Discussion

Main Findings

This review is, to our knowledge, the first to systematically identify published studies attempting to provide risk scoring or prognostic models for the prediction of PPH. Of eleven eligible studies, two have presented externally validated risk tools but neither were developed using recommended methods. Kim et al. (predicting blood transfusion (≥8u) following CS for placenta praevia) and Rubio-Alvarez et al. (predicting excessive postpartum blood loss in women with singleton pregnancies who underwent vaginal delivery) did not define candidate predictors and both demonstrated very low events per variable. Chi et al., was the only study to have a tool applicable to the general obstetric population but is of high risk of bias; until it has been validated, it cannot be recommended for clinical use. The other eight studies identified are not deemed suitable for use in clinical practice due to a lack of clinical relevance of some study populations, high risk of bias and lack of external validation. Six out of eleven studies featured substantially less than 10 events per potential predictor being tested such that sample size presents a major threat to reliability of findings.

Strengths and Limitations

A strength of this review is the prospective publication of the protocol in PROSPERO with strict adherence to this. The aim was to find a clinically meaningful formula or tool which could be of use to a clinician in daily practice. Numerous related studies have not published a useable tool or logistic regression model with a formula which a clinician could use in clinical practice – this may reflect poor (or poorer than anticipated) performance of the model. This review benefits from use of broad and general search criteria to maximise identification of relevant studies. Additionally, the results yielded by the search strategy were double-screened by two reviewers (CN and SN). The use of the CHARMS checklist allowed for systematic data extraction and assessment of risk of bias.
A limitation of this review is that three studies were unable to be obtained which may have been appropriate for inclusion. One of these was part of an unpublished PhD thesis and the other two were behind a paywall.
This review highlights shortcomings regarding the risk of bias and reporting of the included studies.
The review included only studies in English language such that this may limit the value of the findings.

Interpretation

This review suggests that there are no published prediction tools for PPH that are ready for clinical use. Future research to generate prognostic models for use specifically in elective CS or in women aiming for vaginal birth would facilitate advanced planning of personnel to optimise care provided.
The clinical usefulness of models generated by some of the identified studies is limited by the target population. Four studies focus on vaginal births which is not clinically meaningful as this cannot be guaranteed in advance. The circumstances during labour are subject to change with a risk of CS present until the fetal head is delivered. Therefore, despite one of these studies, Rubio-Alvarez et al., providing an externally validated user-friendly risk prediction tool in Excel™, its validity in practice is extremely limited as it is not possible to know which women will give birth vaginally and thus for whom the model is valid. Only one study produced a scoring tool aimed at use in the general obstetric population but the study design was unclear and attempts to contact the author were not successful. The study included 923 women in Beijing, China, of whom almost half had a PPH, and it did not assess predictive performance via internal or external validation. Therefore, despite the presentation of an equation to predict PPH with AUC of 0.86, it’s lack of performance assessment means it cannot be recommended for use in clinical practice.
Most studies were retrospective, meaning that some predictors may not have been measured, but the vast majority of relevant risk factors for PPH can be assessed retrospectively such that this is not considered to be a major problem.
Some studies’ prediction models or tools are clinically unhelpful in regard to the final predictors included due to some not being known at time of birth. Both Biguzzi et al., and Rubio-Alvarez et al., included neonatal birthweight as a predictor, which suggests that the intended time for the nomogram and risk tool use is after weighing of the baby, most likely once the highest risk of PPH has passed. These models are therefore of limited value for preparation of resources prior to birth. Estimated birthweight may be a more appropriate measure but this has not been included as a predictor in any model.
Use of intrapartum factors can aid risk assessment in a dynamic scenario. Two studies have included these: duration of the first and second stage of delivery; non-use of uterotonics and cord traction. Intrapartum risk scoring may be facilitated by use of electronic health records, where the tool could be embedded within the system, but otherwise may present logistical difficulties if it requires ongoing computer access as per Rubio-Alvarez et al’s proposed risk tool.
Robust external validation was absent from all prediction models identified, suggesting that this is poorly understood and undervalued. Of the two models externally validated both utilised Hosmer-Lemesow testing which is not recommended, and only one provided validation results. Internal validation is a reasonable alternative as this assesses how well the model performs in the underlying population from which the model was developed, but only five studies did this and only one is considered appropriate for prospective use (in placenta praevia population) and thus this would benefit from future external validation.
The prediction models identified were at high risk of bias overall, with lack of detail of candidate predictors, small sample size and suboptimal statistical analysis being common, and missing data not reported in any study. Without missing data information it is not possibly to fully assess the related risk of bias.39
The need for adequately powered studies is clear. Half the included studies have shown a low EPV (<10) with only one conducting any shrinkage methods to overcome problems arising from overfitting of the model (and risk of optimistic predictions) when there is a low number of events. Despite this, several authors recommended use of affected models without external validation.19,20,22As a result of heterogeneity and low EPV, it was not possible to conduct a meta-analysis of the results. However, there is potential for synthesis of findings for predicting PPH in a population of women with placenta praevia, where individual participant data (IPD) meta-analysis could be used.