For clinicians in India, risk stratification is an essential part of improving maternal care quality. Consequently, the identification and prediction of Severe Maternal Outcomes (SMOs) in non-intensive care unit (non-ICU) settings is paramount. A systematic review evaluated prognostic models designed to predict SMOs, the life-threatening complications that can occur during pregnancy or within 42 days postpartum. However, this critical analysis reveals significant methodological shortcomings in currently available tools.
Critical Appraisal of Severe Maternal Outcomes Prediction Tools
The systematic review included thirteen studies reporting on 13 distinct prognostic models for pregnant or postpartum women in non-ICU settings. Interestingly, model populations included general obstetric admissions and specific high-risk cohorts such as those with acute fatty liver of pregnancy or pulmonary hypertension. Results demonstrated that models generally offer moderate to good discrimination, with C-statistics ranging from 0.74 to 0.95. This indicates a good ability to distinguish between women who will and will not experience an SMO.
However, the review utilized rigorous quality assessment tools (CHARMS and PROBAST AI). Consequently, all 15 model evaluations were assessed as having a high risk of bias. Researchers pointed out several key problems. For example, inadequate sample sizes and inappropriate variable selection compromised the internal validity of the models. Furthermore, poor management of missing data and arbitrary categorisation of continuous predictors introduced further bias. Therefore, clinicians should exercise caution when applying these models in practice.
The Validation Gap in Risk Assessment
A major concern is the lack of robust external validation. The systematic review found that only five models underwent any external validation. Importantly, none of the models reported calibration performance, which measures the agreement between the predicted risk and the actual observed risk. This is a critical metric for a model intended for clinical use. Furthermore, many models displayed temporal misalignments between the measurement of predictors and the intended use of the model.
Doctors in India routinely use tools like the Maternal Severity Score (MSS) and Maternal Severity Index (MSI) to stratify risk. Studies in Indian tertiary centers have affirmed the good discriminative performance of these models, particularly in predicting maternal death in women with potentially life-threatening complications. Nevertheless, similar to the global systematic review findings, research in India also points to the need for recalibration to avoid overfitting and ensure local applicability. Hence, the systematic review’s conclusion—emphasizing the need for enhanced methodological rigor and models tailored to specific gestational stages—is particularly relevant for low- and middle-income country settings where resources are often stretched. Thus, new tools must facilitate dynamic risk assessment for feasible integration into daily obstetric practice.
Frequently Asked Questions
Q1: What was the main weakness of the Severe Maternal Outcomes prediction models identified in the systematic review?
The primary weakness was a consistently high risk of bias across all 15 evaluations. This resulted from issues such as inadequate sample sizes, inappropriate variable selection, poor management of missing data, and insufficient internal validation. Moreover, a lack of reported calibration performance was a critical omission.
Q2: Why are prediction models for SMOs needed in non-ICU settings?
The majority of antenatal and intrapartum care takes place in non-ICU settings. Prediction models allow clinicians to identify high-risk women early in their course, enabling timely and appropriate transfers, resource allocation, and escalation of care, thereby preventing progression to severe morbidity or mortality.
Q3: Are similar maternal risk models used and validated in India?
Yes. Clinicians in India utilize tools like the Maternal Severity Index (MSI) and Maternal Severity Score (MSS) for risk stratification in patients with potentially life-threatening obstetric complications. Local studies have shown good discriminative performance, but they also highlight the importance of model recalibration for better accuracy in the specific population.
References
- Luo Z et al. Prediction Models for Severe Maternal Outcomes in Non-ICU Settings: Systematic Review. BJOG. 2026 Jan 05. doi: 10.1111/1471-0528.70124. PMID: 41487074.
- Keepanasseril A, et al. External validation of the Maternal Severity Index for predicting maternal death following potentially life-threatening complications during pregnancy and childbirth: a single-centre, prospective observational study. BMJ Open. 2022 Dec 29;12(12):e067112.
- Magar JS, Rustagi PS, Malde AD. Retrospective analysis of patients with severe maternal morbidity receiving anaesthesia services using ‘WHO near miss approach’ and the applicability of maternal severity score as a predictor of maternal outcome. Indian J Anaesth. 2020 Jul;64(7):585-593.