Development and Validation of a Predictive Nomogram for Cesarean Delivery in Term Singleton Pregnancies Complicated by Small for Gestational Age Undergoing Labor Induction

doi:10.21203/rs.3.rs-4892379/v1

Objective: This study aimed to identify antenatal and intrapartum risk factors associated with cesarean delivery in term singleton pregnancies complicated by small for gestational age (SGA) and to develop a predictive model.

Methods:We conducted a retrospective case-control study of 507 SGA patients who underwent labor induction between 2017 and 2022 at Fujian Maternity and Child Health Hospital.Comprehensive data on maternal demographics, obstetric complications, labor induction methods, and neonatal outcomes were collected. 354 (70%) experiencing SGA complications enrolled as the derivation cohort and 153 (30%) included in the validation set. Univariate and multivariate logistic regression analyses were performed to identify independent risk factors for cesarean delivery, and a predictive nomogram was developed based on these factors in the derivation cohort,and verified in the validation set.

Results: A total of 134 (26.43%) women in the cohort underwent cesarean delivery following labor induction. Four significant independent risk factors for cesarean delivery were identified: maternal age(aOR1.08, 95%CI 1.01-1.15) , weightat admission (aOR 1.04, 95% CI 1.01 - 1.07), the use of dinoprostone for induction(aOR 2.08, 95% CI 1.13-3.81), and the Bishop score after cervical ripening(aOR0.65, 95% CI:0.54-0.80). The constructed nomogram displayed a discriminative ability with an area under the curve (AUC) of 0.78 in the training cohort and 0.77 in the validation cohort. Calibration curves indicated strong agreement（P＞0.05）between predicted probabilities and observed outcomes, while decision curve analysis confirmed significant net benefits across various various threshold probabilities.

Conclusion:The developed nomogram provides clinicians with a reliable tool for predicting the likelihood of cesarean delivery in SGA pregnancies undergoing labor induction, aiding in informed decision-making and potentially optimizing clinical management strategies to improve perinatal outcomes.

Small for Gestational Age (SGA)

Cesarean Delivery

Labor Induction

Risk Factors

Predictive Model

Small for gestational age (SGA) refers to infants whose birth weight is below the 10th percentile for their same gestational age and sex [1].As a high-risk group among newborns, SGA infants are predisposed to various perinatal diseases and mortality. They are also susceptible to multiple long-term metabolic and cardiovascular conditions. Moreover, they face significantly increased risks of delayed growth, developmental delays, and neurological disorders, profoundly affecting their quality of life in long- and short-term[2–3].

Due to the heightened risk of stillbirth in pregnancies beyond 37 weeks of gestation, there is a common consensus advocating for labor induction during the early term phase [4]. However, the incidence of cesarean delivery remains notably high in this context, primarily due to the associated risks of intrapartum fetal acidosis and fetal heart rate (FHR) abnormalities commonly seen in SGA fetuses[5]. Previous studies have identified several risk factors contributing to cesarean delivery[6–9], including Doppler abnormalities in the umbilical artery, a reduced cerebroplacental ratio (CPR), oligohydramnios, the utilization of prostaglandins, and non-reassuring fetal heart tracing(NRFHT). Determining the appropriate timing and method of delivery for fetuses with SGA poses considerable challenges across all stages of gestational age. Thus, it is vital to identify antenatal and intrapartum risk factors associated with cesarean delivery at labor induction.

The objective of this research is to develop and validate an objective model for predicting cesarean deliveries due to failed labor inductions in pregnancies complicated by the SGA in term singletons.This model seeks to enhance informed decision-making, optimize resource allocation, and mitigate the potential risks associated with failed labor induction.

Study Population

A retrospective case-control study was carried out at Fujian Maternity and Child Health Hospital, involving a series of pregnant women diagnosed as having small for gestational age fetuses between October 2017 and December 2022. Ethical approval was obtained for the study from the hospital’s Ethics Committee (reference number 2023KY016). Comprehensive maternal and neonatal characteristics were systematically collected from medical records. Considering the study’s retrospectinve appoach and the anonymous date of the patients, obtaining informed consent was deemed unnecessary.

The initial inclusion criteria included singleton pregnancies diagnosed with SGA, characterized by a birth weight that falls under the 10th percentile at term (≥ 37 weeks). Exclusion criteria comprised multiple pregnancies, congenital malformations, fetal chromosomal abnormalities, premature rupture of membranes (PROM), prior uterine surgery (such as cesarean section or myomectomy), and cases lost to follow-up. In total, 507 cases were evaluated for labor induction, all presenting cephalically with a Bishop score < 6 for cervical ripening. Figure 1 depicts the selection procedure.

Treatment Protocol for SGA Pregnancies and Induction Methods

In accordance with departmental protocols, all SGA patients underwent routine ultrasound and fetal heart rate monitoring prior to labor induction. A senior physician assessed the cervical condition via the Bishop score. Patients were thoroughly informed about the induction procedure and its potential risks, including those related to the use of Cook's double balloon and dinoprostone. Each SGA patient provided informed consent, selecting their preferred method for cervical ripening. Following manufacturer guidelines, 10mg of dinoprostone was introduced into the posterior vaginal fornix, while 80ml of saline was used to inflate both intrauterine and intravaginal balloons for the Cook's double balloon.

In our labor induction protocol, each method was applied for a maximum of 12 hours, accompanied by intermittent cardiotocographic monitoring. Criteria for removal included reaching the active phase of labor (≥ 2cm cervical dilation), membrane rupture, non-reassuring fetal heart rate, placental abruption, or uterine tachysystole (> 5 contractions in 10 minutes) for either device. If active labor (≥ 2cm cervical dilation) was not achieved within 12 hours, the devices were removed, and intravenous oxytocin was initiated at a rate of 2.5 mIU/min, with adjusted every 15 minutes based on uterine contractions. Women achieving vaginal delivery following induction were classified as successful, whereas those requiring cesarean delivery for any reason were considered failed inductions.

Data Collection

The primary outcome was to identify risk factors linked to cesarean birth in pregnancies complicated by SGA during labor induction. The analyzed factors included maternal demographic characteristics such as age, height, weight, body mass index (BMI), gravidity, and parity;complications during pregnancy like gestational diabetes, pregnancy-induced hypertension and oligohydramnios, obstetric conditions including gestational age, Bishop scores before and after cervical ripening, induction methods, oxytocin augmentation, and mode of delivery, complications during labor such as fever, early rupture of membranes, and blood loss after birth, and neonatal characteristics like estimated fetal weight, neonatal weight, Apgar scores at 1 and 5 minutes, and admission to the neonatal intensive care unit (NICU)).

Statistical Analysis

Numerical variables were presented as mean ± standard deviation or median (interquartile range), whereas categorical variables were expressed as frequencies and percentages.The Student’s t-test or Mann–Whitney U-test was employed for comparing continuous variables, while the chi-square (χ²) test or Fisher’s exact test was utilized for categorical variables.Risk factors correlated with cesarean delivery were identified through multivariate binary logistic regression analysis, applying univariate predictors (p < 0.05) via a stepwise backward elimination method. In the multivariate analysis,only variables that had a p-value of less than 0.05 in the univariate analysis were included,whereas insignificant factors were omitted. Instances of multicollinearity necessitated the retention of only one variable within the model.All statistical tests were performed as two-tailed, considering p-values < 0.05 as statistically significant. Associations were expressed as odds ratios (OR),accompanied by 95% confidence intervals (CIs). A graphical nomogram was generated to visually represent the logistic regression model.

The model’s discriminatory performance was quantified by the area under the receiver operating characteristic (ROC) curve (AUC). The ROC curve was derived from the regression model, with both the AUC and its 95% CIs reported. AUC values exceeding 0.7 suggest robust discriminatory capacity. Calibration was evaluated using the Hosmer-Lemeshow test, accompanied by a calibration curve illustrating predicted versus actual probabilities, with alignment closely following the ideal 45° line indicating strong correlation. Clinical applicability was assessed via decision curve analysis (DCA). All statistical analyses were executed using R (version 4.3.0; The R Project for Statistical Computing, Vienna, Austria; http://www.r-project.org) and SPSS (version 24.0; IBM Corp., Armonk, NY, USA).

Baseline Patient Characteristics

The participant selection flowchart for our case-control study is illustrated in Figure 1. Of 998 patients meeting inclusion criteria, 491 were excluded due to factors such as multiple pregnancies, chromosomal abnormalities, and major malformations. Ultimately, a cohort of 507 participants was included in this study, with 354 (70%) experiencing SGA complications enrolled as the derivation cohort and 153 (30%) included in the validation set. A total of 134 (26.43%) women in the cohort underwent cesarean delivery following labor induction.

As depicted in Table 1,a comparison was made between the maternal, neonatal, and obstetric characteristics of women who achieved vaginal delivery and those who required cesarean section. Women who underwent cesarean section demonstrated higher maternal age (29.68 ± 4.58 vs. 28.19 ± 4.22, P < 0.001), greater admission weight (62.81 ± 8.39 vs. 60.65 ± 9.57, P = 0.015), and elevated admission BMI (24.84 ± 2.93 vs. 24.00 ± 3.66, P = 0.008) in comparison to vaginal delivery patients. Cesarean deliveries were also associated with lower Bishop scores following cervical ripening (6.17 ± 1.41 vs. 7.05 ± 1.60, P < 0.001), An increased incidence of oligohydramnios (16.42% vs. 9.92%, P = 0.044), lower rates of early rupture of membranes during induction (5.97% vs. 12.87%, P = 0.029), and increased postpartum blood loss (397.5 ml vs. 185 ml, P < 0.001). Notably, the utilization of dinoprostone for induction resulted in a statistically significant cesarean delivery rate of 47.01% among SGA patients when compared to the Cook double balloon method. Table 2 provides the characteristics of pregnancies in both the derivation and validation cohorts, which shared similar maternal and fetal features, indicating the robustness of random group assignment.

Table 1 Univariate analysis of demographic and clinical characteristics of pregnancies with SGA stratified by mode of delivery (N=507)

Variables	Total (N= 507)	vaginal delivery (N= 373)	cesarean delivery (N = 134)	P
Variables	Total (N= 507)	vaginal delivery (N= 373)	cesarean delivery (N = 134)	P
Antenatal variables
Maternal age(y)	28.59 ± 4.36	28.19 ± 4.22	29.68 ± 4.58	<.001*
Maternal age, n(%)				0.006*
＜35	458 (90.34)	345 (92.49)	113 (84.33)
≥35	49 (9.66)	28 (7.51)	21 (15.67)
Gravidity	1.62 ± 0.95	1.62 ± 0.92	1.63 ± 1.02	0.915
Gestational age at adimission	38.99 ± 1.19	39.00 ± 1.19	38.96 ± 1.20	0.707
Height（cm）	158.95 ± 5.16	158.95 ± 5.05	158.95 ± 5.47	0.993
Prepregnancy weight（kg)	50.44 ± 7.34	50.17 ± 7.32	51.17 ± 7.35	0.178
Prepregnancy BMI(kg/m2)	19.95 ± 2.68	19.85 ± 2.76	20.22 ± 2.42	0.174
Prepregnancy BMI，n(%)				0.314
Underweight(BMI < 18.5)	154 (30.37)	121 (32.44)	33 (24.63)
Normal(18.5 ≤ BMI < 25.0)	312 (61.54)	222 (59.52)	90 (67.16)
Overweigt(25.0 ≤BMI < 30.0)	38 (7.50)	28 (7.51)	10 (7.46)
Obese (BMI ≥ 30.0)	3 (0.59)	2 (0.54)	1 (0.75)
Admission weight(kg)	61.22 ± 9.31	60.65 ± 9.57	62.81 ± 8.39	0.015*
Admission BMI(kg/m2)	24.23 ± 3.50	24.00 ± 3.66	24.84 ± 2.93	0.008*
Admission BMI (%)				0.003*
Underweight(BMI < 18.5)	27 (5.33)	25 (6.70)	2 (1.49)
Normal(18.5 ≤ BMI < 25.0)	210 (41.42)	165 (44.24)	45 (33.58)
Overweight(25.0 ≤ BMI < 30)	205 (40.43)	135 (36.19)	70 (52.24)
Obese (BMI ≥ 30.0)	65 (12.82)	48 (12.87)	17 (12.69)
Parity , n(%)				0.263
0	383 (75.54)	277 (74.26)	106 (79.10)
≥1	124 (24.46)	96 (25.74)	28 (20.90)
GDM, n(%)	74 (14.60)	58 (15.55)	16 (11.94)	0.31
Gestational hypertension, n(%)	34 (6.71)	27 (7.24)	7 (5.22)	0.424
Oligoamnios, n(%)	59 (11.64)	37 (9.92)	22 (16.42)	0.044*
Bishop score before labor induction	2.85 ± 0.86	2.88 ± 0.86	2.76 ± 0.83	0.181
Estimated fetal weight(kg)	2650.20 ± 266.55	2655.31 ± 249.88	2635.96± 308.83	0.515
Intrapartum variables
Method of inducing labor, n(%)				0.007*
cook double balloon	318 (62.72)	247 (66.22)	71 (52.99)
dinoprostone	189 (37.28)	126 (33.78)	63 (47.01)
Bishop score after medication	6.82 ± 1.60	7.05 ± 1.60	6.17 ± 1.41	<.001*
Oxytocin agument, n(%)	230 (45.36)	171 (45.84)	59 (44.03)	0.717
Early Rupture of Membranes, n(%)	56 (11.05)	48 (12.87)	8 (5.97)	0.029*
Intrapartum fever, n(%)	12 (2.37)	9 (2.41)	3 (2.24)	1
Postpartum blood loss, M (Q₁, Q₃)	205(155.0,361.5)	185(150,278)	397.5(196.25,420)	<.001*
Variables at birth
Gestational age at delivery(wk)	39.43 ± 1.16	39.44 ± 1.15	39.40 ± 1.18	0.714
Neonatal weight(kg)	2595.37 ± 206.66	2605.42 ± 195.89	2567.40 ± 232.55	0.068
1min Apgar score	9.89 ± 0.65	9.90 ± 0.62	9.85 ± 0.71	0.419
5min Apgar＜7, n(%)	3 (0.59)	2 (0.54)	1 (0.75)	1
NICU, n(%)	187 (36.88)	138 (37.00)	49 (36.57)	0.929
Small for gestational age, n(%)				0.147
mild	418 (82.45)	313 (83.91)	105 (78.36)
severe	89 (17.55)	60 (16.09)	29 (21.64)

*P≤0.05. GDM:gestational diabetes mellitus; BMI:body mass index;NICU: Neonatal Intensive Care Units.

Table 2 Demographic and Clinical Characteristics of Patients With SGA Undergoing Induction of Labor

Variables	Training set (N = 354)	Validation set (N= 153)	P
Variables	Training set (N = 354)	Validation set (N= 153)	P
Maternal age(y)	28.38 ± 4.39	29.07 ± 4.28	0.104
Maternal age, n(%)			0.293
＜35	323 (91.24)	135 (88.24)
≥35	31 (8.76)	18 (11.76)
Prepregnancy weight（kg）	50.91 ± 7.63	49.33 ± 6.48	0.026^*
Prepregnancy BMI（kg/m2）	20.02 ± 2.80	19.79 ± 2.36	0.386
Prepregnancy BMI，n(%)			0.79
Underweight(BMI < 18.5)	110 (31.07)	44 (28.76)
Normal(18.5 ≤ BMI < 25)	215 (60.73)	97 (63.40)
Overweight(25.0 ≤ BMI < 30)	26 (7.34)	12 (7.84)
Obese (BMI ≥ 30.0)	3 (0.85)	0 (0.00)
Height（cm）	157.82 ± 5.09	159.44 ± 5.12	0.001*
Parity , n(%)			0.896
0	268 (75.71)	115 (75.16)
≥1	86 (24.29)	38 (24.84)
Gravidity	1.69 ± 0.88	1.59 ± 0.98	0.297
Admission weight（kg）	60.53 ± 9.01	61.52 ± 9.44	0.269
Admission BMI（kg/m2）	24.28 ± 3.38	24.20 ± 3.55	0.812
Admission BMI (%)			0.586
Underweight(BMI < 18.5)	22 (6.21)	5 (3.27)
Normal(18.5 ≤ BMI < 25)	145 (40.96)	65 (42.48)
Overweight(25.0 ≤ BMI < 30)	141 (39.83)	64 (41.83)
Obese (BMI ≥ 30.0)	46 (12.99)	19 (12.42)
Estimated fetal weight（kg）	2662.67±264.79	2621.35±269.23	0.109
Bishop score before labor induction	2.85 ± 0.86	2.84 ± 0.86	0.952
Oxytocin agument, n(%)	157 (44.35)	73 (47.71)	0.49
Gestational age at adimission（wk)	38.96 ± 1.23	39.01 ± 1.18	0.709
Gestational age at delivery(wk）	39.42 ± 1.19	39.44 ± 1.15	0.885
GDM, n(%)	54 (15.25)	20 (13.07)	0.523
Gestational hypertension, n(%)	23 (6.50)	11 (7.19)	0.775
Oligoamnios, n(%)	43 (12.15)	16 (10.46)	0.586
Method of inducing labor, n(%)			0.836
cook double balloon	221 (62.43)	97 (63.40)
dinoprostone	133 (37.57)	56 (36.60)
Bishop score after medication	6.84 ± 1.58	6.81 ± 1.61	0.834
Oxytocin agument, n(%)	157 (44.35)	73 (47.71)	0.485
Delivery model, n(%)			0.451
vaginal delivery	257 (72.60)	116 (75.82)
cesarean section	97 (27.40)	37 (24.18)
Early Rupture of Membranes, n(%)	44 (12.43)	12 (7.84)	0.13
Intrapartum fever, n(%)	9 (2.54)	3 (1.96)	0.938
Postpartum blood loss M (Q₁, Q₃)	205.0(150.0,325.0)	202.5(158.5,373.7)	0.516
Neonatal weight（kg）	2590.86±227.16	2597.32±197.45	0.747
1min Apgar score	9.89 ± 0.71	9.87 ± 0.56	0.854
5min Apgar score＜7, n(%)	2 (0.56)	1 (0.65)	1
NICU, n(%)	141 (39.83)	46 (30.07)	0.036^*

^*P≤0.05. GDM:gestational diabetes mellitus; BMI:body mass index;NICU: Neonatal Intensive Care Units.

Predictive Variable Screening

Following univariate logistic regression analysis in the training set, several factors emerged with a p-value < 0.05 .There were then integrated into the logistic regression model,including maternal age, prepregnancy weight, weight at admission,body mass index (BMI) at admission, utilization of dinoprostone for induction, and Bishop score after cervical ripening. The independent risk factors linked to cesarean delivery following labor induction were uncovered through subsequent backward stepwise multivariate logistic regression analysis,as depicted in Table3. Four significant risk factors were identified: maternal age, admission weight, the use of dinoprostone for labor induction, and the Bishop score after medication. Notably, for each 1-point increase in Bishop score after medication, the odds of cesarean delivery decreased by 35% (aOR 0.65, 95% CI 0.54 - 0.80). Increments of 1 kg in admission weight and 1 year in maternal age corresponded to a 4.0% (aOR 1.04, 95% CI 1.01-1.07) and an 8.0% (aOR 1.08, 95% CI 1.01-1.15) rise in the likelihood of cesarean delivery, respectively. Furthermore, when comparing dinoprostone with the Cook double balloon method for labor induction, dinoprostone emerged as a notable risk factor, showing more than double the risk of cesarean delivery (aOR 2.08, 95% CI 1.13-3.81).

Table 3 Independent risk factors for cesarean delivery among patients with SGA undergoing induction of labor at term in training set.

Risk factors	Unadjusted OR(95%CI)	P	Adjusted OR(95%CI)	P
Risk factors	Unadjusted OR(95%CI)	P	Adjusted OR(95%CI)	P
Antenatal factors
Maternal age	1.11 (1.04 ~ 1.18)	0.001*	1.08 (1.01 ~ 1.15)	0.024*
Parity
0	1.00 (Reference)
≥1	1.09 (0.58 ~ 2.05)	0.79
Gravidity	1.12 (0.89 ~ 1.42)	0.328
Gestational age at adimission	0.96 (0.77 ~ 1.19)	0.721
Height	1.04 (0.98 ~ 1.09)	0.192
Prepregnancy weight	1.04 (1.01 ~ 1.08)	0.041*	1.02 (0.99 ~ 1.05)	0.246
Prepregnancy BMI	1.08 (0.98 ~ 1.20)	0.137
Prepregnancy BMI
Underweight(BMI < 18.5)	0.58 (0.32 ~ 1.06)	0.078
Normal(18.5 ≤ BMI < 25.0)	1.00 (Reference)
Overweight(25.0 ≤ BMI < 30.0)	0.94 (0.32 ~ 2.76)	0.903
Obese (BMI ≥ 30.0)	0.00 (0.00 ~ Inf)	0.988
Admission weight	1.05 (1.02 ~ 1.08)	<.001*	1.04 (1.01 ~ 1.07)	0.027*
Admission BMI	1.12 (1.04 ~ 1.21)	0.003*
Admission BMI
Underweight(BMI < 18.5)	0.59 (0.13 ~ 2.73)	0.498	0.53 (0.15 ~ 1.93)	0.338
Normal(18.5 ≤ BMI < 25.0)	1.00 (Reference)		1.00 (Reference)
Overweight(25.0 ≤ BMI < 30.0)	2.87 (1.58 ~ 5.20)	<.001*	2.58 (1.32 ~ 5.06)	0.078
Obese (BMI ≥ 30.0)	1.96 (0.83 ~ 4.63)	0.124	0.94 (0.32 ~ 2.76)	0.903
Gestational diabetes mellitus	0.53 (0.24 ~ 1.19)	0.122
Gestational hypertension	0.90 (0.32 ~ 2.54)	0.838
Oligoamnios	1.36 (0.61 ~ 3.03)	0.446
Bishop score before labor induction	0.93 (0.69 ~ 1.26)	0.646
Estimated fetal weight	1.00 (1.00 ~ 1.00)	0.716
Intrapartum factors
Gestational age at delivery	0.99 (0.79 ~ 1.23)	0.902
Method of inducing labor
cook double balloon	1.00 (Reference)
dinoprostone	2.00 (1.17 ~ 3.43)	0.011*	2.08 (1.13 ~ 3.81)	0.018*
Bishop score after medication	0.69 (0.57 ~ 0.83)	<.001*	0.65 (0.54 ~ 0.80)	<.001*
Oxytocin agument	1.23 (0.73 ~ 2.08)	0.445
Early rupture of membranes, n(%)	0.48 (0.16 ~ 1.42)	0.183
Intrapartum fever, n(%)	0.96 (0.19 ~ 4.88)	0.964
^*P≤0.05,OR: Odds Ratio, CI: Confidence Interval

Development of the Nomogram

Utilizing the findings from the logistic regression analysis, we constructed a predictive nomogram for SGA patients undergoing labor induction. This model incorporates four predictive variables: maternal age, admission weight, the use of dinoprostone as an induction method, and the Bishop score after cervical ripening, along with the outcome variable of cesarean delivery (Figure 2).The individual scores for each predictive factors were added together, suggesting that a high overall score implies a greater likelihood of cesarean birth.

Model Performance in the Training Set

The prognostic efficacy of our model was performed within the training cohort. Discrimination is defined as the model's capacity to distinguish between events and non-events, measured by the area under the curve (AUC). As illustrated in Figure 3A, the nomogram achieved an AUC of 0.78 (95% CI: 0.73–0.84) in the training cohort, reflecting robust discriminative ability. With an optimal cutoff value of 36%, the nomogram demonstrated an accuracy of 0.79 (95% CI: 0.75–0.83), sensitivity of 0.86 (95% CI: 0.82–0.91), specificity of 0.61 (95% CI: 0.51–0.71), a positive predictive value (PPV) of 0.85 (95% CI: 0.81–0.90), and a negative predictive value (NPV) of 0.63 (95% CI: 0.53–0.73).

Calibration refers to the degree of alignment between predicted probabilities and actual outcomes. The calibration plots in the training cohort reveal that the predictions of the nomogram are strongly correlated with the data observed(P=0.397)(Figure 3B). We further applied decision curve analysis (DCA) to assess alternative prognostic strategies, illustrating that the model conferred higher net benefits across probability thresholds from 5% to 80%, highlighting its practical applicability in clinical decision-making within this probability range.

Performance of the model in the validation set

The prediction model was evaluated with our validation cohort. To assess discriminative ability, calibration accuracy, and clinical effectiveness of the nomogram, we employed a receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA). As demonstrated in Figure 4A, the AUC for the validation cohort stood at 0.77 (95% CI: 0.68–0.86), suggesting strong discriminative power in predicting outcomes. Nomogram accuracy was 0.75, sensitivity 0,84, and specificity 0.46.The calibration curve in the validation cohort closely mirrored the diagonal, evidencing robust calibration ability(P=0.812) (Figure 4B). Furthermore, the DCA curve remained consistently elevated across a broad spectrum of threshold probabilities (Figure 4C), indicating that our model provides noteworthy net benefits in forecasting the likelihood of cesarean delivery for SGA patients following labor induction.

Main Findings

This study identified crucial hazards linked to cesarean section following failure induction in pregnancies complicated by SGA fetuses.Maternal age, admission weight, use of dinoprostone for induction, and Bishop score after cervical ripening were found to be independent predictors of cesarean delivery. The developed nomogram, based on these parameters serves as a visual tool for assessing individualized risk, thereby enhancing clinical decision-making. The nomogram in the model group demonstrated an area under the curve (AUC) of 0.78 (95% CI: 0.73–0.84), while the validation cohort confirmed an AUC of 0.77 (95% CI: 0.68–0.86). These findings emphasize the nomogram's robust discriminative power and predictive accuracy, offering a comprehensive reference for developing clinical guidelines that inform medical decisions.

Interpretation of Findings and Comparison with Existing Literature

The nomogram incorporates risk factors that have been previously documented in various publications.Nwabuobi C[6] developed a prediction model for cesarean birth following labor induction in SGA patients based on maternal age, gestational age, and the initial method of induction. Our research verified the observation that advancing maternal age elevates the likehood of cesarean section following labor induction in SGA cases, consistent with existing literature highlighting a correlation between older maternal age and increased cesarean rates[10-11]. Attali E[12] reported a significant correlation between maternal age and the occurrence of cesarean delivery,observed in a dose-dependent manner with [aOR1.56,95% CI:1.39-1.76) and 2.53 (2.07-3.09)] in women aged between 35-40 y and those over 40 y. Our study revealed that mothers aged over 35 years are 1.58 times more likely to undergo cesarean delivery following induction failure in SGA pregnancies (aOR 2.58, 95% CI: 1.09-6.09). This heightened risk may stem from complications associated with advanced maternal age, which could indicate poorer placental function and vascularity[13].

Additionally, our analysis identified admission weight as a significant factor contributing to cesarean delivery, suggesting that higher body weight alters maternal metabolic status and physiological conditions, exacerbating potential maternal and fetal complications.Quach D [14]showed that higher BMI (aOR, 1.05 (95% CI, 1.03-1.08)) were correlated with an gradual rise in the likelihood of cesarean delivery following IOL.Study has shown that obesity (BMI >30 kg/m2) can be an independent risk factor for CS[15].While univariate logistic regression indicated a correlation between elevated BMI and increased cesarean risk, after adjusting for confounders, only admission weight remained statistically significant (aOR 1.04, 95% CI: 1.01 - 1.07). This suggests that while overweight status might influence outcomes, the unique characteristics of the Chinese population potentially limit the generalizability of this finding.

Conventionally, Bishop score has served as the standard assessment for planning inductions .Our research indicated that there was no notable disparity in the Bishop score prior to labor induction among the groups. This might due to all cases presenting with an immature cervix, characteriaed by Bishop scores approximately 3. Previous studies have suggested that the Bishop score for the cervix is not a reliable predictor of labor induction effectiveness[16]. However, the cervical Bishop score exhibited a noteworthy positive predictive value subsequent to the use of a cervical ripening device. Lee DS et al [17] discovered that a favorable Bishop score subsequent to cervical ripening was correlated with a decreased rate of cesarean delivery,even after adjusting for parity and Bishop score at admission. Our study similarly found that Bishop score after cervical ripeing was associated with decrease rate of cesarean delivery (aOR0.65, 95% CI:0.54 ~ 0.80).However,this method is subjective and susceptible to significant variation between observers.It is proposed that using ultrasound score (USG) and cervical length as a whole method can be employed to predict the probability of cesarean delivery during induction[18-19].

Dinoprostone is widely approved for cervical ripening before labor induction.Familiari A [20]found that the dinoprostone group had a cesarean delivery rate of 18.1% (95% CI 9.9-28.3) due to induction failure, while the misoprostol group had a rate of 9.4% (95% CI 1.4-22.0), and the mechanical method group had a rate of 8.1% (95% CI 5.0-11.6) in SGA patients..Di Mascio D [21]demonstrated that the rate of cesarean birth (25.6% vs. 17.2%; p = 0.027), composite negative neonatal outcome (26.1% vs. 16.7%; p = 0.013) and NICU admission (16.9% vs. 5.6%; p < 0.001) was greater in pregnancies affected by late fetal growth restriction undergoing IOL with dinoprostone than those performed with mechanical methods.Our findings revealed that dinoprostone doubled the risk of cesarean section (aOR 2.08, 95% CI: 1.13-3.81) compared to the Cook double balloon method. This elevated risk may be attributed to the potential for uterine hyperstimulation following dinoprostone administration[22], particularly in SGA cases complicated by placental insufficiency, necessitating a prompt cesarean delivery.In our research, 318 patients chose cook doble balloon as method of labor induction with safety ,only 189 pregnancies chose dionprostone.Therefore,the safety aspect of dinoprostone usage in inducing labor in SGA pregnancies continues to be a matter of concern.

Clinical and Research Implications

The nomogram established in this study serve as an invaluable clinical tool for individual risk assessment in pregnancies affected by SGA. By integrating key predictive factors—including maternal age, admission weight, dinoprostone usage, and the Bishop score after cervical ripein—healthcare providers can personalize management strategies for SGA pregnancies. Early identification of high-risk patients empowers individuals to make informed decisions regarding their delivery plans and fosters shared decision-making between clinicians and patients. By presenting risk probabilities visually, the nomogram encourages expectant mothers to actively engage in discussions around their delivery options, potentially enhancing patient satisfaction and compliance with medical recommendations.

Moreover, the nomogram's ability to accurately determine patients at risk for cesarean delivery facilitates improved resource allocation within healthcare systems. Hospitals can make necessary preparations in advance, ensuring adequate surgical and neonatal care resources are available when needed. Such proactive measures not only enhance patient safety but also optimize operational efficiency within obstetric care facilities. Noneetheless,it’s important to realize that the nomogram is designed to assist in patients consultation,not to directly make clinical decisions.

Study Strengths and Limitations

This research has numerous significant advantages such as: (1) a fairly substantial sample size, (2) random division of the group into model creation and validation groups, and (3) the inclusion of easily measurable antepartum variables in the nomogram, making it practical for daily clinical use.

However, There are several limitations to our study. As a retrospective, single-center investigation, there may be biases impacting the results. The findings may lack general applicability, as the specific characteristics of our patient population and institutional practices may not be reflective of broader hospital settings. Consequently, the identified risk factors may differ in varied contexts, necessitating validation in larger, multi-center cohorts. Additionally, while we examined key predictors, numerous other factors influencing labor outcomes in SGA pregnancies were not accounted for, such as psychological aspects, socioeconomic status, prenatal care differences, and genetic factors. The exclusion of these variables may limit the comprehensiveness of our predictive model. Finally, further external validation of the nomogram is necessary before widespread implementation in clinical practice. Future research should prioritize multi-center prospective studies to validate our findings and explore the nomogram's applicability in diverse clinical settings.

Our study validates the predictive capabilities of identified factors for cesarean delivery risk following induction failure in SGA patients through the development of a nomogram. This tool enhances the identification of high-risk patients and supports individualized management. Subsequent investigations should focus on optimizing the utilization of these risk factors to enhance clinical outcomes, reduce cesarean delivery rates, and promote maternal and neonatal well-being.

Author contributions

All authors helped to perform the research; Mingxing Yan: project development, data analysis, manuscript writing; Liping Hu: projecti development,data collection;Liying Li: Writing-Reviewing and Editing;Mengting Chen:data collection;Jun Shi:Data preparation; Jinji Wang: Investigation;data collection.Feng Li:Writing-Reviewing and Editing.All authors read and approved the final manuscript.

Conflict of interest

All authors declare that there was no conflict of interest.

Funding

Innovation Platform Project of Science and Technology,Fujian province（2021Y2012）

National Key Clinical Specialty Construction Program of China（Obstetric）

Ethical approval

This study adheres to the principles of the Declaration of Helsinki (2000) of the World Medical Association. Ethics approval was obtained from the Ethics Review Committee of Fujian Maternal and Child Health Hospital [2023KY016]. As this is a retrospective study, the Institutional Review Board waived the need for individual written informed consent from the patients.

Consent to participate

All patients provided signed informed consent for labor induction. Written informed consent for study participation was obtained from all women included in the data analysis.

Availability of data and materials

The datasets utilized or analyzed during the current study are accessible from the corresponding author upon reasonable request.

Acknowledgements

Not applicable.

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No competing interests reported.

Development and Validation of a Predictive Nomogram for Cesarean Delivery in Term Singleton Pregnancies Complicated by Small for Gestational Age Undergoing Labor Induction

Status:

Version 1

Abstract

Figures

Introduction

Materials and Methods

Study Population

Treatment Protocol for SGA Pregnancies and Induction Methods

Data Collection

Statistical Analysis

Results

Discussion

Conclusion

Declarations

References

Additional Declarations

Status:

Version 1