Study population

In the current analysis, patients were included who underwent CD. The primary goal for this analysis was to identify predictors of peripartum blood transfusion among women who underwent CD. Patients were excluded from the secondary analysis who had a vaginal delivery or incomplete data for both intrapartum and postpartum transfusion (Fig 1). Women considered as having the primary outcome in our analysis if they had intraoperative and/or postoperative blood transfusion.

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Fig 1. Flow diagram of women included in prediction model.

Outlines who was included in the prediction model derivation and validation.

https://doi.org/10.1371/journal.pone.0208417.g001

Candidate predictors

We included candidate predictors for the antepartum model based on prior known risk factors for postpartum hemorrhage and also availability in daily clinical practice[6,8,12,13]. Predictors for model 1 (antepartum risk factors) included the following: maternal age (extremes including age <21 or age >36), BMI (body mass index, defined as maternal height in meters divided by kilograms squared) at delivery, number of previous term deliveries, gestational age, gestational age >37 weeks, total years of schooling, African American ethnicity, insurance status for prenatal care, white blood cell count, platelet count, hematocrit, previous cesarean delivery, asthma, heart disease, connective tissue disorder, hypertensive disorder (gestational hypertension/preeclampsia/HELLP) and abruption. Predictors for model 2 (antepartum and intrapartum risk factors) included the same as model 1 plus the following: non-elective cesarean delivery, use of general anesthesia, abruption at the time of delivery, failure to progress (FTP), preeclampsia/eclampsia or HELLP, abnormal placentation and intrapartum antibiotic use (including treatment for chorioamnionitis or prophylaxis for GBS/cesarean delivery).

Lab values were obtained closest to admission. Suspected clinical abruption on presentation (antepartum) was distinguished separately from abruption as indication for cesarean delivery (intrapartum). FTP was collected as an indication for cesarean delivery and abnormal placentation (accreta/increta/percreta) clinically made diagnosis at time of delivery when removing placenta. Gestational hypertension and preeclampsia were considered antepartum risk factors while preeclampsia/eclampsia was considered an intrapartum risk factor. HELLP was classified both as an antepartum and intrapartum risk factor since this could be diagnosed in both time periods. To empirically determine whether the model improved upon the prediction accuracy that would be obtained by simply coding high risk based on known risk factors, we created a simple high-risk indicator based on the presence of preeclampsia, HELLP, low HCT, or abruption. Patients with any of these were considered “Hi-Risk”. Patients with none of these were considered “Low-risk”. (Only a few patients had more than one of these risk factors). We compared the area under the ROC curve (AUC) for this simple risk indicator with the AUC for our risk score based on our regression model, using a chi-square (Gonen M. Analyzing receiver operating characteristic curves with SAS 2007. Cary, NC, SAS Institute Inc.).

To test for possible sampling bias that could have been introduced by over-sampling women with repeat CD in the later years of data collection, we performed a sensitivity analysis in which we examined the performance of the pre-delivery model in the validation sample, using only women who gave birth during the years when repeat CD was not over-represented (1999–2000).

Data analysis

We used 2/3 of cases, selected randomly, for the derivation sample (n = 38,094) and the remaining 1/3 (n = 18,873) for the validation sample. Two separate models were constructed, one model using only variables that would be known pre-delivery or antepartum, and the other model also including variables that would be known intrapartum. All model development steps were done using a complete-case analysis, only using the derivation sample. This included examining univariable associations of ante- and intra-partum variables with transfusion status, building multivariable models, testing for multicollinearity and determining the test performance at false positive rate of 10%. Multivariable logistic regression was used to examine prediction models, with backward elimination of predictors that had p>.20. Final selection of variables for the multivariable logistic regression was subsequently based on clinical relevance and ease with which it can be externally validated.

The final regression model was of the form: y = intercept + b₁x₁ + b₂x₂ + … b_nx_n, where the b_i’s are the parameter estimates and the x_i’s are the predictor values for each subject. The probability of peripartum transfusion was then defined as p = e^y / (1 + e^y)[21]. Multicollinearity was tested using weighted regression, with any variance inflation factor > 2.0 indicating a problem (Allison, 1999). If this occurred, the collinearity diagnostic matrix was examined to identify overlapping variables so one of them could be eliminated. Since the intent is to use this marker in a general pre-delivery population, we wanted to maintain a low rate of false positives to minimize the number of women who would be subjected to unneeded interventions, while we could still identify a high proportion of those who might need such interventions. Therefore, we defined the test performance at false positive rate (FPR) of 10%, and we hoped to be able to identify at least 50% of those who would go on to have transfusions (true positive rate, TPR).

We then tested the resulting models in the validation sample, using a variety of tests for model replication. These included: a) the area under the receiver operating characteristic curve (AUC) using the probability estimate calculated using the above equation as a univariable predictor; b) the sensitivity and specificity using the test performance at false positive rate of 10%; and c) model calibration was tested in the validation sample by examining the association between predicted probability of transfusion and observed incidence in deciles of the risk score distribution. The risk of over-fitting was minimized by ensuring that k > 10n, where k = number of predictors in the model, and n = number of outcome events, and by using a separate validation sample. SAS (version 9.3, Cary, NC) was used for data analysis with a two-tailed p < .05 considered significant. No imputation for missing data was performed.

Model 1 antepartum model

The final multivariable model predicting perinatal transfusion in the derivation sample had AUC = 0.77 (95% confidence interval 0.75–0.78), indicating moderate discrimination. Variance inflation factors were all <1.8. Predictors that were retained in the final model included mother’s age (coded as either <21 or >36, versus 21–36), race, BMI, type of insurance, presence of abruption, platelet count < 150 x 10³ units, hematocrit < 32%, presence of gestational HTN or preeclampsia, presence of HELLP, history of asthma, history of heart disease, gestational age < 37 weeks, and 3 or more previous term deliveries (versus <3) (Table 2). The calibration graph indicated good agreement of predicted probability and observed incidence (Fig 2A). However, the highest risk decile only had incidence of 0.11 and the risk deciles were not evenly distributed. An FPR of 0.10 was achieved using a probability cut point of p ≥ 0.055, and at this cut point the TPR was 0.42. At the other end of the probability distribution, using a cut point of 0.01, TPR of 0.92 was achieved with the corresponding FPR of 0.68. The predicted chance for peripartum transfusion can be calculated for women undergoing cesarean delivery using the following formula:

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Fig 2. Calibration curves for Model 1 and Model 2.

Model 1 includes antepartum risk factors only and Model 2 includes antepartum and intrapartum risk factors. a. Model 1—Derivation Sample. Calibration of the model predicting transfusion using only antepartum variables. b. Model 1—Validation Sample. Calibration of the model predicting transfusion using only antepartum variables.

https://doi.org/10.1371/journal.pone.0208417.g002

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Table 2. Multivariable models for predicting perinatal transfusion.

https://doi.org/10.1371/journal.pone.0208417.t002

When compared with assignment of high-risk status based on known risk factors, we found that while the risk score calculated from our model had AUC 0.75 (95% CI 0.33–0.78), the simpler risk model had AUC of 0.59 (0.57–0.61), a difference that was significant (p < .0001). Therefore, the model we propose significantly improves prediction accuracy compared with using a simpler approach to risk stratification based on a few known risk factors.

In the validation sample the incidence of transfusion was also 2.6%. When the risk equation was used in the validation sample as the only predictor of transfusion, the AUC for the univariable logistic regression model was 0.75 (95% CI 0.73–0.78). Using p = 0.055 as the cut point for predicting transfusion in the validation sample, the TPR was 0.43 with FPR of 0.10. The model remained well-calibrated (Fig 2B). In the validation sample, the observed incidence of transfusion was 10.8% in mothers with the highest probability decile and 0.5% in mothers with the lowest probability decile. Mean predicted probability of transfusion in deciles 10 and 1 was 10.9% (95% CI 10.6–11.2) and 0.6% (95% CI 0.60–0.61), respectively.

Model 2 antepartum and intrapartum model

Variables that would only be known during the delivery process, that were added to the Intra-partum model included general anesthesia, placenta accreta, eclampsia, use of oxytocin, intrapartum decision for CD, and intra-partum use of antibiotic. All of these were more common in the group with transfusions (Table 1). The final multivariable intrapartum prediction model had AUC = 0.83 (95% CI 0.81–0.84) in the derivation sample, and included the intrapartum predictors general anesthesia, FTP, eclampsia, accreta/increta/percreta, antibiotic use, and non-elective repeat CD (Table 2). All variance inflation factor scores were < 1.75. The predicted chance for peripartum transfusion can be calculated for women undergoing cesarean delivery using the following formula:

The model calibration graph in the derivation sample indicated good agreement between predicted probability and observed incidence of peri-partum transfusion (Fig 2C). However, again, the risk deciles clustered toward the lower end of the probability distribution rather than being spread out evenly. The incidence of transfusion in the highest risk decile was 13.8%, versus 0.2% in the lowest decile. Using a cut point of p = 0.047, TPR was 0.55, while FPR was 0.10. In the validation sample, AUC for this model was 0.82 (95% CI 0.79–0.84), indicating good discrimination. Model calibration continued to be good (Fig 2D). Using the cut point of p = 0.047, the FPR in the validation sample was 0.10 with TPR 0.54. In other words, for the intra-partum model, the probability cut point generated from the derivation data produced nearly identical results in the validation sample.

Among the patients with asthma who had uterine atony but no transfusion (n = 1,074), 8.5% received methergine versus 4.0% received hemabate (p = 0.004). More specifically, among the patients with asthma who had uterine atony with transfusion (n = 164), 19.4% received methergine versus 8.8% received hemabate (p = 0.05).

Main findings

In this study, we have built a prediction model that accurately estimates a patient’s odds of receiving blood transfusion during or after a CD. Our results demonstrate a distinct group of risk factors that predispose women for peripartum blood transfusion, a clear objective marker signifying morbidity. In our model, internal validation had good discrimination and validation. Our observed rate of transfusion is 2.6% (1,488 / 56,967 * 100), slightly above the previously reported overall rate when combining vaginal and cesarean deliveries[13,22,23]. Our rate is comparable to previously observed rates among CD, which have been reported between 3.2–3.3%[23–25].

In a study by Helman et al, risks factors associated with severe PPH (defined as 5 or more units of pRBCs) were comparable with our findings, including abnormal placentation, hypertensive disorders, and multifetal gestation[16]. Though extensive research has been done evaluating risk factors for severe hemorrhage, our model is novel in that we are attempting to isolate individuals who are at highest likelihood of transfusion prior to delivery. In a previous study by Bateman et al, looking at the risk for any transfusion during vaginal or cesarean delivery, they were unable to identify risk factors with good predictability or discrimination (ROC < 0.7). However, they did identify similar risk factors to those in our study such as advanced maternal age, hypertensive disease and multiple gestations[12]. The explanation for the differences in the model accuracies is likely attributable to the level of detail provided in the MFMU Cesarean Registry compared with the Nationwide Inpatient Sample.

We observed that deliveries at an earlier gestational age, and correspondingly with lower birthweight, had a significantly increased odds for massive transfusion. Although we did not use birthweight as a variable in the risk prediction model, it is highly correlated with gestational age. Mothers who have more comorbidities such as hypertension or diabetes are more likely to have preterm deliveries, lower birthweight, and other risk factors that contribute to obstetric hemorrhage[9,12,16,18].

One of the most significant risk factors for blood transfusion is preoperative anemia[9,14,17]. We found preoperative anemia (hematocrit <32%) also to be a significant predictor for need of transfusion (OR 3.72, 95% CI 3.25–4.25). In a study by Al-Zirqi, et al, severe anemia with a hemoglobin < 9.0 g/dL was associated with an increased odds of transfusion (OR 2.2; CI 1.63–3.15)[17]. Another study by Ehrenthal, et al, demonstrated that a starting hemoglobin < 10.5 g/dL was associated with some risk of transfusion where a hemoglobin < 9.5 g/dL was associated with considerable risk[9].

Given that blood transfusion at delivery is a relatively rare occurrence, most women identified at risk by the model will likely not have the outcome. The true positive rate of at least 50% was achieved by model 2 with a small margin although not achieved by model 1, which suggests that the additional variables included in model 2 improved the detection ability. In terms of the utility of the model given the relatively high false positive rate, that all depends on how providers use this information. If used for delivery preparedness or consideration for prophylactic medications to reduce bleeding, whether for clinical or research purposes, this model still can be useful. Further improvement of the model will likely require larger sample sizes.

The current analysis differs from published literature in that it recognizes one prior CD is associated with lower odds for hemorrhage. Among patients with CD, a patient with a prior CD is more likely to have an elective or scheduled CD rather than an emergency or non-elective repeat CD. Many prior studies find that a prior CD increases a patient’s risk for severe obstetric hemorrhage[6,9,12,17]. Al-Zirqi, et al found that emergency CD was the most significant risk factor for severe obstetric hemorrhage (OR of 4.75; CI 1.32–12.96)[17]. We suspect our finding is due to the fact emergency CD is preceded by labor, either spontaneous or induced (which is becoming increasingly more common with the known complication of prolonged induction), possibly FTP, possibly prolonged oxytocin use and possibly chorioamnionitis, which increase the risk for uterine atony and postpartum hemorrhage[15,16].

Furthermore, the developed algorithm offers an objective way to consider use of prophylactic interventions that are being studied in research protocols such as antifibrinolytic therapy (i.e. tranexamic acid use) prior to delivery or immediately after delivery of the baby. Utilization a validated algorithm for prophylactic use of a medication would be more acceptable than consideration of only clinical risk factors left up to clinical discretion.

Strengths and limitations

A strength of this study is the ability to analyze a large cohort of primary and repeat CD, making it generalizable to a large number of patient populations. Predictors included within the model are factors that are often identifiable in perioperative setting, making the model useful and applicable in every day practice. In addition, our model allows for identification of patients who are at increased risk of transfusion in the perioperative setting, which could allow for pre-delivery optimization. For example, this could include aggressive management of pre-delivery anemia with intravenous iron and/or transfusion. Furthermore, in rural regions that are not as equipped with transfusion medicine services or subspecialty surgical services, consideration for transfer to higher level of care should be given when the prediction model estimates a higher than baseline risk for transfusion (assuming stable condition for patient prior to delivery).

Some limitations of our study include the retrospective aspect of the data and a focus that exclusively evaluates hemorrhage and transfusion in the setting of CD, which does not provide direct insight to similar complications that arise during a vaginal delivery. Another important limitation of the model is a possibility to underestimate the risk of transfusion in the setting of an unexpected outcome or evolving clinical situation. For example, encountering an undiagnosed accreta or abruption, or an unexpected conversion to general anesthesia. Furthermore, for some predictors it is not possible to evaluate temporal associations (i.e. was general anesthesia a predictor of hemorrhage requiring transfusion or the result after transfusion initiated). Although our model demonstrated good discrimination during internal validation, important next steps include external validation of the model by using another cohort of patients. Finally, an optimal threshold for risk of transfusion should be based on further risk-benefit analysis and was beyond the scope of our study.

Interpretation

This model has promising clinical use in identification of patients who are at increased risk of transfusion during or after CD. This offers a unique opportunity for providers to improve delivery preparedness for both patients and patient care teams. In the office, patient counseling could increase awareness of potential complications at time of delivery. In addition, setting patient expectations and allowing opportunities for providers to answer questions could potentially increase patient preparedness and decrease the emotional trauma that is commonly associated with sentinel maternal events like massive hemorrhage[26]. Our risk calculator using the key antepartum and intrapartum factors can be found on https://www.gwdocs.com/mfm/peripartum-prediction-of-blood-transfusion.