[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100637678":3},{"organization":4,"armGroups":7,"interventions":14,"overallOfficials":10,"centralContacts":19,"locations":25,"responsibleParty":39,"collaborators":10,"id":43,"slug":44,"hasResults":45,"nctId":46,"briefTitle":47,"officialTitle":48,"acronym":49,"eligibilityCriteria":50,"healthyVolunteers":45,"sex":51,"minAge":52,"maxAge":10,"enrollmentInfo":53,"targetDuration":10,"studyType":56,"phases":10,"briefSummary":57,"conditions":58,"keywords":60,"overallStatus":65,"whyStopped":10,"lastUpdateSubmitDate":66,"lastUpdatePostDateStruct":67,"startDateStruct":70,"completionDateStruct":72,"leadSponsor":74,"locationsCount":75},{"fullName":5,"class":6},"Fondazione Policlinico Universitario Agostino Gemelli IRCCS","OTHER",[8],{"label":9,"type":10,"description":11,"interventionNames":12},"All parturients between January 2020 and March 2026",null,"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. Labour analgesia was maintained through manual top-up boluses or PIEB (Programmed Intermittent Epidural Bolus).",[13],"Other: Machine-learning prediction model",[15],{"type":6,"name":16,"description":17,"armGroupLabels":18,"otherNames":10},"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.",[9],[20],{"name":21,"role":22,"phone":23,"phoneExt":10,"email":24},"Bruno A Zanfini, MD","CONTACT","06 3015 3105","brunoantonio.zanfini@policlinicogemelli.it",[26],{"facility":27,"status":10,"city":28,"state":29,"zip":30,"country":31,"countryCode":32,"cosmosGeoPoint":33,"geoPoint":38,"contacts":10},"Fondazione Policlinico Universitario A. Gemelli IRCCS","Rome","RM","00168","Italy","IT",{"type":34,"coordinates":35},"Point",[36,37],12.51133,41.89193,{"lat":37,"lon":36},{"type":40,"investigatorFullName":41,"investigatorTitle":42,"investigatorAffiliation":5,"oldNameTitle":10,"oldOrganization":10},"PRINCIPAL_INVESTIGATOR","ZANFINI BRUNO ANTONIO","Principal Investigator","100637678","prediction-of-local-anaesthetic-dosing-during-labour-epidural-analgesia-100637678",false,"NCT07614516","Prediction of Local Anaesthetic Dosing During Labour Epidural Analgesia","Prediction of Local Anaesthetic Dosing During Labour Epidural Analgesia: a Machine-learning Approach. The DIANA (Dosing Intrapartum ANAesthetics) Study.","DIANA","Inclusion Criteria:\n\n* Parturients receiving neuraxial analgesia for labour, as clinical practice\n\nExclusion Criteria:\n\n* Planned caesarean delivery","FEMALE","18 Years",{"count":54,"type":55},12500,"ESTIMATED","OBSERVATIONAL","Epidural analgesia is the gold standard for controlling labour pain. However, labour pain happens during neuraxial analgesia, due to anaesthetic, obstetric, maternal factors.\n\nThe 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.",[59],"Labour Analgesia",[61,62,63,64],"Machine Learning","Anesthesia, Obstetric","Anesthesia, Epidural","Anesthetics, Local","NOT_YET_RECRUITING","2026-05-22",{"date":68,"type":69},"2026-05-29","ACTUAL",{"date":71,"type":55},"2026-06-13",{"date":73,"type":55},"2026-10-31",{"name":5,"class":6},1]