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Original Research ajog.org

OBSTETRICS

Prediction of vaginal birth after cesarean deliveriesusing machine learning
Michal Lipschuetz, RN, MPH, MSc; Joshua Guedalia, MBA; Amihai Rottenstreich, MD; Michal Novoselsky Persky, MD;Sarah M. Cohen, MPH; Doron Kabiri, MD; Gabriel Levin, MD; Simcha Yagel, MD; Ron Unger, PhD;Yishai Sompolinsky, MD, MPH
BACKGROUND: Efforts to reduce cesarean delivery rates to 12e15% attempted a trial of labor, with a success rate of 88%. A machine-

have been undertaken worldwide. Special focus has been directed to-

wards parturients who undergo a trial of labor after cesarean delivery to

reduce the burden of repeated cesarean deliveries. Complication rates are

lowest when a vaginal birth is achieved and highest when an unplanned

cesarean delivery is performed, which emphasizes the need to assess, in

advance, the likelihood of a successful vaginal birth after cesarean de-

livery. Vaginal birth after cesarean delivery calculators have been devel-

oped in different populations; however, some limitations to their

implementation into clinical practice have been described. Machine-

learning methods enable investigation of large-scale datasets with input

combinations that traditional statistical analysis tools have difficulty

processing.

OBJECTIVE: The aim of this study was to evaluate the feasibility of

using machine-learning methods to predict a successful vaginal birth after

cesarean delivery.STUDY DESIGN: The electronic medical records of singleton, term la-

bors during a 12-year period in a tertiary referral center were analyzed. With

the use of gradient boosting, models that incorporatedmultiple maternal and

fetal features were created to predict successful vaginal birth in parturients

who undergo a trial of labor after cesarean delivery. One model was created

to provide a personalized risk score for vaginal birth after cesarean delivery

with the use of features that are available as early as the first antenatal visit;

a second model was created that reassesses this score after features are

added that are available only in proximity to delivery.

RESULTS: A cohort of 9888 parturients with 1 previous cesarean

delivery was identified, of which 75.6% of parturients (n¼7473)

Cite this article as: Lipschuetz M, Guedalia J, Rotten-streich A, et al. Prediction of vaginal birth after cesarean

deliveries using machine learning. Am J Obstet Gynecol

2020;222:613.e1-12.

0002-9378/$36.00ª 2020 Elsevier Inc. All rights reserved.https://doi.org/10.1016/j.ajog.2019.12.267

learningebased model to predict when vaginal delivery would be suc-

cessful was developed. When features that are available at the first

antenatal visit are used, the model showed a receiver operating char-

acteristic curve with area under the curve of 0.745 (95% confidence

interval, 0.728e0.762) that increased to 0.793 (95% confidence in-

terval, 0.778e0.808) when features that are available in proximity to the

delivery process were added. Additionally, for the later model, a risk

stratification tool was built to allocate parturients into low-, medium-,

and high-risk groups for failed trial of labor after cesarean delivery. The

low- and medium-risk groups (42.4% and 25.6% of parturients,

respectively) showed a success rate of 97.3% and 90.9%, respectively.

The high-risk group (32.1%) had a vaginal delivery success rate of

73.3%. Application of the model to a cohort of parturients who elected a

repeat cesarean delivery (n¼2145) demonstrated that 31% of these

parturients would have been allocated to the low- and medium-risk

groups had a trial of labor been attempted.

CONCLUSION: Trial of labor after cesarean delivery is safe for mostparturients. Success rates are high, even in a population with high rates of

trial of labor after cesarean delivery. Application of a machine-learning

algorithm to assign a personalized risk score for a successful vaginal

birth after cesarean delivery may help in decision-making and contribute to

a reduction in cesarean delivery rates. Parturient allocation to risk groups

may help delivery process management.

Key words: machine-learning, personalized, prediction, trial of labor,vaginal birth after cesarean delivery

fforts are ongoing to reduce the
E rates of cesarean deliveries (CD)worldwide, according to the WorldHealth Organization recommendationto achieve a suitable rate of 10e15%,1,2
reserving CD for those cases in whichmaternal or fetal health potentially

would be compromised in a vaginal de-livery.3,4 This goal for CD rates has notyet been met, with current rates of 32%in the United States,5 25.5% in England,6

and 16.2% in Israel.7

Planned repeat CD is associated withmaternal adverse outcomes and com-plications in future pregnancies8e15 andis less cost-effective.16 Rates of successfulvaginal birth after CD (VBAC) when atrial of labor after CD (TOLAC) isattempted are relatively high, reaching72e75%6,17 with low rates of adverseoutcomes,6,10,18,19 even when labor isinduced20,21 or an instrumental deliveryis performed.22 These findings have ledprofessional organizations to reassurepatients and clinicians to attempt

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TOLAC.6,8 However, at present, rates ofTOLAC range from as low as 10e14%23

in some reports up to 44% in others,24

which contributes to the high rate ofCD.25,26

TOLAC complications have also beenreported that arise mainly from the needfor emergency repeat CD in cases inwhich VBAC has not beenachieved.22,24,25,27,28 To avoid thesecomplications, it is imperative todistinguish between parturients who arelikely to achieve a successful VBAC andthose who are not.

Various methods and calculators havebeen developed to estimate the likeli-hood of successful TOLAC that providean individualized risk assessment. The

can Journal of Obstetrics & Gynecology 613.e1

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https://doi.org/10.1016/j.ajog.2019.12.267

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AJOG at a Glance

Why was this study conducted?The purpose of this study was do develop a personalized prediction tool forvaginal birth after cesarean delivery with the use of amachine-learning algorithm.

Key findingsA personalized prediction tool for successful vaginal birth after cesarean deliverywas built. This tool has an area under the curve of 0.745 (95% confidence interval,0.728e0.762) when data that are available at the first antenatal visit (first-trimester model) are used and increases to an area under the curve of 0.793 (95%confidence interval, 0.778e0.808) with the addition of delivery unit admissionfeatures (prelabor model). With the use of the prelabor model, most parturients(67.9%) were allocated to a low- and medium-risk group with high vaginal birthafter cesarean delivery rates of 94.9%, compared with 73.3% in the high-riskgroup (32.1% of parturients). Vaginal birth after cesarean delivery success rateswere high (88%), even in a population with high rates of trial of labor after ce-sarean delivery attempts (75.6%).

What does this add to what is known?This is a proof of concept study for the applicability of machine-learning algo-rithms in clinical decision-making regarding vaginal births after cesareandeliveries.

Original Research OBSTETRICS ajog.org

predicted probability nomogram ofGrobman et al,29 followed by a well-validated calculator for VBAC success,has been described, with a receiveroperating characteristic curve with areaunder the curve (AUC) of 0.75 in the firstprenatal visit,29 which increases up to0.77e0.80 with the addition of deliveryunit admission features.30,31 This modelhas been validated based on a Europeancohort, with a lower AUC of 0.69.32e36

However, more recent studies demon-strated decreased accuracy of these cal-culators when applied to otherdatasets.37

Machine-learning methods compriseseveral algorithms that allow investiga-tion of large-scale sets of data with inputcombinations that traditional statisticalanalysis tools have difficulty processing.Some elements of these methods aresimilar to traditional regression models,mainly the need to label data accuratelyand to define features and outcomesmeticulously. However, machine-learning outperforms traditional regres-sion models in processing large datasets,for which numerous predictors may bepresent with intricate associations be-tween these predictors and when some

613.e2 American Journal of Obstetrics & Gynecol

parameters have a nonmonotonic in-fluence on the outcome.38,39

In this retrospective study, we aimedto use machine-learning tools to build apersonalized prediction algorithm forsuccessful VBAC in a population with ahigh TOLAC rate. Such a model can beused to evaluate VBAC feasibility duringthe course of pregnancy and perhapsallow further modulation in closerproximity to the onset of labor.

MethodsPatientsThis is a retrospective, electronicmedicalrecordebased study performed withdata from the Hadassah Medical CenterObstetrics Departments database be-tween January 2003 and December 2014.Multifetal gestations, antepartum fetaldeath, major fetal anomalies, pretermdeliveries, and cases in which the uterinescar was other than low cervical trans-verse were excluded, as were cases inwhich TOLAC was attempted after 2previous CDs.Planned repeat CD cases were

excluded from the main analysis,regardless of whether the end result was aplanned CD or unplanned CD.

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Data collectionWe collected multiple obstetric data,including maternal demographic fea-tures (eg, age, parity, smoking status),fetal features (eg, gestational age, esti-mated fetal weight [EFW] within 2weeks of delivery), and, if available,previous obstetrics outcomes (eg, pre-vious neonatal Apgar scores, previousneonatal weight, and headcircumferences40e42).

As noted, all the data used in themodel are collected routinely, eitherduring antenatal visits or admission tothe delivery unit. Features that aregathered after delivery (eg, infant sex,actual fetal weight) were discarded. A fulllist of all the collected features can befound in Supplemental Table 1.

We sought to develop 2 models. Thefirst-trimester model was designed topredict VBAC success according to fea-tures already known at the first antenatalvisit. The second, a prelabor model, tookinto account features that are gatheredonly during delivery unit admission(including cervical dilation and efface-ment, need for induction of labor21).

The primary obstetric outcome wasmode of delivery (vaginal vs unplannedCD). Secondary outcomes that wereevaluated included maternal outcomefeatures: death, uterine rupture (definedas complete uterine scar dehiscence asobserved by surgical staff and coded inelectronic medical records), postpartumhemorrhage (defined as estimated bloodloss of>500mL and 1000mL for vaginaland cesarean deliveries, respectively).Neonatal outcome features includeddeath, 5-minute Apgar score �7, andneonatal intensive care unit admission.

Staff members who extracted andanalyzed the data (M.L., J.G., S.M.C.)were not involved in patient care; care-givers who recorded data in real time atpoint of care were not aware of the study.

Institutional ethical review boardapproval was obtained for this study(0632-15-HMO. 10/01/2016).

Statistical analysisStatistical analysis was performed withPython 3.7.3, scikit-learn library 0.21.2,catboost 0.15.2, Office Excel 2010

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FIGURE 1Cohort flowchart of study population

One previous cesarean delivery

(n=13,085)

Subsequent delivery

(n=9,888)

Trial of labor after cesarean delivery

(n=7,473)

Vaginal delivey

(n=6,576)

Normal vaginal delivery

(n=5,787)

Instrumental vaginal delivery

(n=789)

Unplanned cesarean delivery

(n=897)

Planned repeated cesarean delivery

(n=2,415)

Excluded (n=3197):

Non-vertex presentation (n=1677)

Preterm deliveries (n=1188)

Major fetal anomalies (n=1061)

Multiple gestations (n=385)

Previous vertical cesarean delivery scar (n=36)

Antepartum fetal death (n=14)

Note: parturients can be excluded due to multiple categories

Lipschuetz et al. Prediction of vaginal birth after searean deliveries using maching learning. Am J Obstet Gynecol 2020.

ajog.org OBSTETRICS Original Research

(Microsoft Corporation, Redmond,WA) and IBM SPSS 24 for Windows(IBM Corp, Armonk, NY). Dichoto-mous features were compared with theuse of c2 test or Fisher’s exact test incases of small numbers, as appropriate;the Mann-Whitney U test was applied toanalyze differences in nonparametriccontinuous features. Confidence intervalwas calculated with the use of DeLong’smethod.43

For this study, we used the gradient-boosting method (using the CatBoostimplementation). This method builds amodel that contains a collection of de-cision trees based on training data, whereeach consecutive tree is trained withfocus on the previous trees’ errors. Themodel can then be used to predict out-comes by combining the predictions ofthe ensemble of decision trees44

(Supplemental Table 2).Two gradient-boosting models were

built to assess parturient personalizedlikelihood for VBAC success, a first-trimester model, and a prelabor model,as described.

Validation of the robustness of themachine-learning model was donewith 2 methods: 10-fold cross valida-tion and removing a portion of thedata. Cross validation is a process thatinvolves splitting the dataset intorandomized training and test sets. Themodel is trained on a training set andtested on the test set; the AUC that isreported is compared across randomsplits. In addition, validation wasperformed by the removal of a portionof the data as a validation set and by acomparison of the AUC of the modelon the untrained validation set. Weused the years 2013 and 2014 as avalidation set and compared this sub-cohort to the total cohort as a vali-dation test.

Machine-learning algorithms createpredictive models that overcome situ-ations of incomplete data. Medianimputation, a common approach todealing with missing values inmachine-learning algorithms,45 wasapplied in this study. This enablesanalysis of the whole cohort, asopposed to analysis of only a subset ofthe cohort with complete datasets.

Even when a significant feature ismissing in a large proportion of cases,machine-learning algorithms mightstill produce better results using thefeature (and imputing the missingvalues) than with a smaller cohort thatis limited to full datasets.After a decision is made to attempt

TOLAC, the same algorithm can assistthe clinician to translate the personalizedrisk score into a simpler feature, whichaids clinical management, by sortingparturients into low-, medium-, andhigh-risk groups. For this purpose, weused the prelabor model, because theadditional features that it incorporates

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are available when the labor process ismanaged.

Low-risk group allocation was set to95%, which is the rate of vaginal de-livery in the total birth cohort ofHadassah medical center (approxi-mately 100,000 deliveries). We used thiscut-off point between the low- andmedium-risk groups to evaluate theadded risk for unplanned CD that aprevious CD entail.

The cut-off point between medium-and high-risk parturients was set to 88%,which was the total VBAC rate in thiscohort, to distinguish those parturientswho have a higher risk for unplanned


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TABLE 1Demographic and maternal and neonatal outcome data for parturients who underwent trial of labor after cesareandelivery (n[7473)

Variable Vaginal delivery Cesarean delivery P valuec

Demographic data

N 6574 897

Mean age, y�SD 31.7�5.1 31.6�5.0 .537

Mean parity, n�SD 3.0�2.2 1.9�1.7 <.001

Smoking, n/N (%) 198/6576 (3) 38/897 (4.2) .053

Gestational diabetes mellitus, n/N (%) 151/5606 (2.7) 20/703 (2.8) .806

Male fetus, n/N (%) 3309/6576 (50.3) 518/897 (57.7) <.001

Mean gestational age, wk�SD 39.5�1.18 39.7�1.34 .001

Mean estimated fetal weight, g�SDa 3335�375 3447�366 <.001

Mean neonatal birthweight, g�SD 3339�413 3372�457 .053

Mean neonatal head circumference, cm�SDb 34.4�1.14 34.7�1.21 <.001

Maternal and neonatal outcome data

Maternal death, n 0 0

Neonatal death, n 4 0 1

Postpartum hemorrhage, n/N (%) 357/5936 (6.0) 14/275(5.1) .604

Uterine rupture, n/N (%) 4/6576 (0.1) 32/897 (3.6) <.001

5-Minute Apgar score �7, n/N (%) 11/6553(0.2) 16/893 (1.8) <.001

Neonatal intensive care unit admission, n/N (%) 31/6576 (0.5) 7/897 (0.8) .211

SD, standard deviation.

a Available for 3025 vaginal deliveries and for 403 cesarean deliveries; b Available in the electronic record since 2010 for 2722 vaginal deliveries and for 392 cesarean deliveries; c Indicates acomparison of vaginal deliveries and cesarean deliveries and were calculated for c2 test for dichotomous features, Mann-Whitney U test for continuous features.



CD than similar parturients who un-dergo TOLAC.

These cut-off points can be alteredlocally in each delivery unit to reflectlocal caregivers or the institutes defini-tion of high-risk.

ResultsBetween the years 2003 and 2014,100,988 deliveries occurred in the de-livery units of Hadassah Medical Center,13,085 of which were to parturients with1 previous CD. After exclusions, 9888cases were identified with 1 previouslow-cervical transverse CD and asingleton, vertex, term fetus with noknown major fetal anomalies. Amongthese parturients, 24.4% women (2415/9888) opted for a planned repeat CD,and 75.6% women (7473/9888) optedfor a TOLAC.


Of the 7473 parturients who attemp-ted a TOLAC, 77.4% (5787/7473) ach-ieved a vaginal delivery, and 10.6% (789/7473) underwent an instrumentalvaginal delivery. Unplanned CD wasperformed in 12% (897/7473) of thedeliveries (Figure 1).Table 1 shows the demographic fea-

tures of the cohort and maternal andneonatal outcomes by mode of delivery.Mean maternal age, rate of gesta-

tional diabetes mellitus, and neonatalbirthweight were similar between the 2groups. Women who underwent un-planned CD had lower parity. HigherEFW and higher estimated fetal headcircumference were also noted in theCD group with no clinically significantdifference. There were no recordedcases of neonatal death in the CD group,and 4 cases of neonatal death in the

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vaginal delivery group (P¼1). The rateof Apgar score �7 was higher amongthe CD parturients; however, similarrates of neonatal intensive care unitadmissions were observed. Postpartumhemorrhage rates were similar between2 groups.

Table 2 summarizes the most signifi-cant features that were included in the 2models described earlier: the first-trimester model and the prelabor model.

Figure 2 presents the calibration plotsof the proposed models and shows thepredicted probability of successful VBACvs the observed proportion of successfulVBAC for each model. The gradient-boosting model allows for individual-ized risk assessment with an AUC of0.745 (95% confidence interval [CI],0.728e0.762) for the first-trimestermodel and increases to an AUC of

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TABLE 2The 5 most important features used by the models to predict success of trial of labor after cesarean delivery (in adescending ranking order)

Importancerank

First-trimester model Prelabor model

FeatureDirection ofassociationa Feature

Direction ofassociationa

1 Parity \ Parity \

2 Age Z First cervical examination: dilation \

3 Minimal gestational week in previousdeliveries

Z First cervical examination: headstation

\

4 Previous delivery newborn infant weight Z Gestational week Not monotonicb

5 Vaginal birth after cesarean deliveryin the past

\ Previous delivery newborn infantweight

Not monotonicb

a Direction of association refers to the association between the feature and the rate of successful trial of labor (ie, increased parity is associated with increased rate of successful trial of labor [\] andincreased birthweight in previous deliveries is associated with decreased rate of successful trial of labor [Z]); b Refers to cases in which the associated is not consistent in 1 direction (ie, gestationalweek begins Z and then changes to \, which creates a U-shaped curve.)



0.793 (95% CI, 0.778e0.808) when theprelabor model is applied.

The gradient-boosting method wasfor its simplicity of use and easy access tofeature importance. A comparison of theprelabor model with the use of gradient-boosting to other machine-learningmodels showed similar results (Table 3).

In addition to cross validation, avalidation was performed by removing aset of data and using it as a validation set.To do this, we used the years 2013e2014as a validation set; the prelabor modelwas calculated based on only the data of6502 deliveries from the years2003e2012 and tested on the 971 de-liveries recorded in 2013 and 2014. Thistest yielded similar results to the previ-ous model (AUC, 0.802; 95% CI,0.761e0.842) supporting the robustnessof the model.

Several features regarding data fromprevious deliveries were found to beimportant features for the prediction ofTOLAC failure. Among these features, a5-minute Apgar score of �8 in previousdeliveries increased the risk for TOLACfailure, with an odds ratio of 2.09 (95%CI, 1.25e3.49): 10.5% in CD cases (454/4301) vs 19.8% in vaginal delivery cases(19/96). The same trend was shown withApgar score of �7, but statistical signif-icance was not reached, probablybecause of the small number of cases(n¼44).

Analysis of the datasets revealed thatEFW was recorded for only 46% ofparturients (3431/7473) for the prelabormodel. Nevertheless, when featureimportance was analyzed (SupplementalTable 2), EFW was among the top 10most important features. To validaterobustness of the model with andwithout EFW, we created a smaller sub-cohort of parturients with EFWdata andcompared this to the larger cohort inwhich missing data were imputed. Thesmall cohort model had an AUC of 0.777(95% CI, 0.756e0.798), which is similarto the large cohort’s AUC of 0.793 (95%CI, 0.778e0.808).Application of the prelabor model to

create low-, medium-, and high-riskgroups demonstrated that 42.4% ofthe parturients (3167/7473) were allo-cated to the low-risk group with suc-cessful VBAC rates of 97.3% (3081/3167); 25.6% of the parturients (1910/7473) were allocated to the medium-risk group with successful VBAC ratesof 90.9% (1,736/1,910); 32.1% of theparturients (2396/7473) were consid-ered to be high-risk with rates of 73.3%(1758/2396) for a successful VBAC(Figure 3).Additionally, the application of the

same prelabor model to the cohort of2415 parturients who underwent electiverepeat cesarean delivery revealed 756(31%) parturients who would have been

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allocated to the low- or medium-riskgroup of TOLAC.

Application of a commonly used first-trimester VBAC calculator29 on a smallercohort (for which the datasets werecomplete for all parturients [n¼406])yielded an AUC of 0.693 (95% CI,0.624e0.763). The application of ourfirst-trimester model to the same subsetpopulation showed a higher AUC of0.745 (95%CI, 0.681e0.810), whichwasnot statistically significant.

CommentPrincipal findingsSuccessful VBAC is feasible for most par-turients, even in a high TOLAC ratepopulation. The machine-learningebasedmodel accurately predicted successfulVBAC. The model offers a personalizedrisk assessment for each parturient basedinitially on features that are present at thefirst antenatal visit but shows improvedprediction accuracy when applied inproximity to the delivery process. Withthe use of the same model, designation ofparturients to low-, medium-, and high-risk groups allows caregivers to put addi-tional emphasis on the parturients whoare allocated to the high-risk group butshould not deter from TOLAC attemptsbecause the success rates are still high,even in the high-risk group (73.3%).

Both models show similar results incomparison with previously published


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FIGURE 2Personalized prediction for successful vaginal birth after cesarean delivery

Calibration plot represents the degree to which the prediction of the models (x-axis) agrees with theobserved outcomes (y-axis). Perfect model calibration is represented by the diagonal line. The size ofthe population identified at each prediction is represented by the size of the circles. A, first-trimesterplot; B, prelabor plot.Lipschuetz et al. Prediction of vaginal birth after searean deliveries using maching learning. Am J Obstet Gynecol 2020.


models: those based on data from thefirst prenatal visit29 and those based ondelivery unit admission data.31,46,47

However, most of these studies investi-gated populations in which rates ofTOLAC were low48,49 (as low as21.5%30) and perhaps were subjected to


a selection bias towards low-risk cases inwhich TOLAC success is more likely. Inour population, TOLAC rates were high(75.6%), yet rates of successful VBACremained high, while maintaining theability to accurately predict successfulVBAC. This is in contrast to a previous

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study that demonstrated that as the useof TOLAC increased, VBAC ratesdecreased.18 Differences between pop-ulations might account for this discrep-ancy; further investigation into thefactors associated with VBAC success isnecessary.

Clinical implicationsPrevious published calculators concurwith our observation that adding fac-tors that are only available close to de-livery (ie, cervical dilation andeffacement, EFW, and others) increasesthe prediction accuracy of VBACmodels. Therefore, perhaps reassess-ment of the decision regarding mode ofdelivery should be considered at thatstage.31,46

Research implicationsThis study shows that incorporatingmachine-learning methods into clinicalobstetric practice might be feasible andshould be investigated thoroughly inlarger, prospective trials. Future researchshould focus on refining the machine-learning models, better understandingtheir pitfalls, and investigating thosecases in which the model prediction wasless accurate. Additionally, researchshould focus on algorithms that “learn”the delivery process, allowing for real-time modulation of the prediction ac-cording to the progress of labor.

Strengths and limitationsThe strength of this study is the highrates of TOLAC attempts. Previousstudies were done in populations withlow rates of TOLAC that subjected themto a selection bias towards low-risk cases.In this study, 75.6% of parturientsattempted a TOLAC, which reduced theselection bias significantly. The presentstudy demonstrates the robustness ofmachine-learning as a modality in pre-diction model development in obstet-rics. We show the feasibility of applyingsuch models in clinical practice. Inaddition, we demonstrate that the addi-tion of features improves model perfor-mance. Our study validated the ability ofthe model to predict VBAC in a largecohort of parturients with high TOLACrates, based on routinely collected

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TABLE 3Performance comparison of different machine-learning methods in the prelabor model

Method Area under the curve 95% Confidence interval

Gradient boosting 0.793 0.778e0.808

Random forest 0.756 0.740e0.773

Balanced random forest 0.782 0.767e0.797

AdaBoost ensemble 0.784 0.769e0.800



electronic medical records data. Manyobstetric models are not applicable toclinical practice50 owing to the need foradditional features that are collected onlyto serve the model.

One of the main advantages ofmachine-learning methods is that theymight shed light on associations thatwere not revealed in previous studies.Wefound that a low 5-minute Apgar score ina previous delivery increases the risk forTOLAC failure. We can speculate thatthe parturient and staff are aware of theprevious bad obstetric outcome andtherefore tend to discontinue TOLACattempts earlier than in other parturi-ents. Previous studies that investigated

FIGURE 3Risk stratification of trial of labor afte

A, Success rates for trial of labor after cesarean delB, Distribution of parturients in the risk groups.AUC, area under the curve.

Lipschuetz et al. Prediction of vaginal birth after searean deliver

features that are collected during pre-natal visits were unlikely to have beenaware of such an association.29,47

The main limitation of our study is thefact that datasets that are based on clinicalpractice are often incomplete. Although alarge dataset was obtained for each de-livery, data were not always complete andideal. We addressed this limitation bycreating a smaller cohort with morecomplete datasets and compared it withthe larger cohort in which the missingdata were imputed with the use ofmedianvalues. The small and large cohort showedsimilar prediction accuracy. Although thelack of full datasets may affect the resultsgiven by the model and more accurate

r cesarean delivery

ivery in comparison with total cohort success rate, d

ies using maching learning. Am J Obstet Gynecol 2020.

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results may be provided by a more com-plete dataset, the algorithm can overcomethis limitation by imputation of missingdata.

In addition, this is a single-centerretrospective study with a limited rateof heterogeneity in the study population,which may differ from other hospitalsettings.18 However, 1 advantage ofmachine-learning is that these models,when applied in other populations, mayreveal different features that predictVBAC success based on local data, whilestill retaining the strength of the struc-tural model used to find those features.

The increased rates of low 5-minuteApgar scores in the CD group indicates

ivided into low-, medium-, and high- risk groups.


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that, for some parturients, TOLACmight not be the safest mode of delivery.However, the rarity of these cases did notallow for a significant prediction. Largerscale databases might allow for suchprediction and investigation of otherundesirable obstetric outcomes51 (eg,uterine rupture, for which previous at-tempts to build predictive models werenot successful52,53) by the incorporationof these outcomes into these models.

ConclusionIn conclusion, the use of machine-learning to predict successful VBAC isfeasible and allows for a comprehensiveassessment of previous and current preg-nancy features, especially towards the endof pregnancy, to facilitate a prelabor dis-cussion of delivery mode and manage-ment during labor. Thismodelmight helpreduce the burden of repeated CDs, reas-sure many parturients regarding thefeasibility of a successful VBAC, andempower them to attempt TOLAC. n

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25. Hehir MP, Ananth CV, Siddiq Z, Flood K,Friedman AM, D’Alton ME. Cesarean delivery inthe United States 2005 through 2014: apopulation-based analysis using the Robson10-group classification system. Am J ObstetGynecol 2018;219:105.e1–11.26. Zhang J, Troendle J, Reddy UM, et al.Contemporary cesarean delivery practice in theUnited States. Am J Obstet Gynecol 2010;203:326.e1–10.27. Lehmann S, Baghestan E, Bordahl PE,Irgens LM, Rasmussen S. Perinatal outcome inbirths after a previous cesarean section at hightrial of labor rates. Acta Obstet Gynecol Scand2019;98:117–26.28. Grobman WA, Lai Y, Landon MB, et al. Cana prediction model for vaginal birth after cesar-ean also predict the probability of morbidityrelated to a trial of labor? Am J Obstet Gynecol2009;200:56.e1–6.29. Grobman WA, Lai Y, Landon MB, et al.Development of a nomogram for prediction ofvaginal birth after cesarean delivery. ObstetGynecol 2007;109:806–12.30. Metz TD, Allshouse AA, Faucett AM,Grobman WA. Validation of a vaginal birth aftercesarean delivery prediction model in womenwith two prior cesarean deliveries. ObstetGynecol 2015;125:948–52.31. GrobmanWA, Lai Y, LandonMB, et al. Doesinformation available at admission for deliveryimprove prediction of vaginal birth after cesar-ean? Am J Perinatol 2009;26:693–701.32. Annessi E, Del Giovane C, Magnani L, et al.A modified prediction model for VBAC, in a Eu-ropean population. J Matern Fetal Neonatal Med2016;29:435–9.33. Chaillet N, Bujold E, Dube E, Grobman WA.Validation of a prediction model for vaginal birthafter caesarean. J Obstet Gynaecol Can2013;35:119–24.34. Costantine MM, Fox KA, Pacheco LD, et al.Does information available at delivery improvethe accuracy of predicting vaginal birth aftercesarean? Validation of the published models inan independent patient cohort. Am J Perinatol2011;28:293–8.35. Mone F, Harrity C, Mackie A, et al. Vaginalbirth after caesarean section prediction models:a UK comparative observational study. Eur JObstet Gynecol Reprod Biol 2015;193:136–9.36. Schoorel EN, Melman S, van Kuijk SM, et al.Predicting successful intended vaginal deliveryafter previous caesarean section: external vali-dation of two predictive models in a Dutchnationwide registration-based cohort with a highintended vaginal delivery rate. BJOG 2014;121:840–7.37. Harris BS, Heine RP, Park J, et al. Are pre-diction models for vaginal birth after cesareanaccurate? Am J Obstet Gynecol 2019;220:492.e1–7.38. Deo RC. Machine-learning in medicine. Cir-culation 2015;132:1920–30.39. Obermeyer Z, Emanuel EJ. Predicting thefuture: big data, machine-learning, and clinicalmedicine. N Engl J Med 2016;375:1216–9.

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40. Lipschuetz M, Cohen SM, Israel A, et al.Sonographic large fetal head circumference andrisk of cesarean delivery. Am J Obstet Gynecol2018;218:339.e1–7.41. Lipschuetz M, Cohen SM, Ein-Mor E, et al.A large head circumference is more stronglyassociated with unplanned cesarean or instru-mental delivery and neonatal complications thanhigh birthweight. Am J Obstet Gynecol2015;213:833.e1–12.42. Pavlicev M, Romero R, Mitteroecker P.Evolution of the human pelvis and obstructedlabor: new explanations of an old obstetricaldilemma. Am J Obstet Gynecol 2020;222:3–16.43. Sun X, Xu W. Fast implementation ofDeLong’s algorithm for comparing the areasunder correlated receiver operating character-istic curves. IEEE Signal Process Lett 2014;21:1389–93.44. Prokhorenkova L, Gusev G, Vorobev A,Dorogush AV, Gulin A. Catboost: unbiasedboosting with categorical features. Adv NeuralInf Process Syst 2018:6638–48.45. Batista GEAPA, Monard MC. An analysis offour missing data treatment methods for su-

pervised learning. Applied Artificial Intelligence2003;17:519–33.46. Metz TD, StoddardGJ, Henry E, JacksonM,Holmgren C, Esplin S. Simple, validated vaginalbirth after cesarean delivery prediction model foruse at the time of admission. Obstet Gynecol2013;122:571–8.47. Hashima JN, Guise JM. Vaginal birth aftercesarean: a prenatal scoring tool. Am J ObstetGynecol 2007;196:e22–3.48. Uddin SF, Simon AE. Rates and successrates of trial of labor after cesarean delivery in theUnited States, 1990-2009. Matern Child HealthJ 2013;17:1309–14.49. National Institutes of Health. National In-stitutes of Health Consensus DevelopmentConference statement vaginal birth after cesar-ean: new insights March 8e10, 2010. SeminPerinatol 2010;34:351–65.50. Kleinrouweler CE, Cheong-See FM,Collins GS, et al. Prognostic models in obstet-rics: available, but far from applicable. Am JObstet Gynecol 2016;214:79–90.e36.51. Dietz HP, Campbell S. Toward normal birth-but at what cost? Am J Obstet Gynecol2016;215:439–44.

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52. Macones GA, Cahill AG, Stamilio DM,Odibo A, Peipert J, Stevens EJ. Can uterinerupture in patients attempting vaginal birth aftercesarean delivery be predicted? Am J ObstetGynecol 2006;195:1148–52.53. Smith GC, White IR, Pell JP, Dobbie R.Predicting cesarean section and uterinerupture among women attempting vaginal birthafter prior cesarean section. PLoSMed 2005;2:e252.

Author and article informationFrom The Mina and Everard Goodman Faculty of Life

Sciences, Bar-Ilan University, Ramat-Gan, Israel (Dr

Unger and Ms Lipschuetz and Mr Guedalia); the Obstet-

rics & Gynecology Division, Hadassah-Hebrew University

Medical Center, Jerusalem, Israel (Drs Rottenstreich,

Persky, Kabiri, Levin, Yagel, and Sompolinsky and Ms

Lipschuetz and Ms Cohen).

Received Dec. 19, 2019; accepted Dec. 30, 2019.

Supported by the Ministry of Science and Technology,

Israel (M.L.).

The authors report no conflict of interest.

Corresponding author: Simcha Yagel, MD. simcha.

[email protected]
































































mailto:[email protected]

mailto:[email protected]

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SUPPLEMENTAL TABLE 1List of features evaluated for the model

Features Feature type Notes First-trimester model Prelabor model

Maternal

Maternal age Discrete Years þ þGravidity Discrete þ þParity Discrete þ þPrevious abortions Discrete þ þPrevious ectopic pregnancies Discrete þ þNumeric of live children Discrete þ þMaternal blood type Categoric ABO/� þ þSmoking status Categoric and discrete Yes/No and numeric of packs

per yearþ þ

Maternal height Numeric Centimeter e þInterpregnancy

Interpregnancy interval Mean, minimum, maximum,standard deviation

Years þ þ

Interpregnancy interval with cesarean delivery Discrete Years þ þPrevious delivery weights Mean, minimum, maximum

standard deviationGrams þ þ

Head circumferences in previous deliveries Mean, minimum, maximum,standard deviation

Centimeters þ þ

Gestational age at previous deliveries Mean, minimum, maximum,standard deviation

Complete weeks of gestation þ þ

Gestational age in last delivery Numeric Complete weeks of gestation þ þMaternal age at previous deliveries Mean, minimum, maximum,

standard deviationYears þ þ

Mode of delivery in previous deliveries Categoric with count foreach category

Vaginal delivery, vacuum delivery,or forceps delivery

þ þ

Lipschuetz et al. Prediction of vaginal birth after searean deliveries using maching learning. Am J Obstet Gynecol 2020. (continued)

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SUPPLEMENTAL TABLE 1List of features evaluated for the model (continued)

Features Feature type Notes First-trimester model Prelabor model

Vaginal birth after cesarean delivery in the past Categoric Yes/No þ þ5-Minute Apgar score in previous deliveries Mean, minimum, maximum,

standard deviationþ þ

Presence of diagnosis of dysfunctionallabor in previous deliveries

Categoric Yes/No þ þ

Indication for previous cesarean sectiondelivery

Categoric Failed vacuum extraction, fetaldistress, maternal indication,placental indication,or other indications

þ þ

Index pregnancy

Prepregnancy maternal body weight Numeric Kilograms þ þPrepregnancy maternal body mass index Numeric Kilogram/meter square e þCurrent maternal weight Numeric Kilograms e þCurrent maternal body mass index Numeric Kilogram/meter square e þNeed for assisted reproductive technology Categoric Yes/No e þGroup B Strep status Categoric Yes/No e þGestation diabetes mellitus status Categoric Yes/No e þOnset of labor Categoric Spontaneous, induction, augmentation e þEstimated fetal weight Numeric Grams e þGestational age at labor Numeric Days and completed weeks of

pregnancye þ

First cervical examination

Dilation Discrete Centimeters e þEffacement Numeric Percentage e þHead station Discrete Range, e3 to þ3 e þ


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SUPPLEMENTAL TABLE 2Glossary

Method Definition

Machine-learning A group of algorithms that can perform a task, such asprediction or classification, relying on the identificationof patterns in the data rather than receiving explicitinstructions regarding the possible associationsbetween features (ie, instead of reviewing whether a orb leads to c); these algorithms infer patterns in thelinkage among a, b, and c.

Random-forest A machine-learning method in which multiple decisiontrees are created, incorporating the various features indifferent orders and evaluating the ability of theensemble of decision trees to predict the desiredoutcome.

Gradient boosting A machine-learning method that builds multipledecision trees, where each successive tree is fine-tuned to focus on the errors of the previous trees. Theensemble of trees is then used for the final prediction,working in a stage-wise manner rather than a parallelmanner (as is done in the random forest approach).

Area under the curve Abbreviated form of “area under the receiver operativecharacteristic curve” that refers to the curve that iscreated by the trade-off between sensitivity andspecificity of a diagnostic model. The area under thecurve describes the discriminatory effect of thesuggested model and serves as a means to assess theprediction accuracy of the model; an area under thecurve of 0.5 represents random classification, and anarea under the curve of 1 represents perfectclassification.

Feature An individual measurable property or characteristicthat is used as input to the machine-learning model.Features can be numeric, categoric, textual, or imagebased. Features are also sometimes referred to as“variables,” “parameters,” or “attributes.”

Feature importance A machine-learning term that describes the influencethat a feature has on the prediction of the model. Thebigger the value of the importance, the bigger thechange to the prediction value, if this feature ischanged.



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