An observational study in Intrapartum Fever, sponsored by Chengdu Jinjiang Maternity and Child Health Hospital. Not yet recruiting. Open to female participants. Per ClinicalTrials.gov, last updated 2026-10-19.
Sponsored by Chengdu Jinjiang Maternity and Child Health Hospital · Observational
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.
438 studies on the registry are indexed under Fever; 55 are open to participants now.
This study's planned enrollment of 6,144 is above the median of 408 across 140 observational studies indexed under Fever.
Browse Fever studies →Chengdu Jinjiang Maternity and Child Health Hospital is the lead sponsor of 10 studies on the registry; 7 are open to participants now.
Counted across the registry records on this site, refreshed daily.
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.
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Exclusion Criteria:
Other: Labor analgesia
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.
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.
Time frame: From initiation of labor analgesia through delivery, an average of 10 hours
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.
Time frame: 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.
Time frame: Through study completion, an average of 1 year
No study locations are listed for this record.
Plan to share: Undecided
No publications or documents are linked to this record.
Start is estimated for Oct 2026; primary completion for Dec 2026, 2 months later.
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From the registry record's own update history. This site started tracking changes on Sep 25, 2026; for anything earlier, see the record history on ClinicalTrials.gov ↗
This study is not yet recruiting, as verified in Oct 2026. You cannot join it, but the record below documents what was studied.
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Chengdu Jinjiang Maternity and Child Health Hospital