Prediction of preterm birth in nulliparous women using logistic regression and machine learning

Reza Arabi Belaghi; Joseph Beyene; Sarah D. McDonald

doi:10.1371/journal.pone.0252025

Peer Review History

Original SubmissionSeptember 30, 2020
18 Jan 2021 Decision Letter - Pal Bela Szecsi, Editor PONE-D-20-30837 Prediction of Preterm Birth in Nulliparous Women Using Logistic Regression and Machine Learning PLOS ONE Dear Dr. McDonald, Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process. In addition to the issues raised by the reviewers, please provide the code for AI if possible. It is an interesting approach and might be further improved with new data. Please submit your revised manuscript by Feb 13 2021 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. 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Please include captions for your Supporting Information files at the end of your manuscript, and update any in-text citations to match accordingly. Please see our Supporting Information guidelines for more information: http://journals.plos.org/plosone/s/supporting-information [Note: HTML markup is below. Please do not edit.] Reviewers' comments: Reviewer's Responses to Questions Comments to the Author 1. Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: Yes Reviewer #2: Yes ******** 2. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: Yes Reviewer #2: Yes ****** 3. Have the authors made all data underlying the findings in their manuscript fully available? The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified. Reviewer #1: Yes Reviewer #2: Yes ****** 4. Is the manuscript presented in an intelligible fashion and written in standard English? PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here. Reviewer #1: No Reviewer #2: Yes ****** 5. Review Comments to the Author Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters) Reviewer #1: In this study Authors constructed by using logistic regression analysis and machine learning technique an algorithm to predict preterm labor defined as < 37 weeks. The argument is of interest, the number of women considered relevant and an elegant statistical approach was used. So I would like to congratulate with Authors for their effort My comments are as follows 1) did Authors differentiate spontaneous from iatrogenic preterm delivery? This is of crucial since women with pregestational diseases (diabetes) or developing medical complications are frequently induced preterm and this may flaw the algorithm 2)although stated as a limitation I suggest Authors to perform their analysis also at earlier gestational age (e.g. < 34 and or 32 weeks) that are more clinical significant 3)it should be acknowledged that data on ultrasonographic measurement of the uterine cervix are missing since at present it is considered the powerful predictive variables. Reviewer #2: In this manuscript, Belaghi et al use a database of nulliparous women who delivered in Ontario, Canada to predict PTB using both logistic regression and machine learning techniques. They found that using data available from the second trimester improved their prediction models using both approaches. The paper is well-written and easy to understand. However, several important questions arise from this study in its current form: 1. Spontaneous PTB: How was this defined? This is not clear from the manuscript. This should be clarified. Further, as the authors allude but do not directly discuss, PTB can be broadly classified into provider-initiated PTB and spontaneous PTB. The pathophysiology of spontaneous PTB is very different than that of provider-initiated PTB. Although this study is by no means the first study to group PTB broadly into one category, it should directly address the reality that PTB has many phenotypes and that a prediction algorithm that is trying to predict all PTB inherently has many limitations. An algorithm that predicts spontaneous PTB may be of greater utility and greater accuracy than an algorithm that tries to predict both spontaneous PTB and HELLP syndrome necessitating provider-initiated delivery. Further, in the abstract, the authors compare their model to the negative predictive value of a fetal fibronectin test. A fetal fibronectin test is ONLY used to predict spontaneous PTB, not all PTB. Consequently, this comparison is of little utility. Further, what percentage of PTB included in this study was spontaneous? This is not clear from the manuscript. If possible, the authors should provider information on the various phenotypes of PTB and how they were ascertained. This information is of significant clinical utility. 2. PAPP-A: In the abstract, the authors mention "abnormal pregnancy-associated plasma protein-A contractions" as being strongly associated with PTB. However, how a PAPP-A contraction was defined is unclear, as this contraction is never mentioned again. Is this meant to read concentration, not contraction? 3. Complications during pregnancy: No definition of moderate complications is provided in the manuscript. Additionally, what percentage of women had each of the severe complications listed on page 6 is not clear. The only clarity regarding this variable is provided on page 6: "The variable, complications during pregnancy, had more than 600 categories and we classified those data into three groups based on the expert opinion of our in-home maternal-fetal specialist, including no complications, moderate complications, and severe complications (including hypertensive disorder, placental abnormalities, and maternal complications during this pregnancy, such as antepartum bleeding)." As severe complications of pregnancy were highly associated with PTB, it would be helpful to better understand this variable. Further, if possible, these complications should be separated and included in the model, as one would expect preeclampsia and HELLP are more likely to lead to provider-initiated PTB and antepartum bleeding to be associated with abruption and preterm labor, which would likely lead to spontaneous PTB. 4. Aneuploidy: This study does not directly address aneuploidy or trisomy pregnancies. However, the most significant predictors of PTB in this study were diabetes and PAPP-A. Diabetes and low PAPP-A are both associated with trisomy pregnancies, and trisomy pregnancies have increased risks of PTB. Consequently, this should be addressed/clarified in this manuscript. 5. Grammar: Please carefully review the manuscript at length for typos. Below are several that were identified on my review: - A parenthesis is missing after "(Supplemental Table 2" on page 6. - In the last paragraph on page 9, the first sentence should include an "s" after "other model." - An extra parenthesis should be removed after "(logistic regression, Supplementary Figure 2))" and "(logistic regression, Supplementary Figure 3))" on page 10. - "Table S8" should be renamed "Supplemental Table 8" to be consistent with the rest of the manuscript. ****** 6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). 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Revision 1
8 Mar 2021 Author Response Dear Editor and Reviewers, We greatly appreciate your careful reading of our manuscript and the helpful feedback you have provided. We have revised the manuscript based on your comments, as detailed below. We thank you again for your valuable time and we hope that you find our revised manuscript acceptable for publication. Our responses to your comments are given in Blue font. We also added the requested revisions in the text in the Blue font. We also reformatted the paper according to the journal guidelines. Reviewer #1: In this study Authors constructed by using logistic regression analysis and machine learning technique an algorithm to predict preterm labor defined as < 37 weeks. The argument is of interest, the number of women considered relevant and an elegant statistical approach was used. So I would like to congratulate with Authors for their effort My comments are as follows 1) did Authors differentiate spontaneous from iatrogenic preterm delivery? This is of crucial since women with pregestational diseases (diabetes) or developing medical complications are frequently induced preterm and this may flaw the algorithm We thank the reviewer for this important comment. We did examine spontaneous PTB as a secondary outcome, excluding medically induced PTB and women with PPROM. Results from this analysis are presented in the last paragraph of the Results section and are included below for your convenience. Prediction of spontaneous PTB For models predicting spontaneous PTB, during the first trimester the AUC ranged from 55% (random forests) to 59% (logistic regression, Supplementary Figure 2). During the second trimester, AUC ranged from 58% (decision trees) to 64% (logistic regression, Supplementary Figure 3). Both machine learning and logistic regression generated negative predictive values of approximately 94% for spontaneous PTB during the first and second trimesters (Supplementary Table 8). We emphasize that pregnancy complications, hypertensive disorder, and other medically induced PTB were not included in these analyses. 2) Although stated as a limitation I suggest Authors to perform their analysis also at earlier gestational age (e.g. < 34 and or 32 weeks) that are more clinical significant We previously developed separate prediction models for preterm birth <32 weeks and <28 weeks. Because of the differing prevalence, and risk factors, between these outcomes and PTB <37 weeks and because the analyses for the present manuscript are focused on nulliparous women, we chose to publish the models for earlier PTB outcomes in a separate manuscript, which is currently in press elsewhere. 3) It should be acknowledged that data on ultrasonographic measurement of the uterine cervix are missing since at present it is considered the powerful predictive variables. We thank the reviewer for this suggestion. Although ultrasonographic measurement of the uterine cervix during the second trimester is indeed a strong predictor of preterm birth, the BORN database does not include data on such measurements. We now discuss this in the limitations section (last paragraph of the Discussion). Reviewer #2: In this manuscript, Belaghi et al use a database of nulliparous women who delivered in Ontario, Canada to predict PTB using both logistic regression and machine learning techniques. They found that using data available from the second trimester improved their prediction models using both approaches. The paper is well-written and easy to understand. However, several important questions arise from this study in its current form: 1. Spontaneous PTB: How was this defined? This is not clear from the manuscript. This should be clarified. We defined spontaneous PTB using the definition from Maghsouldu et al. (2019), as follows: not “induced”, not “caesarean section” and not “augmented labor We now clarify the definition of spontaneous PTB in the outcome subsection (second paragraph of the Methods). Maghsoudlou, S., Yu, Z. M., Beyene, J., & McDonald, S. D. (2019). Phenotypic classification of preterm birth among nulliparous women: a population-based cohort study. Journal of Obstetrics and Gynaecology Canada, 41(10), 1423-1432. Further, as the authors allude but do not directly discuss, PTB can be broadly classified into provider-initiated PTB and spontaneous PTB. The pathophysiology of spontaneous PTB is very different than that of provider-initiated PTB. Although this study is by no means the first study to group PTB broadly into one category, it should directly address the reality that PTB has many phenotypes and that a prediction algorithm that is trying to predict all PTB inherently has many limitations. An algorithm that predicts spontaneous PTB may be of greater utility and greater accuracy than an algorithm that tries to predict both spontaneous PTB and HELLP syndrome necessitating provider-initiated delivery. In addition to our principal analyses, we also developed prediction models for spontaneous PTB as a secondary outcome. The results from this analysis are reported in the last paragraph of the Results section. Further, in the abstract, the authors compare their model to the negative predictive value of a fetal fibronectin test. A fetal fibronectin test is ONLY used to predict spontaneous PTB, not all PTB. Consequently, this comparison is of little utility. In line with the reviewer’s suggestion, we have removed the comparison between our predictive model for overall PTB and FFN from the abstract, and we now refer to FFN only in connection with spontaneous PTB. Further, what percentage of PTB included in this study was spontaneous? This is not clear from the manuscript. If possible, the authors should provider information on the various phenotypes of PTB and how they were ascertained. This information is of significant clinical utility. There were a total of 3468 spontaneous preterm births in our analytic data set, accounting for 46.7% of all PTB (3468/7430) and yielding a spontaneous PTB rate of 6.9% (3468/(46213+3468)). We now clarify this in the footnote to Table 1. We ascertained spontaneous PTB using the definition from Maghsouldu et al., as discussed in our response to point 1 above and in the second paragraph of the Methods section. Maghsoudlou, S., Yu, Z. M., Beyene, J., & McDonald, S. D. (2019). Phenotypic classification of preterm birth among nulliparous women: a population-based cohort study. Journal of Obstetrics and Gynaecology Canada, 41(10), 1423-1432. 2. PAPP-A: In the abstract, the authors mention "abnormal pregnancy-associated plasma protein-A contractions" as being strongly associated with PTB. However, how a PAPP-A contraction was defined is unclear, as this contraction is never mentioned again. Is this meant to read concentration, not contraction? We thank the reviewer for drawing our attention to this error. This word was indeed intended to be “concentration” and we have changed it accordingly. 3. Complications during pregnancy: No definition of moderate complications is provided in the manuscript. Additionally, what percentage of women had each of the severe complications listed on page 6 is not clear. The only clarity regarding this variable is provided on page 6: "The variable, complications during pregnancy, had more than 600 categories and we classified those data into three groups based on the expert opinion of our in-home maternal-fetal specialist, including no complications, moderate complications, and severe complications (including hypertensive disorder, placental abnormalities, and maternal complications during this pregnancy, such as antepartum bleeding)." As severe complications of pregnancy were highly associated with PTB, it would be helpful to better understand this variable. Further, if possible, these complications should be separated and included in the model, as one would expect preeclampsia and HELLP are more likely to lead to provider-initiated PTB and antepartum bleeding to be associated with abruption and preterm labor, which would likely lead to spontaneous PTB. We thank the reviewer for bringing this point to our attention. Categorization of complications as mild-moderate complications versus severe was based on expert maternal-fetal input (Dr. Sarah McDonald). We now clarify this in the last paragraph of the Predictors subsection, just above Statistical Analysis. The distribution of complications during pregnancy, which we have added to the end of Table 1, is as follows: Complications during pregnancy N Percent No complications 90302 79.94 Mild-Moderate complications 14255 12.62 Severe complications 4676 4.14 Missing 3730 3.30 This variable was not included in the first-trimester prediction models for overall or spontaneous PTB, whereas it was included in the second-trimester prediction model for overall PTB but not for the spontaneous PTB. We report on the significant predictor variables included in the different models beginning in paragraph 4 of the Results. We examined the predictive power of all models without complications during pregnancy as a sensitivity analysis, reported in the second to last paragraph of the Results. The AUC in models without complications during pregnancy ranged from 58% (decision trees) to 65% (artificial neural networks, Supplementary Figure 1). 4. Aneuploidy: This study does not directly address aneuploidy or trisomy pregnancies. However, the most significant predictors of PTB in this study were diabetes and PAPP-A. Diabetes and low PAPP-A are both associated with trisomy pregnancies, and trisomy pregnancies have increased risks of PTB. Consequently, this should be addressed/clarified in this manuscript. We have added this to the limitations section of the discussion section in line with the reviewer’s suggestion. 5. Grammar: Please carefully review the manuscript at length for typos. Below are several that were identified on my review: - A parenthesis is missing after "(Supplemental Table 2" on page 6. - In the last paragraph on page 9, the first sentence should include an "s" after "other model." - An extra parenthesis should be removed after "(logistic regression, Supplementary Figure 2))" and "(logistic regression, Supplementary Figure 3))" on page 10. - "Table S8" should be renamed "Supplemental Table 8" to be consistent with the rest of the manuscript. We thank the reviewer for drawing our attention to these errors. Our manuscript has now undergone additional editorial review, through which we have addressed these and other points. ________________________________________ Attachments Attachment Submitted filename: Response to the Reviwers comments_R1 GS.docx https://doi.org/10.1371/journal.pone.0252025.r002
10 May 2021 Decision Letter - Pal Bela Szecsi, Editor Prediction of Preterm Birth in Nulliparous Women Using Logistic Regression and Machine Learning PONE-D-20-30837R1 Dear Dr. McDonald, We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements. Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication. An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org. If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org. Kind regards, Pal Bela Szecsi, M.D. D.M.Sci. Academic Editor PLOS ONE Additional Editor Comments (optional): Please in the poof correct some misspellings (ie. diabetes i fig 1) Comments to the Author 1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation. Reviewer #1: All comments have been addressed ******** 2. Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: Yes ****** 3. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: Yes ****** 4. Have the authors made all data underlying the findings in their manuscript fully available? The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified. Reviewer #1: Yes ****** 5. Is the manuscript presented in an intelligible fashion and written in standard English? PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here. Reviewer #1: Yes ****** 6. Review Comments to the Author Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters) Reviewer #1: (No Response) ****** 7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files. If you choose “no”, your identity will remain anonymous but your review may still be made public. Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy. Reviewer #1: Yes:** Giuseppe Rizzo https://doi.org/10.1371/journal.pone.0252025.r003
Formally Accepted
18 Jun 2021 Acceptance Letter - Pal Bela Szecsi, Editor PONE-D-20-30837R1 Prediction of Preterm Birth in Nulliparous Women Using Logistic Regression and Machine Learning Dear Dr. McDonald: I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department. If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org. If we can help with anything else, please email us at plosone@plos.org. Thank you for submitting your work to PLOS ONE and supporting open access. Kind regards, PLOS ONE Editorial Office Staff on behalf of Dr. Pal Bela Szecsi Academic Editor PLOS ONE https://doi.org/10.1371/journal.pone.0252025.r004

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