Implementing a Preeclampsia Prediction Model in Obstetrics: Cutoff Determination and Health Care Professionals' Adherence

Pim van Montfort*, Luc J. M. Smits, Ivo M. A. van Dooren, Stephanie M. P. Lemmens, Maartje Zelis, Iris M. Zwaan, Marc E. A. Spaanderman, Hubertina C. J. Scheepers

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review


Background. Despite improved management, preeclampsia remains an important cause of maternal and neonatal mortality and morbidity. Low-dose aspirin (LDA) lowers the risk of preeclampsia. Although several guidelines recommend LDA prophylaxis in women at increased risk, they disagree about the definition of high risk. Recently, an externally validated prediction model for preeclampsia was implemented in a Dutch region combined with risk-based obstetric care paths. Objectives. To demonstrate the selection of a risk threshold and to evaluate the adherence of obstetric health care professionals to the prediction tool. Study Design. Using a survey (n = 136) and structured meetings among health care professionals, possible cutoff values at which LDA should be discussed were proposed. The prediction model, with chosen cutoff and corresponding risk-based care paths, was embedded in an online tool. Subsequently, a prospective multicenter cohort study (n = 850) was performed to analyze the adherence of health care professionals. Patient questionnaires, linked to the individual risk profiles calculated by the online tool, were used to evaluate adherence. Results. Health care professionals agreed upon employing a tool with a high detection rate (cutoff: 3.0%; sensitivity 75%, specificity 64%) followed by shared decision between patients and health care professionals on LDA prophylaxis. Of the 850 enrolled women, 364 women had an increased risk of preeclampsia. LDA was discussed with 273 of these women, resulting in an 81% adherence rate. Conclusion. Consensus regarding a suitable risk cutoff threshold was reached. The adherence to this recommendation was 81%, indicating adequate implementation.

Original languageEnglish
Article numberARTN 0272989X19889890
Pages (from-to)81-89
Number of pages9
JournalMedical Decision Making
Issue number1
Early online date2 Dec 2019
Publication statusPublished - Jan 2020


  • aspirin
  • adherence
  • implementation
  • prediction
  • preeclampsia
  • pregnancy
  • shared decision making


Dive into the research topics of 'Implementing a Preeclampsia Prediction Model in Obstetrics: Cutoff Determination and Health Care Professionals' Adherence'. Together they form a unique fingerprint.

Cite this