We preliminarily constructed and validated three ANN models to predict postpartum hemorrhage in vaginal delivery women. The MLP model indicated the best predictive ability in all groups of training, validation and test. Moreover, compared with the traditional logistic regression model, both MLP and BP models showed better discrimination ability to predict postpartum hemorrhage. In addition, A user-friendly CDSS was developed according to the MLP model.
In terms of clinical application, midwives could prepare full in advance for example triage expectant mothers with high risk of postpartum hemorrhage to the corresponding obstetric care level and initiate interventions. Prediction of our model mainly focused on the time of labor and birth, therefore, we adopted the variables available during the same instances. It is also appropriate to construct prediction model employing high-risk factors after delivery, such as cervical lacerations and newborn weight. Importantly, in order to facilitate clinical use immediately, we recommend the information departments in hospital to integrate the aid decision-making system in electronic medical records, in the form of a risk calculator or outputting the result automatically. When a pregnant woman is laboring by an obstetrician, the midwife or other assistant could input the numerical values of the 15 predictors into the built CDSS (Fig. 3), and the prediction outcome (postpartum hemorrhage or non-postpartum hemorrhage) would be output by clicking the button of “submit” in the designed interface of the system. Once the system identifies some pregnancy women as postpartum hemorrhage, the one should be considered carefully by the obstetricians. According to the predicted outcome, the obstetricians can make optimal decision for the pregnancy women about personalized treatment in the next step. In this way, the obstetrician can manage the labor stage more skillfully by early identification, early warning, early treatment, in order to mitigate adverse outcome for these high-risk patients. For example, midwives can make pre-transfusion preparations for high-risk patients in advance, cross-match blood tests, and inform the blood transfusion department to prepare blood products, particularly fresh frozen plasma can take up an hour to thaw. At the same time, it is conducive to the rational allocation of medical resources by arranging high-risk bleeding women for delivering in different time to avoid insufficient staff resources.
In recent years, medical institutions and obstetric experts at home and abroad have made a lot of exploration in the early warning evaluation and prediction of postpartum hemorrhage. The risk score calculators formulated by them for different subgroups of people could indicate the possibility of postpartum hemorrhage to some extent and achieved certain results. Ana [31] developed and validated a predictive model based on the binary logistic regression and the ridge regression to measure the risk of excessive blood loss in 2336 vaginal delivery women, but this analysis only collected thirteen variables and the sensitivity was low. Michelle [32] trained and tested a logistic regression prediction models involving 74 variables for hemorrhage and transfusion by a data set from 63973 deliveries, however, the results were not visualized. Kartic [15] et al. used two traditional statistical models (logistic regression with and without lasso regularization) and two machine learning models (random forest and extreme gradient boosting) to predict postpartum hemorrhage, the extreme gradient boosting model showed the best in both the discrimination and decision curve analysis, but some variables in the model were difficult to collect so that limited the clinical practicability. Ahmadzia et al. [33] developed an online calculator for postpartum hemorrhage risk scores, Dunkerton [34] et al. established a decision tree model for postpartum hemorrhage prediction based on a non-parametric recursion algorithm, Bingnan Chen [35] created a nomogram model to predict postpartum hemorrhage individually, however, all of them were only used for cesarean section population. Due to the heterogeneity of discriminant criteria and risk factors between cesarean section and vaginal delivery, there are some limitations in predicting postpartum hemorrhage using the same warning model for different mode of delivery. In addition, most of current prediction models for postpartum hemorrhage were linear algorithms, logistic regression is a traditional statistics algorithm which can screen out the limited variables associated with postpartum hemorrhage and eliminate confounding variables [36]. However, when there are too many variables to observe, the LR neither detect complicated nonlinear relationships between independent and dependent variables, nor has the ability to address collinearity between variables, so some potential valid variables were removed [37]. Compare with above models, our study indicated that both neural network and logistic regression can provide excellent discrimination for the prediction of postpartum hemorrhage, hence, a finally selected model to build a CDSS should rely on a combination of model performance (discrimination, calibration and net benefit), clinical applicability, and acceptability by obstetricians and expectant women [38].
The ANN prediction model established in this paper aims to provide personalized prediction results and achieve effective risk stratification. The results of our study revealed that the ANN model was more accurate to predict postpartum hemorrhage among vaginal delivery. At present, the ANN model has been widely used in disease prediction and diagnosis, chronic disease management, medical image recognition and other aspects [16–21], it had shown superior performance than conventional predictive models even employing the same input variables [16, 21]. Postpartum hemorrhage, as an obstetric emergency, has various factors and complex mechanism. If we take the epidemiological data and use the traditional linear discriminant function to predict it, there are great limitations. As an information processing system abstracted from biological neural network, ANN has the ability of self-learning and identifying the relationship between variables, which can approximate arbitrary nonlinear functions with arbitrary accuracy [17]. Previous studies implied that neural network models reflected a stronger fit to address complex nonlinear relationships and cost less effort to generate than the traditional regression algorithms [18–21]. It is imperative that ANN techniques automatically conduct variables selection, missing values imputing and other data preprocessing procedures.
There was a lot of literature support for the included variables as risk factors for PPH. Almost all the variables in our model are clinically available and have been identified in previous models, including newborn weight, cervical laceration, history of uterine surgery, parity, manual removal of placenta, episiotomy [31–35, 38]. However, one important predictor that has not been considered yet is the WBC count during the first stage of labor, it is identified of 100% importance in this study, which extended the previous work in predicting PPH and may provide insights of underlying pathophysiology links between inflammation participation and PPH onset. In our results, the WBC count of hemorrhage women were higher than that of non-hemorrhage women during the first stage of labor. Possible reasons for this may be as follows: infection leads to weak state of pregnancy women and those were vulnerable to PPH, and labor is a state of stress which also leads high WBC count [39, 40]. Further, more clinical laboratory studies are needed to uncover the underlying pathophysiology mechanism between high WBC count during the first stage of labor and postpartum hemorrhage. Our outcomes for the WBC showed a 100% standardized importance, this point maybe because the sample size was large, but the morbidity of PPH was low and the number of cases was small in this model. Nevertheless, another statistical algorithm was conducted (binary logistic regression) to explore the odds ratio (OR) of the variables and confirm their statistical significance.
The second important risk factor was the newborn weight of 75.5% standardized importance in our study, which again matched previous studies [13, 15, 24, 31]. This may because that increased newborn weight is associated with hyperextension of uterine muscle fibers and affects uterine contractions. In addition, pregnant women with large fetal weight may have other complications, such as cephalic pelvic asymmetricity, prolonged first and second stages of labor, shoulder dystocia, laceration of the soft birth canal, uterine contraction [41], which also increases the incidence of postpartum hemorrhage. The newborn weight in our study was postpartum variable when the risk period of bleeding had passed, we can consider introducing a prenatal diagnosis of macrosomia or estimated fetal weight according to the ultrasound into model. Some other risk factors in our model for hemorrhage have been accepted generally, namely operative vaginal delivery, induced labor, manual removal of placenta, episiotomy and cervical tears [2,3,6,31−35], despite their fewer links to hemorrhage with standardized importance less than 30%, the less noticeable whose effect is, the more severe the situation goes.
4.1. Limitations
Our study has several limitations. Firstly, neural network algorithms are driven by big data and rely on a large sample size. We developed a CDSS based on electronic medical record from only one center, the sample size was far insufficient, the external validation was just conducted in our hospital, so the result may be not generalizable, robust internal and external validation is needed before promotion and application widely. Secondly, Missing data is another limitation of our study, the study was restricted in the part of valid blood loss data from the original dataset, the missing value of the covariable accounted for a considered proportion, though we adopted multiple imputation techniques reported by the PROBAST [26], the proportion of incomplete data also limited the generalization of the model. Furthermore, it is more likely that the missing values will continue to hinder integrating the models into electronic medical records. Thirdly, the predictors in our study all were static, hourly data such as temperature, heart rate, systolic blood pressure, oxygen saturation, physical examination findings were not included.
In addition, despite embedding all risk factors in the CDSS will be promising, but it may increase clinical workload burden and could potentially delay intervention [42]. The impact of a model on aiding decision making depends on multiple characteristics of health providers and circumstances, including ability to initiate immediately intervene response and weigh the risks against benefits, capacity to take actions, and expectant women (or obstetricians) compliance with the recommended measures [43]. Other environmental restrictions consist of staff, space, and facilities, which are not considered into current CDSS. Meanwhile, estimated blood loss (EBL) is considered to be inaccurate, subjective and always underestimated, there is a mismatch between the actual blood loss and the vital signs, urine volume and mental state of the individual shown. A physical compensatory period of blood loss exists in the early bleeding stage [44–45]. Our definition of postpartum hemorrhage followed the current clinical guidelines [22–25], however, we did not evaluate other relevant clinical indicators of acute blood loss, including a deep perineal hematoma from a laceration of the birth canal, blood flow velocity and properties, the bleeding was fluid or turbulent or exudative, and whether it clotted or contained clots [46]. The blood loss in predictive models needs to be measured by means of the quantitative blood loss (QBL) methods, such as basic methemoglobin colorimetric method [47] or image spectral analysis [45], Shock Index (SI) [48] and so on.