Evidence map›Paper›PMID 39723193›Full record

ArticleFrontiers in cellular and infection microbiology2024

A simple predictive model for puerperal infections: emphasizing risk factors and pathogen analysis.

Yanqing Wen, Xin Ming, Jing Yang, Hongbo Qi

Abstract read
In one paragraph

Article in Frontiers in cellular and infection microbiology, 2024. 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

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Yanqing WenDepartment of Obstetrics and Gynecology, Women and Children's Hospital of Chongqing Medical University, Chongqing, China.
Xin MingDepartment of Obstetrics and Gynecology, Chongqing Health Center of Women and Children, Chongqing, China.
Jing YangDepartment of Obstetrics and Gynecology, Women and Children's Hospital of Chongqing Medical University, Chongqing, China.
Hongbo QiDepartment of Obstetrics and Gynecology, Women and Children's Hospital of Chongqing Medical University, Chongqing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Puerperal infection (PI) accounting for approximately 11% of maternal deaths globally is an important preventable cause of maternal morbidity and mortality. This study aims to analyze the high-risk factors and pathogenic bacteria of PI, design a nomogram to predict the risk of PI occurrence, and provide clinical guidance for prevention and treatment to improve maternal outcomes. Methods: A total of 525 pregnant women were included in the study. The mothers were randomly divided into a training cohort (n=367) and a test cohort (n=158). The performance of our model was assessed using the area under the receiver operating characteristic (ROC) curve, calibration curve, and decision curve analyses. All the women in the group of PI underwent blood culture tests, if the bacteria were detected, drug sensitivity tests were performed. The drug sensitivity spectrum was recorded and analyzed. Results: Univariate analysis showed that 12 indicators were significantly different (P < 0.05). Logistic regression analysis showed 6 factors, such as parity, number of vaginal examinations, amount of postpartum bleeding, antibiotics administered in one week before admission, induced labor, and indwelling catheter were significantly different between the PI group and control group (P < 0.05). The area under the ROC curve was 0.904 (95% CI: 0.871-0.936) in the training set and 0.890 (95% CI: 0.837-0.942) in the test set. The calibration curve of the nomogram showed good agreement between prediction and observation. The analysis of the clinical decision curve showed that the nomogram is of practical significance. There were 100 patients with positive blood cultures in the PI group, and Conclusion: The risk of PI will be significantly reduced by controlling the number of vaginal examinations less than 4 times, postpartum hemorrhage less than 414ml, and reducing the time of urethral catheter indwelling. If PI was clinically diagnosed or highly suspected, it was recommended to use antibiotics that were sensitive to

Indexed as

Anti-Bacterial AgentsNomogramsPuerperal InfectionROC CurveAdultBacteriaFemaleHumansMicrobial Sensitivity TestsPregnancyRisk FactorsYoung AdultAnti-Bacterial Agentsdrug sensitivitynomogrampathogenic bacteriapredictive modelpuerperal infection

Identifiers

PMID39723193
PMCPMC11669357

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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.