Dr Fergus McCarthy takes a monthly look at articles just published in the area of hypertension & pre-eclampsia.

Professor Shakila Thangaratinam and collaborators in the PREP Collaborative Network recently published their Prediction of complications in early-onset pre-eclampsia (PREP) model along with the release of a complementary ap or desktop programme with which users can calculate risk scores.1 The aim of this prognostic model is to inform clinicians and safely prolong preterm gestation in women with pre-eclampsia and allow accurate and timely prediction of complications.

Prognostic Models

This large prospective cohort study recruited women from 53 maternity units in the UK and developed prognostic models for the overall risk of experiencing a complication using logistic regression (PREP-L), and for predicting the time to adverse maternal outcome using a survival model (PREP-S). External validation of the models were carried out in a multinational cohort (PIERS-634) and another cohort from the Netherlands (PETRA-216).

A total of 169 mothers (18%) in the PREP dataset had adverse outcomes by 48 hours, and 633 (67%) by discharge. The C-statistics of the models for predicting complications by 48 hours and by discharge were 0.84 (95% CI, 0.81-0.87; PREP-S) and 0.82 (0.80-0.84; PREP-L), respectively. On validation in the external PIERS dataset, the reduced PREP-S model showed reasonable calibration (slope 0.80) and discrimination (C-statistic 0.75) for predicting adverse outcome by 48 hours. Reduced PREP-L model showed excellent calibration (slope: 0.93 PIERS, 0.90 PETRA) and discrimination (0.81 PIERS, 0.75 PETRA) for predicting risk by discharge in the two external datasets.

The PREP-S model included maternal age, gestation, medical history, systolic blood pressure, deep tendon reflexes, urine protein creatinine ratio, platelets, serum alanine amino transaminase, urea, creatinine, oxygen saturation and treatment with antihypertensives or magnesium sulfate.

The PREP-L model included the above except deep tendon reflexes, serum alanine amino transaminase and creatinine.

The complementary app (available at http://stg.pocketapp.co.uk/qmul/#home) allows users select one of two options; Overall risk by discharge PREP-L or risks at various time points before 34 weeks (PREP-S).

Inputting Data

With the PREP-L, users then input maternal age, gestational age at diagnosis of pre-eclampsia and confirm if there are certain pre-existing maternal conditions. Maternal characteristics and test results are then inputted including blood pressure, serum urea, platelet count and whether MgS04 or anti hypertensives were administered. The users then receive an output stating “The risk of having any one of the following maternal complications* or delivery before 34 weeks is x%”. Complications include Maternal death, Pulmonary oedema, Intubation, Postpartum haemorrhage, Hepatic dysfunction, Hepatic haematoma, Hepatic rupture, Glasgow Coma Score <13, Stroke, Cortical blindness, Reversible Ischaemic Neurologic Deficit (RIND), Retinal detachment, Acute renal insufficiency, Dialysis, Blood transfusion, Positive ionotropic support, Myocardial ischaemia or infarction, >50% oxygen requirement for >1hour.

The PREP-S model ap works in a similar way. However, the ap starts by requesting information on the number of days from baseline along with maternal age and gestation at diagnosis of pre-eclampsia. Additional test results are required including creatinine, serum urea, systolic blood pressure, pulse oximetry, ALT, Protein creatinine ratio and platelet count. Users then receive an output stating “The risk of having any one of the following maternal complications* or delivery before 34 weeks within the next 28 days is x%”. The 28 days refers to the number of days inputted at the start of the process (from baseline) and options include 2-42 days. Maternal complications are as defined above.

Systematic Review

Dr James Duffy along with collaborators at iHOPE: International Collaboration to Harmonise Outcomes in Pre-Eclampsia published their work examining outcome reporting across randomised controlled trials evaluating therapeutic interventions for pre-eclampsia.2 The authors performed a systematic review with the aim of mapping maternal and offspring outcome reporting across randomised trials evaluating therapeutic interventions for pre-eclampsia.

Seventy-nine randomised trials, reporting data from 31,615 maternal participants and 28,172 of their offspring, were included. Fifty-five different interventions were evaluated. Included trials reported 119 different outcomes, including 72 maternal outcomes and 47 offspring outcomes. Maternal outcomes were inconsistently reported across included trials; for example, 11 trials (14%) reported maternal mortality, reporting data from 12 422 participants, and 16 trials (20%) reported cardiovascular morbidity, reporting data from 14 963 maternal participants. Forty-three trials (54%) reported fetal outcomes and 23 trials (29%) reported neonatal outcomes. Twenty-eight trials (35%) reported offspring mortality. There was poor reporting of childhood outcomes: six trials (8%) reported neurodevelopmental outcomes. Less than half of included trials reported any relevant information regarding harms for maternal participants and their offspring.

This analysis highlights the need for development of a minimum data set (core outcome set), in future pre-eclampsia trials to help to address these issues.

Prediction of Pre-Eclampsia

Finally, this month, Vestgaard et al published their systematic review on the prediction of pre-eclampsia in type 1 diabetes in early pregnancy by clinical predictors.3

The authors conducted a systematic search of PubMed and included studies which had at least 100 women with type 1 diabetes, dealing with either the prevalence of pre-eclampsia or possible clinical predictors of pre-eclampsia identified in early pregnancy.

11,518 pregnancies in 11 articles were included which gave an overall prevalence of pre-eclampsia in women with type 1 diabetes of 17%, five to six times more than in the background population. In early pregnancy, the following clinical predictors were associated with increased prevalence of pre-eclampsia: diabetic nephropathy (OR 3.7-23.5), microalbuminuria (OR 3.8-11.7), diabetic retinopathy (OR 1.9-2.9) and pre-existing hypertension (OR 3.8-17.1) as well as high blood pressure within the normotensive range.

HbA1C, body mass index and nulliparity were positively associated with pre-eclampsia, but not consistently. This study certainly confirms type 1 diabetics as a high risk group for development of pre-eclampsia.


  1. Thangaratinam S, Allotey J, Marlin N, Dodds J, Cheong-See F, von Dadelszen P, Ganzevoort W, Akkermans J, Kerry S, Mol BW, et al. Prediction of complications in early-onset pre-eclampsia (PREP): development and external multinational validation of prognostic models. BMC medicine. Mar 30 2017;15(1):68.
  2. Duffy J, Hirsch M, Kawsar A, Gale C, Pealing L, Plana MN, Showell M, Williamson PR, Khan KS, Ziebland S, et al. Outcome reporting across randomised controlled trials evaluating therapeutic interventions for pre-eclampsia. BJOG : an international journal of obstetrics and gynaecology. Apr 22 2017.
  3. Vestgaard M, Sommer MC, Ringholm L, Damm P, Mathiesen ER. Prediction of preeclampsia in type 1 diabetes in early pregnancy by clinical predictors: a systematic review. The journal of maternal-fetal & neonatal medicine : the official journal of the European Association of Perinatal Medicine, the Federation of Asia and Oceania Perinatal Societies, the International Society of Perinatal Obstet. Jun 02 2017:1-7.