Personalized Medicine innovation grant 2017 enabled the analysis of the RAVEL trials

Personalized Medicine innovation grant 2017 enabled the analysis of the RAVEL trials

The Personalized Medicine innovation grant of 2017 enabled the analysis of the RAVEL trials. Due to the PM grant a collaboration among several APH researchers was possible to make a multi-marker model to identify those laboring women who satisfied with controlled analgesia with remifentanil.

The research is part of the PhD project of Sabine Logtenberg (Amsterdam UMC department of Obstetrics and Gynaecology - location VUmc) and will be submitted to international peer-reviewed journals.

Other APH-researchers involved in this research are:  Corine Verhoeven (AUmc department of Midwifery science, location VUmc), Patrick Bossuyt (AUmc department of Clinical Epidemiology, Biostatistics and Bioinformatics, location VUmc), Martijn Heymans (AUmc department of Epidemiology and Biostatistics, location AMC), Francois Schellevis ( AUmc department of General Practice & Elderly Care Medicine, location VUmc) and Mohammad Hadi Zafarmand (AUmc department of of Clinical Epidemiology, Biostatistics and Bioinformatics, location AMC).


Summary of the research: Identifying women satisfied with remifentanil patient-controlled analgesia rather than with epidural analgesia: a secondary analysis of the RAVEL trials.
Background

The RAVEL studies failed to show that remifentanil patient-controlled analgesia (PCA) is equivalent to epidural analgesia with respect to satisfaction with pain relief during labour. Since remifentanil-PCA is less invasive and more readily available than epidural analgesia, we investigated whether we could identify women with a request for pain relief who would be as satisfied with remifentanil-PCA compared to epidural analgesia. 

Methods

We used data from two randomised controlled RAVEL studies, in which 1,832 women with a low obstetric risk or an intermediate to high obstetric risk were allocated to remifentanil PCA or epidural analgesia in case of a request for pain relief during labour. We developed a multivariable model using logistic regression analysis to identify labouring women who would be satisfied with remifentanil-PCA and other women who would be satisfied with epidural analgesia. We included the following potential predictor-treatment variables in the analysis: education level, ethnicity, age, BMI, previous vaginal delivery, antepartum fear of childbirth, risk category, gestational age, onset of labour, augmentation with oxytocin and dilatation. The outcome was satisfaction with pain relief during labour expressed as area under the curve.

Results

The final multivariable model contained treatment and the following variables: education level, ethnicity, age, BMI, previous vaginal delivery, antepartum fear of childbirth, risk category, gestational age, onset of labour, augmentation with oxytocin and dilatation as well as a treatment - ethnicity interaction and treatment - risk category interaction. The model identified 18.3% of the study group as women who would be satisfied with remifentanil-PCA. Using remifentanil in this group and epidural in all others would lead to a mean area under the curve for satisfaction with pain relief of 51.27 (95% CI 48.41-54.23), compared to 50.86 (95% CI 48.06-53.83) when epidural analgesia would be used for all women.

Conclusion

We developed and internally validated a multivariable treatment selection model for satisfaction with pain relief during labour. After external validation this model could be used to guide decisions about remifentanil-PCA for labour analgesia.