**Study type, setting and participants**

This was a retrospective cross-sectional study that covered the period from 1 July 2016 to 30 November 2019, at Mpilo Central Hospital, a government teaching and tertiary referral centre. Mpilo Central Hospital is located in the township of Mzilikazi in Bulawayo. Bulawayo, located in Matabeleland is the second largest city in Zimbabwe after the capital city Harare, with a population of 653, 337 as of the 2012 census [11]. Mpilo Central Hospital is a 1000-bedded hospital and its maternity unit delivers 8000-10 000 babies per year. The objective of the study was to develop and validate a diagnostic multivariable parsimonious predictive model for composite adverse outcomes in PPH.

**Inclusion and exclusion criteria**

The study included participants that had a diagnosis of postpartum haemorrhage of ≥500ml blood loss after the delivery of the baby or ≥1000ml for caesarean section within 24 hours of delivery at Mpilo Central Hospital. Both singleton and twin/higher order pregnancies were included. Women who delivered outside the hospital were excluded from the study even if they had PPH since their birth records were kept at the place of their delivery.

**Independent variables**

The independent variables included socio-demographic factors, known risk factors for PPH, current obstetric history, antenatal haemoglobin levels, mode of delivery, fetal outcome and birth weight, estimated blood loss, post-delivery haemoglobin and platelet count, causes of PPH and the management of PPH.

**Main outcome measure**

The outcome of interest for this research was composite adverse outcome. This was defined as maternal death or serious morbidity (either of hypovolaemic shock or haemoglobin <4g/dL or massive blood transfusion >4 units or hysterectomy or admission to ICU or coagulopathy or major organ dysfunction). Some of these outcome measures were part of the core outcome sets for prevention and treatment of postpartum haemorrhage in the Delphi consensus study [12].

**Sample size calculation**

The Cochran sample size formula was used to calculate the sample size as follows; n0 =z2pq/e2

where n0 =sample size

z = is the selected critical value of desired confidence level

p = is the estimated proportion of an attribute that is present in the population

q = is 1-p and e is the desired level of precision

Assuming the maximum variability, which is equal to 50% (p = 0.5) and taking 95% confidence level with ±5% precision, the calculation for the required sample size was as follows;

p = 0.5 and hence q = 1-0.5 = 0.5, e = 0.05; z = 1.96

So, n0 = (1.96)2(0.5) (0.5)/(0.05)2

= 384.16

=385

**Data collection**

A paper-based data collection tool was used to collect data for the above study. This was used to collect secondary data from the labour ward delivery registers, and mortality registers. Patients’ hospital case notes were retrieved from the Hospital Records Department using hospital numbers obtained from the above registers. Clinical information was then collected onto the paper data collection sheet.

**Data analysis**

Prior to analysis the data were cleaned, coded and entered into a Microsoft Excel spreadsheet, then exported to SPSS Version 20 (IBM, Armonk, NY, USA) for analysis. Multiple imputation was used for missing data. Bivariate correlations of association between main independent variables and the outcome measures were performed using Pearson 2-tailed chi-square test.

**Candidate predictor variables considered for the model**

Table 1 shows the candidate predictor variables collected for the predictive model.

**Model building**

Those variables that had a p<0.2 from the bivariate correlations analyses, were considered for the multivariable stepwise linear logistic regression. Stepwise backward elimination on SPSS, was used to produce a predictive model that was parsimonious and accurate as this excluded variables that do not contribute to explaining differences in the dependent variable, with a stopping rule of *p*<0.20. Continuous variables were checked for non-colinearity. Co-linearity was avoided by determining correlation between variables and only those clinically relevant pairs of highly related pairs were retained.

**The final model**

In developing the final logistic regression model (logit), the predictor variables with a *P* value of < 0.2 will be considered for the following models;

**Assessment of model’s performance and validation**

Performance of the models was assessed using a calibration slope. Discrimination ability was evaluated on the basis of area under curve of the receiver operating characteristic (AU ROC). Internal validation of the model was assessed using Efron’s enhanced bootstrap method [13].