Allouche, M., Huissoud, C., Guyard-Boileau, B., Rouzier, R., Parant, O. (2011). Development and validation of nomograms for predicting preterm delivery. American Journal of Obstetrics & Gynecology, 204(3), 242.e241–248. https://doi.org/10.1016/j.ajog.2010.09.030
Google Scholar Altman, D. G., Royston, P. (2006). The cost of dichotomising continuous variables. BMJ, 332(7549), 1080. https://doi.org/10.1136/bmj.332.7549.1080
Google Scholar Baer, R. J., McLemore, M. R., Adler, N., Oltman, S. P., Chambers, B. D., Kuppermann, M., Pantell, M. S., Rogers, E. E., Ryckman, K. K., Sirota, M., Rand, L., Jelliffe-Pawlowski, L. L. (2018). Pre-pregnancy or first-trimester risk scoring to identify women at high risk of preterm birth. European Journal of Obstetrics, Gynecology, and Reproductive Biology, 231, 235–240. https://doi.org/10.1016/j.ejogrb.2018.11.004
Google Scholar Blencowe, H., Cousens, S., Oestergaard, M. Z., Chou, D., Moller, A. B., Narwal, R., Adler, A. J., Garcia, C. V., Rohde, S., Say, L., Lawn, J. E. (2012). National, regional, and worldwide estimates of preterm birth rates in the year 2010 with time trends since 1990 for selected countries: a systematic analysis and implications. The Lancet, 379(9832), 2162–2172. https://doi.org/10.1016/S0140-6736(12)60820-4
Google Scholar Creasy, R. K., Gummer, B. A., Liggins, G. C. (1980). System for predicting spontaneous preterm birth. Obstetrics & Gynecology, 55(6), 692–695.
Google Scholar | Medline Creasy, R., Krowley, S. (1978). Early detection of premature labor. Perinatal Press, 2, 51.
Google Scholar de Caunes, F., Alexander, G. R., Berchel, C., Guengant, J. P., Papiernik, E. (1990). Anamnestic pregnancy risk assessment. International Journal of Gynecology & Obstetrics, 33(3), 221–227. https://doi.org/10.1016/0020-7292(90)90005-6
Google Scholar Debray, T. P., Damen, J. A., Snell, K. I., Ensor, J., Hooft, L., Reitsma, J. B., Riley, R. D., Moons, K. G. (2017). A guide to systematic review and meta-analysis of prediction model performance. BMJ, 356, i6460. https://doi.org/10.1136/bmj.i6460
Google Scholar Edenfield, S. M., Thomas, S. D., Thompson, W. O., Marcotte, J. J. (1995). Validity of the Creasy risk appraisal instrument for prediction of preterm labor. Nursing Research, 44(2), 76–81.
Google Scholar | Crossref | Medline Eick, S. M., Meeker, J. D., Swartzendruber, A., Rios-McConnell, R., Brown, P., Vélez-Vega, C., Shen, Y., Alshawabkeh, A. N., Cordero, J. F., Ferguson, K. K. (2020). Relationships between psychosocial factors during pregnancy and preterm birth in Puerto Rico. PLoS One, 15(1), e0227976. https://doi.org/10.1371/journal.pone.0227976
Google Scholar Gilman-Sachs, A., Dambaeva, S., Salazar Garcia, M. D., Hussein, Y., Kwak-Kim, J., Beaman, K. (2018). Inflammation induced preterm labor and birth. Journal of Reproductive Immunology, 129, 53–58. https://doi.org/10.1016/j.jri.2018.06.029
Google Scholar Gioan, M., Fenollar, F., Loundou, A., Menard, J. P., Blanc, J., D’Ercole, C., Bretelle, F. (2018). Development of a nomogram for individual preterm birth risk evaluation. Journal of Gynecology Obstetrics and Human Reproduction, 47(10), 545–548. https://doi.org/10.1016/j.jogoh.2018.08.014
Google Scholar Giurgescu, C., Misra, D. P. (2018). Psychosocial factors and preterm birth among black mothers and fathers. MCN: The American Journal of Maternal/Child Nursing, 43(5), 245–251. https://doi.org/10.1097/nmc.0000000000000458
Google Scholar Goldenberg, R. L., Culhane, J. F., Iams, J. D., Romero, R. (2008). Epidemiology and causes of preterm birth. The Lancet, 371(9606), 75–84. https://doi.org/10.1016/s0140-6736(08)60074-4
Google Scholar Goodfellow, L., Care, A., Sharp, A., Ivandic, J., Poljak, B., Roberts, D., Alfirevic, Z. (2019). Effect of QUiPP prediction algorithm on treatment decisions in women with a previous preterm birth: A prospective cohort study. BJOG, 126(13), 1569–1575. https://doi.org/10.1111/1471-0528.15886
Google Scholar Goyal, N. K., Hall, E. S., Greenberg, J. M., Kelly, E. A. (2015). Risk prediction for adverse pregnancy outcomes in a medicaid population. Journal of Women’s Health, 24(8), 681–688. https://doi.org/10.1089/jwh.2014.5069
Google Scholar Holbrook, R. H., Laros, R. K., Creasy, R. K. (1989). Evaluation of a risk-scoring system for prediction of preterm labor. American Journal of Perinatology, 6(1), 62–68.
Google Scholar | Crossref | Medline Hueston, W. J. (1998). Preterm contractions in community settings: II. Predicting preterm birth in women with preterm contractions. Obstetrics & Gynecology, 92(1), 43–46.
Google Scholar | Crossref | Medline Keirse, M. J. N. C. (1989). An evaluation of formal risk scoring for preterm birth. American Journal of Perinatology, 6(2), 226–233. https://doi.org/10.1055/s-2007-999582
Google Scholar Kim, J. I. (2018). Preterm labor and birth: Definition, assessment, and management. Korean Journal of Women Health Nursing, 24(3), 231–232. https://doi.org/10.4069/kjwhn.2018.24.3.231
Google Scholar Kim, J. I., Cho, M. O., Choi, G. Y. (2017). Multiple factors in the second trimester of pregnancy on preterm labor symptoms and preterm birth. Journal of Korean Academy of Nursing, 47(3), 357–366. https://doi.org/10.4040/jkan.2017.47.3.357
Google Scholar Kleinrouweler, C. E., Cheong-See, F. M., Collins, G. S., Kwee, A., Thangaratinam, S., Khan, K. S., Mol, B. W. J., Pajkrt, E., Moons, K. G. M., Schuit, E. (2016). Prognostic models in obstetrics: Available, but far from applicable. American Journal of Obstetrics and Gynecology, 214(1), 79–90 e36. https://doi.org/10.1016/j.ajog.2015.06.013
Google Scholar Koullali, B., Oudijk, M. A., Nijman, T. A., Mol, B. W., Pajkrt, E. (2016). Risk assessment and management to prevent preterm birth. Seminars in Fetal & Neonatal Medicine, 21(2), 80–88. https://doi.org/10.1016/j.siny.2016.01.005
Google Scholar Lee, K. J., Yoo, J., Kim, Y. H., Kim, S. H., Kim, S. C., Kim, Y. H., Kwak, D. E., Kil, K., Park, M. H., Park, H., Shim, J. Y., Son, G. H., Lee, K. A., Oh, S.-Y., Oh, K. J., Cho, G. J., Shim, S. Y., Cho, S. J., Cho, H. Y.…Kim, Y. J. (2020). The clinical usefulness of predictive models for preterm birth with potential benefits: A KOrean preterm collaborate network (KOPEN) registry-linked data-based cohort study. International Journal of Medical Sciences, 17(1), 1–12. https://doi.org/10.7150/ijms.37626
Google Scholar Luo, W., Huning, E. Y., Tran, T., Phung, D., Venkatesh, S. (2016). Screening for post 32-week preterm birth risk: How helpful is routine perinatal data collection? Heliyon, 2(6), e00119. https://doi.org/10.1016/j.heliyon.2016.e00119
Google Scholar Mercer, B. M., Goldenberg, R. L., Das, A., Moawad, A. H., Iams, J. D., Meis, P. J., Copper, R. L., Johnson, F., Thom, E., McNellis, D., Miodovnik, M., Menard, M. K., Caritis, S. N., Thurnau, G. R., Bottoms, S. F., Roberts, J. (1996). The preterm prediction study: A clinical risk assessment system. American Journal of Obstetrics and Gynecology, 174(6), 1885–1893; discussion 1893-1885. https://doi.org/10.1016/s0002-9378(96)70225-9
Google Scholar Misra, D. P., O’Campo, P., Strobino, D. (2001). Testing a sociomedical model for preterm delivery. Paediatric and Perinatal Epidemiology, 15(2), 110–122. https://doi.org/10.1046/j.1365-3016.2001.00333.x
Google Scholar Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G. (2009). Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. BMJ, 339, b2535. https://doi.org/10.1136/bmj.b2535
Google Scholar Moons, K. G. M., Royston, P., Vergouwe, Y., Grobbee, D. E., Altman, D. G. (2009). Prognosis and prognostic research: What, why, and how? BMJ, 338, b375. https://doi.org/10.1136/bmj.b375
Google Scholar Moons, K. G. M., Wolff, R. F., Riley, R. D., Whiting, P. F., Westwood, M., Collins, G. S., Reitsma, J. B., Kleijnen, J., Mallett, S. (2019). PROBAST: A tool to assess risk of bias and applicability of prediction model studies: Explanation and elaboration. Annals of Internal Medicine, 170(1), W1–W33. https://doi.org/10.7326/M18-1377
Google Scholar Moons, K. G., Altman, D. G., Reitsma, J. B., Ioannidis, J. P., Macaskill, P., Steyerberg, E. W., Vickers, A. J., Ransohoff, D. F., Collins, G. S. (2015). Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): Explanation and elaboration. Annals of Internal Medicine, 162(1), W1–73. https://doi.org/10.7326/M14-0698
Google Scholar Moons, K. G., de Groot, J. A., Bouwmeester, W., Vergouwe, Y., Mallett, S., Altman, D. G., Reitsma, J. B., Collins, G. S. (2014). Critical appraisal and data extraction for systematic reviews of prediction modelling studies: The CHARMS checklist. PLoS Medicine, 11(10), e1001744. https://doi.org/10.1371/journal.pmed.1001744
Google Scholar Morken, N. H., Källen, K., Jacobsson, B. (2014). Predicting risk of spontaneous preterm delivery in women with a singleton pregnancy. Paediatric and Perinatal Epidemiology, 28(1), 11–22. https://doi.org/10.1111/ppe.12087
Google Scholar Nast, I., Bolten, M., Meinlschmidt, G., Hellhammer, D. H. (2013). How to measure prenatal stress? A systematic review of psychometric instruments to assess psychosocial stress during pregnancy. Paediatric and Perinatal Epidemiology, 27(4), 313–322. https://doi.org/10.1111/ppe.12051
Google Scholar Oskovi Kaplan, Z. A., Ozgu-Erdinc, A. S. (2018). Prediction of preterm birth: Maternal characteristics, ultrasound markers, and biomarkers: An updated overview. Journal of Pregnancy, 2018, 8367571. https://doi.org/10.1155/2018/8367571
Google Scholar Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372, n71. https://doi.org/10.1136/bmj.n71
Google Scholar Riley, R. D., Hayden, J. A., Steyerberg, E. W., Moons, K. G., Abrams, K., Kyzas, P. A., Malats, N., Briggs, A., Schroter, S., Altman, D. G., Altman, D. G. (2013). Prognosis research strategy (PROGRESS) 2: Prognostic factor research. PLoS Medicine, 10(2), e1001380. doi:https://doi.org/10.1371/journal.pmed.1001380
Google Scholar Ruiz, R. J., Dwivedi, A. K., Mallawaarachichi, I., Balcazar, H. G., Stowe, R. P., Ayers, K. S., Pickler, R. (2015). Psychological, cultural and neuroendocrine profiles of risk for preterm birth. BMC Pregnancy and Childbirth, 15, 204. https://doi.org/10.1186/s12884-015-0640-y
Google Scholar Sun, G.-W., Shook, T. L., Kay, G. L. (1996). Inappropriate use of bivariable analysis to screen risk factors for use in multivariable analysis. Journal of Clinical Epidemiology, 49(8), 907–916. https://doi.org/10.1016/0895-4356(96)00025-X
Google Scholar Takagi, K., Satoh, K., Muraoka, M., Takagi, K., Seki, H., Nakabayashi, M., Takeda, S., Yoshida, K., Nishioka, N., Ikenoue, T., Kanayama, N., Kanzaki, T., Sagawa, T., Matsuda, Y. (2012). A mathematical model for predicting outcome in preterm labour. Journal of International Medical Research, 40(4), 1459–1466. https://doi.org/10.1177/147323001204000424
Google Scholar Tan, H., Wen, S. W., Chen, X. K., Demissie, K., Walker, M. (2007). Early prediction of preterm birth for singleton, twin, and triplet pregnancies. European Journal of Obstetrics & Gynecology and Reproductive Biology, 131(2), 132–137. https://doi.org/10.1016/j.ejogrb.2006.04.038
Google Scholar Vogel, J. P., Chawanpaiboon, S., Moller, A. B., Watananirun, K., Bonet, M., Lumbiganon, P. (2018). The global epidemiology of preterm birth. Best Practice & Research: Clinical Obstetrics & Gynaecology, 52, 3–12. https://doi.org/10.1016/j.bpobgyn.2018.04.003
Google Scholar Weber, A., Darmstadt, G. L., Gruber, S., Foeller, M. E., Carmichael, S. L., Stevenson, D. K., Shaw, G. M. (2018). Application of machine-learning to predict early spontaneous preterm birth among nulliparous non-Hispanic black and white women. Annals of Epidemiology, 28(11), 783–789.e781. https://doi.org/10.1016/j.annepidem.2018.08.008
Google Scholar World Health Organization . (2018). Preterm birth. https://www.who.int/news-room/fact-sheets/detail/preterm-birth
Google Scholar Wilson, E. W., Sill, H. K. (1973). Identification of the high risk pregnancy by a scoring system. The New Zealand Medical Journal, 78(503), 437–440.
Google Scholar | Medline Wolff, R. F., Moons, K. G. M., Riley, R. D., Whiting, P. F., Westwood, M., Collins, G. S., Reitsma, J. B., Kleijnen, J., Groupdagger, P. (2019). PROBAST: A tool to assess the risk of bias and applicability of prediction model studies. Annals of Internal Medicine, 170(1), 51–58. https://doi.org/10.7326/M18-1376
Google Scholar Woolery, L. K. F. (1992). Knowledge acquisition for assessment of preterm labor in pregnant women. University of Kansas. http://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=109871619&&site=ehost-live Available from EBSCOhostccm database.(PH.D.)
Google Scholar Zhu, Y. Z., Peng, G. Q., Tian, G. X., Qu, X. L., Xiao, S. Y. (2017). New model for predicting preterm delivery during the second trimester of pregnancy. Scientific Reports, 7(1), 11294. https://doi.org/10.1038/s41598-017-11286-x
Google Scholar