Prediction of Local Anaesthetic Dosing During Labour Epidural Analgesia
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- The protocol lists: Machine-learning prediction model.
- Who it may be relevant to
- Registry conditions: Labour Analgesia. Basic parameters: from 18 years · Female.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- Italy
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
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Official title
Prediction of Local Anaesthetic Dosing During Labour Epidural Analgesia: a Machine-learning Approach. The DIANA (Dosing Intrapartum ANAesthetics) Study.
Overview
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.
Interventions
- Other Machine-learning prediction model
A 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.
Primary outcome measures
- Machine-learning algorithm able to predict the LA consumption [Time frame: From the epidural catheter placement to delivery.]
Secondary outcome measures (12)
- Clinical score [Time frame: At admission in the Labour Suite and before the epidural catheter is placed]
- Time-dependent AUC of LA manual boluses [Time frame: From epidural catheter placement to delivery.]
- Total LA consumption [Time frame: From epidural catheter placement to delivery.]
- Ratio of time to first bolus demand to duration of labor [Time frame: From epidural catheter placement to delivery.]
- Total demand of LA boluses [Time frame: From epidural catheter placement to delivery.]
- Total adjunctive rescue LA epidural boluses [Time frame: From epidural catheter placement to delivery.]
- Length of active phase [Time frame: From start of the active phase to full cervical dilation, up to 24 hours.]
- Length of second stage [Time frame: From full cervical dilation to delivery.]
- Rate of postpartum haemorrhage [Time frame: In the first hour after delivery.]
- Rate of perineal laceration [Time frame: In the first hour after delivery.]
- Rate of episiotomy [Time frame: At delivery.]
- Rate of shoulder dystocia [Time frame: At delivery.]
Eligibility criteria
Inclusion criteria
- Parturients receiving neuraxial analgesia for labour, as clinical practice
Exclusion criteria
- Planned caesarean delivery
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: No
Study design
- Observational model
- Cohort
Study locations
Italy · 1 center
- Fondazione Policlinico Universitario A. Gemelli IRCCS — Rome
Identifiers
NCT: NCT07614516 · 27389