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Labor estimation by informational objective assessment (LEIOA) for preterm delivery prediction

  • General Gynecology
  • Published:
Archives of Gynecology and Obstetrics Aims and scope Submit manuscript

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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Acknowledgements

This work was supported by the Basque Government (Grants PRE-2015-1-194 and IT974-16) and the Ferring S.A. Laboratories (Grant BC/A/16/004). Authors would also like to thank the Archive Service of Cruces University Hospital for their continuous help and effort on the sample acquisition.

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Authors and Affiliations

  1. Department of Mathematics, University of the Basque Country UPV/EHU, Barrio Sarriena, s/n, 48940, Leioa, Spain

    Iker Malaina, Luis Martínez, Ildefonso M. De la Fuente & Martín Blás Pérez

  2. Department of Applied Mathematics, Statistics and Operation Research, University of the Basque Country UPV/EHU, Leioa, Spain

    Larraitz Aranburu

  3. Obstetrics and Gynecology Department, Cruces University Hospital, Barakaldo, Spain

    Luis Fernández-Llebrez, Leire González, Itziar Arana & Roberto Matorras

  4. Department of Cell Biology and Histology, University of the Basque Country UPV/EHU, Leioa, Spain

    Carlos Bringas

  5. Department of Nutrition, CEBAS-CSIC Institute, Murcia, Spain

    Ildefonso M. De la Fuente

  6. Department of Medical-Surgical Specialties, University of the Basque Country UPV/EHU, Leioa, Spain

    Roberto Matorras

Authors
  1. Iker Malaina
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  2. Larraitz Aranburu
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  3. Luis Martínez
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  4. Luis Fernández-Llebrez
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  5. Carlos Bringas
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  6. Ildefonso M. De la Fuente
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  7. Martín Blás Pérez
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  8. Leire González
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  9. Itziar Arana
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  10. Roberto Matorras
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Contributions

IM: protocol/project development, data collection or management, data analysis, and manuscript writing/editing. LA: protocol/project development, data collection or management, data analysis, and manuscript writing/editing. LM: protocol/project development, data analysis, and manuscript writing/editing. LF-L: protocol/project development, data collection, or management. CB: data analysis and manuscript writing/editing. IMdlF: protocol/project development and manuscript writing/editing. MBP: data collection or management and data analysis. LG: data collection or management. IA: data collection or management. RM: protocol/project development, data collection or management, data analysis, and manuscript writing/editing.

Corresponding author

Correspondence to Iker Malaina.

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Malaina, I., Aranburu, L., Martínez, L. et al. Labor estimation by informational objective assessment (LEIOA) for preterm delivery prediction. Arch Gynecol Obstet 297, 1213–1220 (2018). https://doi.org/10.1007/s00404-018-4729-1

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  • DOI: https://doi.org/10.1007/s00404-018-4729-1

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