Epidural analgesia is the gold standard for controlling labour pain. However, labour pain happens during neuraxial analgesia, due to anaesthetic, obstetric, maternal factors.
The investigators hypothesized that relevant variables, able to predict the local anaesthetic (LA) requirement during labour, can be identified at admission and each parturient may therefore be accordingly classified in "low-requirement" and "high-requirement". In this way, a predictive score may be developed, and the analgesic regimen may be matched to the individual patient, thus ensuring a timely and appropriate treatment of patients likely to require higher doses of LA, while minimizing potentially side effects of excessive treatment in the low-dose group.
Parturients receiving neuraxial analgesia for labour, as clinical practice
Planned caesarean delivery
All parturients who delivered via vaginal route or intrapartum caesarean section and received neuraxial analgesia (epidural analgesia (EA) or combined spinal-epidural \[CSE\] analgesia or dural puncture epidural (DPE)) in Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, between January 1st, 2020 and March 01th, 2026 (including follow-up data), meeting the inclusion criteria.
Cohort
Retrospective
Machine-learning prediction model
Other interventionA machine-learning prediction model will be developed to anticipate the parturient's requirement of LA at admission in the Labour Suite, according to demographic, obstetric and anaesthetic features ongoing before administration of the first epidural bolus.
Machine-learning algorithm able to predict the LA consumption
To develop a machine-learning algorithm able to predict the mean hourly cumulative LA consumption administered via the epidural catheter from the catheter placement up to delivery, expressed as time-weighted LA consumption per hour (mg/h) in patients receiving top-up analgesia or the need for adjunctive rescue LA boluses in patients receiving PIEB (Programmed Intermittent Epidural Bolus) analgesia.
Clinical score
To develop a clinical score, based on the machine learning algorithm, with the aim of predicting the patient's LA requirement at admission, thus categorizing parturients as "low requirement" or "high requirement".
Time-dependent AUC of LA manual boluses
To compare (between low- and high-requirement parturients) the time-dependent AUC of LA boluses (in case of analgesia maintenance through top-up) and the time-dependent AUC of adjunctive rescue LA boluses (in case of analgesia maintenance through PIEB)
Total LA consumption
To compare between low- and high-requirement parturients the total LA consumption (mg), mean (+-SD)
Ratio of time to first bolus demand to duration of labor
To compare between low- and high-requirement parturients the ratio of time to first bolus demand to duration of labor (%), mean (+-SD), in case of analgesia maintenance through top-up.
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