Preeclampsia is a multisystem disorder that can cause devastating complications for mothers and babies, from brain and liver injury in mothers to premature birth, and it is one of the top causes of maternal-fetal mortality worldwide.

The condition is actually highly treatable, as shown by the advances achieved in some developed countries [1]. When treated, preeclampsia is fatal for only 1 out of every million births [2]. 

In many developing countries, however, particularly those with limited resources, this condition still poses a formidable threat to maternal-fetal health. In developing countries, roughly 18% of maternal deaths are due to preeclampsia; this represents more than 570 deaths per million births.

Another major reason why preeclampsia can be so deadly is delay diagnosis and poor monitoring of pregnant women with risk factors for the condition. Till date, delivery of the placenta and baby under intensive care remain the only approach that remits preeclampsia. 

From the prognostic standpoint, several molecular markers, including angiogenic growth factors, have been recommended as promising markers for early detection. However, these markers are mainly for research purposes. These markers are costly, require expertise and are not available in routine practice and to women from resource-limited countries.

In an effort to solve this problem, we created an algorithm that predicts whether a pregnant woman is likely to develop preeclampsia and needs further monitoring and/or treatment. A key component of the algorithm is the suboptimal health status questionnaire-25 (SHSQ-25), a survey developed by our team that asks questions about fatigue, cardiovascular health, digestive health, immune health, and mental health [3-5]. This questionnaire is a simple, low-cost method, require less expertise and can be available to both women from develop and underdeveloped countries.

To develop this algorithm, we first administered the SHSQ-25 in addition to measuring relevant physiological metrics such as magnesium and calcium levels. Based on participants’ median SHS questionnaire scores, we classified the women as being in “poor overall health” (high SHS) and being in “good health” (low SHS). Ultimately, a higher proportion of the high SHS women went on developing preeclampsia, compared with just very few of the low SHS scored category. The high SHS score women who developed preeclampsia also exhibited significantly lower magnesium and calcium levels, leading to incorporation of these tests into the predictive algorithm along with SHSQ-25 scores. Overall, the algorithm was able to identify 77.7% of the women who developed preeclampsia.

“This [algorithm] could be used to predict hypertensive disorders of pregnancy as well as lower the high maternal death rate by identifying women who need treatment for this often fatal condition, thus creating a window of opportunity for predictive, preventive, and personalized medicine [3, 6]. Hence, SHSQ-25 can be applied as a subjective screening tool in resource limited communities to assess early risk of poor health among normotensive pregnant women who are like to develop cardiovascular disorders such as preeclampsia [3].

Information regarding this patented algorithm was also published and cited by The European Journal of Predictive Preventive and Personalised Medicine (EPMA Journal) [3]. In addition, its value has been recognized by a panel of The AACC Academy Fellows in a special AACC Press release [7] and by The News Medical Life Science Media in Australia [8].


  1. Harmon QE, Huang L, Umbach DM, Klungsøyr K, Engel SM, Magnus P, et al. (2015) Risk of fetal death with preeclampsia. Obstetrics and gynecology 125: 628-635.
  2. Alkema L, Chou D, Hogan D, Zhang S, Moller AB, Gemmill A, et al. (2016) Global, regional, and national levels and trends in maternal mortality between 1990 and 2015, with scenario-based projections to 2030: a systematic analysis by the UN Maternal Mortality Estimation Inter-Agency Group. Lancet 387: 462-474.
  3. Anto EO, Roberts P, Coall D, Turpin CA, Adua E, Wang Y, et al. (2019) Integration of suboptimal health status evaluation as a criterion for prediction of preeclampsia is strongly recommended for healthcare management in pregnancy: a prospective cohort study in a Ghanaian population. EPMA Journal 10: 211-226.
  4. Wang W, Yan Y (2012) Suboptimal health: a new health dimension for translational medicine. Clin Transl Med 1: 28.
  5. Yan Y-X, Liu Y-Q, Li M, Hu P-F, Guo A-M, Yang X-H, et al. (2009) Development and evaluation of a questionnaire for measuring suboptimal health status in urban Chinese. Journal of Epidemiology 19: 333-341.
  6. High Maternal Death Rate in Resource Limited Countries Could Be Reduced With a Simple Questionnaire. Retrieved October 21, 2019