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Malaina I, Aranburu L, Martínez L, Fernández-Llebrez L, Bringas C, De la Fuente IM, Pérez MB, González L, Arana I, Matorras R. Labor estimation by informational objective assessment (LEIOA) for preterm delivery prediction. Arch Gynecol Obstet 2018; 297:1213-1220. [PMID: 29508063 DOI: 10.1007/s00404-018-4729-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2017] [Accepted: 02/28/2018] [Indexed: 10/17/2022]
Abstract
PURPOSE To introduce LEIOA, a new screening method to forecast which patients admitted to the hospital because of suspected threatened premature delivery will give birth in < 7 days, so that it can be used to assist in the prognosis and treatment jointly with other clinical tools. METHODS From 2010 to 2013, 286 tocographies from women with gestational ages comprehended between 24 and 37 weeks were collected and studied. Then, we developed a new predictive model based on uterine contractions which combine the Generalized Hurst Exponent and the Approximate Entropy by logistic regression (LEIOA model). We compared it with a model using exclusively obstetric variables, and afterwards, we joined both to evaluate the gain. Finally, a cross validation was performed. RESULTS The combination of LEIOA with the medical model resulted in an increase (in average) of predictive values of 12% with respect to the medical model alone, giving a sensitivity of 0.937, a specificity of 0.747, a positive predictive value of 0.907 and a negative predictive value of 0.819. Besides, adding LEIOA reduced the percentage of incorrectly classified cases by the medical model by almost 50%. CONCLUSIONS Due to the significant increase in predictive parameters and the reduction of incorrectly classified cases when LEIOA was combined with the medical variables, we conclude that it could be a very useful tool to improve the estimation of the immediacy of preterm delivery.
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Affiliation(s)
- Iker Malaina
- Department of Mathematics, University of the Basque Country UPV/EHU, Barrio Sarriena, s/n, 48940, Leioa, Spain.
| | - Larraitz Aranburu
- Department of Applied Mathematics, Statistics and Operation Research, University of the Basque Country UPV/EHU, Leioa, Spain
| | - Luis Martínez
- Department of Mathematics, University of the Basque Country UPV/EHU, Barrio Sarriena, s/n, 48940, Leioa, Spain
| | | | - Carlos Bringas
- Department of Cell Biology and Histology, University of the Basque Country UPV/EHU, Leioa, Spain
| | - Ildefonso M De la Fuente
- Department of Mathematics, University of the Basque Country UPV/EHU, Barrio Sarriena, s/n, 48940, Leioa, Spain.,Department of Nutrition, CEBAS-CSIC Institute, Murcia, Spain
| | - Martín Blás Pérez
- Department of Mathematics, University of the Basque Country UPV/EHU, Barrio Sarriena, s/n, 48940, Leioa, Spain
| | - Leire González
- Obstetrics and Gynecology Department, Cruces University Hospital, Barakaldo, Spain
| | - Itziar Arana
- Obstetrics and Gynecology Department, Cruces University Hospital, Barakaldo, Spain
| | - Roberto Matorras
- Obstetrics and Gynecology Department, Cruces University Hospital, Barakaldo, Spain.,Department of Medical-Surgical Specialties, University of the Basque Country UPV/EHU, Leioa, Spain
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