Evidence map›Paper›PMID 42492377›Full record

ArticleInternational journal of obstetric anesthesia2026

Predictive modeling of labor epidural catheter replacement or conversion to spinal or general anesthesia: a multicenter retrospective cohort study (2017-2024).

Daniel F Berenson, Braden E Eberhard, Ricardo Kleinlein, Nolan K Wheeler, Ayumi Maeda, Vesela P Kovacheva

Abstract read
In one paragraph

Article in International journal of obstetric anesthesia, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Daniel F BerensonDepartment of Anesthesiology, Mass General Brigham, Brigham and Women's Hospital, Boston, MA, USA. Electronic address: dberenson@bwh.harvard.edu.
Braden E EberhardDepartment of Anesthesiology, Mass General Brigham, Brigham and Women's Hospital, Boston, MA, USA.
Ricardo KleinleinDepartment of Anesthesiology, Mass General Brigham, Brigham and Women's Hospital, Boston, MA, USA. Electronic address: rfaundez-carrasco@bwh.harvard.edu.
Nolan K WheelerDepartment of Anesthesiology, Mass General Brigham, Brigham and Women's Hospital, Boston, MA, USA. Electronic address: nkwheeler@bwh.harvard.edu.
Ayumi MaedaDepartment of Anesthesiology, Mass General Brigham, Brigham and Women's Hospital, Boston, MA, USA. Electronic address: amaeda@bwh.harvard.edu.
Vesela P KovachevaDepartment of Anesthesiology, Mass General Brigham, Brigham and Women's Hospital, Boston, MA, USA. Electronic address: vkovacheva@bwh.harvard.edu.

Funding

Personalized Postpartum Hemorrhage Prediction Using Machine Learning And Polygenic Risk ScoresK08HL161326 · NHLBI · BRIGHAM AND WOMEN'S HOSPITAL · PI Vesela Kovacheva · 2022 to 2026
$842k
NHLBI NIH HHS K08 HL161326
6 · The paper itself

Abstract

backgroundLabor epidural analgesia may be inadequate during labor or for cesarean delivery anesthesia. No prediction model identifies cases of labor epidural analgesia with increased risk for epidural catheter replacement or conversion to spinal or general anesthesia.

methodsWe conducted a retrospective cohort study of patients receiving labor epidural analgesia initiated with standard epidural or dural puncture epidural technique in a large, multihospital healthcare system between 2017 and 2024. Demographic, comorbidity, vital-sign, and procedural data were extracted from the electronic health record. The composite outcome was epidural catheter replacement, subsequent spinal anesthesia, or advanced airway placement indicating general anesthesia for cesarean delivery within the same anesthesia encounter. Statistical and machine-learning models were developed; one hospital was held out for external validation.

resultsThe cohort included 82,823 cases of epidural catheter placement among 65,419 unique patients; 5010 cases (6.0%) met the composite outcome. Logistic regression and XGBoost performed similarly on external validation (area under the receiver operating characteristic curve, 0.67). Important predictors included prior replacement or conversion during the same encounter, end-of-pregnancy body mass index, pre-placement pain score, prior cesarean delivery, parity, and delivery hospital. Decision-curve analysis and Monte Carlo simulation suggested possible utility for screening and triage when anesthesia staffing is limited.

conclusionsWe developed and externally validated Risk Estimation for Anesthesia Catheter Technique replacement or conversion in Obstetrics (REACT-OB) to estimate the risk of this outcome. Prospective validation is required before clinical use.

Indexed as

Artificial intelligenceEpidural analgesiaLabor painMachine learningObstetric anesthesiaPredictive modelsRisk assessment

Identifiers

PMID42492377
PMCPMC13483115

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.