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Maternal age and body mass index and risk of labor dystocia after spontaneous labor onset among nulliparous women: A clinical prediction model
Clinical risk prediction of labor dystocia
https://orcid.org/0000-0002-6871-9420
Nathan Nina Olsén Conceptualization Data curation Formal analysis Funding acquisition Investigation Methodology Project administration Visualization Writing – original draft Writing – review & editing 1 2 *
https://orcid.org/0000-0002-7284-6094
Bergholt Thomas Conceptualization Supervision Visualization Writing – review & editing 3 4
Sejling Christoffer Formal analysis Methodology Visualization Writing – review & editing 1 5
Ersbøll Anne Schøjdt Conceptualization Writing – review & editing 1
Ekelund Kim Conceptualization Supervision Writing – review & editing 6 7
Gerds Thomas Alexander Formal analysis Investigation Methodology Supervision Writing – review & editing 5
Gam Christiane Bourgin Folke Conceptualization Writing – review & editing 8
Rode Line Conceptualization Data curation Supervision Writing – review & editing 9
Hegaard Hanne Kristine Conceptualization Data curation Funding acquisition Supervision Writing – review & editing 1 2 4
1 The Interdisciplinary Unit of Women’s, Children’s and Families’ Health, the Juliane Marie Centre, Copenhagen University Hospital ‐ Rigshospitalet, Copenhagen, Denmark
2 Department of Obstetrics, Copenhagen University Hospital ‐ Rigshospitalet, Copenhagen, Denmark
3 Department of Obstetrics and Gynecology, Copenhagen University Hospital ‐ Herlev, Herlev, Denmark
4 Institute of Clinical Medicine, Faculty of Health Sciences and Medicine, University of Copenhagen, Copenhagen, Denmark
5 Department of Biostatistics, University of Copenhagen, Copenhagen, Denmark
6 Department of Anesthesia- and Operation, the Juliane Marie Centre, Copenhagen University Hospital ‐ Rigshospitalet, Copenhagen, Denmark
7 Copenhagen Academy of Medical Education and Simulation (CAMES), Copenhagen University Hospital–Herlev, Herlev, Denmark
8 Department of Obstetrics and Gynecology, Nordsjællands Hospital, Hillerød, Denmark
9 Department of Clinical Biochemistry, Copenhagen University Hospital–Rigshospitalet, Glostrup, Denmark
Desseauve David Editor
Lausanne University Hospital: Centre Hospitalier Universitaire Vaudois (CH), FRANCE
Competing Interests: The authors have declared that no competing interests exist.

* E-mail: nina.olsen.nathan@regionh.dk
6 9 2024
2024
19 9 e030801828 2 2024
16 7 2024
© 2024 Nathan et al
2024
Nathan et al
https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Introduction

Obstetrics research has predominantly focused on the management and identification of factors associated with labor dystocia. Despite these efforts, clinicians currently lack the necessary tools to effectively predict a woman’s risk of experiencing labor dystocia. Therefore, the objective of this study was to create a predictive model for labor dystocia.

Material and methods

The study population included nulliparous women with a single baby in the cephalic presentation in spontaneous labor at term. With a cohort-based registry design utilizing data from the Copenhagen Pregnancy Cohort and the Danish Medical Birth Registry, we included women who had given birth from 2014 to 2020 at Copenhagen University Hospital–Rigshospitalet, Denmark. Logistic regression analysis, augmented by a super learner algorithm, was employed to construct the prediction model with candidate predictors pre-selected based on clinical reasoning and existing evidence. These predictors included maternal age, pre-pregnancy body mass index, height, gestational age, physical activity, self-reported medical condition, WHO-5 score, and fertility treatment. Model performance was evaluated using the area under the receiver operating characteristics curve (AUC) for discriminative capacity and Brier score for model calibration.

Results

A total of 12,445 women involving 5,525 events of labor dystocia (44%) were included. All candidate predictors were retained in the final model, which demonstrated discriminative ability with an AUC of 62.3% (95% CI:60.7–64.0) and Brier score of 0.24.

Conclusions

Our model represents an initial advancement in the prediction of labor dystocia utilizing readily available information obtainable upon admission in active labor. As a next step further model development and external testing across other populations is warranted. With time a well-performing model may be a step towards facilitating risk stratification and the development of a user-friendly online tool for clinicians.

http://dx.doi.org/10.13039/501100005111 Rigshospitalet https://orcid.org/0000-0002-6871-9420
Nathan Nina Olsén http://dx.doi.org/10.13039/501100008140 Hans og Nora Buchards Fond https://orcid.org/0000-0002-6871-9420
Nathan Nina Olsén This study (author NON) was supported by Copenhagen University Hospital – Rigshospitalet’s Research grant. Hans og Nora Buchards Fond (grant no Journal nr. 7334, ID 1894) provided funding for publication. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Data AvailabilityOur dataset originates from the Danish National Birth Registry and is subject to legal restrictions that prohibit its sharing with other researchers. These restrictions are imposed by Danish data protection laws and regulations governing the use of sensitive health information, including data related to births and individuals' health records. As researchers, we are committed to transparency and the advancement of scientific knowledge. However, due to the legal constraints, we are unable to directly provide access to the raw data from the Danish registries. For inquiries regarding data access or further information on the legal restrictions surrounding our dataset, please contact The Research Services at The Danish Health Data Authority at kontakt@sundhedsdata.dk or +45 7221 6800. We are fully prepared to facilitate the sharing of aggregated results, summary statistics, and any analysis code used in our study. Additionally, we are more than willing to collaborate with interested parties to explore avenues for replication or further investigation within the boundaries of Danish data protection laws.
Data Availability

Our dataset originates from the Danish National Birth Registry and is subject to legal restrictions that prohibit its sharing with other researchers. These restrictions are imposed by Danish data protection laws and regulations governing the use of sensitive health information, including data related to births and individuals' health records. As researchers, we are committed to transparency and the advancement of scientific knowledge. However, due to the legal constraints, we are unable to directly provide access to the raw data from the Danish registries. For inquiries regarding data access or further information on the legal restrictions surrounding our dataset, please contact The Research Services at The Danish Health Data Authority at kontakt@sundhedsdata.dk or +45 7221 6800. We are fully prepared to facilitate the sharing of aggregated results, summary statistics, and any analysis code used in our study. Additionally, we are more than willing to collaborate with interested parties to explore avenues for replication or further investigation within the boundaries of Danish data protection laws.
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pmcIntroduction

Labor dystocia, also referred to as prolonged labor, is defined by slow progression of cervical dilation in the first stage of labor and/or fetal descent in the second stage [1, 2]. Being common among nulliparous women, affecting as many as 20–47% in the Scandinavian countries [3–6], labor dystocia remains a challenge for women in labor, midwives, and obstetricians.

Labor dystocia is a major indication for instrumental vaginal delivery and cesarean section [3, 7, 8]. It is also a key contributor to women’s negative birth experiences [9–11]. Additionally, although synthetic oxytocin is extensively used for labor augmentation [12, 13] it is important to acknowledge that the Institute For Safe Medication Practices has classified the drug as one of twelve high-alert medications signifying a heightened risk of imposing serious acidotic harm to the fetus due to risk of hyperstimulation of the feto-placenta entity [14].

The current emphasis in obstetrics is to find better ways to diagnose and treat labor dystocia, and until now research has primarily focused on either management [12, 15–17] or identification of factors associated with labor dystocia [18–22]. Still, clinicians have no tools to appropriately risk-stratify women at the onset of labor. Generally, in medicine, prediction models provide individualized risk estimates for clinically important outcomes in patients with a particular characteristic, disease, or condition. Prediction models can provide more accurate prognoses than clinicians working on their own [23]. In recent years, risk prediction models have been developed in feto-maternal medicine [24–27] and implemented in daily clinical practice [28]. In comparison, prediction models in the field of obstetrics are less prevalent and are often related to predicting mode of birth [29–32]. Notably, two recently published studies from Japan and China have sought to predict the risk of delivery by emergency caesarean section due to labor dystocia [33, 34]. To our knowledge, no prediction model estimating a woman’s risk of labor dystocia at time of labor onset exists.

Identification of women with increased risk of labor dystocia could have several benefits. First, a prediction model would provide an individualized risk assessment incorporating multiple predictor variables while considering their combined effect. Second, a well-performing model could be further developed into an online calculator and serve as a supportive decision tool for pregnant women and clinicians, enabling them to implement preventive measures to potentially mitigate the risk.

The aim of the study is to develop a prediction model for labor dystocia among nulliparous women in spontaneous labor at term with a single baby in cephalic presentation to be used at onset of active labor.

Material and methods

Population

The source population for this study was derived from Copenhagen University Hospital–Rigshospitalet. Since 2012 pregnant women have been sent an electronic clinical questionnaire when booking their combined first-trimester screening for chromosomal anomalies. More than 90% of all pregnant women in Denmark participate in this screening [35]. The questionnaire collects self-reported data related to socio-demographic characteristics, medical and obstetric history, mental health and lifestyle factors [36, 37] and this is transferred to the electronic medical journal with the aim of qualifying and optimizing antenatal care. When researchers, with approvals from relevant authorities, utilize anonymized data from this questionnaire it is cited as Copenhagen Pregnancy Cohort.

Because all persons in Denmark have a unique identity number unambiguous linkage of data from Copenhagen Pregnancy Cohort with corresponding birth data in the Danish Medical Birth Registry (MBR) was possible. From the period October 2012 to December 2020 39,162 questionnaires were sent and the response rate was 89% (n = 34,883). After removing pregnancies not eligible for the study (i.e., miscarried before questionnaire completion or self-reported multiparous pregnancies) 20,271 pregnancies remained for linkage.

For obtaining the target population the following stepwise exclusion criteria were applied to births identified in MBR: stillbirth, multipara, preterm birth (gestational age ≤36+6 weeks), multiple gestations, induction of labor or elective caesarean section, non-vertex cephalic or breech presentations, and year of birth before 2014, as this was the year the Danish National Clinical Guideline concerning labor dystocia was published [1]. Additionally, if discrepancies were identified between due date from Copenhagen Pregnancy Cohort and in MBR, the birth was not included. Subsequently, 12,717 nulliparous women with spontaneous onset of labor at term with a cephalic presentation constituted the target population. Due to incomplete data an additional 272 births (2.1%) were excluded resulting in a study population of 12,445 (Fig 1).

10.1371/journal.pone.0308018.g001 Fig 1 Flowchart of study population derived from Copenhagen Pregnancy Cohort and linked with corresponding birth data from the Danish Medical Birth Registry.

Abbreviations: CPC, Copenhagen Pregnancy Cohort; MBR, the Danish Medical Birth Registry; GA, gestational age. aA woman from CPC could have more than one pregnancy in MBR.

Selection of predictors and classification of outcome

Eight candidate predictors were pre-selected based on clinical reasoning and established associations in the literature [8, 18, 20, 22, 38–40]. Inferential associations or causality reflections were not a requirement as the goal was to optimize predictive accuracy and not explain causality between variables. There was no targeted maximum number of variables but a prespecified aim to achieve a model applicable to clinical practice guided the process. The predictors included were maternal age at time of birth in (years, continuous), pre-pregnancy body mass index (kg/m2, continuous), height (≤ 160 cm and >160 cm), gestational age (weeks + days, 37+0–37+6, 38+0–38+6, 39+0–39+6,40+0–40+6, ≥41+0), weekly physical activity in 1st trimester (none, 1–3.5 hours, ≥3.5 hours) [41]. In relation to general and reproductive health, we included self-reported medical conditions (none, somatic, psychiatric, both somatic and psychiatric), WHO-5 Well-being Index (score of ≤50, >50) [42], and fertility treatment (yes/no). We omitted smoking and alcohol consumption during pregnancy as the prevalence was very low (2%).

The outcome we sought to predict was labor dystocia derived from MBR–in which coding is based on the Danish Healthcare Classification System including the International Classification of Disease version 10 [43]. In the period up to 2019 treatment with oxytocin augmentation was available, while the diagnosis code ‘O62*: obstructed labor’ was available from 2019 and onward. Supporting information presents all predictor candidates and outcome with definition/diagnostic codes, measurement scale, and source of data (S1 Table).

Statistical analysis

The study population was split randomly [44], resulting in a training dataset of 63.2% of the complete cases (no missing values) and a validation dataset of the remaining 36.8% of the complete cases. Population characteristics were presented as medians with interquartile ranges and frequencies with percentages as appropriate. We pre-specified a list of prediction models based on logistic regression (Table 1). Utilizing the training dataset, a discrete super learner—an algorithm designed to ease the challenge of choosing the most suitable predictive model by considering various strategies—chose the logistic regression model from the pre-specified list with the lowest 10-fold cross-validated prediction performance (Brier score) [44]. The Brier score is the squared difference between the predicted probability of labor dystocia and the observed status, the lower the Brier score the higher the predictive performance. The validation dataset was used to calculate the receiver operating characteristic (ROC) curve, the calibration plot, the area under the ROC curve (AUC), and the Brier score of the selected super learner model. Presented are personalized risk predictions from the super learner model. The statistical analysis was carried out with R [45].

10.1371/journal.pone.0308018.t001 Table 1 Candidate prediction models.

Candidate prediction models for outcome labor dystocia built by logistic regression using the following candidate predictor variables:
Category I: maternal age, body mass index (BMI), height, gestational age (GA), physical activity
Category II: medical condition disease, WHO-5 score, fertility treatment	
Model 1	Additive effects of category I variables, assumed linear relationship between linear predictor (logit odds) and maternal age, BMI	
Model 2	Additive effects of category I, restricted cubic splines model the relationship for maternal age, BMI	
Model 3	Alike Model 1, but with statistical interactions, allowing the effects of the variables maternal age, BMI, GA to depend on the three categories defined physical activity	
Model 4	Alike Model 2, but with statistical interactions, allowing the effects of the variables maternal age, BMI, GA to depend on the three categories defined physical activity	
Model 5	Alike Model 1 now adding additive effects of all category II variables	
Model 6 a	Alike Model 2 now adding additive effects of all category II variables	
Model 7	Alike Model 3 now adding additive effects of all category II variables.	
Model 8	Alike Model 4 now adding additive effects of all category II variables	
aThe final model selected, based on best performance (Brier score S1 Fig), tested in validation dataset

Ethics statement

The study was approved by the National Data Protection Agency (journal no.: RH-2016-202, I-Suite no.: 04778). Patient consent was waived due to the Danish Patient Safety Authorities having granted permission to disclose patient information from medical registries for research use (journal no.: H-21032059).

Results

The final cohort included 12,445 births involving 5,525 women with labor dystocia (44%). The mean age of women was 30.1 years and 87% were of Danish nationality. Median gestational age at birth was 283 days (40+3). Mode of delivery was distributed with 77% vaginal birth, 14% instrumental birth and 9% caesarean birth. Maternal characteristics and predictors are presented in Table 2.

10.1371/journal.pone.0308018.t002 Table 2 Characteristics for study population.

	Training dataset	Validation dataset	Total	
	N = 7,866	N = 4,579	N = 12,445	
General characteristics	
Maternal age at birth years, median (IQR)	30.2 (28–33)	30 (28–33)	30.1 (28–33)	
Nationality n (%)				
 Danish	6,743 (87.2)	3,901 (86.5)	10,644 (86.9)	
 other	987 (12.8)	611 (13.5)	1,598 (13.1)	
 missing	136	67	203	
Cohabitating n (%)				
 Yes	7,232 (92.2)	4,149 (90.9)	11,381 (91.8)	
 No	609 (7.8)	413 (9.1)	1,022 (8.2)	
 missing	25	17	42	
Level of education n (%)				
 Compulsory	511 (6.6)	311 (7.0)	822 (6.8)	
 Skilled	228 (3.0)	126 (2.8)	354 (2.9)	
 Tertiary (1–2 years)	382 (5.0)	241 (5.4)	623 (5.1)	
 Bachelor or equivalent (3–4 years)	2,270 (29.5)	1,286 (28.8)	3,556 (29.2)	
 Master or equivalent (≥5 years)	4,298 (55.9)	2,507 (56.1)	6,805 (56.0)	
 missing	177	108	285	
Height cm, median (IQR)	169 (165–173)	168 (164–173)	169 (165–173)	
 >160	825 (10.5)	498 (10.9)	1,323 (10.6)	
 ≤160	7,041 (89.5)	4,081 (89.1)	11,122 (89.4)	
Pre-pregnancy BMI kg/m2, median (IQR)	21.7 [20.2, 23.8]	21.8 [20.2, 23.8]	21.8 [20.2, 23.8]	
Health	
WHO-5 Well-being Index score median (IQR)	64 (52–72)	64 (52–72)	64 (52–72)	
 >50	6,335 (80.5)	3,733 (81.5)	10,068 (80.9)	
 ≤50a	1,531 (19.5)	846 (18.5)	2,377 (19.1)	
Medical condition n (%)				
 None	7,073 (89.9)	4,101 (89.6)	11,174 (89.8)	
 Somatic	572 (7.3)	326 (7.1)	898 (7.2)	
 Psychiatric	192 (2.4)	136 (3.0)	328 (2.6)	
 Somatic and psychiatric	29 (0.4)	16 (0.3)	45 (0.4)	
Lifestyle	
Physical activity hours weekly, n (%)				
 None	2,941 (37.4)	1,770 (38.7)	4,711 (37.9)	
 <3.5	1,064 (13.5)	580 (12.7)	1,644 (13.2)	
 ≥3.5b	3,861 (49.1)	2,229 (48.7)	6,090 (48.9)	
Obstetric characteristics	
Fertility treatment n (%)				
 Yes	1,014 (12.9)	579 (12.6)	1,593 (12.8)	
 No	6,852 (87.1)	4,000 (87.4)	10,852 (87.2)	
Gestational age at birth days, median (IQR)	283 (277–288)	283 (277–288)	283 (277–288)	
 weeks + days, n (%)				
 37+0–37+6	361 (4.6)	183 (4.0)	544 (4.4)	
 38+0–38+6	750 (9.5)	443 (9.7)	1,193 (9.6)	
 39+0–39+6	1,642 (20.9)	893 (19.5)	2,535 (20.4)	
 40+0–40+6	2,686 (34.1)	1,532 (33.5)	4,218 (33.9)	
 ≥41+0	2,427 (30.9)	1,528 (33.4)	3,955 (31.8)	
Labor dystocia n (%)				
 Yes	3470 (44.1)	2055 (44.9)	5525 (44.4)	
 No	4396 (55.9)	2524 (55.1)	6920 (55.6)	
Mode of birth n (%)				
 Vaginal	6,033 (77.0)	3,479 (76.3)	9,512 (76.7)	
 Instrumental	1,094 (14.0)	643 (14.1)	1,737 (14.0)	
 Cesarean section	713 (9.0)	438 (9.6)	1,151 (9.3)	
 missing	26	19	45	
Abbreviation: BMI, body mass index

aIndicates reduced psychological well-being

bIndicates adherence with national recommendations

Model development

The discriminative ability for all candidate models was better than of a benchmark null model that ignores predictor candidates and predicts an average risk. Brier score was not substantially different across models (S1 Fig). In the best performing selected model, model six, all candidate predictors were retained, and restricted cubic splines were used for the continuous candidate predictors age and BMI (Table 1). Regression coefficients for the predictors in the selected model are available in supplementary material (S2 Table).

Internal validation and model performance

We inspected the performance of the selected model in the validation dataset on a calibration plot (Fig 2). When the predicted risk of labor dystocia was compared with the observed rate, we saw a well calibrated model for all risk strata. The calculated AUC for the ability of the model to predict labor dystocia was 62.3% (95% CI:60.7–64.0) indicating poor discrimination (Fig 3). Brier score was 0.24 and corresponding Index of Predictive Accuracy 0.046.

10.1371/journal.pone.0308018.g002 Fig 2 Calibration plot showing the agreement between predicted probabilities and observed frequency of labor dystocia in the validation data grouped according to deciles of predicted risk of the selected model.

10.1371/journal.pone.0308018.g003 Fig 3 Receiver operating characteristic (ROC) curve with calculated area under the curve (AUC) in the validation dataset.

Risk scenarios

To illustrate the impact of each predictor we modelled three scenarios–a low-, reference, and high-risk (Table 3). In the low-risk scenario, a 20-year-old woman giving birth in gestational week 40, with BMI of 20 kg/m2, height >160 cm, no medical conditions, physically active ≥3.5 hours weekly, reporting a WHO-5 score of >50 and having received fertility treatment had a predicted risk of labor dystocia of 24%. Conversely in the high-risk scenario a 40-year-old woman in gestational week 41 or later, with a BMI of 35 kg/m2, height ≤160 cm, somatic medical condition, not physically active, reporting low well-being defined as a WHO-5 score of ≤50 and having conceived spontaneously had a prediction risk of 88%. For a selected overview of personalized predicted risk for labor dystocia with unique combinations of maternal age and BMI, see supporting information (S3 Table) [46].

10.1371/journal.pone.0308018.t003 Table 3 Predicted risk for three scenarios.

Scenario	Age years	BMI kg/m²	GA weeks+days	Physical activity hours weekly	Height cm	Medical condition	Fertility treatment	WHO-5 score	Predicted risk %	
Low-risk	20	20	40+0–40+6	≥3.5	>160	none	yes	>50	24.0	
Reference	30	27	40+0–40+6	≥3.5	>160	none	no	>50	41.7	
High-risk	40	35	≥41+0	none	≤160	somatic	no	≤50	88.1	
Abbreviations: BMI, body mass index; GA, gestational age

Also, in the supporting information a figure of the running risk as a function for sequentially ‘switching-on’ categorical predictors is available (S2 Fig).

Maternal age and BMI

Evidently increasing maternal age and BMI were strong predictors. Specifically, for these we show the span of predicted risks when restraining all other predictors as described in the previous scenarios (Fig 4). We noted an increasing predicted risk by increasing age and BMI for all three scenarios. Exemplified a 40-year-old woman demonstrated a two-fold increase in her projected risk compared to a 20-year-old also in the low-risk scenario.

10.1371/journal.pone.0308018.g004 Fig 4 Predicted risk for three scenarios allowing change in maternal BMI and age.

Abbreviations: BMI, body mass index. aAll predictors, other than BMI and age, are retained as described in Table 3.

Discussion

In this cohort-based registry study, we developed a prediction model for labor dystocia among nulliparous women in spontaneous term labor with a singleton baby in cephalic presentation. The selected model included all eight candidate predictors, and the predicted risk of labor dystocia was higher with increasing age and BMI and substantially higher among women with all predictors. The predictive performance was poor with Brier score of 24% and AUC of 63%.

The finding that pre-pregnancy BMI has predictive value is supported by previous epidemiological studies showing a relationship between obesity and length of labor [47–49]. There are indications that obese pregnant women at term have cellular alterations in the uterine myocytes which could decrease contractility [50–52]. Also the association between a women’s age and labor dystocia is well-established, with evidence suggesting a negative effect of ageing [20, 22, 53]. In concordance with our model a recent study, aimed at developing a prediction model for caesarean section due labor dystocia after induction of labor, found age to be an important predictor [32]. Still, the explanatory mechanism is not known and in vitro studies of human uterine myocyte contractility have failed to demonstrate reduced contractility associated with age [54], as such validated physiological explanations have yet to be found.

An unexpected finding was that not receiving fertility treatment was a predictor for labor dystocia. This was contrary to the expected direction of the effect as existing evidence has found that pregnancies conceived after fertility treatment are at increased risk for obstetric complications [55, 56]. The finding could potentially be attributed to the ‘healthy-user effect’, wherein women who receive fertility treatment engage in a healthier diet and lifestyle compared to those who conceive spontaneously. The available evidence supporting this explanation is however lacking and a previous study conducted in the Copenhagen Pregnancy Cohort population found that fertility treated women were less likely to engage in physical activity [57]. Another possible explanation might be that the least healthy women receiving fertility treatment were excluded due to preterm birth and induction of labor in relation to the preliminary steps of identifying the target population in the present study.

To develop a more robust and accurate prediction model for labor complications, future models could consider a broader range of relevant predictors to capture the complexity of the underlying mechanisms and enhance model performance. The occurrence of labor dystocia is likely attributed to a complex interplay of numerous contributing factors and intrapartum events undoubtedly play a role. Furthermore, it is probable that there is a genetic component to labor dystocia, and studies have found an increased risk in women whose mothers or sisters also experienced the complication [58, 59]. Still, calculations of heritability leave the majority of labor dystocia with no genetic cause [59].

In summary, several complex factors will affect labor dystocia, and the complication requires a comprehensive approach. Nonetheless, the aim of this model was not to inform decisions regarding mode of birth or influence the clinical treatment of labor dystocia, but to serve as a first step in developing a model that can support risk stratification of women at the onset of labor to target resources and facilitate discussion of prevention. Though evidence on optimal prevention is not straightforward there are studies that support certain practices and interventions. Avoidance of hospital admission in the latent stage of labor may reduce the risk of receiving augmentation [60–62]. Also the utilization of continuous labor support has been associated with a reduction in the incidence [63]. While adequate hydration, specifically intravenously administrated, has provided conflicting evidence [64]. It is the position of the authors that prevention and stepwise progression of interventions aimed at women at high risk is the preferred mode of action.

A central strength of this study is the large sample size and the number of events observed. Another strength is that the model’s source population comes from at large cohort with a high coverage combined with national registry-based data. The latter ensuring a representative sample of the target population and limiting non-response and loss to follow-up. We pre-selected candidate predictors based on available evidence and clinical reasoning instead of observed significant relationships with outcome in the dataset, thereby ensuring higher external validity and less overfitting of the model [44]. Furthermore, we chose only to include predictor candidates present at the onset of labor, enabling the model’s applicability in current labor care.

There are also limitations. Firstly, and importantly, is the lack of external validation for the prediction model, as it was solely internally validated. Given the unique demographic characteristics of our population, the low levels of smoking and alcohol consumption and high level of education, it is crucial to recognize that the performance of the model may vary when applied to other populations. This is also true for the candidate predictors the model has been explicitly trained to consider and model performance is heavily influenced by for example the BMI distribution in the Copenhagen Pregnancy Cohort study. Another key limitation of MBR in the period 2014–2019 is the lack of diagnostic codes related to labor complications. Using the proxy of treatment by oxytocin augmentation entails the risk of introducing misclassification bias, occurring if oxytocin augmentation does not consistently align with the presence of labor dystocia. Though diagnosis and treatment are used interchangeably in many publications [16, 65, 66], no studies have validated the accuracy of oxytocin augmentation as a measure of labor dystocia in the Danish registries [67]. It is, however, reassuring that publicly accessible aggregated data shows the prevalence of the labor dystocia diagnosis is consistent with the prevalence in our data and has remained relatively stable since 2014 [68]. Furthermore, the distribution of the outcome is roughly the same across the study period (S3 Fig).

Most candidate predictors were patient-reported outcome data (PRO-data), and this has enabled the model to cover a broad range of domains on which detailed information is not available in the standard registries, but would be obtainable by clinicians, like hours of physical activity per week. On the other hand, the data was collected in the first trimester, and changes across pregnancy may be significant. One study found that while a pre-pregnancy BMI was an independent risk factor, the risk of dystocia increased as a function of gestational weight gain [69]. Additionally, intrapartum actions and interventions may also predict the risk of labor dystocia but were unavailable for incorporation in the model. Neither were we able to subclassify labor dystocia according to the phase of labor This distinction could have a clinically relevant implication, as research suggests that the mode of birth is influenced by the timing of labor dystocia diagnosis. Specifically, women diagnosed at ≤5 cm of cervical dilation exhibit higher cesarean section rates [70]. However, obtaining information on the phase of labor from MBR is not possible. While a robust prediction model requires a large population; making the optimal data source a national registry, this approach also poses a limitation due to national registries lacking the additional clinical details sometimes desired.

We applied a single-random split to our dataset to account for potential temporal changes due to revisions of the MBR or clinical guideline updates, but introducing randomness can inadvertently influence the performance of the prediction model [44]. Alternatively, with a calendar year split we would have avoided this while mimicking real life where data from women in the past is used to build a model applicable for future use.

Another methodological choice was to perform a complete case analysis, excluding observations of with missing values. Hereby we could maintain the original relationship and patterns in the complete dataset and avoid introducing assumptions or imputed values that potentially may affect the relationship. In contrast, we acknowledge the loss of information when discarding observations in the model.

Conclusion

The present model serves as a first step in predicting labor dystocia using information obtainable upon admission. Maternal age and BMI were found to be important predictors influencing nulliparous women’s risk of labor dystocia. The model’s prediction ability was reasonable, but not satisfactory for clinical application. As with any new model, there is a need for further development, extensive testing, and external validation in other settings to ensure that the model performs well in different populations or clinical settings. We hope other researchers will build upon our work ‐ ultimately advancing the field towards more accurate predictive models. Over time, as the development of robust models for predicting labor dystocia progresses, creating a user-friendly online calculator tool, primarily aimed at clinicians, emerges as a logical next step.

Supporting information

S1 Table Definition and description of predictors and outcome.

(PDF)

S2 Table Selected model input with associated coefficients and standard error.

(PDF)

S3 Table Predicted risk for labor dystocia for selected combinations of age and BMI.

(PDF)

S1 Fig Brier scores for each candidate model at different ridge penalty values–model selection.

Abbreviation: m, model. Model six was the final selected model for which model performance was tested in validation dataset. See Table 1 for specifications for all eight candidate models.

(TIF)

S2 Fig Switch-on risk development.

The figure shows the effect of ‘switching on’ exposures starting from ‘None’ which is the low-risk scenario defined in Table 3. Only categorical candidate predictors are incorporated in the figure. The order is as follows: Gestational age from 40+0–40+6 to ≥41+0; Height from >160cm to ≤160cm; Physical activity from ≥3.5 hours weekly to no physical activity; Fertility treatment from yes to no; Medical condition from none to somatic; WHO-5 score from >50 to ≤50.

(TIF)

S3 Fig Distribution of outcome across period of study (2014–2020).

In the years 2014–2018, the outcome is defined by oxytocin augmentation, while from year 2019 the outcome is defined by ICD-10 diagnosis codes.

(TIF)

Camilla Byskou Eriksen substantially contributed to data preparation of the Copenhagen Pregnancy Cohort in the early stages of project.

10.1371/journal.pone.0308018.r001
Decision Letter 0
Desseauve David Academic Editor
© 2024 David Desseauve
2024
David Desseauve
https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Submission Version0
26 Apr 2024

PONE-D-24-07911Maternal age and body mass index and risk of labor dystocia after spontaneous labor onset among nulliparous women: A clinical prediction modelPLOS ONE

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Academic Editor

PLOS ONE

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Reviewers' comments:

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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: No

Reviewer #3: Yes

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: I Don't Know

Reviewer #3: 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: No

Reviewer #3: 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: Yes

Reviewer #2: Yes

Reviewer #3: 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: Thank you for giving me the opportunity to read this paper. The issue of labor dystocia is of real interest in obstetrics. Any serious work that can help the obstetrician in this field is important.

The authors describe their objective and how they achieved it. They also highlight the limitations of the study, which are mostly the lack of external validation limiting the application of these results to other populations.

One of its strengths is that it uses patient centered indicators such as the WHO well-being score and self reported outcomes.

I found this work to be well written and well explained, with a great deal of thought given to both the value of this work and its current and future limitations.

I would like to thank the authors for their work.

NB : Flow chart is not readable.

Reviewer #2: I thank the authors for their work, which I found interesting. Indeed, it would be interesting to have a model that predicts the risk of dystocia and more importantly to predict whether therapeutic measures are efficient (ie. worth the wait and the risk of oxytocin augmentation)

Nevertheless, I feel that several modifications need to be made in the design of the study to assess this important problematic.

First and mostly: the outcome to be predicted.

1. The authors took labour dystocia, without differentiating dystocia during the active phase / descent phase or expulsion phase, which would potentially have different impact, namely a caesarean section is more likely in case of dystocia during the active phase than during the expulsive phase, where an instrumental delivery would be possible. The consequences for maternal morbidity are different.

2. they choose to predict all dystocia, without taking into account the measures taken ie. amniotomy or oxytocin augmentation, at least after 2019 there is no information, and therefore I find it difficult to draw conclusions on whether the dystocia was resolved or not, and more importantly whether it was associated with an “adverse” outcome such as caesarean section, instrumental delivery.

Second :I find it difficult to understand who was the training dataset and the validating dataset chosen. How many cases of dystocia were in each data set. Figure 1 is unreadable

Third : I feel that the variables testing should be presented as main results. It is indeed the whole point of the study and should not be presented as supplementary data.

Finally In the end their model seems to have a poor discrimination value…

Reviewer #3: To sum up, the study is interesting. A bibliography review shows a few articles on the subject, confirming the review you have already carried out on the subject. The idea of including only predictors present at the onset of labor is very interesting, so we can also screen for them upstream and use them clinically.

The statistical method used in the study seems rigorous and appropriate for developing a predictive model.

It's worth noting that the model's predictive performance is currently modest, with a Brier score of 24% and an AUC of 63%. However, this also presents an opportunity for future research to strengthen its predictive capabilities.

This article is only a first step, but it makes us think about the risk factors for labor dystocia.

The summary is well written.

It respects the form and content of the article.

Regarding the introduction, it leads to an apparent problem by showing the benefits of prediction scores.

The results of fertility treatments are interesting. Whether they are confirmed or just the result of chance (or a confounding factor) remains to be seen.

**********

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Reviewer #1: No

Reviewer #2: No

Reviewer #3: Yes: Buisson Alexandre

**********

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10.1371/journal.pone.0308018.r002
Author response to Decision Letter 0
Submission Version1
10 May 2024

Manuscript PONE-D-24-07911

‘Maternal age and body mass index and risk of labor dystocia after spontaneous labor onset among nulliparous women: A clinical prediction model’

Dear Editor David Desseauve and peer-reviewers,

We want to thank you very much for your consideration of our manuscript and suggested revisions. We appreciate the points raised, which have helped improve the manuscript considerably. Please see our point-by-point response to the comments below in red font. The yes/no answers pertaining to questions (Q) 1-4 and 6 are discussed when relevant together with the reviewer’s detailed comments under question 5.

Comments to the Authors

Q1. 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 No

Reviewer #3 Yes

________________________________________

Q2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1 Yes

Reviewer #2 I Don’t Know

Reviewer #3 Yes

________________________________________

Q3. 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 No

Reviewer #3 Yes

________________________________________

Q4. 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

Reviewer #2 Yes

Reviewer #3 Yes

________________________________________

Q5. 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: Thank you for giving me the opportunity to read this paper. The issue of labor dystocia is of real interest in obstetrics. Any serious work that can help the obstetrician in this field is important.

Thank you for acknowledging the importance of labor dystocia, the focus of our prediction model. Indeed, we authors undertook this work to support obstetricians through research aimed at qualifying the management of this prevalent labor complication. Your assessment of the manuscript as relevant and technically sound with supported conclusions (Q1), statistically rigorous (Q2), with data underlying the findings (Q3), and effectively disseminated (Q4) is greatly appreciated.

The authors describe their objective and how they achieved it. They also highlight the limitations of the study, which are mostly the lack of external validation limiting the application of these results to other populations.

One of its strengths is that it uses patient centered indicators such as the WHO well-being score and self reported outcomes.

As noted by Reviewer 1 and addressed in the manuscript's discussion section, a limitation of this study is the lack of external validation (L287). This was anticipated, given that the prediction model is the first of its kind, and in the study conclusion we encourage other researchers to explore the model's performance across diverse populations.

I found this work to be well written and well explained, with a great deal of thought given to both the value of this work and its current and future limitations.

I would like to thank the authors for their work.

Thank you for recognising the effort and academic considerations invested in this work.

NB : Flow chart is not readable.

Thank you for drawing our attention to this. We apologise for the poor resolution in the flowchart and have corrected this.

Reviewer #2: I thank the authors for their work, which I found interesting. Indeed, it would be interesting to have a model that predicts the risk of dystocia and more importantly to predict whether therapeutic measures are efficient (ie. worth the wait and the risk of oxytocin augmentation)

We are grateful to Reviewer 2 for their careful consideration of our manuscript and your positive feedback on the manuscript language and presentation (Q4).

It was not within the scope of this study to predict the efficiency of treatment by oxytocin augmentation, but it is indeed of interest. As stated in the introduction paragraph of the manuscript, two recent studies have sought to predict the risk of emergency cesarean due to labor dystocia – or in other words the result of inefficient therapeutic measures for treating labor dystocia. We aim for our study to add to this current body of evidence and have therefore targeted the diagnosis of labor dystocia to facilitate a discussion of prevention: “... developing a model that can support risk stratification of women at the onset of labor to target resources and facilitate discussion of prevention.” (L269-270).

Nevertheless, I feel that several modifications need to be made in the design of the study to assess this important problematic.

First and mostly: the outcome to be predicted.

1. The authors took labour dystocia, without differentiating dystocia during the active phase / descent phase or expulsion phase, which would potentially have different impact, namely a caesarean section is more likely in case of dystocia during the active phase than during the expulsive phase, where an instrumental delivery would be possible. The consequences for maternal morbidity are different.

We fully concur with Reviewer 2 that differentiating dystocia based on the stage of labor could provide a more informative prediction model. Recent research, and our clinical experience, aligns with Reviewer 2’s comment indicating that the mode of birth is influenced by the stage at which labor dystocia is diagnosed . In the original manuscript we point to this the limitation in our discussion: “Neither were we able to subclassify labor dystocia according to the phase of labor.” (L311-312). To clarify that distinguishing between stages of labor would have been relevant, but was a condition of the data source, we have now added the following text: “This distinction could have a clinically relevant implication, as research suggests that the mode of birth is influenced by the timing of labor dystocia diagnosis. Specifically, women diagnosed at ≤5 cm of cervical dilation exhibit higher cesarean section rates [70]. However, obtaining information on the phase of labor from MBR is not possible. While a robust prediction model requires a large population; making the optimal data source a national registry, this approach also poses a limitation due to national registries lacking the additional clinical details sometimes desired.” (L312-318)

2. they choose to predict all dystocia, without taking into account the measures taken ie. amniotomy or oxytocin augmentation, at least after 2019 there is no information, and therefore I find it difficult to draw conclusions on whether the dystocia was resolved or not, and more importantly whether it was associated with an “adverse” outcome such as caesarean section, instrumental delivery.

Thank you for this comment that seems related to Reviewer 2’s first point. Unfortunately, it was not within the scope of this study to examine the effect of treatment, intrapartum events or take into account whether labor dystocia was resolved. Instead, it attempts model development for the prediction of dystocia to facilitate prevention.

Second :I find it difficult to understand who was the training dataset and the validating dataset chosen. How many cases of dystocia were in each data set. Figure 1 is unreadable.

We apologise for the poor resolution in the flowchart and have corrected this. With Figure 1 now readable, it should be clear that the training and validation datasets were generated through a random split of the study population and not manually selected. Some readers may not be acquainted with the statistical considerations underlying prediction modelling, and we have therefore added a reference to L147 in the statistical methods section to establish that the approach is substantiated.

Regarding the distribution of the predicted outcome, ‘labor dystocia’ has been added to Table 2, depicting the study population’s characteristics. This illustrate an almost equal distribution of dystocia cases (44.1% dystocia in the training dataset vs. 44.9% in the validation dataset).

Third : I feel that the variables testing should be presented as main results. It is indeed the whole point of the study and should not be presented as supplementary data.

We sincerely thank Reviewer 2 for this point, which has been highly helpful in improving the coherence of the manuscript. Our study was two-part. Firstly, we aimed to develop the prediction model and then present the final best-performing model. While model development has received attention in text, as Reviewer 2 states, the related tables should not be supplementary. We now more clearly communicate model development in the manuscript by moving ‘S2 Table’ presenting the eight candidate prediction models to the main manuscript, and it is now Table 1.

We have considered moving the supplementary table of model input with associated coefficients but are uncertain whether it would contribute significantly to the manuscript. For readers unfamiliar with the statistical differences between association or regression studies and predictive modelling, there might be confusion about the significance of coefficients in selecting candidate predictors. In predictive modelling, the primary concern lies in whether these predictors enhance predictive accuracy rather than demonstrating association. Additionally, the use of restricted cubic splines for the two continuous candidate predictors age and BMI means they are presented twice, which might further complicate interpretation of the table. Due to these considerations, we have decided to retain this table as supplementary for now (S2 Table Selected model input with associated coefficients and standard error). However, if the Editors find it recommendable to include it in the main manuscript, we are willing to do so.

Finally In the end their model seems to have a poor discrimination value…

This point raised by Reviewer 2 is accurate. The final model performed poorly assessed by evaluation measures such as AUC. Because of this, we conclude that the model should be further developed before integration in clinical practice.

Still, the authors find it important to publish the results of imperfect prediction models as this aligns with transparency and scientific integrity principles. By openly sharing our findings, we not only acknowledge the challenges encountered but also contribute to the collective understanding of predictive modelling in our field. Negative results are valuable contributions, as they inform researchers about what approaches may not be effective and prevent the duplication of efforts. Furthermore, while the described prediction model may not be perfect, it opens opportunities for improvement. Encouraged by Reviewer 2’s comment, we now have unfolded this point in the conclusion:

The last part of our conclusion previously read: “The prediction ability of the model was satisfactory, and developing a user-friendly online calculator tool, primarily intended for clinicians, is a logical next step. Nevertheless, as with any new model, there is a need for further development, extensive testing, and external validation in other settings to ensure that the model performs well in different populations or clinical settings.”

This has been changed to: “The model's prediction ability was reasonable, but not satisfactory for clinical application. As with any new model, there is a need for further development, extensive testing, and external validation in other settings to ensure that the model performs well in different populations or clinical settings. We hope other researchers will build upon our work - ultimately advancing the field towards more accurate predictive models. Over time, as the development of robust models for predicting labor dystocia progresses, creating a user-friendly online calculator tool, primarily aimed at clinicians, emerges as a logical next step.” (L335-342)

Reviewer #3: To sum up, the study is interesting. A bibliography review shows a few articles on the subject, confirming the review you have already carried out on the subject. The idea of including only predictors present at the onset of labor is very interesting, so we can also screen for them upstream and use them clinically.

Thank you to reviewer 3 for highlighting two central positives of our work. We sincerely believe the novelty of developing a prediction model for the commonly occurring yet enduring complication of labor dystocia holds the interest for many clinicians and researchers alike. Our emphasis on clinical applicability guided our decision to include predictors known at onset of labor.

The statistical method used in the study seems rigorous and appropriate for developing a predictive model. It's worth noting that the model's predictive performance is currently modest, with a Brier score of 24% and an AUC of 63%. However, this also presents an opportunity for future research to strengthen its predictive capabilities.

We appreciate Reviewer 3’s positive feedback regarding the manuscript’s provision of data to substantiate the conclusions (Q1) and the application of appropriate and rigorous statistical methods (Q2). Like Reviewer 3 writes, we view our model's modest performance as an opportunity for further refinement and development. While we understand that the initial development of a prediction model rarely provides an immediate 'ready-to-use' tool, we firmly believe in the importance of not only publishing positive outcomes but also detailing initial advances, enabling other researchers to benefit from our progress. Please see the changes to the manuscript's conclusion related to this in extension of your and Reviewer 2’s comments.

The summary is well written.

It respects the form and content of the article.

Regarding the introduction, it leads to an apparent problem by showing the benefits of prediction

Thank you. We appreciate the acknowledgement of the clarity and structure of the summary and introduction.

The results of fertility treatments are interesting. Whether they are confirmed or just the result of chance (or a confounding factor) remains to be seen.

The authors very much agree, and ‘fertility treatment’ as a predictor will be interesting to follow when the model is applied to other populations.

________________________________________

Q6. 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 No

Reviewer #2 No

Reviewer #3 Yes: Buisson Alexandre

______________________________________

Attachment Submitted filename: Response to Reviewers.docx

10.1371/journal.pone.0308018.r003
Decision Letter 1
Desseauve David Academic Editor
© 2024 David Desseauve
2024
David Desseauve
https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Submission Version1
16 Jul 2024

Maternal age and body mass index and risk of labor dystocia after spontaneous labor onset among nulliparous women: A clinical prediction model

PONE-D-24-07911R1

Dear Dr. Nathan,

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 will be generated when your article is formally accepted. Please note, if your institution has a publishing partnership with PLOS and your article meets the relevant criteria, all or part of your publication costs will be covered. Please make sure your user information is up-to-date by logging into Editorial Manager at Editorial Manager® and clicking the ‘Update My Information' link at the top of the page. If you have any questions relating to publication charges, 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,

David Desseauve, MD, MPH, PhD

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

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: Thank you for giving me the opportunity to reread this article. I consider that the clarifications I requested have been provided. This work opens up the possibility for other teams to test the model in order to judge its effectiveness.

**********

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: No

**********

10.1371/journal.pone.0308018.r004
Acceptance letter
Desseauve David Academic Editor
© 2024 David Desseauve
2024
David Desseauve
https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
26 Jul 2024

PONE-D-24-07911R1

PLOS ONE

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