Artículo

Early warning systems for identifying severe maternal outcomes: findings from the WHO global maternal sepsis study

Chimwaza, Yamikani ; Hunt, Alexandra ; Oliveira Ciabati, Livia ; Bonnett, Laura ; Abalos, Edgardo ; Cuesta, Cristina ; Souza, João Paulo ; Bonet, Mercedes ; Brizuela, Vanessa ; Lissauer, David ; Mohammad Iqbal Aman, null ; Noormal, Bashir ; Espinoza, Marisa ; Pasquale, Julia ; Leroy, Charlotte ; Roelens, Kristien ; Vandenberghe, Griet ; Urlyss Agossou, M. Christian ; Keke, Sourou Goufodji ; Aguemon, Christiane Tshabu ; Apaza Peralta, Patricia Soledad ; Altamirano, Víctor Conde ; Muñoz, Rosalinda Hernández ; Cecatti, José Guilherme ; Ribeiro do Valle, Carolina ; Batiene, Vincent ; Cisse, Kadari ; Ouedraogo, Henri Gautier ; Cheang, Kannitha ; Lam, Phirun ; Rathavy, Tung ; Simo, Elie ; Tebeu, Pierre-Marie ; Yakana, Emah Irene ; Carvajal, Javier ; Escobar, María Fernanda ; Fernández, Paula ; Colmorn, Lotte Berdiin ; Langhoff-Roos, Jens ; Mereci, Wilson ; Vélez, Paola ; Eldin, Yasser Salah ; Sultan, Alaa ; Teklu, Alula M. ; Worku, Dawit ; Adanu, Richard ; Govule, Philip ; Lwanga, Charles Noora ; Arriaga Romero, William Enrique ; Flores Aceituno, María Guadalupe ; Bustillo, Carolina ; Lara, Bredy ; Kumar, Vijay ; Suri, Vanita ; Trikha, Sonia ; Cetin, Irene ; Donati, Serena ; Personeni, Carlo ; Baimussanova, Guldana ; Kabylova, Saule ; Sagyndykova, Balgyn ; Gwako, George ; Osoti, Alfred ; Qureshi, Zahida ; Asylbasheva, Raisa ; Boobekova, Aigul ; Seksenbaeva, Damira ; Itani, Saad Eddine ; Minkauskienė, Meilė ; Ramašauskaitė, Diana ; Chikhwaza, Owen ; Gadama, Luis ; Malunga, Eddie ; Dembele, Haoua ; Sangho, Hamadoun ; Zerbo, Fanta Eliane ; Serapio, Filiberto Dávila ; Maldonado, Nazarea Herrera ; Islas Castañeda, Juan I. ; Cauaus, Tatiana ; Curteanu, Ala ; Petrov, Victor ; Buyanjargal, Yadamsuren ; Khishgee, Seded ; Lkhagvasuren, Bat-Erdene ; Essolbi, Amina ; Moulki, Rachid ; Jaze, Zara ; Mariano, Arlete ; Osman, Nafissa Bique ; Thway Einda, Hla Mya ; Maung, Thae Maung ; Tin, Khaing Nwe ; Gurung, Tara ; Shrestha, Amir Babu ; Shrestha, Sangeeta ; Bloemenkamp, Kitty ; Rijken, Marcus J. ; Van Den Akker, Thomas ; Estrada, María Esther ; Pavón Gómez, Néstor J. ; Adesina, Olubukola ; Aimakhu, Chris ; Fawole, Bukola ; Chaudhri, Rizwana ; Hamid, Saima ; Khan, M. Adnan ; Hernández, María del Pilar Huatuco ; Zavaleta Pimentel, Nelly M. ; Andal, Maria Lu ; Recidoro, Zenaida Dy ; Martin, Carolina Paula ; Budianu, Mihaela ; Pușcașiu, Lucian ; Diouf, Léopold ; Guirassy, Dembo ; Moreira, Philippe Marc ; Borovsky, Miroslav ; Kovac, Ladislav ; Kristufkova, Alexandra ; Cebekhulu, Sylvia ; Cornelissen, Laura ; Soma Pillay, Priya ; Cararach, Vicenç ; López, Marta ; Vidal Benedé, María José ; Jayakody, Hemali ; Jayaratne, Kapila ; Althabe, Fernando Icon

Otros responsables

Fecha

01/2025

Editorial

Elsevier

Revista

eClinicalMedicine

Título del libro

Nombre del evento

Fecha del evento

Institución organizadora

ISSN

2589-5370

eISSN

ISBN

Idioma

Inglés

Tipo del evento

Clasificación temática

Resumen

Background: 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.Methods: 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.Findings: 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.Interpretation: 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.Funding: 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.

Colecciones

Aprobación

Revisión

Complementado por

Referenciado por

Licencia Creative Commons

Excepto donde se indique lo contrario, la licencia de este ítem se describe como by