Skip to main content

Screening high-risk population of persistent postpartum hypertension in women with preeclampsia using latent class cluster analysis

Abstract

Background

A significant proportion of women with preeclampsia (PE) exhibit persistent postpartum hypertension (PHTN) at 3 months postpartum associated with cardiovascular morbidity. This study aimed to screen patients with PE to identify the high-risk population with persistent PHTN.

Methods

This retrospective cohort study enrolled 1,000 PE patients with complete parturient and postpartum blood pressure (BP) profiles at 3 months postpartum. The enrolled patients exhibited new-onset hypertension after 20 weeks of pregnancy, while those with PE superimposed upon chronic hypertension were excluded. Latent class cluster analysis (LCCA), a method of unsupervised learning in machine learning, was performed to ascertain maternal exposure clusters from eight variables and 35 subordinate risk factors. Logistic regression was applied to calculate odds ratios (OR) indicating the association between clusters and PHTN.

Results

The 1,000 participants were classified into three exposure clusters (subpopulations with similar characteristics) according to persistent PHTN development: high-risk cluster (31.2%), medium-risk cluster (36.8%), and low-risk cluster (32.0%). Among the 1,000 PE patients, a total of 134 (13.4%) were diagnosed with persistent PHTN, while the percentages of persistent PHTN were24.68%, 10.05%, and 6.25% in the high-, medium-, and low-risk clusters, respectively. Persistent PHTN in the high-risk cluster was nearly five times higher (OR, 4.915; 95% confidence interval (CI), 2.92–8.27) and three times (OR, 2.931; 95% CI, 1.91–4.49) than in the low- and medium-risk clusters, respectively. Persistent PHTN did not differ between the medium- and low-risk clusters. Subjects in the high-risk cluster were older and showed higher BP, poorer prenatal organ function, more adverse pregnancy events, and greater medication requirement than the other two groups.

Conclusion

Patients with PE can be classified into high-, medium-, and low-risk clusters according to persistent PHTN severity; each cluster has cognizable clinical features. This study’s findings stress the importance of controlling persistent PHTN to prevent future cardiovascular disease.

Peer Review reports

Introduction

Preeclampsia (PE) is a common and severe complication of pregnancy that manifests as new-onset hypertension and proteinuria and can progress to severe PE with multi-organ involvement [1,2,3]. An estimated 2-8% of pregnant women worldwide suffer from PE [4, 5]. In the last decade, mounting evidence has suggested that preeclamptic women are susceptible to developing cardiovascular disease (CVD) later in life [6,7,8].

Maternal hypertension and proteinuria usually disappear in most PE patients within the first week postpartum; until 3 months, blood pressure (BP) mostly returns to normal [1, 9, 10]. However, about 20% of patients with PE exhibit persistent postpartum hypertension (PHTN), and the occurrence of some forms of PHTN, including sustained hypertension, masked hypertension, and white-coat hypertension, could be as high as 41.5% at 1 year after discharge in patients with severe PE [11,12,13], who require long-term antihypertensive medication. One proposed risk factor for cardiovascular morbidity is the persistence of hypertension in the postpartum period [14, 15]. Hypertensive women who experience PE are reportedly at a two-fold risk of developing CVD in the next decades compared to those with PE but become normotensive after delivery [16]. Notably, postpartum follow-up of PE is inadequate, with reported rates of 20-60%, and a large proportion of obstetricians neglect to follow up postpartum BP in PE patients [13, 15, 17, 18]. Therefore, early screening for those at high risk of developing persistent PHTN helps clinicians provide accurate postpartum BP monitoring and timely intervention for in such patients.

PE is associated with multiple risk factors [2], and its risk assessments require methods that can integrate them. Current PE models mostly involve the early prediction of adverse pregnancy outcomes and long-term cardiovascular disease risk [19,20,21]. Prognostic data on BP profiles at short-term follow-up during the postpartum period in patients with PE are scarce, although some other risk factors of PHTN have been suggested, including preexisting hypertension before pregnancy and a higher body mass index (BMI) [11], older age, smoking, pre-pregnancy obesity, comorbidities such as thyroid disorders [12], and decreased serum placental growth factor [22]. Few studies to date have reported the clustered and combined effects of multiple risk factors on BP recovery during the 3-month or longer period after delivery in patients with PE. It remains unclear whether PE increases the occurrence of PHTN through the known risk factors associated with PE and CVD. The present study aimed to identify the categories of PE patients using latent class cluster analysis (LCCA) by combining multiple risk factors. Unlike traditional single-factor approaches, LCCA is a machine learning method and has certain advantages; for example, it can explore the interrelationships among multiple risk factors and classify similar objects into groups, and thus can be applied for screening high-risk populations [23, 24]. This study may help identify high-risk clusters in the PE population and provide appropriate treatment strategies for those who may develop persistent PHTN and CVD.

Methods

Study population

This retrospective multicenter cohort study recruited patients from the First Hospital of Shanxi Medical University (FHSMU) between June 2017 and May 2020 and Shanxi Children's Hospital and Women Health Center (SCWHC) from September 2019 to May 2020 with a confirmed diagnosis of PE or who developed PE after admission. A study flowchart is shown in Fig. 1. The diagnosis of PE conformed to the 2018 definition of International Society for the Study of Hypertension in Pregnancy and ACOG 2019 [1, 2]. PE is characterized by new-onset of hypertension (systolic BP ≥ 140 mmHg and/or diastolic BP ≥ 90 mmHg) and exhibits at least one of the following new-onset symptoms during or after 20 weeks of gestation: 1) proteinuria (24-h urinary protein ≥ 300 mg/day or dipstick reading ≥ 2+); 2) other maternal organ dysfunction, such as acute kidney injury (creatinine ≥ 90 μmol/L or 1.0 mg/dL), hepatic dysfunction (alanine aminotransferase or aspartate aminotransferase > 40 IU/L, with or without epigastric abdominal or right upper quadrant pain), neurological dysfunction (such as eclampsia and change in mental status), or hematologic complications (blood platelet count < 150,000/μL, disseminated intravascular coagulation, or hematolysis); and 3) uteroplacental complications (abnormal Doppler waveform of the umbilical artery, fetal growth restriction, or stillbirth). Patients who were diagnosed with PE superimposed upon chronic hypertension, pre-pregnancy hypertension, or hypertension that occurred within the first 20 weeks of pregnancy were not included in the study.

Fig. 1
figure 1

Study flowchart outlining the composition of final PE cohort using the datasets from the First Hospital of Shanxi Medical University (FHSMU) and Shanxi Children’s Hospital and Women Health Center (SCWHC)

The study flowchart and patient information are presented in Fig. 1. Between June 2017 and May 2020, a total of 722 PE patients who were admitted to the FHSMU and ended their pregnancies were included, excluding 16 without follow-up and 16 without postpartum BP measurement data. Thus, 690 of the 722 PE patients treated at the FHSMU were included; the other 32 were excluded. Of the 690 patients from the FHSMU, the BP data of 94 (13.62%) remained higher at 3 months postpartum. Among patients treated at the SCWHC between September 2019 and May 2020, 328 with PE ended their pregnancies, excluding 16 patients without follow-up and two without postpartum BP measurement data; the remaining 310 were included in the study. Among the 310 patients in the SCWHC group, the BP values did not return to normal by 3 months postpartum in 40 (12.90%). Thus, 1,000 (690 + 310) PE patients from the two hospitals were included, while 50 were excluded due to lack of postpartum BP data or phone interview failure.

Defining persistent PHTN

The recruited PE patients were followed up by telephone interviews to confirm persistent PHTN at 3 months postpartum. BP was measured twice a day by trained nurses in community clinics and/or by trained family members at home using mercurial or electronic arm cuff BP meters after a 5-min rest. BP measurements were repeated three times within 10 min, with a 1-min interval between measurements. Mean systolic and diastolic BP values were recorded. In the present study, patients with persistent PHTN were defined as those who experienced PE and still showed hypertension (average systolic BP ≥ 140 mmHg; average diastolic BP ≥ 90 mmHg) within 3 months postpartum and the requirement for cardiovascular consults for further investigation and medication. The end date of follow-up was October 2020. Patients with BP that did not return to normal by 3 months postpartum were included as outcome events. Patients who could not be reached by telephone for follow-up and whose BP was not monitored within 3 months postpartum were excluded from the study cohort.

Data collection

The data and diagnoses of the enrolled patients were collected, including maternal demographic characteristics and relevant clinical laboratory tests performed within 7 days prior to the end of pregnancy. If the index visit involved multiple tests, the worst value was selected. A total of 35 candidate risk factors, including laboratory test results, were entered from the literature reviews [1, 2, 20, 25,26,27]. Laboratory indicators were converted from continuous to categorical variables based on whether they were outside the normal range (Table 1).

Table 1 Description of the eight indicator variables for LCCA and the baseline characteristics of the 1,000 enrolled PE patients

Latent class cluster analysis

The 35 candidate risk factors were categorized into eight important indicator variables, including maternal delivery age, mean arterial pressure (MAP = diastolic BP + 1/3 pulse pressure difference; maximum MAP levels during pregnancy were used in the study), drug use during pregnancy, medical history, adverse pregnancy outcomes, blood cell and coagulation tests performed within 7 days before delivery, altered liver and renal functions within 7 days before delivery, and elevated blood myocardial enzymes and electrolyte disbalance within 7 days before delivery. Among the 35 risk factors, no categorical variable data were missing. Some continuous variable data were missing, including up to 2.5% of those for serum creatine kinase and serum lactate dehydrogenase, while data for prothrombin activity (%), international normalized ratio, albumin, serum creatinine, serum urea nitrogen, and serum potassium were missing for fewer than five cases; instead, mean values were used. Each of the eight indicator variables except maternal age and MAP included multiple risk factors. These variables were aggregated and assessed as total risk factor scores, with a dimensionality reduction of 0-N (Table 1) [28]. All eight indicators were considered continuous variables and standardized by the LCCA.

LCCA, a model-based clustering approach, was conducted to analyze the eight indicator variables using R 3.6.1 software. It assumes that heterogeneous populations are a mixture of populations; that is, a latent class is used to classify populations. This method classifies the population by probability; that is, the individual belongs to a cluster with a certain probability, and the individual is ultimately assigned to the cluster with the highest posterior probability [29]. LCCA for categorical indicator variables is called latent class analysis, while that for continuous indicator variables is called latent profile analysis (LPA). The eight indicators we studied were continuous variables, and the basic principle of LPA was to suppose that the probability density function of the P-dimensional continuous manifest variable vector yi can be expressed as the following equation 1:

$$f\left({y}_i\right)=\sum_{k=1}^K{\eta}_k{f}_k\left({y}_i\left|{\mu}_k,{\Sigma}_k\right.\right)$$
(1)

Here ηk denotes the latent class probabilities and K is the number of clusters (= 1, 2, …, K); yi is the score of object i on a set of manifest variables, assuming that within the cluster k, yi came from an independent multivariate normal distribution; μis the mean vector; and ∑k is the variance-covariance matrix. After model establishment using Bayesian theory, the posterior probability of assigning patients to class k was calculated using the following equation 2:

$$P\left(k\left|{y}_i\right.\right)=\frac{\eta_k{f}_k\left({y}_i\left|{\mu}_k,{\Sigma}_k\right.\right)}{\sum_{k=1}^K{\eta}_k{f}_k\left({y}_i\left|{\mu}_k,{\Sigma}_k\right.\right)}$$
(2)

LPA with the mclust package was used to define clusters of participants with similar clinical profiles. We used mclustBIC to observe the Bayesian Information Criterion (BIC) for different profiles and the integrated completed likelihood (ICL) to penalize the model’s instability to stabilize the number of obtained models. Finally, PE patients were classified into different latent classes.

Statistical analysis

Continuous variables in the baseline information are expressed as median and quartile [M (P25, P75)], and comparisons between PE patients with versus without persistent PHTN were made using the Mann-Whitney U test. Categorical variables are expressed as count and percentage, and the chi-squared test was used to compare PE patients with versus without persistent PHTN. Standardized characteristics of clusters are expressed as mean ± standard deviation (SD), while cluster comparisons were performed using analysis of variance. The logistic regression analysis was performed to explore the association between exposure clusters and persistent PHTN. The statistical analysis was performed using SPSS 22.0, and statistical significance was set at P < 0.05. Bonferroni correction was used to adjust the P values for multiple tests.

Results

Baseline characteristics of PE patients

Indicator variables and patients’ baseline characteristics are presented in Table 1. Except for the first and second indicator variables (maternal delivery age and MAP, respectively), all indicator variables consisted of 33 risk factors that were aggregated into a score for each indicator variable according to the presence or absence of the corresponding risk factor. The scoring standards (a series of yes/no questions) are shown in the Data collection section of the Methods section.

Among the 1,000 PE subjects, 134 developed persistent PHTN and required transfer to a cardiovascular department for further evaluation and antihypertensive medication. Maternal delivery age, MAP, and prenatal use of antihypertensive drugs in the 134 patients with PHTN were significantly higher than those in the 866 patients without PHTN (P < 0.001, Mann-Whitney U test and chi-squared test). Compared with normotensive subjects at 3 months postpartum, patients who developed persistent PHTN showed PE features earlier (earlier than 32 weeks’ gestation), longer intervals between births (/pregnancies) (> 10 years), a higher incidence of adverse pregnancy outcomes, and worse laboratory results.

LCCA results

Table S1 presents the analytical results of the five models assessed for goodness of fit. The BIC model 2- and model 3-clusters were smaller, while the model 3-cluster was more suitable for screening high-risk study populations.

According to the model 3-cluster, three clusters of maternal exposure were identified and the standardized values of the eight indicator variables were compared (Table 2). The risk factors among the three clusters are shown in Table S2. The numbers (percentages) of 1,000 PE patients distributed in the three clusters are as follows: cluster 1 (low-risk), 320 (32.0%); cluster 2 (medium-risk), 368 (36.8%); and cluster 3 (high-risk), 312 (31.2%).

Table 2 Comparison of standardized characteristics among the three clusters of PE patients

Cluster 3 exhibited the highest levels of the eight indicator variables. Thus, cluster 3 was characterized as a high-risk cluster. Cluster 2 did not differ from cluster 1 in maternal delivery age and medical history indicators and had a lower drug use indicator level, but higher levels of the remaining five indicators than cluster 1. Consequently, cluster 2 was characterized as a medium-risk cluster, while cluster 1 was a low-risk cluster. Figure 2 shows the normalized mean values of the eight indicator variables in the three clusters. There were statistically significant differences among the eight indicator variables in the high-, medium-, and low-risk clusters.

Fig. 2
figure 2

Standardized means of the indicator variables for different risk clusters. Cluster 1 indicated the low-risk cluster, cluster 2 was the medium-risk cluster, and cluster 3 was the high-risk cluster

Each patient with PE was assigned to the most likely cluster based on the parameters constructed by the trichotomous model. Table S3 describes the standardized values and LCCA cluster assignments for selected patients with PE.

Association of persistent PHTN with identified latent classes

The event rate of persistent PHTN was significantly higher in the high-risk cluster (24.68%) than in the medium-risk (10.05%) and low-risk (6.25%) clusters, and all statistical differences were significant, but the differences in the latter two were statistically insignificant (Table 3). In addition, the probability of persistent PHTN in the high-risk population was 4.915 and 2.931 times higher than that in the low- and medium-risk populations, respectively. The probability of persistent PHTN was 1.677 times higher in the medium- versus low-risk population, but the difference was statistically insignificant (Table 3).

Table 3 Event rates of PHTN in different clusters and associations of PHTN with risk clusters

Discussion

The present study investigated the trends in BP changes and other cardiovascular risk factors in 1,000 PE patients at 3 months postpartum, mainly using the LCCA method. We found that these PE patients showed differential risks of persistent PHTN and could be subdivided into high-, medium-, and low-risk clusters according to persistent PHTN severity as indicated by the eight indicator variables and the subordinate 35 risk factors shown in Table 1. Overall, approximately 13.40% of patients with PE exhibited persistent PHTN at 3 months postpartum. The incidence of persistent PHTN in the high-risk cluster was nearly five and three times higher than those in the low-risk and medium-risk clusters, respectively. Those who developed persistent PHTN were usually older; had higher BMI and MAP values; used antihypertensive drugs before delivery; and had abnormal pregnancy features (including earlier onset of PE and higher abortion rate), larger birth numbers, longer intervals between births, and worse laboratory results. These findings may help clinicians and patients realize the severity of persistent PHTN, thus strengthening the importance of follow-up, timely intervention, and improved patient awareness of postpartum BP self-regulation. As a result, such monitoring and treatment may reduce the incidence of long-term CVD in women.

PE is a severe hypertensive disorder of pregnancy (HDP). The follow-up of HDP patients for 5-21 years after delivery demonstrated that they had a four-fold higher risk of developing PHTN than those without HDP [6, 8, 21]. One-year follow-up studies showed that PHTN occurred in 17-29% of patients with PE after discharge and that the probability of postpartum CVD was significantly higher in the PE group than in those with normal pregnancies; among those with PE, the BP returned to normal [12, 13, 22, 30]. The 13.4% incidence of PHTN among the 1,000 PE subjects here reflects the morbidity of persistent PHTN in Taiyuan City, Shanxi Province, China.

An appropriate approach may help improve evaluations of PHTN severity and subsequent cardiovascular events in pregnant women after delivery. A combination of maternal and fetal parameters may reportedly detect the higher risk of PE [31]. The evaluation and management of PHTN requires a multidisciplinary approach [10]. Studies of cardiovascular risk factors before versus after pregnancy reported that half of the increased risk of future hypertension in women with PE is attributable to prenatal risk factors [32]. LCCA, an unsupervised machine learning method, can be used to screen high-risk populations. LCCA assumes that a heterogeneous group consists of a mixture of aggregates, and a latent class variable determines the optimal model. With the help of goodness-of-fit evaluation metrics, LCCA may achieve “dimensionality reduction” at the variable level and cluster at the individual level. The latent classes extracted by LCCA both reflect the comprehensive effects of different influencing factors and aid the further analysis of the characteristics of different population clusters. No single current test can reliably predict the risk of PE. As a multidisciplinary approach that integrates multiple risk factors, LCCA has certain advantages over single tests for evaluating the risks of pregnancy-associated diseases in later life. LCCA has been used to identify high-risk populations requiring clinical treatment and identify distinct subgroups within the clinical risk population [33, 34]. LCCA has also been used effectively to screen populations at high risk of birth defects [35, 36]. Using LCCA, we successfully clustered the subpopulations of patients with PE in terms of persistent PHTN and identified the characteristics of each clustered population. This assessment model can be applied to assess the risk of persistent PHTN.

Strengths and limitations

The present study’s main strengths were as follows: 1) over 1,000 PE patients were screened; and 2) the LCCA assessment model had good accuracy, could be applied to evaluate persistent PHTN in PE patients, and may help establish early and precise guidance for managing persistent PHTN and reducing the risk of future CVD. The study also had some limitations, including: 1) its relatively shorter follow-up time duration (3 months); 2) potential bias caused by its relatively small cohort size; and 3) its retrospective study design inevitably leading to incomplete medical history data, such as routine testing of blood pro-brain-type natriuretic peptide, anticardiolipin antibodies, proteinuria within the follow-up period, and blood lipids, especially in the week before delivery. This situation may prevent some of the predictive variables from being included in the model. In future studies, we may expand the cohort size, prolong the follow-up duration, and perform prospective validation, which might compensate for the limitations of the current study.

Conclusion

This study leveraged LCCA, screened a subpopulation of PE patients at high risk of persistent PHTN, and identified some related risk factors, including older age, higher BMI, earlier PE onset, longer interval between births, higher incidence of abnormal pregnancy, and worse laboratory results. This study’s findings may help clinicians realize the severity of persistent PHTN, encourage patients to actively seek early medical advice, facilitate the early identification of high-risk PE women, and encourage precise monitoring and management of postpartum BP.

Availability of data and materials

The original datasets of the present study are available from the corresponding author on reasonable request.

Abbreviations

BMI:

Body mass index

BP:

Blood pressure

CVD:

Cardiovascular disease

HDP:

Hypertensive disorder of pregnancy

LCCA:

Latent class cluster analysis

LPA:

Latent profile analysis

MAP:

Mean arterial pressure

OR:

Odds ratios

PE:

Preeclampsia

PHTN:

Postpartum hypertension

References

  1. Brown MA, Magee LA, Kenny LC, et al. Hypertensive Disorders of Pregnancy: ISSHP Classification, Diagnosis, and Management Recommendations for International Practice. Hypertension. 2018;72(1):24–43. https://doi.org/10.1161/HYPERTENSIONAHA.117.10803.

    CAS  Article  PubMed  Google Scholar 

  2. ACOG Practice Bulletin No 202. Gestational Hypertension and Preeclampsia. Obstet Gynecol. 2019;133(1):1. https://doi.org/10.1097/AOG.0000000000003018.

    Article  Google Scholar 

  3. Steegers EA, von Dadelszen P, Duvekot JJ, Pijnenborg R. Pre-eclampsia. Lancet. 2010;376(9741):631–44. https://doi.org/10.1016/S0140-6736(10)60279-6.

    Article  PubMed  Google Scholar 

  4. Khan KS, Wojdyla D, Say L, Gülmezoglu AM, Van Look PF. WHO analysis of causes of maternal death: a systematic review. Lancet. 2006;367(9516):1066–74. https://doi.org/10.1016/S0140-6736(06)68397-9.

    Article  PubMed  Google Scholar 

  5. Duley L. The global impact of pre-eclampsia and eclampsia. Semin Perinatol. 2009;33(3):130–7. https://doi.org/10.1053/j.semperi.2009.02.010.

    Article  PubMed  Google Scholar 

  6. Bellamy L, Casas JP, Hingorani AD, Williams DJ. Pre-eclampsia and risk of cardiovascular disease and cancer in later life: systematic review and meta-analysis. BMJ. 2007;335(7627):974. https://doi.org/10.1136/bmj.39335.385301.BE.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Chen CW, Jaffe IZ, Karumanchi SA. Pre-eclampsia and cardiovascular disease. Cardiovasc Res. 2014;101(4):579–86. https://doi.org/10.1093/cvr/cvu018.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  8. Leon LJ, McCarthy FP, Direk K, et al. Preeclampsia and Cardiovascular Disease in a Large UK Pregnancy Cohort of Linked Electronic Health Records: A CALIBER Study. Circulation. 2019;140(13):1050–60. https://doi.org/10.1161/CIRCULATIONAHA.118.038080.

    Article  PubMed  Google Scholar 

  9. Lowe SA, Bowyer L, Lust K, et al. The SOMANZ Guidelines for the Management of Hypertensive Disorders of Pregnancy 2014. Aust N Z J Obstet Gynaecol. 2015;55(1):11–6. https://doi.org/10.1111/ajo.12253.

    Article  PubMed  Google Scholar 

  10. Sibai BM. Etiology and management of postpartum hypertension-preeclampsia. Am J Obstet Gynecol. 2012;206(6):470–5. https://doi.org/10.1016/j.ajog.2011.09.002.

    Article  PubMed  Google Scholar 

  11. Benschop L, Duvekot JJ, Versmissen J, van Broekhoven V, Steegers E. Roeters van Lennep JE. Blood Pressure Profile 1 Year After Severe Preeclampsia. Hypertension. 2018;71(3):491–8. https://doi.org/10.1161/HYPERTENSIONAHA.117.10338.

    CAS  Article  PubMed  Google Scholar 

  12. Chen KH, Chen LR. Provoking factors for postpartum chronic hypertension in women with preceding gestational hypertension/preeclampsia: A longitudinal cohort study of 22,798 pregnancies. Int J Med Sci. 2020;17(4):543–8. https://doi.org/10.7150/ijms.39432.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  13. Levine LD, Nkonde-Price C, Limaye M, Srinivas SK. Factors associated with postpartum follow-up and persistent hypertension among women with severe preeclampsia. J Perinatol. 2016;36(12):1079–82. https://doi.org/10.1038/jp.2016.137.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  14. Edlow AG, Srinivas SK, Elovitz MA. Investigating the risk of hypertension shortly after pregnancies complicated by preeclampsia. Am J Obstet Gynecol. 2009;200(5):e60–2. https://doi.org/10.1016/j.ajog.2008.10.012.

    Article  PubMed  Google Scholar 

  15. Berks D, Steegers E, Molas M, Visser W. Resolution of hypertension and proteinuria after preeclampsia. Obstet Gynecol. 2009;114(6):1307–14. https://doi.org/10.1097/AOG.0b013e3181c14e3e.

    Article  PubMed  Google Scholar 

  16. Breetveld NM, Ghossein-Doha C, van Kuijk S, et al. Cardiovascular disease risk is only elevated in hypertensive, formerly preeclamptic women. BJOG. 2015;122(8):1092–100. https://doi.org/10.1111/1471-0528.13057.

    CAS  Article  PubMed  Google Scholar 

  17. Traylor J, Chandrasekaran S, Limaye M, Srinivas S, Durnwald CP. Risk perception of future cardiovascular disease in women diagnosed with a hypertensive disorder of pregnancy. J Matern Fetal Neonatal Med. 2016;29(13):2067–72. https://doi.org/10.3109/14767058.2015.1081591.

    CAS  Article  PubMed  Google Scholar 

  18. Mito A, Arata N, Sakamoto N, et al. Present status of clinical care for postpartum patients with hypertensive disorders of pregnancy in Japan: findings from a nationwide questionnaire survey. Hypertens Pregnancy. 2015;34(2):209–20. https://doi.org/10.3109/10641955.2014.1001902.

    Article  PubMed  Google Scholar 

  19. Perales A, Delgado JL, de la Calle M, et al. sFlt-1/PlGF for prediction of early-onset pre-eclampsia: STEPS (Study of Early Pre-eclampsia in Spain). Ultrasound Obstet Gynecol. 2017;50(3):373–82. https://doi.org/10.1002/uog.17373.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  20. von Dadelszen P, Payne B, Li J, et al. Prediction of adverse maternal outcomes in pre-eclampsia: development and validation of the fullPIERS model. Lancet. 2011;377(9761):219–27. https://doi.org/10.1016/S0140-6736(10)61351-7.

    Article  Google Scholar 

  21. Stuart JJ, Tanz LJ, Cook NR, et al. Hypertensive Disorders of Pregnancy and 10-Year Cardiovascular Risk Prediction. J Am Coll Cardiol. 2018;72(11):1252–63. https://doi.org/10.1016/j.jacc.2018.05.077.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Neuman RI, Figaroa A, Nieboer D, et al. Angiogenic markers during preeclampsia: Are they associated with hypertension 1 year postpartum. Pregnancy Hypertens. 2021;23:116–22. https://doi.org/10.1016/j.preghy.2020.11.011.

    Article  PubMed  Google Scholar 

  23. Funderburk JS, Maisto SA, Sugarman DE, Wade M. The covariation of multiple risk factors in primary care: a latent class analysis. J Behav Med. 2008;31(6):525–35. https://doi.org/10.1007/s10865-008-9176-1.

    Article  PubMed  Google Scholar 

  24. Mori M, Krumholz HM, Allore HG. Using Latent Class Analysis to Identify Hidden Clinical Phenotypes. JAMA. 2020;324(7):700–1. https://doi.org/10.1001/jama.2020.2278.

    Article  PubMed  Google Scholar 

  25. Lisonkova S, Bone JN, Muraca GM, et al. Incidence and risk factors for severe preeclampsia, hemolysis, elevated liver enzymes, and low platelet count syndrome, and eclampsia at preterm and term gestation: a population-based study. Am J Obstet Gynecol. 2021;225(5):538.e1–538. https://doi.org/10.1016/j.ajog.2021.04.261.

    CAS  Article  Google Scholar 

  26. MacKay AP, Berg CJ, Atrash HK. Pregnancy-related mortality from preeclampsia and eclampsia. Obstet Gynecol. 2001;97(4):533–8. https://doi.org/10.1016/s0029-7844(00)01223-0.

    CAS  Article  PubMed  Google Scholar 

  27. Jhee JH, Lee S, Park Y, et al. Prediction model development of late-onset preeclampsia using machine learning-based methods. PLoS One. 2019;14(8):e0221202. https://doi.org/10.1371/journal.pone.0221202.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  28. Hausdorf K, Eakin E, Whiteman D, Rogers C, Aitken J, Newman B. Prevalence and correlates of multiple cancer risk behaviors in an Australian population-based survey: results from the Queensland Cancer Risk Study. Cancer Causes Control. 2008;19(10):1339–47. https://doi.org/10.1007/s10552-008-9205-y.

    Article  PubMed  Google Scholar 

  29. Vermunt JK, Magidson J. Latent class cluster analysis. In: Hagenaars J, McCutcheon A, editors. Applied latent class analysis. Cambridge: Cambridge University Press; 2002. p. 89–106.

    Chapter  Google Scholar 

  30. Black MH, Zhou H, Sacks DA, et al. Hypertensive disorders first identified in pregnancy increase risk for incident prehypertension and hypertension in the year after delivery. J Hypertens. 2016;34(4):728–35. https://doi.org/10.1097/HJH.0000000000000855.

    CAS  Article  PubMed  Google Scholar 

  31. Toal M, Chan C, Fallah S, et al. Usefulness of a placental profile in high-risk pregnancies. Am J Obstet Gynecol. 2007;196(4):363.e1–7. https://doi.org/10.1016/j.ajog.2006.10.897.

    CAS  Article  Google Scholar 

  32. Romundstad PR, Magnussen EB, Smith GD, Vatten LJ. Hypertension in pregnancy and later cardiovascular risk: common antecedents. Circulation. 2010;122(6):579–84. https://doi.org/10.1161/CIRCULATIONAHA.110.943407.

    Article  PubMed  Google Scholar 

  33. Ryan AT, Addington J, Bearden CE, et al. Latent class cluster analysis of symptom ratings identifies distinct subgroups within the clinical high risk for psychosis syndrome. Schizophr Res. 2018;197:522–30. https://doi.org/10.1016/j.schres.2017.12.001.

    Article  PubMed  Google Scholar 

  34. Orri M, Pingault JB, Rouquette A, et al. Identifying affective personality profiles: A latent profile analysis of the Affective Neuroscience Personality Scales. Sci Rep. 2017;7(1):4548. https://doi.org/10.1038/s41598-017-04738-x.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  35. Cao H, Wei X, Guo X, et al. Screening high-risk clusters for developing birth defects in mothers in Shanxi Province, China: application of latent class cluster analysis. BMC Pregnancy Childbirth. 2015;15:343. https://doi.org/10.1186/s12884-015-0783-x.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  36. Pei L, Zeng L, Zhao Y, Wang D, Yan H. Using latent class cluster analysis to screen high risk clusters of birth defects between 2009 and 2013 in Northwest China. Sci Rep. 2017;7(1):6873. https://doi.org/10.1038/s41598-017-07076-0.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

The authors thank FHSMU and SCWHC for providing the registration information of patients.

Funding

This study was financed by Shanxi “1331” Project Quality and Efficiency Improvement Plan (1331KFC) and partially by Key Medical Science and Technology Program of Shanxi Province (2020XM01) and National Natural Science foundation of China (82170523).

Author information

Authors and Affiliations

Authors

Contributions

Y.Y.L., H.L.Y., L.Y.H. and J.M.C.: organized and designed study. Y.Y.L., J.C., J.L.L., J.Y.Z. and Y.M.L.: collected data. Y.B.Z.: provided suggestions of study design and supervised data analysis. Y.Y.L. and C.Z.: conducted data analysis. Y.Y.L.: wrote the manuscript. J.M.C., D.P.W., H.L., Y.F.H. and R.Z.: revised manuscript. All authors read and approved manuscript submission.

Corresponding authors

Correspondence to Yan-Bo Zhang or Ji-Min Cao.

Ethics declarations

Ethics approval and consent to participate

The study was approved by the Ethics Committee of First Hospital of Shanxi Medical University and the Institutional Review Board of Shanxi Children’s Hospital and Women Health Center, and followed the principles of the Declaration of Helsinki. The protocol numbers were 2020-K083 and KYYN-2021-002, respectively. Informed consents were obtained from all subjects and/or their legal guardian(s) in the study.

Consent for publication

Not applicable (No such identifying information/images of participants are provided).

Competing interests

The authors declare that they have no conflict of interests in this study.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1: Table S1.

Goodness-of-fit indicators for the five different class models. Table S2. Comparison of risk factors among the three clusters of PE patients. Table S3. Class assignment for a minority of PE patients.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Li, YY., Cao, J., Li, JL. et al. Screening high-risk population of persistent postpartum hypertension in women with preeclampsia using latent class cluster analysis. BMC Pregnancy Childbirth 22, 687 (2022). https://doi.org/10.1186/s12884-022-05003-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12884-022-05003-4

Keywords

  • preeclampsia
  • postpartum hypertension
  • cardiovascular disease
  • latent class cluster analysis