Assessment and validation of the Community Maternal Danger Score algorithm.

Area of Science:

• Obstetrics and Gynecology
• Global Health
• Maternal Health

Background:

• High maternal mortality in low- and middle-income countries (LMICs) is linked to a lack of skilled birth attendants (SBAs).
• Risk analysis tools can help identify high-risk pregnancies in LMICs.
• The Community Maternal Danger Score (CMDS) was developed to identify pregnant women requiring an SBA.

Purpose of the Study:

• To develop and validate a low-cost maternal risk assessment tool, the CMDS.
• To identify pregnant women who need an SBA at delivery.
• To improve maternal care in LMICs.

Main Methods:

• A scoping review identified predictors for SBA need.
• Medical records of 589 women in Nigeria (2019-2020) were analyzed.
• Multivariate logistic regression and AUC assessed the CMDS algorithm's predictive ability.

Main Results:

• Seven factors predicted SBA need: age, parity, BMI, fundal height, obstetrical history, pre-eclampsia signs, and medical conditions.
• 67% of women required an SBA; 1% died, yielding a maternal mortality ratio of 1189/100,000.
• The CMDS algorithm showed an AUC of 0.73 for predicting SBA need and 0.85 for in-hospital mortality.

Conclusions:

• The CMDS is a low-cost, evidence-based tool utilizing 7 risk factors.
• Non-specialist health workers can use CMDS to standardize assessments.
• CMDS encourages pregnant women to seek SBAs, potentially reducing maternal mortality.