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What’s the best way to predict the success of a trial of labor after a previous C-section?
While 8 scoring tools predict success rates for a trial of labor after previous cesarean section (TOLAC), it’s unclear which is the best because no trials have compared prediction tools against each other, and each tool has a unique set of variables.
A “close-to-delivery” scoring nomogram predicting the success rate of TOLAC correlates well (90% accuracy) with actual outcomes (strength of recommendation [SOR]: B, prospective and retrospective cohort studies) and has been externally validated with multiple additional cohorts.
All other point-prediction scoring tools are accurate within 10% when predicting the success rate of TOLAC (SOR: B, prospective and retrospective cohort studies).
EVIDENCE SUMMARY
Seven validated prospective scoring systems, and one unvalidated system, predict a successful TOLAC based on a variety of clinical factors (TABLE1-11). The systems use different outcome statistics, so their predictive accuracy can’t be directly compared.12
Grobman: Entry-to-care and close-to-delivery nomograms
Grobman et al created 2 prediction models, an “entry-to-care” model (used at the first prenatal visit), and a “close-to-delivery” model (used on admission to the labor ward).1,2 Both models display a graphic nomogram forecasting the probability of TOLAC success (with 95% confidence intervals [CIs]). The authors compared predicted TOLAC outcomes with actual TOLAC outcomes and found that the model predictions most successfully correlated with high-likelihood outcomes (70% to 90% chance of successful TOLAC, plus or minus approximately 5%). Both models were less accurate with low-likelihood outcomes (40% chance of successful TOLAC, plus or minus approximately 10%).
Many independent authors have validated the close-to-delivery model, comparing predicted with actual TOLAC success rates. In a retrospective cohort study of 490 women, Constantine et al found the correlation between the observed and predicted TOLAC rates to have an r of 0.90, P=.002, with an area under the curve (AUC) of 0.70.3 Yoki et al validated the model in a Japanese cohort of 729 women with an AUC of 0.81, consistent with the AUC of 0.75 reported in the development of the original model.4
Tessmer-Tuck: The close-to-delivery model without the race variable
Tessmer-Tuck et al developed a model similar to Grobman’s close-to-delivery model, but removed race/ethnicity as a variable and compared it to the accuracy of the Grobman nomogram.5 Variables considered in this model were maternal age <30 years (odds ratio [OR]=1.53; 95% CI, 1.00-2.36), body mass index (BMI) <30 kg/m2 (OR=1.82; 95% CI, 1.11-2.97), any previous vaginal delivery (OR=3.17; 95% CI, 1.50-6.80), previous vaginal delivery after cesarean (OR=2.24; 95% CI, 1.25-4.18), and absence of a recurrent indication for cesarean delivery (OR=1.81; 95% CI, 1.18-2.76).
The model provided a successful probability of vaginal birth after cesarean ranging from 38% to 98% with AUC of 0.723 (95% CI, 0.680-0.767). When compared with the Grobman model, the AUC for features in the Tessmer-Tuck model was 0.757 (95% CI, 0.713-0.801), similar to the AUC of 0.75 reported in the development of the original model. The predictive accuracy of TOLAC success between 70% and 90% was quite poor at only ±29%.
Metz: A 5-point scoring tool
Metz et al created a point scoring tool for use on admission to the labor ward, based on 5 variables weighted by degree of correlation with TOLAC success: a history of vaginal birth (OR=2.7; 95% CI, 1.8-4.1), absence of a recurrent indication for initial cesarean delivery (OR=2.0; 95% CI, 1.3-3.1), age <35 years (OR=2.0; 95% CI, 1.1-3.4), BMI <30 kg/m2 (OR=1.6; 95% CI, 1.1-2.4), and each point of Bishop score on admission (OR=1.3; 95% CI, 1.2-1.4).6
The authors internally validated this scoring tool with an AUC of 0.70 (95% CI, 0.67-0.74), then externally validated the tool with an independent cohort of 585 women and found an AUC of 0.80 (95% CI, 0.76-0.84). In the external validation cohort, TOLAC success rates were 37.4% (95% CI, 27.2-47.5) with a score <10 and 94.4% (95% CI, 90.9-97.8) with a score >16, performing within 8% of the prediction model.
Troyer: A simple 4-point tool
Troyer et al created a simple 4-point scoring tool for use on admission to the labor ward.7 The tool’s 4 variables—previous dysfunctional labor, no previous vaginal birth, nonreassuring fetal heart tracing (NRFHT) on admission, and induced labor—were found to reduce the success rate of a trial of labor (P<.05). Dinsmoor et al used this scoring tool in a group of 156 women with an overall TOLAC success rate of 76% (3% higher than Troyer’s group) and found that for labors with a favorable score (0), the tool performed within 5% and for labors with an unfavorable score (≥3), the tool performed within 10%.8
Flamm: 5 variables weighted by correlation with TOLAC success
Flamm et al also created a scoring tool for use on admission to the labor ward, based on 5 variables weighted according to degree of correlation with TOLAC success: age <40 years (OR=2.58; 95% CI, 1.55-4.3), history of a vaginal birth (OR=1.53-9.11 depending on where the vaginal birth fell in the woman’s reproductive history), reason other than failure to progress for the first cesarean delivery (OR=1.93; 95% CI, 1.58-2.35), cervical effacement ≥75% on admission (OR=2.72; 95% CI, 2.00-3.71), and cervical dilation ≥4 cm on admission (OR=2.16; 95% CI, 1.66-2.82).9 Dinsmoor validated this scoring tool as well in 156 women and found 100% TOLAC success for scores ≥7 (within 5% of the original tool) and 56% TOLAC success for scores ≤4 (compared with 49% for scores 0-2 in the original work).8
Hashima and Guise: A 3-point scoring tool
Hashima and Guise evaluated 16 variables and identified 7 associated with TOLAC outcome: indication for cesarean delivery (recurrent vs nonrecurrent), chorioamnionitis, macrosomicinfant, age, anemia, diabetes, and infant sex, from which they created a 3-point scoring tool using the variables most associated with TOLAC outcome. Each variable was assigned a score of 0 or 1, and the likelihood of TOLAC success was calculated.10
They found a relationship between score and TOLAC success. The original study population of 10,828 was randomly divided into a score development and validation group. TOLAC success percentages were most discordant between the tool development and internal validation groups for score 0 at 7%. Scores 1 to 3 were within 4% of each other.
Schoorel: A model designed for Western Europeann women
Finally, Schoorel et al developed and internally validated a prediction model for a Western European population, to be used during counseling in the third trimester of pregnancy.11 Six variables were identified and entered into the model calculations: prepregnancy BMI (entered as a continuous variable), (OR=0.96; 95% CI, 0.92-1.00); previous cesarean for nonprogressive labor (OR=0.50; 95% CI, 0.33-0.76); previous vaginal delivery (OR=3.81; 95% CI, 2.10-6.92); induction of labor (OR=0.52; 95% CI, 0.33-2.10); estimated fetal weight >90th percentile (OR=0.54; 95% CI, 0.14-2.02); and white ethnicity (OR=1.61; 95% CI, 0.97-2.66). The authors noted that the predicted probability of TOLAC success ranged from 39% to 93%, with a mean of 72% (standard deviation, 11%), and only noted the predicted probabilities were well calibrated from 65% upwards without additional data on specific performance.
RECOMMENDATIONS
The American College of Obstetricians and Gynecologists (ACOG) lists strong predictors of a successful vaginal birth after cesarean as previous vaginal birth and spontaneous labor. Factors associated with decreased probability of success are recurrent indication for initial cesarean delivery (labor dystocia), increased maternal age, nonwhite ethnicity, gestational age greater than 40 weeks, maternal obesity, preeclampsia, short interpregnancy interval, and increased neonatal birth weight. ACOG does not offer any weighted or risk-based scoring tools for predicting success.13
Neither the American Academy of Family Physicians nor the American College of Nurse Midwives recommend specific scoring tools or success predictors.
1. Grobman WA, Lai Y, Landon MB, et al; National Institute of Child Health and Human Development (NICHD) Maternal- Fetal Medicine Units Network (MFMU). Development of a nomogram for prediction of vaginal birth after cesarean delivery. Obstet Gynecol. 2007;109:806-812.
2. Grobman WA, Lai Y, Landon MB, et al. Does information available at admission for delivery improve prediction of vaginal birth after cesarean? Am J Perinatol. 2009;26:693-701.
3. Costantine MM, Fox KA, Pacheco LD, et al. Does information available at delivery improve the accuracy of predicting vaginal birth after cesarean? Validation of the published models in an independent patient cohort. Am J Perinatol. 2011;28:293-298.
4. Yoki A, Ishikawa K, Miyazaki K, et al. Validation of the prediction model for success of vaginal birth after cesarean delivery in Japanese women. Int J Med Sci. 2012;9:488-491.
5. Tessmer-Tuck JA, El-Nashar SA, Racek AR, et al. Predicting vaginal birth after cesarean section: a cohort study. Gynecol Obstet Invest. 2014;77:121-126.
6. Metz TD, Stoddard GJ, Henry E, et al. Simple, validated vaginal birth after cesarean delivery prediction model for use at the time of admission. Obstet Gynecol. 2013;122:571-578.
7. Troyer LR, Parisi VM. Obstetric parameters affecting success in a trial of labor: designation of a scoring system. Am J Obstet Gynecol. 1992;167(4 pt 1):1099-1104.
8. Dinsmoor MJ, Brock EL. Predicting failed trial of labor after primary cesarean delivery. Obstet Gynecol. 2004;103:282-286.
9. Flamm BL, Geiger AM. Vaginal birth after cesarean delivery: an admission scoring system. Obstet Gynecol. 1997;90:907-910.
10. Hashima JN, Guise JM. Vaginal birth after cesarean: a prenatal scoring tool. Am J Obstet Gynecol. 2007;196:e22-e23.
11. Schoorel ENC, van Kuijk SMJ, Melman S, et al. Vaginal birth after a caesarean section: the development of a Western European population-based prediction model for deliveries at term. BJOG. 2014;121:194-201.
12. Guise JM, Eden K, Emeis C, et al. Vaginal birth after cesarean: New insights. Evidence Report/Technology Assessment No. 191. AHRQ Publication No. 10-E003. Rockville, MD: Agency for Healthcare Research and Quality; 2010.
13. American College of Obstetricians and Gynecologists. ACOG Practice bulletin no. 115: Vaginal birth after previous cesarean delivery. Obstet Gynecol. 2010;116(2 pt 1):450-463.
While 8 scoring tools predict success rates for a trial of labor after previous cesarean section (TOLAC), it’s unclear which is the best because no trials have compared prediction tools against each other, and each tool has a unique set of variables.
A “close-to-delivery” scoring nomogram predicting the success rate of TOLAC correlates well (90% accuracy) with actual outcomes (strength of recommendation [SOR]: B, prospective and retrospective cohort studies) and has been externally validated with multiple additional cohorts.
All other point-prediction scoring tools are accurate within 10% when predicting the success rate of TOLAC (SOR: B, prospective and retrospective cohort studies).
EVIDENCE SUMMARY
Seven validated prospective scoring systems, and one unvalidated system, predict a successful TOLAC based on a variety of clinical factors (TABLE1-11). The systems use different outcome statistics, so their predictive accuracy can’t be directly compared.12
Grobman: Entry-to-care and close-to-delivery nomograms
Grobman et al created 2 prediction models, an “entry-to-care” model (used at the first prenatal visit), and a “close-to-delivery” model (used on admission to the labor ward).1,2 Both models display a graphic nomogram forecasting the probability of TOLAC success (with 95% confidence intervals [CIs]). The authors compared predicted TOLAC outcomes with actual TOLAC outcomes and found that the model predictions most successfully correlated with high-likelihood outcomes (70% to 90% chance of successful TOLAC, plus or minus approximately 5%). Both models were less accurate with low-likelihood outcomes (40% chance of successful TOLAC, plus or minus approximately 10%).
Many independent authors have validated the close-to-delivery model, comparing predicted with actual TOLAC success rates. In a retrospective cohort study of 490 women, Constantine et al found the correlation between the observed and predicted TOLAC rates to have an r of 0.90, P=.002, with an area under the curve (AUC) of 0.70.3 Yoki et al validated the model in a Japanese cohort of 729 women with an AUC of 0.81, consistent with the AUC of 0.75 reported in the development of the original model.4
Tessmer-Tuck: The close-to-delivery model without the race variable
Tessmer-Tuck et al developed a model similar to Grobman’s close-to-delivery model, but removed race/ethnicity as a variable and compared it to the accuracy of the Grobman nomogram.5 Variables considered in this model were maternal age <30 years (odds ratio [OR]=1.53; 95% CI, 1.00-2.36), body mass index (BMI) <30 kg/m2 (OR=1.82; 95% CI, 1.11-2.97), any previous vaginal delivery (OR=3.17; 95% CI, 1.50-6.80), previous vaginal delivery after cesarean (OR=2.24; 95% CI, 1.25-4.18), and absence of a recurrent indication for cesarean delivery (OR=1.81; 95% CI, 1.18-2.76).
The model provided a successful probability of vaginal birth after cesarean ranging from 38% to 98% with AUC of 0.723 (95% CI, 0.680-0.767). When compared with the Grobman model, the AUC for features in the Tessmer-Tuck model was 0.757 (95% CI, 0.713-0.801), similar to the AUC of 0.75 reported in the development of the original model. The predictive accuracy of TOLAC success between 70% and 90% was quite poor at only ±29%.
Metz: A 5-point scoring tool
Metz et al created a point scoring tool for use on admission to the labor ward, based on 5 variables weighted by degree of correlation with TOLAC success: a history of vaginal birth (OR=2.7; 95% CI, 1.8-4.1), absence of a recurrent indication for initial cesarean delivery (OR=2.0; 95% CI, 1.3-3.1), age <35 years (OR=2.0; 95% CI, 1.1-3.4), BMI <30 kg/m2 (OR=1.6; 95% CI, 1.1-2.4), and each point of Bishop score on admission (OR=1.3; 95% CI, 1.2-1.4).6
The authors internally validated this scoring tool with an AUC of 0.70 (95% CI, 0.67-0.74), then externally validated the tool with an independent cohort of 585 women and found an AUC of 0.80 (95% CI, 0.76-0.84). In the external validation cohort, TOLAC success rates were 37.4% (95% CI, 27.2-47.5) with a score <10 and 94.4% (95% CI, 90.9-97.8) with a score >16, performing within 8% of the prediction model.
Troyer: A simple 4-point tool
Troyer et al created a simple 4-point scoring tool for use on admission to the labor ward.7 The tool’s 4 variables—previous dysfunctional labor, no previous vaginal birth, nonreassuring fetal heart tracing (NRFHT) on admission, and induced labor—were found to reduce the success rate of a trial of labor (P<.05). Dinsmoor et al used this scoring tool in a group of 156 women with an overall TOLAC success rate of 76% (3% higher than Troyer’s group) and found that for labors with a favorable score (0), the tool performed within 5% and for labors with an unfavorable score (≥3), the tool performed within 10%.8
Flamm: 5 variables weighted by correlation with TOLAC success
Flamm et al also created a scoring tool for use on admission to the labor ward, based on 5 variables weighted according to degree of correlation with TOLAC success: age <40 years (OR=2.58; 95% CI, 1.55-4.3), history of a vaginal birth (OR=1.53-9.11 depending on where the vaginal birth fell in the woman’s reproductive history), reason other than failure to progress for the first cesarean delivery (OR=1.93; 95% CI, 1.58-2.35), cervical effacement ≥75% on admission (OR=2.72; 95% CI, 2.00-3.71), and cervical dilation ≥4 cm on admission (OR=2.16; 95% CI, 1.66-2.82).9 Dinsmoor validated this scoring tool as well in 156 women and found 100% TOLAC success for scores ≥7 (within 5% of the original tool) and 56% TOLAC success for scores ≤4 (compared with 49% for scores 0-2 in the original work).8
Hashima and Guise: A 3-point scoring tool
Hashima and Guise evaluated 16 variables and identified 7 associated with TOLAC outcome: indication for cesarean delivery (recurrent vs nonrecurrent), chorioamnionitis, macrosomicinfant, age, anemia, diabetes, and infant sex, from which they created a 3-point scoring tool using the variables most associated with TOLAC outcome. Each variable was assigned a score of 0 or 1, and the likelihood of TOLAC success was calculated.10
They found a relationship between score and TOLAC success. The original study population of 10,828 was randomly divided into a score development and validation group. TOLAC success percentages were most discordant between the tool development and internal validation groups for score 0 at 7%. Scores 1 to 3 were within 4% of each other.
Schoorel: A model designed for Western Europeann women
Finally, Schoorel et al developed and internally validated a prediction model for a Western European population, to be used during counseling in the third trimester of pregnancy.11 Six variables were identified and entered into the model calculations: prepregnancy BMI (entered as a continuous variable), (OR=0.96; 95% CI, 0.92-1.00); previous cesarean for nonprogressive labor (OR=0.50; 95% CI, 0.33-0.76); previous vaginal delivery (OR=3.81; 95% CI, 2.10-6.92); induction of labor (OR=0.52; 95% CI, 0.33-2.10); estimated fetal weight >90th percentile (OR=0.54; 95% CI, 0.14-2.02); and white ethnicity (OR=1.61; 95% CI, 0.97-2.66). The authors noted that the predicted probability of TOLAC success ranged from 39% to 93%, with a mean of 72% (standard deviation, 11%), and only noted the predicted probabilities were well calibrated from 65% upwards without additional data on specific performance.
RECOMMENDATIONS
The American College of Obstetricians and Gynecologists (ACOG) lists strong predictors of a successful vaginal birth after cesarean as previous vaginal birth and spontaneous labor. Factors associated with decreased probability of success are recurrent indication for initial cesarean delivery (labor dystocia), increased maternal age, nonwhite ethnicity, gestational age greater than 40 weeks, maternal obesity, preeclampsia, short interpregnancy interval, and increased neonatal birth weight. ACOG does not offer any weighted or risk-based scoring tools for predicting success.13
Neither the American Academy of Family Physicians nor the American College of Nurse Midwives recommend specific scoring tools or success predictors.
While 8 scoring tools predict success rates for a trial of labor after previous cesarean section (TOLAC), it’s unclear which is the best because no trials have compared prediction tools against each other, and each tool has a unique set of variables.
A “close-to-delivery” scoring nomogram predicting the success rate of TOLAC correlates well (90% accuracy) with actual outcomes (strength of recommendation [SOR]: B, prospective and retrospective cohort studies) and has been externally validated with multiple additional cohorts.
All other point-prediction scoring tools are accurate within 10% when predicting the success rate of TOLAC (SOR: B, prospective and retrospective cohort studies).
EVIDENCE SUMMARY
Seven validated prospective scoring systems, and one unvalidated system, predict a successful TOLAC based on a variety of clinical factors (TABLE1-11). The systems use different outcome statistics, so their predictive accuracy can’t be directly compared.12
Grobman: Entry-to-care and close-to-delivery nomograms
Grobman et al created 2 prediction models, an “entry-to-care” model (used at the first prenatal visit), and a “close-to-delivery” model (used on admission to the labor ward).1,2 Both models display a graphic nomogram forecasting the probability of TOLAC success (with 95% confidence intervals [CIs]). The authors compared predicted TOLAC outcomes with actual TOLAC outcomes and found that the model predictions most successfully correlated with high-likelihood outcomes (70% to 90% chance of successful TOLAC, plus or minus approximately 5%). Both models were less accurate with low-likelihood outcomes (40% chance of successful TOLAC, plus or minus approximately 10%).
Many independent authors have validated the close-to-delivery model, comparing predicted with actual TOLAC success rates. In a retrospective cohort study of 490 women, Constantine et al found the correlation between the observed and predicted TOLAC rates to have an r of 0.90, P=.002, with an area under the curve (AUC) of 0.70.3 Yoki et al validated the model in a Japanese cohort of 729 women with an AUC of 0.81, consistent with the AUC of 0.75 reported in the development of the original model.4
Tessmer-Tuck: The close-to-delivery model without the race variable
Tessmer-Tuck et al developed a model similar to Grobman’s close-to-delivery model, but removed race/ethnicity as a variable and compared it to the accuracy of the Grobman nomogram.5 Variables considered in this model were maternal age <30 years (odds ratio [OR]=1.53; 95% CI, 1.00-2.36), body mass index (BMI) <30 kg/m2 (OR=1.82; 95% CI, 1.11-2.97), any previous vaginal delivery (OR=3.17; 95% CI, 1.50-6.80), previous vaginal delivery after cesarean (OR=2.24; 95% CI, 1.25-4.18), and absence of a recurrent indication for cesarean delivery (OR=1.81; 95% CI, 1.18-2.76).
The model provided a successful probability of vaginal birth after cesarean ranging from 38% to 98% with AUC of 0.723 (95% CI, 0.680-0.767). When compared with the Grobman model, the AUC for features in the Tessmer-Tuck model was 0.757 (95% CI, 0.713-0.801), similar to the AUC of 0.75 reported in the development of the original model. The predictive accuracy of TOLAC success between 70% and 90% was quite poor at only ±29%.
Metz: A 5-point scoring tool
Metz et al created a point scoring tool for use on admission to the labor ward, based on 5 variables weighted by degree of correlation with TOLAC success: a history of vaginal birth (OR=2.7; 95% CI, 1.8-4.1), absence of a recurrent indication for initial cesarean delivery (OR=2.0; 95% CI, 1.3-3.1), age <35 years (OR=2.0; 95% CI, 1.1-3.4), BMI <30 kg/m2 (OR=1.6; 95% CI, 1.1-2.4), and each point of Bishop score on admission (OR=1.3; 95% CI, 1.2-1.4).6
The authors internally validated this scoring tool with an AUC of 0.70 (95% CI, 0.67-0.74), then externally validated the tool with an independent cohort of 585 women and found an AUC of 0.80 (95% CI, 0.76-0.84). In the external validation cohort, TOLAC success rates were 37.4% (95% CI, 27.2-47.5) with a score <10 and 94.4% (95% CI, 90.9-97.8) with a score >16, performing within 8% of the prediction model.
Troyer: A simple 4-point tool
Troyer et al created a simple 4-point scoring tool for use on admission to the labor ward.7 The tool’s 4 variables—previous dysfunctional labor, no previous vaginal birth, nonreassuring fetal heart tracing (NRFHT) on admission, and induced labor—were found to reduce the success rate of a trial of labor (P<.05). Dinsmoor et al used this scoring tool in a group of 156 women with an overall TOLAC success rate of 76% (3% higher than Troyer’s group) and found that for labors with a favorable score (0), the tool performed within 5% and for labors with an unfavorable score (≥3), the tool performed within 10%.8
Flamm: 5 variables weighted by correlation with TOLAC success
Flamm et al also created a scoring tool for use on admission to the labor ward, based on 5 variables weighted according to degree of correlation with TOLAC success: age <40 years (OR=2.58; 95% CI, 1.55-4.3), history of a vaginal birth (OR=1.53-9.11 depending on where the vaginal birth fell in the woman’s reproductive history), reason other than failure to progress for the first cesarean delivery (OR=1.93; 95% CI, 1.58-2.35), cervical effacement ≥75% on admission (OR=2.72; 95% CI, 2.00-3.71), and cervical dilation ≥4 cm on admission (OR=2.16; 95% CI, 1.66-2.82).9 Dinsmoor validated this scoring tool as well in 156 women and found 100% TOLAC success for scores ≥7 (within 5% of the original tool) and 56% TOLAC success for scores ≤4 (compared with 49% for scores 0-2 in the original work).8
Hashima and Guise: A 3-point scoring tool
Hashima and Guise evaluated 16 variables and identified 7 associated with TOLAC outcome: indication for cesarean delivery (recurrent vs nonrecurrent), chorioamnionitis, macrosomicinfant, age, anemia, diabetes, and infant sex, from which they created a 3-point scoring tool using the variables most associated with TOLAC outcome. Each variable was assigned a score of 0 or 1, and the likelihood of TOLAC success was calculated.10
They found a relationship between score and TOLAC success. The original study population of 10,828 was randomly divided into a score development and validation group. TOLAC success percentages were most discordant between the tool development and internal validation groups for score 0 at 7%. Scores 1 to 3 were within 4% of each other.
Schoorel: A model designed for Western Europeann women
Finally, Schoorel et al developed and internally validated a prediction model for a Western European population, to be used during counseling in the third trimester of pregnancy.11 Six variables were identified and entered into the model calculations: prepregnancy BMI (entered as a continuous variable), (OR=0.96; 95% CI, 0.92-1.00); previous cesarean for nonprogressive labor (OR=0.50; 95% CI, 0.33-0.76); previous vaginal delivery (OR=3.81; 95% CI, 2.10-6.92); induction of labor (OR=0.52; 95% CI, 0.33-2.10); estimated fetal weight >90th percentile (OR=0.54; 95% CI, 0.14-2.02); and white ethnicity (OR=1.61; 95% CI, 0.97-2.66). The authors noted that the predicted probability of TOLAC success ranged from 39% to 93%, with a mean of 72% (standard deviation, 11%), and only noted the predicted probabilities were well calibrated from 65% upwards without additional data on specific performance.
RECOMMENDATIONS
The American College of Obstetricians and Gynecologists (ACOG) lists strong predictors of a successful vaginal birth after cesarean as previous vaginal birth and spontaneous labor. Factors associated with decreased probability of success are recurrent indication for initial cesarean delivery (labor dystocia), increased maternal age, nonwhite ethnicity, gestational age greater than 40 weeks, maternal obesity, preeclampsia, short interpregnancy interval, and increased neonatal birth weight. ACOG does not offer any weighted or risk-based scoring tools for predicting success.13
Neither the American Academy of Family Physicians nor the American College of Nurse Midwives recommend specific scoring tools or success predictors.
1. Grobman WA, Lai Y, Landon MB, et al; National Institute of Child Health and Human Development (NICHD) Maternal- Fetal Medicine Units Network (MFMU). Development of a nomogram for prediction of vaginal birth after cesarean delivery. Obstet Gynecol. 2007;109:806-812.
2. Grobman WA, Lai Y, Landon MB, et al. Does information available at admission for delivery improve prediction of vaginal birth after cesarean? Am J Perinatol. 2009;26:693-701.
3. Costantine MM, Fox KA, Pacheco LD, et al. Does information available at delivery improve the accuracy of predicting vaginal birth after cesarean? Validation of the published models in an independent patient cohort. Am J Perinatol. 2011;28:293-298.
4. Yoki A, Ishikawa K, Miyazaki K, et al. Validation of the prediction model for success of vaginal birth after cesarean delivery in Japanese women. Int J Med Sci. 2012;9:488-491.
5. Tessmer-Tuck JA, El-Nashar SA, Racek AR, et al. Predicting vaginal birth after cesarean section: a cohort study. Gynecol Obstet Invest. 2014;77:121-126.
6. Metz TD, Stoddard GJ, Henry E, et al. Simple, validated vaginal birth after cesarean delivery prediction model for use at the time of admission. Obstet Gynecol. 2013;122:571-578.
7. Troyer LR, Parisi VM. Obstetric parameters affecting success in a trial of labor: designation of a scoring system. Am J Obstet Gynecol. 1992;167(4 pt 1):1099-1104.
8. Dinsmoor MJ, Brock EL. Predicting failed trial of labor after primary cesarean delivery. Obstet Gynecol. 2004;103:282-286.
9. Flamm BL, Geiger AM. Vaginal birth after cesarean delivery: an admission scoring system. Obstet Gynecol. 1997;90:907-910.
10. Hashima JN, Guise JM. Vaginal birth after cesarean: a prenatal scoring tool. Am J Obstet Gynecol. 2007;196:e22-e23.
11. Schoorel ENC, van Kuijk SMJ, Melman S, et al. Vaginal birth after a caesarean section: the development of a Western European population-based prediction model for deliveries at term. BJOG. 2014;121:194-201.
12. Guise JM, Eden K, Emeis C, et al. Vaginal birth after cesarean: New insights. Evidence Report/Technology Assessment No. 191. AHRQ Publication No. 10-E003. Rockville, MD: Agency for Healthcare Research and Quality; 2010.
13. American College of Obstetricians and Gynecologists. ACOG Practice bulletin no. 115: Vaginal birth after previous cesarean delivery. Obstet Gynecol. 2010;116(2 pt 1):450-463.
1. Grobman WA, Lai Y, Landon MB, et al; National Institute of Child Health and Human Development (NICHD) Maternal- Fetal Medicine Units Network (MFMU). Development of a nomogram for prediction of vaginal birth after cesarean delivery. Obstet Gynecol. 2007;109:806-812.
2. Grobman WA, Lai Y, Landon MB, et al. Does information available at admission for delivery improve prediction of vaginal birth after cesarean? Am J Perinatol. 2009;26:693-701.
3. Costantine MM, Fox KA, Pacheco LD, et al. Does information available at delivery improve the accuracy of predicting vaginal birth after cesarean? Validation of the published models in an independent patient cohort. Am J Perinatol. 2011;28:293-298.
4. Yoki A, Ishikawa K, Miyazaki K, et al. Validation of the prediction model for success of vaginal birth after cesarean delivery in Japanese women. Int J Med Sci. 2012;9:488-491.
5. Tessmer-Tuck JA, El-Nashar SA, Racek AR, et al. Predicting vaginal birth after cesarean section: a cohort study. Gynecol Obstet Invest. 2014;77:121-126.
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Evidence-based answers from the Family Physicians Inquiries Network