Chimwaza Yamikani, Hunt Alexandra, Oliveira-Ciabati Livia, Bonnett Laura, Abalos Edgardo, Cuesta Cristina, Souza João Paulo, Bonet Mercedes, Brizuela Vanessa, Lissauer David
Malawi-Liverpool Wellcome Programme, Blantyre, Malawi.
Department of Women's and Children's Health, Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool, United Kingdom.
EClinicalMedicine. 2024 Dec 6;79:102981. doi: 10.1016/j.eclinm.2024.102981. eCollection 2025 Jan.
Infections and sepsis are leading causes of morbidity and mortality in women during pregnancy and the post-pregnancy period. Using data from the 2017 WHO Global Maternal Sepsis Study, we explored the use of early warning systems (EWS) in women at risk of sepsis-related severe maternal outcomes.
On April 27, 2023, we searched the literature for EWS in clinical use or research in obstetric populations. We calculated the proportion of women for whom each existing EWS identified them as at risk for developing severe maternal outcomes by infection severity (complications and severe maternal outcomes). Sensitivity, specificity, positive and negative likelihood ratios, diagnostic odds ratios, and J statistics were calculated to assess EWS performance. Machine learning was used to test the diagnostic potential of routine maternal sepsis markers.
21 EWS were assessed in 2560 women from 46 countries with suspected or confirmed infections. The NICE Risk Stratification tool, Modified Shock Index, maternity Systemic Inflammatory Response Syndrome, and Early Maternal Infection Prompts scores had high sensitivity (88.1-97.5%) for identifying sepsis-related severe maternal outcomes. The quick Sequential Organ Failure Assessment (SOFA) in Pregnancy score and Obstetrically modified SOFA had high specificity (90.4-100%) for identifying women with sepsis-related severe maternal outcomes. Furthermore, combinations of sepsis markers had very low sensitivity and high specificity using machine learning.
No score demonstrated enough diagnostic accuracy to be used alone to identify sepsis. However, obstetric-and sepsis-specific EWS performed better for early identification of maternal sepsis than non-obstetric and non-sepsis-specific scoring systems. There are limitations to applying EWS to real-world data, mainly due to the incompleteness of medical data that hinders EWS effectiveness. There is a need to continue developing and testing criteria for early identification of maternal sepsis.
UNDP-UNFPA-UNICEF-WHO-World Bank Special Programme of Research, Development and Research Training in Human Reproduction (HRP), WHO, Merck for Mothers, U.S. Agency for International Development, Wellcome Trust, and National Institute for Health and Care Research.
感染和脓毒症是孕期及产后女性发病和死亡的主要原因。利用2017年世界卫生组织全球孕产妇脓毒症研究的数据，我们探讨了预警系统（EWS）在有脓毒症相关严重孕产妇结局风险的女性中的应用。
2023年4月27日，我们检索了有关产科人群中正在临床使用或研究的EWS的文献。我们计算了每种现有EWS将女性识别为有因感染严重程度（并发症和严重孕产妇结局）而发生严重孕产妇结局风险的比例。计算敏感性、特异性、阳性和阴性似然比、诊断比值比和J统计量以评估EWS的性能。使用机器学习来测试常规孕产妇脓毒症标志物的诊断潜力。
对来自46个国家的2560名疑似或确诊感染的女性评估了21种EWS。英国国家卫生与临床优化研究所（NICE）风险分层工具、改良休克指数、孕产妇全身炎症反应综合征和早期孕产妇感染提示评分在识别脓毒症相关严重孕产妇结局方面具有较高的敏感性（88.1 - 97.5%）。妊娠快速序贯器官衰竭评估（SOFA）评分和产科改良SOFA在识别有脓毒症相关严重孕产妇结局的女性方面具有较高的特异性（90.4 - 100%）。此外，使用机器学习，脓毒症标志物组合的敏感性非常低而特异性高。
没有一个评分显示出足够的诊断准确性可单独用于识别脓毒症。然而，产科和脓毒症特异性EWS在早期识别孕产妇脓毒症方面比非产科和非脓毒症特异性评分系统表现更好。将EWS应用于实际数据存在局限性，主要是由于医疗数据不完整阻碍了EWS的有效性。需要继续制定和测试早期识别孕产妇脓毒症的标准。
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