Systolic hypertension, caesarean duration and prenatal visits as predictors of maternal near miss in Peru

TL;DR

The study identifies systolic hypertension, longer caesarean duration, and fewer prenatal visits as risk factors for maternal near miss in Peruvian hospitals.

Contribution

This study identifies novel predictors of maternal near miss in caesarean patients in a Peruvian hospital setting.

Findings

Systolic blood pressure ≥140 mmHg before caesarean increases the risk of maternal near miss.

Longer caesarean duration is associated with higher risk of maternal near miss.

Fewer prenatal visits correlate with increased risk of maternal near miss.

Abstract

To identify the predictive factors of maternal near miss in caesarean patients in the obstetrics and gynaecology service at Hospital III Daniel Alcides Carrión, Tacna, Peru.

A retrospective cohort study was conducted from 1 January 2022 to 31 December 2023. Preoperative, intraoperative and postoperative clinical and laboratory characteristics of caesarean patients hospitalized in the obstetrics and gynaecology service were analysed. Cox proportional hazards regression was used to identify predictors.

We identified 264 caesarean patients, of which 49 experienced maternal near miss. The mean age was 32.81±5.13 y, the median number of prenatal visits was 7 (interquartile range [IQR] 6–9) and the median gestational age was 39 weeks (IQR 37.5–40). Identified predictive factors for maternal near miss were systolic blood pressure ≥140 mmHg before caesarean (adjusted hazard ratio [aHR] 2.20), duration of the caesarean (aHR 1.02) and number of prenatal visits (aHR 0.90).

The findings suggest that systolic hypertension before caesarean delivery, caesarean duration and number of prenatal visits are significant predictors of maternal near miss. These results underscore the importance of early prenatal care, monitoring blood pressure levels and optimizing surgical duration to improve maternal outcomes. Future research should focus on the implementation of targeted interventions based on these predictors to reduce maternal morbidity and improve health policies in low-resource settings.

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