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A Controlled Mouse Model for Neonatal Polymicrobial Sepsis
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Risk factors for postpartum sepsis: a nested case-control study.

Samina Bakhtawar1, Sana Sheikh2, Rahat Qureshi3

  • 1Department of Community Health Sciences, Aga Khan University, Karachi, Pakistan.

BMC Pregnancy and Childbirth
|May 16, 2020
PubMed
Summary

A new model using risk factors and symptoms accurately identifies postpartum sepsis, offering a non-invasive alternative to lab-based criteria in low-resource settings.

Keywords:
Lower-middle income countryPostpartum womenRisk factorsSepsis

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Area of Science:

  • Obstetrics and Gynecology
  • Infectious Disease Epidemiology
  • Clinical Diagnostics

Background:

  • Maternal deaths are concentrated in low- and middle-income countries, with postpartum hemorrhage, pre-eclampsia, and puerperal sepsis as leading causes.
  • Current sepsis diagnostic criteria, like systemic inflammatory response syndrome (SIRS), often require laboratory tests not feasible in resource-limited settings.
  • Early sepsis identification in postpartum women is crucial for timely intervention and improved outcomes.

Purpose of the Study:

  • To develop and validate a predictive model for early sepsis detection in postpartum women.
  • To create a non-invasive tool utilizing clinical signs, symptoms, and risk factors.
  • To provide a feasible alternative to laboratory-dependent diagnostic criteria in resource-constrained environments.

Main Methods:

  • A case-control study nested within a cohort of 4000 postpartum women.
  • Recruitment of 100 sepsis cases and 498 controls based on SIRS criteria.
  • Data collection included socio-demographics, antenatal care, clinical signs/symptoms, and delivery information; analysis used multivariable logistic regression.

Main Results:

  • Significant risk factors for postpartum sepsis included fewer antenatal visits, multiple vaginal examinations, home delivery, preterm delivery, diabetes in pregnancy, lower abdominal pain, and vaginal discharge.
  • Low SpO2 (<93%) and elevated blood glucose were also strongly associated with sepsis.
  • The developed model demonstrated good discriminative ability with an Area Under the Curve (AUC) of 0.84 (95% CI 0.80-0.89).

Conclusions:

  • A novel, non-invasive tool was developed to identify postpartum sepsis with accuracy comparable to SIRS criteria.
  • The model shows strong potential for early sepsis detection using readily available clinical information.
  • Validation and scale-up for community use by frontline healthcare workers are recommended.