Intrapartum FeverNCT07880665
Machine Learning to Predict Fever After Labor Analgesia
An observational study of Intrapartum Fever, run by Chengdu Jinjiang Maternity and Child Health Hospital.
Trial summary
Post-labor analgesia intrapartum fever (PAIF) is a serious complication that increases the risk of cesarean delivery, postpartum bleeding, and health problems for newborns. Currently, doctors lack a simple tool to predict which mothers will develop a fever before they start labor analgesia. Read more about this study
- Who is it for?
- Any age, women only
- Conditions the study is for
- Intrapartum Fever
- What is being tested?
- Labor analgesia
- How many people
- 6,144 participants
- Where
- No sites listed yet
- Study dates
- 15 Oct 2026to 1 Jan 2027
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages Any age
- Women only
- Child, Adult, Older adult
Study population: The study population consists of parturients with singleton pregnancies who opted for and received labor analgesia and delivered at term (37-42 weeks of gestation) at three participating hospitals in Sichuan, China. Data will be retrospectively retrieved from the electronic medical record (EMR) databases of these hospitals. The derivation cohort will include parturients from two hospitals, and the independent external validation cohort will include parturients from a third hospital during the same period.
Who may be able to join
- Parturients with singleton pregnancies.
- Opted for and received labor analgesia.
- Delivered at term (37-42 weeks of gestation).
- Admitted to the participating study hospitals.
Who cannot join
- Diagnosed with active infections at the onset of labor.
- Axillary temperature ≥ 37.3°C after entering labor but before receiving labor analgesia.
- Missing data on key variables for the prediction model.
Trial locations and contacts
No sites are listed yet. Contact the study team or a site near you to ask about taking part.
About this study
Official title: An Ensemble Machine Learning Model to Predict Post-Labor Analgesia Intrapartum Fever: A Multicentre Real-World Study
Post-labor analgesia intrapartum fever (PAIF) is a serious complication that increases the risk of cesarean delivery, postpartum bleeding, and health problems for newborns. Currently, doctors lack a simple tool to predict which mothers will develop a fever before they start labor analgesia.
This study aims to develop and test a computer prediction tool (using machine learning) to estimate the risk of PAIF. The investigators will review the medical records of mothers who received labor analgesia and delivered at term in three hospitals in China. Information that is available before the mothers receive analgesia, such as maternal age, gestational age, blood test results, and labor progress, will be collected. Using artificial intelligence, the investigators will build a prediction model. This model will then be tested on a separate group of women from a different hospital to ensure it works well in different settings. Finally, an open-access, interactive online calculator will be developed for clinicians.
The goal of this study is to provide doctors with a practical tool to quickly assess a mother's risk level before starting analgesia. This will help doctors monitor high-risk mothers more closely and prepare for possible complications. This study only uses existing medical records and will not change the routine care of any patients. Further studies will be needed to see if using this tool improves the health of mothers and babies.
- Conditions
- Intrapartum Fever
- Being tested
- Labor analgesia
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Labor analgesiaOther
This is an observational study. All participants will receive standard labor analgesia as part of routine clinical care. The investigators will not assign or control the analgesia protocol or timing. The administration of labor analgesia serves as the universal exposure for this cohort, and relevant data will be retrospectively extracted from electronic medical records for the development of the prediction model.
| Study group | Role | What they receive |
|---|---|---|
| Parturients receiving labor analgesia | Not described Receives: Other: Labor analgesia |
What is being measured
The primary outcome is the main question the study is designed to answer.
Post-labor Analgesia Intrapartum Fever
Number of participants with PAIF, defined as maternal axillary temperature ≥38.0°C occurring after initiation of labor analgesia and before delivery. Axillary temperature was measured hourly during labor. A participant with at least one febrile measurement (≥38.0°C) after initiation of labor analgesia and before delivery was classified as having PAIF. Febrile events were confirmed through nursing charts and electronic health records.
Measured: From initiation of labor analgesia through delivery, an average of 10 hours
Secondary outcomes (2)
Area Under the Receiver Operating Characteristic Curve (AUROC)
AUROC of the ensemble machine learning model for predicting PAIF. The model outputs a predicted probability of PAIF; AUROC quantifies discrimination between participants with and without PAIF in the internal and external validation sets.
Measured: Through study completion, an average of 1 year
Area Under the Precision-Recall Curve (AUPRC)
UPRC of the ensemble machine learning model for predicting PAIF. AUPRC summarizes the trade-off between precision and recall across classification thresholds in the internal and external validation sets.
Measured: Through study completion, an average of 1 year
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted19 Oct 2026
- Study start15 Oct 2026 (estimated)
- Primary completion30 Dec 2026 (estimated)
- Study completion1 Jan 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Intrapartum FeverStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 6,144 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Chengdu Jinjiang Maternity and Child Health Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Fei Jia, Chengdu Jinjiang Maternity and Child Health Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07880665
- Sponsor's protocol ID
- 20230807-216
- Status last verified
- Oct 2026
- First submitted
- 20 Sep 2026
- Last updated
- 19 Oct 2026
- Results posted
- No
- Individual data sharing
- Undecided
Study at a glance
From the registry record, last updated by the sponsor on 19 Oct 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- An Ensemble Machine Learning Model to Predict Post-Labor Analgesia Intrapartum Fever: A Multicentre Real-World Study
- ClinicalTrials.gov ID
- NCT07880665
- Acronym / sponsor ID
- 20230807-216
- Conditions
- Intrapartum Fever
- Study type
- Observational
- Recruitment status
- Not yet recruiting
- Enrolment
- 6,144 participants (estimated)
- Lead sponsor
- Chengdu Jinjiang Maternity and Child Health HospitalUniversity, hospital or other
- Study start
- 15 Oct 2026 (estimated)
- Primary completion
- 30 Dec 2026 (estimated)
- Study completion
- 1 Jan 2027 (estimated)
- First posted
- 19 Oct 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
Post-labor analgesia intrapartum fever (PAIF) is a serious complication that increases the risk of cesarean delivery, postpartum bleeding, and health problems for newborns. Currently, doctors lack a simple tool to predict which mothers will develop a fever before they start labor analgesia.
This study aims to develop and test a computer prediction tool (using machine learning) to estimate the risk of PAIF. The investigators will review the medical records of mothers who received labor analgesia and delivered at term in three hospitals in China. Information that is available before the mothers receive analgesia, such as maternal age, gestational age, blood test results, and labor progress, will be collected. Using artificial intelligence, the investigators will build a prediction model. This model will then be tested on a separate group of women from a different hospital to ensure it works well in different settings. Finally, an open-access, interactive online calculator will be developed for clinicians.
The goal of this study is to provide doctors with a practical tool to quickly assess a mother's risk level before starting analgesia. This will help doctors monitor high-risk mothers more closely and prepare for possible complications. This study only uses existing medical records and will not change the routine care of any patients. Further studies will be needed to see if using this tool improves the health of mothers and babies.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Parturients receiving labor analgesia | – | Other: Labor analgesia |
Who may qualify
- Sex
- Female only
- Age groups
- Child, Adult, Older adult
- Study population
- The study population consists of parturients with singleton pregnancies who opted for and received labor analgesia and delivered at term (37-42 weeks of gestation) at three participating hospitals in Sichuan, China. Data will be retrospectively retrieved from the electronic medical record (EMR) databases of these hospitals. The derivation cohort will include parturients from two hospitals, and the independent external validation cohort will include parturients from a third hospital during the same period.
Key inclusion criteria (4 listed)
- Parturients with singleton pregnancies.
- Opted for and received labor analgesia.
- Delivered at term (37-42 weeks of gestation).
- Admitted to the participating study hospitals.
Key exclusion criteria (3 listed)
- Diagnosed with active infections at the onset of labor.
- Axillary temperature ≥ 37.3°C after entering labor but before receiving labor analgesia.
- Missing data on key variables for the prediction model.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Labor analgesiaThis is an observational study. All participants will receive standard labor analgesia as part of routine clinical care. The investigators will not assign or control the analgesia protocol or timing. The administration of labor analgesia serves as the universal exposure for this cohort, and relevant data will be retrospectively extracted from electronic medical records for the development of the prediction model. | Other | Parturients receiving labor analgesia |
Primary outcome
Post-labor Analgesia Intrapartum Fever
Time frame: From initiation of labor analgesia through delivery, an average of 10 hours
Number of participants with PAIF, defined as maternal axillary temperature ≥38.0°C occurring after initiation of labor analgesia and before delivery. Axillary temperature was measured hourly during labor. A participant with at least one febrile measurement (≥38.0°C) after initiation of labor analgesia and before delivery was classified as having PAIF. Febrile events were confirmed through nursing charts and electronic health records.
2 secondary outcomes
Area Under the Receiver Operating Characteristic Curve (AUROC)
Time frame: Through study completion, an average of 1 year
Area Under the Precision-Recall Curve (AUPRC)
Time frame: Through study completion, an average of 1 year
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 19 Oct 2026.
- Individual participant dataSharing planned: Undecided
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Questions about this trial
What is this clinical trial studying?
An observational study of Intrapartum Fever, run by Chengdu Jinjiang Maternity and Child Health Hospital.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 3 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: opening soon, as of the sponsor's last update on 19 Oct 2026. Status can change between updates, so confirm with the study contact before making plans.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Bo Liu, MS (+8618502846036, liubojjfy@163.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Post-labor Analgesia Intrapartum Fever (time frame: From initiation of labor analgesia through delivery, an average of 10 hours). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 19 Oct 2026; AzTrials last refreshed its copy on 30 Oct 2026.