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Helguera-Repetto AC, Soto-Ramírez MD, Villavicencio-Carrisoza O, Yong-Mendoza S, Yong-Mendoza A, León-Juárez M, González-Y-Merchand JA, Zaga-Clavellina V, Irles C. Neonatal Sepsis Diagnosis Decision-Making Based on Artificial Neural Networks. Front Pediatr 2020; 8:525. [PMID: 33042902 PMCID: PMC7518045 DOI: 10.3389/fped.2020.00525] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/15/2020] [Accepted: 07/24/2020] [Indexed: 12/21/2022] Open
Abstract
Neonatal sepsis remains difficult to diagnose due to its non-specific signs and symptoms. Traditional scoring systems help to discriminate between septic or not patients, but they do not consider every single patient particularity. Thus, the purpose of this study was to develop an early- and late-onset neonatal sepsis diagnosis model, based on clinical maternal and neonatal data from electronic records, at the time of clinical suspicion. A predictive model was obtained by training and validating an artificial Neural Networks (ANN) algorithm with a balanced dataset consisting of preterm and term non-septic or septic neonates (early- and late-onset), with negative and positive culture results, respectively, using 25 maternal and neonatal features. The outcome of the model was sepsis or not. The performance measures of the model, evaluated with an independent dataset, outperformed physician's diagnosis using the same features based on traditional scoring systems, with a 93.3% sensitivity, an 80.0% specificity, a 94.4% AUROC, and a regression coefficient of 0.974 between actual and simulated results. The model also performed well-relative to the state-of-the-art methods using similar maternal/neonatal variables. The top 10 factors estimating sepsis were maternal age, cervicovaginitis and neonatal: fever, apneas, platelet counts, gender, bradypnea, band cells, catheter use, and birth weight.
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Affiliation(s)
| | - María Dolores Soto-Ramírez
- Department of Immunobiochemistry, Instituto Nacional de Perinatología, Mexico City, Mexico.,Department of Microbiology, Escuela Nacional de Ciencias Biológicas, Instituto Politécnico Nacional, Mexico City, Mexico
| | - Oscar Villavicencio-Carrisoza
- Department of Immunobiochemistry, Instituto Nacional de Perinatología, Mexico City, Mexico.,Department of Microbiology, Escuela Nacional de Ciencias Biológicas, Instituto Politécnico Nacional, Mexico City, Mexico
| | - Samantha Yong-Mendoza
- Department of Immunobiochemistry, Instituto Nacional de Perinatología, Mexico City, Mexico.,Department of Microbiology, Escuela Nacional de Ciencias Biológicas, Instituto Politécnico Nacional, Mexico City, Mexico
| | - Angélica Yong-Mendoza
- Department of Immunobiochemistry, Instituto Nacional de Perinatología, Mexico City, Mexico
| | - Moisés León-Juárez
- Department of Immunobiochemistry, Instituto Nacional de Perinatología, Mexico City, Mexico
| | - Jorge A González-Y-Merchand
- Department of Microbiology, Escuela Nacional de Ciencias Biológicas, Instituto Politécnico Nacional, Mexico City, Mexico
| | - Verónica Zaga-Clavellina
- Department of Physiology and Cellular Development, Instituto Nacional de Perinatología, Mexico City, Mexico
| | - Claudine Irles
- Department of Physiology and Cellular Development, Instituto Nacional de Perinatología, Mexico City, Mexico
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