Cytokines in Preterm Delivery: Proposal of a New Diagnostic Algorithm
Predicting preterm delivery within 7 days is very important for the proper timing of glucocorticosteroid administration. If within 7 days after glucocorticosteroid administration, the delivery does not occur, it remains questionable if repeated glucocorticosteroid therapy results in improved infant...
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| Format: | Article |
| Language: | English |
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Wiley
2018-01-01
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| Series: | Journal of Immunology Research |
| Online Access: | http://dx.doi.org/10.1155/2018/8073476 |
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| _version_ | 1832560565542715392 |
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| author | Grzegorz Raba Jacek Tabarkiewicz |
| author_facet | Grzegorz Raba Jacek Tabarkiewicz |
| author_sort | Grzegorz Raba |
| collection | DOAJ |
| description | Predicting preterm delivery within 7 days is very important for the proper timing of glucocorticosteroid administration. If within 7 days after glucocorticosteroid administration, the delivery does not occur, it remains questionable if repeated glucocorticosteroid therapy results in improved infant respiratory function. Therefore, differentiation of preterm delivery from false preterm delivery is clinically significant. The aim of this study was to create a diagnostic algorithm to distinguish preterm delivery from false preterm delivery on the basis of concentrations of selected cytokines. The study group (n=622) were patients hospitalized due to threatened preterm delivery. To assess the concentration of cytokines in the serum, we used a multiplex method, which allows simultaneous determination of 13 cytokines. The sets consist of the following cytokines: IGFBP-1, IGFBP-2, BDNF, L-Selectin, E-Selectin, ICAM-1, PECAM, VCAM-1, MIP-1d, MIP-3b, Eotaxin-1, Eotaxin-2, and BLC. In the study group, 67.8% patients had preterm delivery and 32.2% had false preterm delivery. Based on the analysis of cytokine concentrations, a classification tree to distinguish between preterm delivery and false preterm delivery was created. Our findings show the possibility of prediction of preterm delivery with the use of a classification and regression tree of selected cytokine concentration. |
| format | Article |
| id | doaj-art-eb22aad0bd5f4832b272c75b98984dde |
| institution | Kabale University |
| issn | 2314-8861 2314-7156 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Immunology Research |
| spelling | doaj-art-eb22aad0bd5f4832b272c75b98984dde2025-02-03T01:27:17ZengWileyJournal of Immunology Research2314-88612314-71562018-01-01201810.1155/2018/80734768073476Cytokines in Preterm Delivery: Proposal of a New Diagnostic AlgorithmGrzegorz Raba0Jacek Tabarkiewicz1Institute of Obstetric and Emergency Medicine, Faculty of Medicine, University of Rzeszow, Ul. Pigonia 6, 35-310 Rzeszów, PolandCentre for Innovative Research in Medical and Natural Sciences’, Faculty of Medicine, University of Rzeszow, Ul. Warzywna 1a, 35-959 Rzeszów, PolandPredicting preterm delivery within 7 days is very important for the proper timing of glucocorticosteroid administration. If within 7 days after glucocorticosteroid administration, the delivery does not occur, it remains questionable if repeated glucocorticosteroid therapy results in improved infant respiratory function. Therefore, differentiation of preterm delivery from false preterm delivery is clinically significant. The aim of this study was to create a diagnostic algorithm to distinguish preterm delivery from false preterm delivery on the basis of concentrations of selected cytokines. The study group (n=622) were patients hospitalized due to threatened preterm delivery. To assess the concentration of cytokines in the serum, we used a multiplex method, which allows simultaneous determination of 13 cytokines. The sets consist of the following cytokines: IGFBP-1, IGFBP-2, BDNF, L-Selectin, E-Selectin, ICAM-1, PECAM, VCAM-1, MIP-1d, MIP-3b, Eotaxin-1, Eotaxin-2, and BLC. In the study group, 67.8% patients had preterm delivery and 32.2% had false preterm delivery. Based on the analysis of cytokine concentrations, a classification tree to distinguish between preterm delivery and false preterm delivery was created. Our findings show the possibility of prediction of preterm delivery with the use of a classification and regression tree of selected cytokine concentration.http://dx.doi.org/10.1155/2018/8073476 |
| spellingShingle | Grzegorz Raba Jacek Tabarkiewicz Cytokines in Preterm Delivery: Proposal of a New Diagnostic Algorithm Journal of Immunology Research |
| title | Cytokines in Preterm Delivery: Proposal of a New Diagnostic Algorithm |
| title_full | Cytokines in Preterm Delivery: Proposal of a New Diagnostic Algorithm |
| title_fullStr | Cytokines in Preterm Delivery: Proposal of a New Diagnostic Algorithm |
| title_full_unstemmed | Cytokines in Preterm Delivery: Proposal of a New Diagnostic Algorithm |
| title_short | Cytokines in Preterm Delivery: Proposal of a New Diagnostic Algorithm |
| title_sort | cytokines in preterm delivery proposal of a new diagnostic algorithm |
| url | http://dx.doi.org/10.1155/2018/8073476 |
| work_keys_str_mv | AT grzegorzraba cytokinesinpretermdeliveryproposalofanewdiagnosticalgorithm AT jacektabarkiewicz cytokinesinpretermdeliveryproposalofanewdiagnosticalgorithm |