessed whether each study was free of other problems that could have put it at risk of bias:

  • low risk of other bias;

  • high risk of other bias;

  • unclear whether there is risk of other bias.

(7) Overall risk of bias

We made judgments about whether studies are at high risk of bias, according to the criteria given in the Handbook (Higgins 2011). With reference to (1) to (6) above, we assessed the likely magnitude and direction of the bias and whether we considered it was likely to impact on the findings. If necessary, we would have explored the impact of the level of bias through undertaking sensitivity analyses ‐ see Sensitivity analysis.

Measures of treatment effect

Dichotomous data

For dichotomous data, we present results as summary risk ratio with 95% confidence intervals.

Continuous data

For continuous data, we used mean difference to measure continuous data; however, only one trial reported continuous data on outcomes of interest to the review. We planned to use the standardised mean difference to combine trials that measured the same outcome, but used different methods.

Unit of analysis issues

Cluster‐randomised trials

We planned to include cluster‐randomised trials in the analyses along with individually‐randomised trials. If we identify cluster‐randomised trials in future updates of this review, we will adjust their sample sizes using the methods described in the Handbook [Section 16.3.4] using an estimate of the intracluster correlation co‐efficient (ICC) derived from the trial (if possible), from a similar trial or from a study of a similar population. If we use ICCs from other sources, we will report this and conduct sensitivity analyses to investigate the effect of variation in the ICC. If we identify both cluster‐randomised trials and individually‐randomised trials, we plan to synthesise the relevant information. We will consider it reasonable to combine the results from both if there is little heterogeneity between the study designs and the interaction between the effect of intervention and the choice of randomisation unit is considered to be unlikely. We will also acknowledge heterogeneity in the randomisation unit and perform a sensitivity analysis to investigate the effects of the randomisation unit.

Cross‐over trials

Cross‐over trials are unlikely to be a valid study design to address this review's outcomes and will be excluded if identified in searches for future updates.

Other unit of analysis issues

We will exclude results of outcomes from multiple pregnancies if they are identified in searches for future updates.

If we identify trials with more than two treatment groups, we will use the methods described in the Handbook [Section 16.5.4]. We will combine all relevant experimental intervention groups of the study into a single group and combine all relevant control intervention groups into a single control group.

Dealing with missing data

For included studies, we noted levels of attrition. We would have explored the impact of including studies with high levels of missing data in the overall assessment of treatment effect by using sensitivity analysis, should it have occurred.

For all outcomes, we planned to carry out analyses on an intention‐to‐treat basis, i.e. we attempted to include all participants randomised to each group in the analyses, and all participants were analysed in the group to which they were allocated, regardless of whether or not they received the allocated intervention. The denominator for each outcome in each trial was the number randomised minus any participants whose outcomes were known to be missing.

Assessment of heterogeneity

We assessed statistical heterogeneity in each meta‐analysis using the Tau², I² and Chi² statistics. We regarded heterogeneity as substantial if an I² was greater than 30% and either the Tau² was greater than zero, or there was a low P value (< 0.10) in the Chi² test for heterogeneity.

Assessment of reporting biases

If we identify 10 or more studies in a future update of the meta‐analysis, we will investigate reporting biases (such as publication bias) using funnel plots. We will assess funnel plot asymmetry visually (Egger 1997). If asymmetry is suggested by a visual assessment, we will perform exploratory analyses to investigate it.

Data synthesis

We carried out statistical analyses using the Review Manager software (RevMan 2014). We used the fixed‐effect meta‐analysis for combining data where it was reasonable to assume that studies were estimating the same underlying treatment effect: i.e. where trials were examining the same intervention, and the trials’ populations and methods were judged to be sufficiently similar. If there was clinical heterogeneity sufficient to expect that the underlying treatment effects differ between trials, or if substantial statistical heterogeneity was detected, we used random‐effects meta‐analysis to produce an overall summary, if an average treatment effect across trials was considered clinically meaningful. The random‐effects summary was treated as the average range of possible treatment effects and we discussed the clinical implications of treatment effects differing between trials. If the average treatment effect was not clinically meaningful, we would not have combined trials.

Where we used random‐effects analyses, the results were presented as the average treatment effect with 95% confidence intervals, and the estimates of Tau² and I².

Subgroup analysis and investigation of heterogeneity

We planned to conduct subgroup analyses of the primary outcomes classifying whole trials by interaction tests as described by Deeks 2001; however, no data were available to allow these analyses.

We planned to carry out the following subgroup analyses:

  • stage of labour;

  • gestation: less than 37 completed weeks, greater than or equal to 37 completed weeks;

  • concurrent use of alternative tests for assessment of fetal well‐being (for example, fetal scalp blood sampling pH).

If suitable data are available in future updates. we will assess subgroup differences by interaction tests available within RevMan (RevMan 2014). We will report the results of subgroup analyses quoting the Chi² statistic and P value, and the interaction test I² value.

Sensitivity analysis

We planned to carry out sensitivity analysis of the primary outcomes to explore the effect of trial quality if necessary.

Results

Description of studies

See: Characteristics of included studies and Characteristics of ongoing studies.

Results of the search

The original search identified two published randomised controlled trials; we included both. One ongoing study was identified (Flamingo Trial 2011) in the formal search and another (SCALP‐trial 2013) whilst searching lactate literature outside this review process. SeeCharacteristics of ongoing studies. Two further reports were identified in the search for this review update, which represented a conference abstract and the subsequent manuscript of a secondary analysis of the data in the Wiberg‐Itzel 2008 trial, that did not contribute data to this update.

Included studies

The two published randomised controlled trials (RCTs) (Westgren 1998; Wiberg‐Itzel 2008) enrolled 3348 mother‐baby pairs and reported maternal and fetal/neonatal/infant outcomes following fetal scalp blood sampling for pH or lactate measurement.

The RCT reported by Westgren 1998 compared FBS for pH with FBS for lactate analysis in 341 mother‐baby pairs that demonstrated an abnormal fetal heart rate during labour and for whom a FBS was considered clinically appropriate. Outcomes included the frequency and failure rate of the FBS procedures, the interval from incision for the FBS to when result available, mode of birth, birthweight, gestational age, Apgar scores, umbilical arterial blood gas results and admission to neonatal intensive care unit.

Wiberg‐Itzel 2008 reported on a multicentre RCT conducted in 10 Swedish hospitals, comparing FBS for pH and lactate analysis in 3007 mother‐baby pairs (1504 lactate group; 1503 pH group), with similar inclusion and exclusion criteria to those in the Westgren 1998 study. Outcomes included FBS failure, mode of birth and neonatal outcomes.

SeeCharacteristics of included studies for further details.

Excluded studies

There were no excluded studies.

Risk of bias in included studies

Overall, the published RCTs were of low or unclear risk of bias. There was a high risk of performance bias, because it would not have been feasible to blind clinicians or participants. SeeRisk of bias in included studies; Figure 1; Figure 2.


'Risk of bias' graph: review authors' judgements about each risk of bias item presented as percentages across all included studies.

'Risk of bias' graph: review authors' judgements about each risk of bias item presented as percentages across all included studies.


'Risk of bias' summary: review authors' judgements about each risk of bias item for each included study.

'Risk of bias' summary: review authors' judgements about each risk of bias item for each included study.

Allocation

Allocation generation was not reported in the study by Westgren 1998. Overall, allocation concealment was considered to be at low risk of bias for both studies.

Blinding

It was not feasible to blind clinicians or participants to study group allocation. We therefore rated the studies as being at high risk of performance bias. Neither study report provided information about whether the outcome assessors were blinded to group allocation or not.

Incomplete outcome data

Outcome data were not reported in cases of protocol violation (n = 1 lactate group; n = 13 in the pH group) in the RCT reported by Westgren 1998. Umbilical cord gas analysis was incomplete for 12% in the lactate group and up to 9% in the pH group of the Wiberg‐Itzel 2008 study. All other outcome data were reported, so that we judged these trials to be at low risk of attrition bias.

Selective reporting

The lack of information in the study reports and lack of published study protocols meant that we were unclear about the risk of reporting bias.

Other potential sources of bias

There was no information in the study reports to indicate the potential for other sources of bias.

Effects of interventions

Comparison ‐ Lactate versus pH analysis of fetal blood sampling

Primary outcomes

No statistically significant between‐group differences were found for neonatal encephalopathy (risk ratio (RR) 1.00, 95% confidence interval (CI) 0.32 to 3.09, one study, 2992 infants; Analysis 1.1) or death. No studies reported neonatal seizures. We had planned to report death with other morbidities, for example, neonatal encephalopathy; however, the data were not available in a format suitable for this, therefore death due to congenital abnormality was considered alone. The three reported neonatal deaths occurred in babies with diaphragmatic hernias (n = 2) or congenital cardiac fibrosis (n = 1). All three babies had been randomised to the pH group and were not acidaemic at birth (RR 0.14. 95% CI 0.01 to 2.76, one study, 2992 infants; Analysis 1.2).

Secondary outcomes: fetal/neonatal/infant

There were no statistically significant differences for any of the pre‐specified secondary fetal/neonatal/infant outcomes for which data were available. This included low Apgar score at five minutes (RR 1.13, 95% CI 0.76 to 1.68, two studies, 3319 infants; Analysis 1.3) and admission to neonatal intensive care units (RR 1.02, 95% CI 0.83 to 1.25, one study, 2992 infants; Analysis 1.9), or for any categories of low umbilical pH (for example, pH less than 7.00, (RR 0.84, 95% CI 0.47 to 1.50, one study, 2698 infants; Analysis 1.5) or base deficit values (for example, mean difference in base deficit ‐0.70, 95% CI ‐1.62 to 0.22, one study, 327 infants; Analysis 1.7) or metabolic acidaemia (RR 0.91, 95% CI 0.60 to 1.36, one study, 2675 infants; Analysis 1.4) considered within the studies, either overall or where data were available for those where fetal blood sampling had occurred within 60 minutes of delivery.

Similar proportions of fetuses underwent additional tests to further evaluate well‐being during labour, including scalp pH if in the lactate group or scalp lactate if in the pH group (average RR 0.22, 95% CI 0.04 to 1.30, Tau² 1.00, I² = 58%; two studies, 3333 infants; Analysis 1.10). Fetal electrocardiography (specifically ST segment analysis) was used in some hospitals in the RCT reported by Wiberg‐Itzel 2008 as an adjunct to fetal heart rate monitoring, similar numbers were randomised to each of the pH and lactate groups.

Fetal blood sampling attempts for lactate and pH estimation were successful in 98.7% and 79.4% of procedures respectively in the report from Westgren 1998 (Table 1). Failed pH sampling procedures for the 169 original participants of this same study were attributed to birth before the sample could be collected (n = 3), inadequate sample collection (n = 44) and pH meter dysfunction (n = 19). Failed lactate sampling for the study population of 172 was attributed to inadequate sample (n = 1), lactate meter dysfunction (n = 2) or unlikely result (n = 1). Fourteen of these failures were identified as protocol violations and excluded from further analysis, as lactate sampling was then performed in the pH group (n = 13) or pH sampling was performed in the lactate group (n = 1). The report indicated that individuals could have had more than one sampling episode: we have interpreted the reported data to mean that the 14 protocol violations represented 14 mother‐baby pairs, rather than 14 of the total 634 samples taken, given the subsequent exclusion of 14 participants. These exclusions translated to the absence of complete data and meant that we were unable to analyse by intention‐to‐treat. Westgren 1998 suggested that the failure rate was inversely correlated with cervical dilatation in the pH group, but not the lactate group. More scalp incisions were made for each pH sampling attempt (median 2.0, interquartile range (IQR) 1 to 2) than for lactate (median 1.0, IQR 1 to 1) and there was a longer interval from sampling to available result for pH (median 230 seconds (s), IQR 188 to 300s) compared with lactate estimation (120s, IQR 90 to 147s) (Westgren 1998). Although each fetus had a blood sample taken between one and nine times in the study by Wiberg‐Itzel 2008, we were only able to identify the total number of fetuses for whom fetal blood sampling was undertaken (not the overall number of sampling attempts) in the published report, with slightly greater success in the lactate group than the pH group (RR 1.10, 95% CI 1.08 to 1.12, one study, 2992 infants; Analysis 1.11).

Open in table viewer
Table 1. Success rate of fetal blood sampling

Lactate success

Lactate attempts

pH success

pH attempts

Westgren 1998

309

313

255

321

(98.7%)

(79.4%)

Total enrolled: n = 172 Lactate group; n = 169 pH group

No studies reported data on the remaining outcomes pre‐specified for this review, including, for example, fetal scalp laceration infection requiring treatment or long‐term infant disability.

Secondary outcomes: maternal

There were no significant between‐group differences in mode of birth or operative birth for non‐reassuring fetal status, either for all women, or within the group where the fetal blood sample had been taken within 60 minutes of delivery (for example, caesarean section for all enrolled, RR 1.09, 95% CI 0.97 to 1.22, two studies, 3319 women; Analysis 1.14; operative delivery for non‐reassuring fetal status for all enrolled RR 1.02, 95% CI 0.93 to 1.11, one study, 2992 women; Analysis 1.15). No studies reported data for the pre‐specified outcomes of maternal satisfaction with fetal monitoring, maternal anxiety, maternal length of stay or cost‐effectiveness of fetal monitoring.

Data were not available for conduct of the pre‐specified subgroup analyses of stage of labour, gestation or within the subcategories of the additional tests used for assessment of fetal well‐being.

Discussion

Summary of main results

This review identified two randomised controlled trials (RCTs) that considered some aspects of the effectiveness and risks of fetal scalp lactate sampling compared with pH estimation for the assessment of fetal well‐being, following identification of a non‐reassuring cardiotocography trace during 3348 labours. Lactate sampling was more likely to be successful than for pH sampling. This success and speed did not translate to differences in clinical management in terms of mode of birth, or neonatal outcomes evaluated by umbilical cord blood gases, Apgar scores, encephalopathy or admission to the neonatal intensive care unit. The studies were underpowered to assess differences in the low prevalence outcomes.

Overall completeness and applicability of evidence

The studies did not address some of the outcomes of interest for this review, including scalp lacerations/infections, longer‐term measures of infant well‐being, such as long‐term disability, maternal satisfaction with fetal monitoring in labour, maternal anxiety or an economic analysis. Each of these outcomes may impact on the introduction of fetal scalp blood lactate estimation in the clinical setting. One study noted that lower cervical dilatation was associated with greater difficulty in obtaining the fetal blood sampling (FBS) (Westgren 1998). However, it was not clear whether cervical dilatation was similar for each group at the time of sampling. This could be important, particularly for lower dilatations, and may have implications for whether equivalent numbers of women had fetal blood sampling in the latent phase of labour (e.g. less than 3 to 4 cm cervical dilatation) compared with those in the active phase of the first stage of labour.

Further considerations

Cut‐off values

The Westgren 1998 study did not mandate clinical action at any given fetal scalp blood lactate value, as they considered it important that the clinician consider the full clinical picture, rather than act on a single finding. They did acknowledge that a fetal scalp pH of less than 7.20 or lactate greater than 3.08 mmol/L was considered abnormal, while a lactate level between 2.9 and 3.08 was suspicious. The study report did not provide details of lactate values obtained from the FBS attempts or any estimation of the predictive value of the results. The available data would appear to support the action cut‐off value greater than 4.8 mmol/L for intervention, measured with the Lactate Pro (Arkray, Kyoto, Japan) in the RCT reported by Wiberg‐Itzel 2008. Some hospitals publish internal guidelines on cut‐off values: for example, one such guideline uses the same meter as that used by Wiberg‐Itzel 2008 and recommends escalating levels of urgent delivery when lactate values exceed 4.7 mmol/L (The Women's Hospital 2014). These cut‐off values are considerably higher than the cut‐off value of 4.2 mmol/L recommended following an observational study by Allen 2004, using the Accusport (Boeringer, Mannheim, East Sussex, UK) and a report by Smith 1983. Different point‐of‐care meters such as the Lactate Pro and the Nova Biomedical Statstrip Express may provide differing lactate results, with or without correction for the high haematocrit of fetal blood, when compared with each other and with a laboratory reference standard (Orsonneau 2013; Reif 2014; Stewart 2014; Su 2013). The cut‐off lactate value must therefore be considered specifically for the lactate meter in use. Whilst it is beyond the scope of this review to recommend a specific meter or cut‐off values, one option for those developing guidelines for their institution may be to base their recommendations on values used in studies performed with the specific lactate meter in use at the hospital's birthing facility.

Normal lactate in umbilical bloods: gestation

Wiberg 2008 studied umbilical blood lactate concentrations in 10,169 "vigorous" newborns, presumably healthy, born vaginally either spontaneously or with instrumental assistance, to establish reference ranges. The study demonstrated a linear relationship between logarithmic lactate values and gestational age commencing from 34 weeks' gestation. The study had relatively few numbers below 34 weeks' gestation (n = 207), so these results may vary if studied in a larger cohort and in addition, may be influenced by the reason for preterm birth. Following exclusion of instrumental deliveries, linear regression analysis revealed duration of second stage as an independent variable. The variation in umbilical arterial lactate values at term ranged from a mean of 3.5 mmol/L at 37 weeks to 4.3 mmol/L at 42 weeks' gestation. According to these data, the use of the cut‐off value for lactate as 4.8 mmol/L would not result in any unnecessary interventions, even in babies at 42 weeks' gestation. This study excluded babies with complicated pregnancies and those born by caesarean section. No studies have addressed the significance of a relative rise in lactate levels for a given gestation, rather than a critical cut‐off action level applied across all gestations. Future studies could consider receiver operator characteristics for a variety of cut‐off values by gestation. The effect of gestational age on the development of lactic acidosis due to hypoxia, particularly in pregnancies of longer than 42 weeks, also warrants further investigation. We will consider the impact of gestation in this review when data become available from one of the two included studies (Wiberg‐Itzel 2008).

Stage of labour

The relationship between maternal and fetal lactate concentrations have been considered in observational studies in labour. The findings demonstrated no significant correlation between duration of first stage of labour and fetal lactate concentration at the beginning of second stage (Nordström 2001). This confirms previous findings suggesting that fetal lactate concentrations are constant during the first stage of labour, in the absence of hypoxia (Nordström 1994; Nordström 1995). Maternal lactate concentrations increase significantly during the active phase of the second stage of labour. The source of the lactate is thought to be maternal skeletal muscles. One estimate for the maternal increase has been given as 2 mmol/L per 30 minutes (Nordström 2001). Fetal lactate concentrations also correlated positively with the duration of active pushing, however at a rate about half that of the maternal lactate (1 mmol/L per 30 minutes). The question of whether this lactate rise is driven by fetal hypoxia or derived from the mother was investigated by studying the arterio‐venous lactate difference at delivery, the results of which suggested that the main contributor to the fetal lactate increase is the fetus itself, especially with prolonged second stage (Nordström 2001). This is also supported by animal studies (Milley 1988). These findings suggest that fetal scalp lactates would still be an appropriate indicator of fetal hypoxia in second stage, although the clinical appropriateness of performing a lactate in response to abnormal fetal heart rate monitoring in active second stage would have to be considered. We await data from the authors of the Wiberg‐Itzel 2008 study to consider the impact of stage of labour in this review.

Diagnostic accuracy

We constructed this review with the objective of determining the safety and effectiveness of fetal scalp lactate sampling/analysis. The two identified RCTs essentially compared the effectiveness of fetal scalp lactate sampling with what was a more common clinical practice of pH sampling. As such, the available data provide some evidence that the use of lactate monitoring does not result in neonatal or maternal outcomes that differ from those following fetal scalp pH sampling, which had been more commonly used than lactate in many maternity units. Whist the review was not designed to consider the diagnostic accuracy of fetal lactate estimation during labour, the lack of statistically significant clinical outcomes may provide limited de facto support for the equivalence of lactate and pH estimation in identification of the at‐risk fetus. Whilst such a concept may warrant further consideration in a formally constructed review of diagnostic accuracy, a brief overview of some observational studies may be informative here. Several observational studies have investigated the correlation between fetal scalp lactates and clinical outcomes of interest to this review, although we note that it is potentially problematic to draw conclusions about the relationship between lactate and these low prevalence outcomes in the relatively small published studies. Borruto 2008 reported no correlation between scalp lactates and five‐minute Apgar scores, with only 12 of the total 188 neonates assigned an Apgar score less than seven at five minutes. Ramanah 2005 compared fetal scalp lactates to scalp pH, cord blood pH and lactate and Apgar scores. Although good correlations for the former were found in their series of 129 patients, no correlation was found with Apgar scores at one or five minutes. This series included only two babies with Apgars less than seven at one minute of age and in both cases the fetal scalp sample was taken at least 60 minutes prior to delivery. The study of 136 cases by Allen 2004 did not undertake direct correlation of fetal scalp lactate with outcome data. The lack of large, well‐conducted studies with sufficient examples of clearly defined intrapartum asphyxia limits the confidence with which fetal scalp lactate estimation can predict intrapartum fetal well‐being. Such a deficiency needs to be considered in studies (and a review) of diagnostic accuracy. 

Subgroup analyses

The inability to conduct the planned subgroup analyses may be important, particularly given the potential for increases in lactate levels with both advancing gestational age and prolongation of the second stage of labour (Katz 1987; Nordström 2001; Wiberg 2008).

Quality of the evidence

The two RCTs included in this review were judged to be at low or unclear risk of bias overall. The risk of concern was blinding: participants were not blinded, although it could be feasible to achieve this in future studies by analysing all samples outside the birthing room. It was not feasible to blind the clinicians and there was no indication of blinding of outcome assessors. Future studies may incorporate blinding of participants and outcome assessors.

Potential biases in the review process

Potential eligible trials were searched for systematically by the Trials Search Co‐ordinator of the Cochrane Pregnancy and Childbirth Group. We also serendipitously identified another trial in a trial register. We are therefore confident that all potentially eligible trials have been identified. The differences in lactate measurements from point‐of‐care meters represents a potential risk of bias when meta‐analysing the findings of trials. The two included trials used the same meter and in future updates, we will consider sensitivity analysis if different meters were used in trials added to the meta analysis.

Agreements and disagreements with other studies or reviews

The findings of this review are consistent with observational studies and literature reviews available in the peer‐reviewed literature. We are not aware of other published systematic reviews.

'Risk of bias' graph: review authors' judgements about each risk of bias item presented as percentages across all included studies.
Figuras y tablas -
Figure 1

'Risk of bias' graph: review authors' judgements about each risk of bias item presented as percentages across all included studies.

'Risk of bias' summary: review authors' judgements about each risk of bias item for each included study.
Figuras y tablas -
Figure 2

'Risk of bias' summary: review authors' judgements about each risk of bias item for each included study.

Comparison 1 Lactate versus pH analysis of fetal blood sampling, Outcome 1 Neonatal encephalopathy.
Figuras y tablas -
Analysis 1.1

Comparison 1 Lactate versus pH analysis of fetal blood sampling, Outcome 1 Neonatal encephalopathy.

Comparison 1 Lactate versus pH analysis of fetal blood sampling, Outcome 2 Neonatal death.
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Analysis 1.2

Comparison 1 Lactate versus pH analysis of fetal blood sampling, Outcome 2 Neonatal death.

Comparison 1 Lactate versus pH analysis of fetal blood sampling, Outcome 3 Apgar score < 7 at 5 minutes.
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Analysis 1.3

Comparison 1 Lactate versus pH analysis of fetal blood sampling, Outcome 3 Apgar score < 7 at 5 minutes.

Comparison 1 Lactate versus pH analysis of fetal blood sampling, Outcome 4 Metabolic acidemia (umbilical arterial pH < 7.05 + base defecit > 12 mmol/L).
Figuras y tablas -
Analysis 1.4

Comparison 1 Lactate versus pH analysis of fetal blood sampling, Outcome 4 Metabolic acidemia (umbilical arterial pH < 7.05 + base defecit > 12 mmol/L).

Comparison 1 Lactate versus pH analysis of fetal blood sampling, Outcome 5 Low umbilical arterial pH.
Figuras y tablas -
Analysis 1.5

Comparison 1 Lactate versus pH analysis of fetal blood sampling, Outcome 5 Low umbilical arterial pH.

Comparison 1 Lactate versus pH analysis of fetal blood sampling, Outcome 6 Umbilical arterial lactate > 4.68 mmol/L.
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Analysis 1.6

Comparison 1 Lactate versus pH analysis of fetal blood sampling, Outcome 6 Umbilical arterial lactate > 4.68 mmol/L.

Comparison 1 Lactate versus pH analysis of fetal blood sampling, Outcome 7 Umbilical arterial base deficit.
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Analysis 1.7

Comparison 1 Lactate versus pH analysis of fetal blood sampling, Outcome 7 Umbilical arterial base deficit.

Comparison 1 Lactate versus pH analysis of fetal blood sampling, Outcome 8 Umbilical arterial base deficit > 19.2.
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Analysis 1.8

Comparison 1 Lactate versus pH analysis of fetal blood sampling, Outcome 8 Umbilical arterial base deficit > 19.2.

Comparison 1 Lactate versus pH analysis of fetal blood sampling, Outcome 9 Admission to neonatal intensive care unit.
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Analysis 1.9

Comparison 1 Lactate versus pH analysis of fetal blood sampling, Outcome 9 Admission to neonatal intensive care unit.

Comparison 1 Lactate versus pH analysis of fetal blood sampling, Outcome 10 Number of additional tests to evaluate fetal well‐being.
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Analysis 1.10

Comparison 1 Lactate versus pH analysis of fetal blood sampling, Outcome 10 Number of additional tests to evaluate fetal well‐being.

Comparison 1 Lactate versus pH analysis of fetal blood sampling, Outcome 11 Success rate of fetal blood sampling.
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Analysis 1.11

Comparison 1 Lactate versus pH analysis of fetal blood sampling, Outcome 11 Success rate of fetal blood sampling.

Comparison 1 Lactate versus pH analysis of fetal blood sampling, Outcome 12 Normal vaginal birth.
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Analysis 1.12

Comparison 1 Lactate versus pH analysis of fetal blood sampling, Outcome 12 Normal vaginal birth.

Comparison 1 Lactate versus pH analysis of fetal blood sampling, Outcome 13 Assisted vaginal birth.
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Analysis 1.13

Comparison 1 Lactate versus pH analysis of fetal blood sampling, Outcome 13 Assisted vaginal birth.

Comparison 1 Lactate versus pH analysis of fetal blood sampling, Outcome 14 Caesarean section.
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Analysis 1.14

Comparison 1 Lactate versus pH analysis of fetal blood sampling, Outcome 14 Caesarean section.

Comparison 1 Lactate versus pH analysis of fetal blood sampling, Outcome 15 Operative birth for non‐reassuring fetal status.
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Analysis 1.15

Comparison 1 Lactate versus pH analysis of fetal blood sampling, Outcome 15 Operative birth for non‐reassuring fetal status.

Table 1. Success rate of fetal blood sampling

Lactate success

Lactate attempts

pH success

pH attempts

Westgren 1998

309

313

255

321

(98.7%)

(79.4%)

Total enrolled: n = 172 Lactate group; n = 169 pH group

Figuras y tablas -
Table 1. Success rate of fetal blood sampling
Comparison 1. Lactate versus pH analysis of fetal blood sampling

Outcome or subgroup title

No. of studies

No. of participants

Statistical method

Effect size

1 Neonatal encephalopathy Show forest plot

1

2992

Risk Ratio (M‐H, Fixed, 95% CI)

1.0 [0.32, 3.09]

2 Neonatal death Show forest plot

1

2992

Risk Ratio (M‐H, Fixed, 95% CI)

0.14 [0.01, 2.76]

2.1 Death from congenital abnormalities

1

2992

Risk Ratio (M‐H, Fixed, 95% CI)

0.14 [0.01, 2.76]

3 Apgar score < 7 at 5 minutes Show forest plot

2

Risk Ratio (M‐H, Fixed, 95% CI)

Subtotals only

3.1 All study participants

2

3319

Risk Ratio (M‐H, Fixed, 95% CI)

1.13 [0.76, 1.68]

3.2 Fetal blood sample within 60 minutes of delivery

1

1192

Risk Ratio (M‐H, Fixed, 95% CI)

0.99 [0.57, 1.72]

4 Metabolic acidemia (umbilical arterial pH < 7.05 + base defecit > 12 mmol/L) Show forest plot

1

Risk Ratio (M‐H, Fixed, 95% CI)

Subtotals only

4.1 All study participants

1

2675

Risk Ratio (M‐H, Fixed, 95% CI)

0.91 [0.60, 1.36]

4.2 Fetal blood sample within 60 minutes of delivery

1

1192

Risk Ratio (M‐H, Fixed, 95% CI)

0.93 [0.52, 1.65]

5 Low umbilical arterial pH Show forest plot

2

Risk Ratio (M‐H, Fixed, 95% CI)

Subtotals only

5.1 pH < 6.98

1

327

Risk Ratio (M‐H, Fixed, 95% CI)

0.46 [0.14, 1.49]

5.2 pH < 7.00: all study participants

1

2698

Risk Ratio (M‐H, Fixed, 95% CI)

0.84 [0.47, 1.50]

5.3 pH < 7.00: Fetal blood sample within 60 minutes of delivery

1

1192

Risk Ratio (M‐H, Fixed, 95% CI)

0.68 [0.29, 1.58]

5.4 pH < 7.10

1

2698

Risk Ratio (M‐H, Fixed, 95% CI)

0.89 [0.70, 1.12]

6 Umbilical arterial lactate > 4.68 mmol/L Show forest plot

1

327

Risk Ratio (M‐H, Fixed, 95% CI)

0.63 [0.37, 1.07]

7 Umbilical arterial base deficit Show forest plot

1

327

Mean Difference (IV, Fixed, 95% CI)

‐0.70 [‐1.62, 0.22]

8 Umbilical arterial base deficit > 19.2 Show forest plot

1

327

Risk Ratio (M‐H, Fixed, 95% CI)

0.30 [0.03, 2.89]

9 Admission to neonatal intensive care unit Show forest plot

1

2992

Risk Ratio (M‐H, Fixed, 95% CI)

1.02 [0.83, 1.25]

10 Number of additional tests to evaluate fetal well‐being Show forest plot

2

Risk Ratio (M‐H, Random, 95% CI)

Subtotals only

10.1 Fetal scalp pH (lactate group) or lactate estimation (pH group)

2

3333

Risk Ratio (M‐H, Random, 95% CI)

0.22 [0.04, 1.30]

10.2 Fetal ECG (STAN monitor)

1

2992

Risk Ratio (M‐H, Random, 95% CI)

1.00 [0.88, 1.12]

11 Success rate of fetal blood sampling Show forest plot

1

Risk Ratio (M‐H, Fixed, 95% CI)

Subtotals only

11.1 Successful samples (events) in all fetuses sampled (Total)

1

2992

Risk Ratio (M‐H, Fixed, 95% CI)

1.10 [1.08, 1.12]

12 Normal vaginal birth Show forest plot

2

3319

Risk Ratio (M‐H, Random, 95% CI)

0.91 [0.67, 1.24]

13 Assisted vaginal birth Show forest plot

2

3319

Risk Ratio (M‐H, Fixed, 95% CI)

0.90 [0.81, 1.01]

14 Caesarean section Show forest plot

2

3319

Risk Ratio (M‐H, Fixed, 95% CI)

1.09 [0.97, 1.22]

15 Operative birth for non‐reassuring fetal status Show forest plot

1

Risk Ratio (M‐H, Fixed, 95% CI)

Subtotals only

15.1 Fetal scalp blood sampled within 60 minutes of delivery

1

1192

Risk Ratio (M‐H, Fixed, 95% CI)

1.10 [0.98, 1.22]

15.2 All participants

1

2992

Risk Ratio (M‐H, Fixed, 95% CI)

1.02 [0.93, 1.11]

Figuras y tablas -
Comparison 1. Lactate versus pH analysis of fetal blood sampling