Interventional analysis found a significant reduction of puerperal sepsis in postpartum patients using the MEOWS chart, suggesting improved maternal safety.
Background: Puerperal sepsis is a leading cause of maternal morbidity and mortality, particularly in low- and middle-income countries. The Modified Early Obstetric Warning Score (MEOWS) chart is a clinical tool designed to facilitate early detection of deterioration in obstetric patients through structured monitoring of physiological parameters. Objective: To evaluate the utility of the MEOWS chart in reducing the incidence of puerperal sepsis among postpartum women in a tertiary care hospital. Methods: This prospective interventional study was conducted at Shaikh Zaid Women Hospital, Larkana, from January 2025 to April 2025. A total of 185 postpartum patients were enrolled in the study using a non-probability consecutive sampling technique. Patient data were collected using a structured clinical proforma. The study was divided into two equal phases. In the first (pre-intervention) phase, standard postpartum monitoring protocols were followed without the use of the MEOWS chart. In the second (post-intervention) phase, the MEOWS chart was implemented for routine monitoring. Results: The incidence of puerperal sepsis significantly decreased from 20.0% in the pre-intervention group to 6.3% in the post-intervention group (p = 0.006). The mean time to diagnosis was reduced from 14.6 ± 5.2 hours to 6.3 ± 2.8 hours (p < 0.001), and time to antibiotic initiation decreased from 16.2 ± 6.0 hours to 7.4 ± 3.1 hours (p < 0.001). ICU admissions dropped from 5.6% to 1.1% (p = 0.098), and the average hospital stay was shorter in the MEOWS group (4.9 ± 1.3 days vs. 5.6 ± 1.5 days; p = 0.014). Conclusion: The MEOWS chart is a practical and effective tool for early detection of puerperal sepsis. Its implementation significantly reduced sepsis rates and improved response times in postpartum patients. Routine use of MEOWS in obstetric wards can enhance maternal safety, particularly in resource-limited settings.
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Ribat Jawed (2025) studied this question.
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