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Comparison of early warning scores for predicting clinical deterioration and infection in obstetric patients
David E Arnolds1, Kyle A Carey2, Lena Braginsky2
1University of Chicago, Chicago, IL, USA. davidarn@med.umich.edu.
Insights
The electronic Cardiac Arrest Risk Triage (eCART) score demonstrated superior accuracy in predicting patient deterioration and infection on obstetric wards compared to traditional obstetric and general early warning scores. Maternal early warning scores also outperformed general scores.
Area of Science:
- Obstetrics
- Medical Informatics
- Patient Safety
Background:
- Early warning scores (EWS) are crucial for identifying hospitalized patients at risk of clinical deterioration.
- While general EWS exist, obstetric-specific scores are needed due to unique physiological differences and complications in pregnant and postpartum patients.
- The comparative effectiveness of general versus obstetric EWS for predicting deterioration and infection in maternal wards remains unclear.
Purpose of the Study:
- To compare the performance of various obstetric and general early warning scores in predicting clinical deterioration and new infections among hospitalized obstetric patients.
- To evaluate the accuracy of machine learning-based scores against traditional EWS in the obstetric population.
Main Methods:
- An observational cohort study included 19,611 patients on obstetric wards from November 2008 to December 2018.
- Compared obstetric scores (MEOWS, MEWC, MEWT) and general scores (MEWS, NEWS, eCART) using Area Under the Receiver Operating Characteristic Curve (AUC).
- Primary outcomes were transfer to the intensive care unit (ICU) and/or death, and new-onset infection.
Main Results:
- The machine learning-based eCART score showed the highest discrimination for deterioration (AUC 0.86) and infection (AUC 0.77).
- Obstetric scores (MEOWS, MEWC, MEWT) demonstrated higher accuracy than general paper-based scores (MEWS, NEWS) but were less accurate than eCART.
- Deterioration occurred in 0.2% of patients, and infection in 0.4%.
Conclusions:
- The eCART score exhibited superior accuracy in predicting deterioration and infection in antepartum and postpartum patients.
- Maternal early warning scores were more accurate than the MEWS and NEWS scores.
- These findings have significant implications for risk stratification in maternal care, potentially reducing false alarms through improved accuracy.
Background:
Early warning scores are designed to identify hospitalized patients who are at high risk of clinical deterioration. Although many general scores have been developed for the medical-surgical wards, specific scores have also been developed for obstetric patients due to differences in normal vital sign ranges and potential complications in this unique population. The comparative performance of general and obstetric early warning scores for predicting deterioration and infection on the maternal wards is not known.
Methods:
This was an observational cohort study at the University of Chicago that included patients hospitalized on obstetric wards from November 2008 to December 2018. Obstetric scores (modified early obstetric warning system (MEOWS), maternal early warning criteria (MEWC), and maternal early warning trigger (MEWT)), paper-based general scores (Modified Early Warning Score (MEWS) and National Early Warning Score (NEWS), and a general score developed using machine learning (electronic Cardiac Arrest Risk Triage (eCART) score) were compared using the area under the receiver operating characteristic score (AUC) for predicting ward to intensive care unit (ICU) transfer and/or death and new infection.
Results:
A total of 19,611 patients were included, with 43 (0.2%) experiencing deterioration (ICU transfer and/or death) and 88 (0.4%) experiencing an infection. eCART had the highest discrimination for deterioration (p < 0.05 for all comparisons), with an AUC of 0.86, followed by MEOWS (0.74), NEWS (0.72), MEWC (0.71), MEWS (0.70), and MEWT (0.65). MEWC, MEWT, and MEOWS had higher accuracy than MEWS and NEWS but lower accuracy than eCART at specific cut-off thresholds. For predicting infection, eCART (AUC 0.77) had the highest discrimination.
Conclusions:
Within the limitations of our retrospective study, eCART had the highest accuracy for predicting deterioration and infection in our ante- and postpartum patient population. Maternal early warning scores were more accurate than MEWS and NEWS. While institutional choice of an early warning system is complex, our results have important implications for the risk stratification of maternal ward patients, especially since the low prevalence of events means that small improvements in accuracy can lead to large decreases in false alarms.
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