Prediction of Fetal Death in Preterm Preeclampsia Using Fetal Sex, Placental Growth Factor and Gestational Age
by
, , and
Blanca Novillo-Del Álamo
1,*
,
Alicia Martínez-Varea
1,2
,
Carmen Sánchez-Arco
1,
Elisa Simarro-Suárez
1,
Iker González-Blanco
1,
Mar Nieto-Tous
1 and
José Morales-Roselló
1,3 1
Department of Obstetrics and Gynaecology, La Fe University and Polytechnic Hospital, Avenida Fernando Abril Martorell 106, 46026 Valencia, Spain
2
Department of Medicine, CEU Cardenal Herrera University, 12006 Castellón de la Plana, Spain
3
Department of Pediatrics, Obstetrics and Gynecology, Faculty of Medicine, University of Valencia, 46010 Valencia, Spain
*
Author to whom correspondence should be addressed.
J. Pers. Med. 2024, 14(10), 1059; https://doi.org/10.3390/jpm14101059
Submission received: 7 September 2024
/
Revised: 2 October 2024
/
Accepted: 10 October 2024
/
Published: 13 October 2024
(This article belongs to the Section Sex, Gender and Hormone Based Medicine)
Abstract
:Background/Objectives: Preeclampsia (PE) is a systemic disease that affects 4.6% of pregnancies. Despite the existence of a first-trimester screening for the prediction of preterm PE, no consensus exists regarding neither the right moment to end the pregnancy nor the appropriate variables to estimate the prognosis. The objective of this study was to obtain a prediction model for perinatal death in patients with preterm PE, useful for clinical practice. Methods: Singleton pregnant women with PE and preterm delivery were included in an observational retrospective study. Multiple maternal and fetal variables were collected, and several multivariable logistic regression analyses were applied to construct models to predict perinatal death, selecting the most accurate and reproducible according to the highest area under the curve (AUC) and the lowest Akaike Information Criteria (AIC). Results: A group of 148 pregnant women were included, and 18 perinatal deaths were registered. Univariable logistic regression selected as statistically significant variables the following: gestational age (GA) at admission, fetal sex, poor response to antihypertensive drugs, PlGF, umbilical artery (UA) pulsatility index (PI), cerebroplacental ratio (CPR), and absent/reversed ductus venosus (DV). The multivariable model, including all these parameters, presented an AUC of 0.95 and an AIC of 76.5. However, a model including only GA and fetal sex presented a similar accuracy with the highest simplicity (AUC 0.93, AIC 67.6). Finally, in fetuses with a similar GA, fetal death became dependent on PlGF and fetal sex, underlying the role of fetal sex in all circumstances. Conclusions: Female fetal sex and low PlGF are notorious predictors of perinatal death in preterm PE, only surpassed by early GA at birth.
1. Introduction
Preeclampsia (PE) is a multisystem disease characterized by the appearance of hypertension after the 20th week of pregnancy, associated with a widespread organic dysfunction [1,2,3,4,5] that produces significant maternal–fetal comorbidities and has long-term implications on women’s lives. PE is a cardiovascular risk enhancer [6], affecting 4.6% of pregnancies, although this incidence has recently increased [1,2].
Screening for preterm PE is performed in the first trimester of pregnancy [7]. Women with a high risk of PE must initiate acetylsalicylic acid 150 mg daily until week 36 to reduce the possibility of adverse perinatal and maternal outcomes [8,9,10]. During their pregnancy, exhaustive surveillance (analytical, clinical, and ultrasonographical) is carried out to detect the onset of the disease early. However, not infrequently, PE is diagnosed when organ failures have started and consequences in the fetus have already occurred [4,11].
The pathophysiology of PE lies in placental vascular dysfunction [11]. Based on this fact, multiple studies have analyzed the usefulness of the PE ratio sFlt-1/PlGF [12,13,14], agreeing on the high sensitivity and negative predictive value for the development of PE in the subsequent weeks [12,13,14,15]. However, no consensus has been reached, neither about including the ratio as a diagnostic criterion for PE [3,15,16,17,18,19,20] nor about including the ratio to pinpoint the right moment to end the pregnancy in cases of severe PE.
The objective of our study was to evaluate which factors influence perinatal death, creating a prediction model for perinatal death in patients with preterm PE.
2. Materials and Methods
This was an observational retrospective study that included preterm singleton pregnancies diagnosed with PE who attended the Obstetric Department of the tertiary maternity center Hospital Universitario y Politécnico La Fe between 2013 and 2023.
The inclusion criteria were singleton pregnancies diagnosed with PE with a complete follow-up and a preterm delivery. The exclusion criteria were twins, term pregnancies, and pregnancies with loss of follow-up. The Hospital Research Ethics Committee approved the study on the 29 March 2023 (register number: 2023-217-1).
The variables were collected via reviewing the medical records and guaranteeing patient anonymity. Variables were of five types: 1—maternal characteristics: maternal age, number of gestations, parity, prepregnancy weight, maternal height, body mass index (BMI), smoking, gestational age (GA) at admission, and poor response to antihypertensive drugs; 2—maternal medical conditions: use of reproductive techniques, thrombophilia, diabetes; 3—maternal blood analysis: aspartate aminotransferase (AST), alanine transaminase (ALT), lactate dehydrogenase (LDH), platelets, creatinine, soluble fms-like tyrosine kinase-1 (S-Flt-1), placental growth factor (PlGF), S-Flt-1/PlGF, proteinuria, hemolysis, elevated liver enzymes, and low platelets (HELLP) syndrome; 4—maternal symptoms: headache, edema, photopsia, epigastric pain, dyspnea, dizziness; 5—fetal examination: estimated fetal weight (EFW) centile, umbilical artery pulsatility index (UA PI), middle cerebral artery pulsatility index (MCA PI), cerebroplacental ratio (CPR), multiples of the median (MoM), absent or reversed ductus venosus (DV), diagnosis of severe intrauterine growth restriction (IUGR), fetal sex, and abnormal cardiotocogram (CTG). Both the analysis and the ultrasound were performed at admission. On occasions, the ultrasound was delayed up to one day pending a higher definition ultrasound.
Finally, information about labor was also collected to determine the outcome. This included GA at labor, type of labor onset, mode of delivery, birth weight, Apgar score, arterial and venous pH, and neonate destiny (maternal ward, neonatal ward, intensive care unit (ICU), and morgue). We chose perinatal death as the dependent variable for the multivariable analysis.
To evaluate the relationship between the above-mentioned parameters and perinatal death, an univariable regression analysis was initially performed to select plausible determinants. Afterward, a multivariable regression analysis was carried out to create different models that were evaluated considering the area under the curve (AUC) and the Akaike Information Criteria (AIC). In these models, the AIC and the AUC with their 95% confidence interval (CI), p-value, and detection rates (DR) for a false positive rate (FPR) of 5% and 10% were calculated. Statistics and graphs were created using Graph Pad Prism 9® and Stat Plus Pro 7® for Apple Macintosh. Statistical significance was set at p < 0.05.
3. Results
3.1. Descriptive Analysis of the Population
One hundred and forty-eight patients were included in the study. Table 1 shows the descriptive analysis of the population in terms of mean, standard deviation (SD), median, and quartiles (Q) for the continuous parameters and in terms of numbers and percentages for the categorical ones. Two columns were constructed describing the perinatal death (N = 18) and the live fetuses (N = 130) population characteristics, as well as the comparison between them.
Most pregnant women were nulliparous (72.3%) and non-smokers (94.6%). The rate of pregnancies achieved with reproductive techniques was notorious (27%). Regarding clinical manifestations, the most frequent was headache (33.8%), followed by epigastric pain (24.3%) and photopsia (8.7%). HELLP syndrome occurred in only a small percentage (7.4%), and 60.8% of patients presented poor responses to antihypertensive drugs.
The mean maternal age was 35 years old and the mean prepregnancy weight and BMI were 73.9 kg and 27.9 kg/m2. Most fetuses were male (58.8%) and had low birth weight centiles (mean 9.9). In fact, 24.3% presented with severe intrauterine growth restriction (IUGR). All the deliveries were preterm (<37 weeks) (an inclusion criterion), and 70.3% were severe preterm (<34 weeks). Cesarean section (CS) was the main mode of delivery (91.9%), being elective in most cases (81.7%). No spontaneous onset of labor occurred. Low arterial pH was recorded in 14.9%, and a low Apgar score in 8.7% of the newborns. ICU was needed by 52.7% of them, while perinatal deaths (the dependent variable chosen for the outcome) occurred in 12.2% of the studied cases.
The comparison between the population with perinatal death (n = 18) and the live fetuses (n = 130) (Table 1) was significant in terms of all the parameters related to GA, Doppler study (both UA and ACM and, consequently, CPR), as well as birth weight and fetal sex. Regarding analytical parameters, only PlGF and s-Flt-1/PlGF ratio were significant. No clinical parameter showed differences between the two groups. The significance levels are expressed in the last column of Table 1.
3.2. Univariable Logistic Regression for the Prediction of Perinatal Death
3.3. Multivariable Model
The multivariable model created with these parameters [Model 1 in Table 3 and Figure 1] presented high accuracy (AUC 0.95, AIC 76.5). However, the model that included only GA and fetal sex, the most important parameters in the previous model [Model 2 in Table 3 and Figure 1], presented a similar ability with a higher simplicity and reproducibility (lower AIC) (AUC 0.93, AIC 67.6).
This procedure was repeated without including GA in the equation, mimicking a similar GA for all studied patients. In this case, the model [Model 1 in Table 4 and Figure 2] became less accurate (AUC 0.86, AIC 96.12), while fetal death became only dependent on PlGF and fetal sex. Again, the model that included only these two parameters presented a similar ability with a higher level of simplicity (AUC 0.82, AIC 91.9).
3.4. Effect Size of GA, PlGF, and Sex
To illustrate the effect size of GA, fetal sex, and PlGF, Figure 3, Figure 4 and Figure 5 were constructed.
Figure 3 represents the percentage of fetal survival according to the GA at birth (the X axis represents the days remaining until the estimated delivery date). It shows how survival decreases with low GA. A small drop in survival might be seen below 26 weeks (about 100 days before the estimated delivery date).
Figure 4 represents the percentage of fetal survival according to the values of PlGF. Low PlGF values are associated with less fetal survival. An increase in mortality might be seen with PlGF values below 75 pg/mL.
The percentage of each sex among the live and dead fetuses is represented in Figure 5. It shows that 21% of the female fetuses died, in contrast to only 6% of the male fetuses. In fact, despite the diagnosis of preterm PE being more frequent in male than female fetuses (our sample of preterm PE included 61 females and 82 males), female fetuses died between 3 and 4 times more than male fetuses.
4. Discussion
The appropriate time to terminate a pregnancy due to the development of PE remains a challenge in clinical practice. The maternal risk of continuing the pregnancy must be balanced against the newborn prognosis.
Multiple studies demonstrate the existence of a worse neonatal prognosis with low GA due to the extreme prematurity [21,22]. This study is consistent with this evidence and shows that GA at birth is the major determinant of neonatal death [23]. The decrease in survival is a continuum that slightly deepens below 26 weeks (Figure 3). A few combinations have been attempted to improve the prediction of GA for the risk of neonatal death, such as the conjunction of GA and the Apgar score [23], a postnatal parameter. However, to the best of our knowledge, up to now there is no accurate published prediction model for perinatal death in preterm PE.
Sex differences in cardiovascular risk throughout adult life are well known, as sex chromosomes and sex hormones influence blood pressure regulation, the distribution of cardiovascular risk factors, and other comorbidities [24,25,26]. Studies in pregnant rats have shown a different response to prenatal hypoxic stress according to sex via reducing the modulation of nitric oxide in females and increasing expression of thromboxane A2 in males, concluding that complicated pregnancies may lead to a sex difference in the programming of cardiovascular disease in adult offspring [27]. Furthermore, it is suggested there is sexual dimorphism in the feto-placental units, although its clinical relevance remains unknown [28]. A study shows higher levels of sFlt-1 and sFlt-1/PlGF ratio in pregnancies with female fetuses compared with male fetuses both in healthy pregnancies and in women who developed PE. However, a previous study had shown an increased level of sFlt-1 only in first-trimester normal gestations female fetuses, not in gestations that developed PE [29]. Although their results were not concordant, both suggest that fetal sex should be taken into account in the interpretation of angiogenic markers [28]. This study shows a clear and simple model for the prediction of perinatal death in preterm PE, including fetal sex and GA, with a very high prognostic power (AUC 0.93, AIC 67.6). Furthermore, there is a study that suggests a worse prognosis for male fetuses, possibly due to a higher rate of prematurity and IUGR compared with females, although with opposite results for gestations developing PE [30].
Of note, our results show the diagnosis of preterm PE was higher in male fetuses, but strikingly, females died 3.5 times more. To the best of our knowledge, this is a novel finding that launches a hypothesis to be corroborated by larger sample studies.
Regarding the PlGF values, our results show a lower survival with lower values of PlGF, especially below 75 pg/mL (Figure 4). It is known the PlGF values vary with GA [31], and accordingly, it could be argued that in our study the reason for the low PlGF was the low GA, making the use of PlGF MoM mandatory. However, the PlGF normality curve remains very stable around the weeks corresponding to our study period [30]. In addition, in the multivariable analysis we adjusted PlGF for GA, and both GA and PlGF were selected as significant parameters, discarding any type of interaction between both variables, which seem to be independent (Table 3).
Furthermore, for the same GA, the best parameters were PIGF and fetal sex, predicting fetal demise with an AUC of 0.82 and an AIC of 91.90.
These results show a different prognosis in neonates of gestations with PE according to sex, with a higher rate of PE diagnoses in male fetuses but a higher rate of fetal demise in female fetuses. Moreover, at the same GA, fetal death also becomes dependent on PlGF, an influence that seems independent of GA. Although further studies are needed, this finding might be the seed for a new approach to PE management.
The authors acknowledge some limitations of this study, such as the small sample size for the outcome variable (perinatal death, n = 18) and the lack of information in collecting clinical history data due to the study’s retrospective design. Although the study was developed in a center with homogeneous management of clinical cases, more studies with prospective designs are necessary for internal and external validation of the results.
5. Conclusions
Fetal sex and PlGF are notorious predictors of perinatal death in preterm preeclampsia, only surpassed by GA at birth.
Author Contributions
J.M.-R. performed the work’s conceptualization, methodology, and design. C.S.-A., E.S.-S., I.G.-B., M.N.-T. and B.N.-D.Á. collected the information of the patients for the study. J.M.-R. made the statistical analysis. J.M.-R. supervised the research work. B.N.-D.Á. wrote the original draft. J.M.-R. and A.M.-V. meticulously reviewed and made significant enhancements to the draft. All authors have read and agreed to the published version of the manuscript.
Funding
This research received no external funding.
Institutional Review Board Statement
This study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board (or Ethics Committee) of the Hospital La Fe Research Ethics Committee, which approved the study on the 29 March 2023 (register number: 2023-217-1).
Informed Consent Statement
Informed consent was not necessary due to the retrospective design, according to the Hospital Research Ethics Committee.
Data Availability Statement
Data available on request from the authors.
Conflicts of Interest
The authors declare no conflicts of interest.
Abbreviations
| PE | Preeclampsia |
| SD | Standard deviation |
| Q | Quartiles |
| BMI | Body Mass Index |
| GA | Gestational age |
| EFW | Estimated fetal weight |
| BW | Birth weight |
| AST | Aspartate aminotransferase |
| ALT | Alanine transaminase |
| LDH | Lactate dehydrogenase |
| Hemolysis, elevated liver enzymes, and low platelets HELLP syndrome | |
| S-Flt-1 | Soluble fms-like tyrosine kinase-1 |
| PlGF | Placental growth factor |
| UA | Umbilical artery |
| MCA | Middle cerebral artery |
| PI | Pulsatility index |
| CPR | Cerebroplacental ratio |
| MoM | Multiples of the median |
| IVF | In vitro fertilization |
| CTG | Cardiotocogram |
| IUGR | Intrauterine growth restriction |
| ICU | Intensive care unit |
| CS | Cesarean section |
References
- Abalos, E.; Cuesta, C.; Grosso, A.L.; Chou, D.; Say, L. Global and regional estimates of preeclampsia and eclampsia: A systematic review. Eur. J. Obstet. Gynecol. Reprod. Biol. 2013, 170, 1. [Google Scholar] [CrossRef] [PubMed]
- American College of Obstetricians and Gynecologists. Hypertension in pregnancy: Executive summary. Obstet. Gynecol. 2013, 122, 1122–1131. [Google Scholar] [CrossRef] [PubMed]
- Gestational Hypertension and Preeclampsia: ACOG Practice Bulletin, Number 222. Obstet. Gynecol. 2020, 135, e237. [CrossRef] [PubMed]
- Lai, J.; Syngelaki, A.; Nicolaides, K.H.; von Dadelszen, P.; Magee, L.A. Impact of new definitions of preeclampsia at term on identification of adverse maternal and perinatal outcomes. Am. J. Obstet. Gynecol. 2021, 224, 518.e1. [Google Scholar] [CrossRef]
- Lisonkova, S.; Joseph, K.S. Incidence of preeclampsia: Risk factors and outcomes associated with early- versus late-onset disease. Am. J. Obstet. Gynecol. 2013, 209, 544.e1. [Google Scholar] [CrossRef]
- Grand’Maison, S.; Pilote, L.; Okano, M.; Landry, T.; Dayan, N. Markers of Vascular Dysfunction After Hypertensive Disorders of Pregnancy: A Systematic Review and Meta-Analysis. Hypertension 2016, 68, 1447–1458. [Google Scholar] [CrossRef] [PubMed]
- Tan, M.Y.; Syngelaki, A.; Poon, L.C.; Rolnik, D.L.; O’Gorman, N.; Delgado, J.L.; Akolekar, R.; Konstantinidou, L.; Tsavdaridou, M.; Galeva, S.; et al. Screening for pre-eclampsia by maternal factors and biomarkers at 11–13 weeks’ gestation. Ultrasound Obstet. Gynecol. 2018, 52, 186–195. [Google Scholar] [CrossRef] [PubMed]
- Duley, L.; Meher, S.; Hunter, K.E.; Seidler, A.L.; Askie, L.M. Antiplatelet agents for preventing pre-eclampsia and its complications. Cochrane Database Syst. Rev. 2019, 2019, CD004659. [Google Scholar] [CrossRef]
- Henderson, J.T.; Vesco, K.K.; Senger, C.A.; Thomas, R.G.; Redmond, N. Aspirin Use to Prevent Preeclampsia and Related Morbidity and Mortality: Updated Evidence Report and Systematic Review for the US Preventive Services Task Force. JAMA 2021, 326, 1192. [Google Scholar] [CrossRef]
- Ma’ayeh, M.; Costantine, M.M. Prevention of preeclampsia. Semin. Fetal Neonatal Med. 2020, 25, 101123. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
- Ives, C.W.; Sinkey, R.; Rajapreyar, I.; Tita, A.T.N.; Oparil, S. Preeclampsia-Pathophysiology and Clinical Presentations: JACC State-of-the-Art Review. J. Am. Coll. Cardiol. 2020, 76, 1690–1702. [Google Scholar] [CrossRef] [PubMed]
- Duhig, K.E.; Myers, J.E.; Gale, C.; Girling, J.C.; Harding, K.; Sharp, A.; Simpson, N.A.B.; Tuffnell, D.; Seed, P.T.; Shennan, A.H.; et al. Placental growth factor measurements in the assessment of women with suspected Preeclampsia: A stratified analysis of the PARROT trial. Pregnancy Hypertens. 2021, 23, 41–47. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
- Cerdeira, A.S.; O’Sullivan, J.; Ohuma, E.O.; Harrington, D.; Szafranski, P.; Black, R.; Mackillop, L.; Impey, L.; Greenwood, C.; James, T.; et al. Randomized Interventional Study on Prediction of Preeclampsia/Eclampsia in Women With Suspected Preeclampsia: INSPIRE. Hypertension 2019, 74, 983–990. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
- Thadhani, R.; Lemoine, E.; Rana, S.; Costantine, M.M.; Calsavara, V.F.; Boggess, K.; Wylie, B.J.; Simas, T.A.M.; Louis, J.M.; Espinoza, J.; et al. Circulating angiogenic factor levels in hypertensive disorders of pregnancy. NEJM Evid. 2022, 1, EVIDoa2200161. [Google Scholar] [CrossRef]
- Perales, A.; Delgado, J.L.; de la Calle, M.; García-Hernández, J.A.; Escudero, A.I.; Campillos, J.M.; Sarabia, M.D.; Laíz, B.; Duque, M.; Navarro, 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, 373–382. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
- German Society of Obstetrics and Gynecology (DGGG), OEGG and SGGG. Guidelines for Hypertensive Disorders in Pregnancy. Diagnosis and Therapy. Updated May 2019. Available online: https://www.awmf.org/leitlinien/detail/ll/015-018.html (accessed on 28 June 2019).
- Verlohren, S.; Brennecke, S.P.; Galindo, A.; Karumanchi, S.A.; Mirkovic, L.B.; Schlembach, D.; Stepan, H.; Vatish, M.; Zeisler, H.; Rana, S. Clinical interpretation and implementation of the sFlt-1/PlGF ratio in the prediction, diagnosis and management of preeclampsia. Pregnancy Hypertens. 2022, 27, 42–50. [Google Scholar] [CrossRef] [PubMed]
- National Institute for Health and Care Excellence (NICE). PlGF-Based Testing to Help Diagnose Suspected Pre-Eclampsia (Triage PlGF Test, Elecsys Immunoassay sFlt-1/PlGF Ratio, DELFIA Xpress PlGF 1-2-3 Test, and BRAHMS sFlt-1 Kryptor/BRAHMS PlGF Plus Kryptor PE Ratio); NICE: London, UK, 2016. [Google Scholar]
- Magee, L.A.; Brown, M.A.; Hall, D.R.; Gupte, S.; Hennessy, A.; Karumanchi, S.A.; Kenny, L.C.; McCarthy, F.; Myers, J.; Poon, L.C.; et al. The 2021 International Society for the Study of Hypertension in Pregnancy classification, diagnosis & management recommendations for international practice. Pregnancy Hypertens. 2022, 27, 148. [Google Scholar]
- Visintin, C.; Mugglestone, M.A.; Almerie, M.Q.; Nherera, L.M.; James, D.; Walkinshaw, S.; on behalf of the Guideline Development Group. Management of hypertensive disorders during pregnancy: Summary of NICE guidance. BMJ 2010, 341, c2207. [Google Scholar] [CrossRef]
- Yu, Y.L.; Zhong, H.F.; Chen, C.; Gong, W.T.; Huang, Y.C.; Lin, B.C.; Huang, Z.F.; Yang, C.Z. Management and prognosis of extremely preterm infants with gestational age ≤25+6 weeks. Zhonghua Er Ke Za Zhi 2023, 61, 36–42. (In Chinese) [Google Scholar] [CrossRef] [PubMed]
- Backes, C.H.; Rivera, B.K.; Pavlek, L.; Beer, L.J.; Ball, M.K.; Zettler, E.T.; Smith, C.V.; Bridge, J.A.; Bell, E.F.; Frey, H.A. Proactive neonatal treatment at 22 weeks of gestation: A systematic review and meta-analysis. Am. J. Obstet. Gynecol. 2021, 224, 158–174. [Google Scholar] [CrossRef] [PubMed]
- Cnattingius, S.; Johansson, S.; Razaz, N. Apgar Score and Risk of Neonatal Death among Preterm Infants. N. Engl. J. Med. 2020, 383, 49–57. [Google Scholar] [CrossRef] [PubMed]
- Rajendran, A.; Minhas, A.S.; Kazzi, B.; Varma, B.; Choi, E.; Thakkar, A.; Michos, E.D. Sex-specific differences in cardiovascular risk factors and implications for cardiovascular disease prevention in women. Atherosclerosis 2023, 384, 117269. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
- Gerdts, E.; Sudano, I.; Brouwers, S.; Borghi, C.; Bruno, R.M.; Ceconi, C.; Cornelissen, V.; Diévart, F.; Ferrini, M.; Kahan, T.; et al. Sex differences in arterial hypertension. Eur. Heart J. 2022, 43, 4777–4788. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
- Smigoc Schweiger, D.; Battelino, T.; Groselj, U. Sex-Related Differences in Cardiovascular Disease Risk Profile in Children and Adolescents with Type 1 Diabetes. Int. J. Mol. Sci. 2021, 22, 10192. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
- Graton, M.E.; Spaans, F.; He, R.; Chatterjee, P.; Kirschenman, R.; Quon, A.; Phillips, T.J.; Case, C.P.; Davidge, S.T. Sex-specific differences in the mechanisms for enhanced thromboxane A2-mediated vasoconstriction in adult offspring exposed to prenatal hypoxia. Biol. Sex. Differ. 2024, 15, 52. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
- Andersen, L.B.; Jørgensen, J.S.; Herse, F.; Andersen, M.S.; Christesen, H.T.; Dechend, R. The association between angiogenic markers and fetal sex: Implications for preeclampsia research. J. Reprod. Immunol. 2016, 117, 24–29. [Google Scholar] [CrossRef] [PubMed]
- Brown, Z.A.; Schalekamp-Timmermans, S.; Tiemeier, H.W.; Hofman, A.; Jaddoe, V.W.; Steegers, E.A. Fetal sex specific differences in human placentation: A prospective cohort study. Placenta 2014, 35, 359–364. [Google Scholar] [CrossRef] [PubMed]
- Vatten, L.J.; Skjaerven, R. Offspring sex and pregnancy outcome by length of gestation. Early Hum. Dev. 2004, 76, 47–54. [Google Scholar] [CrossRef] [PubMed]
- Tsiakkas, A.; Duvdevani, N.; Wright, A.; Wright, D.; Nicolaides, K.H. Serum placental growth factor in the three trimesters of pregnancy: Effects of maternal characteristics and medical history. Ultrasound Obstet. Gynecol. 2015, 45, 591–598. [Google Scholar] [CrossRef] [PubMed]
Figure 1.
ROC curves of the multivariable models.
Figure 2.
ROC curve of the multivariable model without including gestational age.
Figure 3.
The Kaplan–Meier curve represents the percentage of fetal survival in the days until the estimated delivery date.
Figure 3.
The Kaplan–Meier curve represents the percentage of fetal survival in the days until the estimated delivery date.
Figure 4.
The Kaplan–Meier curve represents the percentage of fetal survival and the PlGF value in pg/mL.
Figure 4.
The Kaplan–Meier curve represents the percentage of fetal survival and the PlGF value in pg/mL.
Figure 5.
Pie chart representing the percentage of female and male fetuses among the live fetuses and the fetal demise.
Figure 5.
Pie chart representing the percentage of female and male fetuses among the live fetuses and the fetal demise.
Table 1.
Description of the study population (N = 148).
| 1-All Cases (N = 148) | 2-Perinatal Death (n = 18) | 3-Live Fetuses (n = 130) | (2 versus 3) | ||||
| Continuous Parameters | Mean (SD) | Median (1st, 3rd Q) | Mean (SD) | Median (1st, 3rd Q) | Mean (SD) | Median (1st, 3rd Q) | p-Value |
| Maternal age | 34.9 (6.1) | 35 (31, 39) | 34 (6.9) | 35 (30.5, 39) | 35 (6) | 35 (31, 39) | 0.7585 |
| Number of gestations | 2.0 (1.3) | 1.5 (1, 3) | 1.4 (0.7) | 1 (1, 2) | 2.1 (1.4) | 2 (1, 3) | 0.0704 |
| Parity | 0.5 (0.9) | 0 (0, 1) | 0.1 (0.3) | 0 (0, 0) | 0.51 (0.94) | 0 (0, 1) | 0.1955 |
| Prepregnancy weight (kg) | 73.9 (17.1) | 71.7 (61.4, 84.9) | 79.6 (17.7) | 76 (63.5, 90) | 73.1 (17) | 70.9 (60, 83.6) | 0.1196 |
| Height (cm) | 162.0 (6.9) | 158 (162, 165) | 163.7 (6.5) | 163 (159, 170) | 161.8 (6.9) | 162 (158, 165) | 0.2655 |
| BMI (kg/m2) | 27.9 (6.1) | 27.1 (23.1, 31.1) | 29.7 (6.3) | 27.2 (24.75, 33.3) | 27.7 (6.1) | 27.1 (23, 31) | 0.2351 |
| GA at admission | 30.9 (3.8) | 31.3 (28.3, 34.0) | 25.6 (2.3) | 25.1 (23.9, 27) | 31.6 (3.3) | 32.1 (29.1, 34.4) | <0.0001 |
| GA at examination (weeks) | 31.25 (3.6) | 31.9 (28.8, 34) | 25.8 (2.4) | 25.5 (23.8, 27.4) | 32 (3) | 32.6 (30, 34.6) | <0.0001 |
| GA at labor | 31.7 (3.5) | 32.4 (29.5, 34.6) | 26 (2.3) | 25.6 (24.5, 28) | 32.5 (2.8) | 33 (30.3, 35) | <0.0001 |
| EFW in grams | 1432 (639.5) | 1373 (945, 1909) | 653 (333) | 579 (438, 750) | 1539 (596) | 1464 (1068, 1956) | <0.0001 |
| EFW centile | 12.6 (24.4) | 1 (0, 10.75) | 5.4 (20.9) | 1 (0, 1) | 13.6 (24.6) | 2 (0, 13) | 0.0031 |
| UA PI | 1.5 (0.61) | 1.3 (1.07, 1.99) | 2.03 (0.5) | 2.3 (1.4, 2.4) | 1.44 (0.58) | 1.23 (1.04, 1.68) | <0.0001 |
| MCA PI | 1.50 (0.41) | 1.44 (1.16, 1.75) | 1.34 (0.46) | 1.21 (1.04, 1.68) | 1.52 (0.4) | 1.46 (1.20, 1.80) | 0.0348 |
| CPR | 1.17 (0.57) | 1.18 (0.64, 1.60) | 0.74 (0.43) | 0.55 (0.47, 0.79) | 1.23 (0.56) | 1.27 (0.73, 1.64) | 0.0002 |
| UA PI MoM | 1.5 (0.6) | 1.31 (1.07, 1.99) | 1.89 (0.45) | 2.05 (1.43, 2.13) | 1.58 (0.60) | 1.37 (1.15, 1.95) | 0.0060 |
| MCA PI MoM | 1.50 (0.41) | 1.44 (1.16, 1.75) | 0.67 (0.24) | 0.61 (0.50, 0.83) | 0.77 (0.20) | 0.75 (0.63, 0.88) | 0.0361 |
| CPR MoM | 1.17 (0.57) | 1.18 (0.64, 1.60) | 0.41 (0.25) | 0.33 (0.27, 0.44) | 0.62 (0.28) | 0.63 (0.37, 0.81) | 0.0019 |
| Interval admission-delivery (days) | 5.5 (9.7) | 3 (1, 6) | 2.6 (3.5) | 1.5 (0, 5.25) | 5.89 (10.2) | 3 (1, 6.2) | 0.0496 |
| AST (GOT) | 56.1 (96.9) | 24.5 (18, 43) | 61.7 (93.6) | 25 (17.5, 71.5) | 55.3 (97.7) | 24 (17.75, 43) | 0.7301 |
| ALT (GPT) | 57.8 (106.6) | 20 (13.2, 50.2) | 64.7 (99.7) | 24.5 (17.5, 86.7) | 56.9 (107.8) | 19 (13, 48.7) | 0.2954 |
| LDH | 277.8 (135.5) | 250 (214.3, 295) | 301 (95.7) | 278 (226, 364) | 274.6 (140.1) | 244 (211.8, 292.3) | 0.0636 |
| Platelets × 1000 | 196.16 (64.22) | 192.5 (157.5, 233) | 201.4 (96) | 202 (138, 234) | 195.4 (58.9) | 192 (158.5, 233.2) | 0.8580 |
| Creatinine | 0.71 (0.24) | 0.58 (0.66, 0.79) | 0.76 (0.51) | 0.66 (0.56, 0.73) | 0.71 (0.18) | 0.66 (0.59, 0.79) | 0.3954 |
| s-Flt-1 | 15,203 (8353) | 13,025 (9888, 17,961) | 15,219 (5581) | 13,343 (11,037, 17,656) | 15,201 (8683) | 13,025 (9563, 18,088) | 0.5313 |
| PlGF | 55.89 (51.1) | 41.9 (22.5, 69) | 24.95 (19.5) | 18.3 (9.6, 36.2) | 60.2 (52.6) | 45.8 (25.5, 74.1) | <0.0001 |
| s-Flt-1/PlGF | 953.9 (4408) | 347.5 (159, 737) | 1001 (654) | 1025 (434, 1606) | 947.3 (4700) | 325 (150, 712) | 0.0003 |
| Proteinuria | 201.9 (295.1) | 100 (0, 300) | 212 (315) | 100 (22.5, 300) | 200.6 (293.5) | 100 (0, 300) | 0.7378 |
| Birth weight in grams | 1432 (671) | 1318 (992, 1798) | 631 (325) | 565 (404, 800) | 1543 (630) | 1415 (1158, 1863) | <0.0001 |
| Birth weight centile | 9.9 (24) | 0 (0, 5) | 3.9 (15) | 0 (0, 0) | 10.7 (24.8) | 1 (0, 5) | 0.0088 |
| Categorical parameters | N (%) | N (%) | N (%) | p-value | |||
| Perinatal deaths | 18 (12.2) | 18 (100) | 0 (0) | <0.0001 | |||
| Fetal sex (male) | 87 (58.8) | 5 (27.8) | 82 (63.1) | 0.0089 | |||
| Smoking | 8 (5.4) | 0 (0) | 0 (0) | 1 | |||
| Headache | 50 (33.8) | 5 (27.8) | 45 (34.6) | 0.7909 | |||
| Edema | 27 (18.2) | 2 (11.1) | 25 (19.2) | 0.5290 | |||
| Photopsia | 13 (8.7) | 2 (11.1) | 11 (8.5) | 0.6600 | |||
| Epigastric pain | 36 (24.3) | 1 (5.5) | 35 (26.9) | 0.0744 | |||
| Dyspnea | 2 (1.3) | 0 (0) | 2 (1.5) | 1 | |||
| Dizziness | 6 (4) | 0 (0) | 6 (4.6) | 1 | |||
| Diagnosis of HELLP syndrome | 11 (7.4) | 3 (16.7) | 8 (6.1) | 0.1335 | |||
| Absent or reversed UA | 34 (23) | 6 (33.3) | 28 (21.5) | 0.3807 | |||
| Absent or reversed Ductus Venosus | 4 (2.7) | 2 (11.1) | 2 (1.5) | 0.0391 | |||
| Diagnosis of severe IUGR | 30 (20.3) | 4 (22.2) | 26 (20) | 0.7621 | |||
| Abnormal CTG | 36 (24.3) | 5 (27.8) | 31 (23.8) | 0.7708 | |||
| Poor response to antihypertensive drugs | 90 (60.8) | 7 (38.9) | 83 (63.8) | 0.0688 | |||
| Reproductive techniques (any) | 40 (27) | 4 (22.2) | 36 (27.7) | 0.7805 | |||
| IVF Ovodon | 27 (18.2) | 2 (11.1) | 25 (19.2) | 0.5290 | |||
| Thrombophilia | 3 (2) | 0 (0) | 3 (2.3) | 1 | |||
| Diabetes | 24 (16.2) | 0 (0) | 24 (18.5) | 0.0450 | |||
| Nulliparity | 107 (72.3) | 15 (83.3) | 92 (70.8) | 0.4001 | |||
| Severe preterm labor incidence (<34) | 104 (70.3) | 18 (100) | 86 (66.1) | 0.0017 | |||
| Preterm labor incidence (<37 weeks) | 148 (100) | 18 (100) | 128 (98.4) | 0.0150 | |||
| Type of labor onset | |||||||
| Cesarean section (elective) | 121 (81.7) | 12 (66.7) | 109 (83.8) | 0.1007 | |||
| Induction of labor | 3 (2) | 3 (16.7) | 21 (16.1) | 1 | |||
| Spontaneous onset of labor | 24 (16.2) | 3 (16.7) | 0 (0) | 0.0015 | |||
| Apgar < 7 at 5 min | 13 (8.7) | 10 (55.5) | 3 (2.3) | <0.0001 | |||
| Arterial pH < 7.10 | 22 (14.9) | 6 (33.3) | 4 (3.1) | 0.0002 | |||
| Via of delivery | |||||||
| CS (elective) | 121 (81.7) | 12 (66.7) | 109 (83.8) | 0.1007 | |||
| CS (failure to progress) | 3 (2) | 0 (0) | 3 (2.3) | 1 | |||
| CS (abnormal CTG) | 12 (8.1) | 2 (11.1) | 10 (7.7) | 0.6413 | |||
| Assisted vaginal delivery | 1 (0.7) | 0 (0) | 1 (0.8) | 1 | |||
| Spontaneous vaginal delivery | 11 (7.4) | 4 (22.2) | 7 (5.4) | 0.0278 | |||
| Neonate destiny | |||||||
| Maternal ward | 12 (8.1) | 0 (0) | 12 (9.2) | 0.3619 | |||
| Neonates ward | 51 (34.4) | 0 (0) | 51 (39.2) | 0.0004 | |||
| Pediatric Intensive care unit | 78 (52.7) | 11 (61.1) | 67 (51.5) | 0.6155 | |||
| Morgue | 7 (4.7) | 7 (38.9) | 0 (0) | <0.0001 | |||
Notes: SD: standard deviation, Q: quartiles, CS: cesarean section, BMI: body mass index, GA: gestational age, EFW: estimated fetal weight, BW: birth weight, UA: umbilical artery, MCA: middle cerebral artery, PI: pulsatility index, CPR: cerebroplacental ratio, MoM: multiples of median, s-Flt-1: soluble fms-like tyrosine kinase 1, PLGF: placental growth factor, AST: aspartate aminotransferase, ALT: alanine transaminase, LDH: lactate dehydrogenase, HELLP: hemolysis, elevated liver enzymes, and low platelets, IVF: in vitro fertilization, CTG: cardiotocogram, IUGR: intrauterine growth restriction.
Table 2.
Univariable logistic regression for the prediction of perinatal death.
| Parameter | Estimate | SE | OR (95% Confidence Interval) | p-Value |
|---|---|---|---|---|
| Maternal characteristics | ||||
| Maternal age | −0.026 | 0.040 | 0.974 (0.900, 1.055) | 0.524 |
| Gestations | −0.555 | 0.302 | 0.574 (0.317, 1.039) | 0.067 |
| Parity | −0.717 | 0.502 | 0.488 (0.182, 1.305) | 0.153 |
| Prepregnancy weight (kg) | 0.020 | 0.014 | 1.020 (0.994, 1.048) | 0.133 |
| Height | 0.042 | 0.037 | 1.043 (0.970, 1.121) | 0.258 |
| BMI | 0.050 | 0.038 | 1.051 (0.975, 1.133) | 0.194 |
| Smoking | −14.279 | 660.925 | 0.000 (0.000, N/A) | 0.983 |
| Gestational age at admission | −0.570 | 0.120 | 0.566 (0.447, 0.716) | <0.0001 |
| Poor response to Anti-HT | −1.021 | 0.516 | 0.360 (0.130, 0.992) | 0.048 |
| Maternal medical conditions | ||||
| Reproductive techniques | −0.293 | 0.600 | 0.746 (0.230, 2.417) | 0.625 |
| Thrombophilia | −13.230 | 694.329 | 0 (0, N/A) | 0.985 |
| Diabetes | −15.411 | 667.290 | 0 (0, N/A) | 0.981 |
| Maternal analysis | ||||
| AST | 0.002 | 0.002 | 1.000 (0.995, 1.005) | 0.794 |
| ALT | 0.000 | 0.002 | 1.000 (0.996, 1.004) | 0.769 |
| LDH | 1.001 | 0.001 | 1.001 (0.998, 1.004) | 0.450 |
| Platelets | 0.000 | 0.000 | 1.000 (1.000, 1.000) | 0.712 |
| Creatinine | 1.975 | 0.809 | 1.975 (0.405, 9.641) | 0.400 |
| s-Flt-1 | 0.000 | 0.000 | 1.000 (1.000, 1.000) | 0.993 |
| PlGF | −0.049 | 0.016 | 0.952 (0.922, 0.983) | 0.003 |
| Ratio s-Flt-1/PlGF | 0.000 | 0.000 | 1.000 (1.000, 1.000) | 0.961 |
| Proteinuria | 0.000 | 0.000 | 1.000 (0.998, 1.001) | 0.881 |
| Diagnosis of HELLP syndrome | 1.1115 | 0.730 | 3.050 (0.729, 12.760) | 0.126 |
| Maternal symptoms | ||||
| Headache | −0.319 | 0.557 | 0.726 (0.243, 2.167) | 0.566 |
| Edema | −0.644 | 0.782 | 0.525 (0.113, 2.433) | 0.410 |
| Photopsia | 0.302 | 0.813 | 1.352 (0.274, 6.661) | 0.711 |
| Epigastric pain | −1.835 | 1.048 | 0.160 (0.020, 1.244) | 0.080 |
| Dyspnea | −13.223 | 850.386 | 0.000 (0.000, N/A) | 0.987 |
| Dizziness | −14.254 | 809.464 | 0.000 (0.000, N/A) | 0.986 |
| Fetal examination | ||||
| EFW centile | −0.020 | 0.020 | 0.980 (0.942, 1.019) | 0.310 |
| UA PI MoM | 0.745 | 0.379 | 2.107 (1.003, 4.425) | 0.049 |
| MCA PI MoM | −2.458 | 1.430 | 0.086 (0.005, 1.440) | 0.088 |
| CPR MoM | −3.232 | 1.174 | 0.039 (0.004, 0.394) | 0.006 |
| Absent or reversed UA flow | 0.600 | 0.543 | 1.820 (0.627, 5.2869) | 0.270 |
| Absent/Reversed DV | 2.079 | 1.034 | 8.000 (1.053, 60.775) | 0.044 |
| Diagnosis of severe IUGR | 0.133 | 0.607 | 1.143 (0.347, 3.762) | 0.826 |
| Fetal sex | −1.491 | 0.557 | 0.225 (0.076, 0.670) | 0.007 |
| Abnormal CTG | 0.205 | 0.565 | 1.228 (0.406, 3.718) | 0.716 |
Notes: BMI: body mass index, SE: standard error, OR: Odds ratio, EFW: estimated fetal weight, UA PI: umbilical artery pulsatility index, CPR: cerebroplacental ratio, MoM: multiples of median, DV: ductus venosus, s-Flt-1: soluble fms-like tyrosine kinase 1, PLGF: placental growth factor, AST: aspartate aminotransferase, ALT: alanine transaminase, LDH: lactate dehydrogenase, HELLP: hemolysis, elevated liver enzymes, and low platelets, IUGR: intrauterine growth restriction, CTG: cardiotocogram.
Table 3.
Multivariable logistic models for the prediction of perinatal death.
| Estimate | SE | OR (95% CI) | p-Value | |
|---|---|---|---|---|
| Model 1 (Parameters with significance or borderline significance in the univariable analysis.) | ||||
| Gestations | −0.60941 | 0.40826 | 0.54367 (0.24424, 1.21021) | 0.13552 |
| GA at admission | −0.54820 | 0.14787 | 0.57799 (0.43256, 0.77231) | 0.00021 |
| Poor response to anti-HTA drugs | 0.37492 | 0.93699 | 1.45488 (0.23186, 9.12872) | 0.68906 |
| PlGF | −0.00944 | 0.01849 | 0.99060 (0.95535, 1.02716) | 0.60961 |
| Epigastric pain | −2.02249 | 1.29514 | 0.13233 (0.01045, 1.67529) | 0.11838 |
| UA PI MoM | −0.22253 | 0.75392 | 0.80049 (0.18265, 3.50836) | 0.76787 |
| MCA PI MoM | −0.09211 | 2.08763 | 0.91201 (0.01524, 54.57851) | 0.96481 |
| Absent or rev DV | 0.89366 | 1.72898 | 2.44405 (0.08248, 72.41841) | 0.60525 |
| Fetal sex | −1.42443 | 0.75276 | 0.24064 (0.05503, 1.05230) | 0.05845 |
| Intercept | 16.01822 | |||
| AIC: 76.5, AUC: 0.95, 95% CI (0.90–0.99), p < 0.0001, DR 78% for a FPR of 5%, DR 78% for a FPR of 10%. | ||||
| Model 2 (Parameters with significance or borderline significance in the multivariable analysis.) | ||||
| GA at admission | −0.55584 | 0.12091 | 0.5735 (0.4525, 0.7269) | p < 0.0001 |
| Fetal sex | −1.37249 | 0.68601 | 0.2534 (0.0660, 0.9724) | 0.04543 |
| Intercept | 14.19035 | |||
| AIC: 67.6, AUC: 0.93, 95% CI (0.88–0.98), p < 0.0001, DR 50% for a FPR of 5%, DR 83% for a FPR of 10%. | ||||
Notes: GA: gestational age, OR: odds ratio, MoM: multiples of the median, UA PI: umbilical artery pulsatility index, CPR: cerebroplacental ratio, MoM: multiples of median, DV: ductus venosus, CI: confidence interval, AIC: Akaike Information Criterion, AUC: area under the curve, DR: discrimination rate, FPR: false positive rate, SE: standard error, CI: confidence interval.
Table 4.
Multivariable logistic models for the prediction of perinatal death in fetuses with the same GA (In this case, GA was excluded).
Table 4.
Multivariable logistic models for the prediction of perinatal death in fetuses with the same GA (In this case, GA was excluded).
| Estimate | SE | OR (95% CI) | p-Value | |
|---|---|---|---|---|
| Model 1 (Parameters with significance or borderline significance in the univariable analysis, excluding GA at admission.) | ||||
| Gestations | −0.48439 | 0.32905 | 0.61607 (0.32325, 1.17416) | 0.14100 |
| Poor response to anti-HTA drugs | −0.67533 | 0.73514 | 0.50899 (0.12049, 2.15017) | 0.35828 |
| PlGF | −0.04605 | 0.01743 | 0.95499 (0.92291, 0.98818) | 0.00824 |
| Epigastric pain | −1.60225 | 1.17818 | 0.20144 (0.02001, 2.02788) | 0.17385 |
| UA PI MoM | −0.03551 | 0.56005 | 0.96512 (0.32200, 2.89272) | 0.94945 |
| MCA PI MoM | 0.15408 | 1.65317 | 1.16658 (0.04568, 29.79332) | 0.92574 |
| Absent or reverse DV | 1.00759 | 1.43756 | 2.73899 (0.16365, 45.84237) | 0.48336 |
| Fetal sex | −1.41739 | 0.62826 | 0.24235 (0.07074, 0.83027) | 0.02407 |
| Intercept | 1.62196 | |||
| AIC: 96.12. AUC: 0.86. 95% CI (0.78–0.95). p < 0.0001. DR 38% for a FPR of 5%. DR 67% for a FPR of 10%. | ||||
| Model 2 (parameters with significance or borderline significance in the multivariable analysis, excluding GA at admission) | ||||
| PlGF | −0.04713 | 0.01618 | 0.95397 (0.92419, 0.98470) | 0.00358 |
| Fetal sex | −1.46315 | 0.58312 | 0.23151 (0.07382, 0.72597) | 0.01210 |
| Intercept | 0.39035 | |||
| AIC: 91.90, AUC: 0.82, 95% CI (0.71–0.93), p < 0.0001, DR 50% for a FPR of 5%, DR 55% for a FPR of 10%. | ||||
Notes: GA: gestational age, OR: odds ratio, MoM: multiples of the median, UA PI: umbilical artery pulsatility index, CPR: cerebroplacental ratio, MoM: multiples of median, DV: ductus venosus, CI: confidence interval, AIC: Akaike Information Criterion, AUC: area under the curve, DR: discrimination rate, FPR: false positive rate, SE: standard error, CI: confidence interval.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
MDPI and ACS Style
Novillo-Del Álamo, B.; Martínez-Varea, A.; Sánchez-Arco, C.; Simarro-Suárez, E.; González-Blanco, I.; Nieto-Tous, M.; Morales-Roselló, J. Prediction of Fetal Death in Preterm Preeclampsia Using Fetal Sex, Placental Growth Factor and Gestational Age. J. Pers. Med. 2024, 14, 1059. https://doi.org/10.3390/jpm14101059
AMA Style
Novillo-Del Álamo B, Martínez-Varea A, Sánchez-Arco C, Simarro-Suárez E, González-Blanco I, Nieto-Tous M, Morales-Roselló J. Prediction of Fetal Death in Preterm Preeclampsia Using Fetal Sex, Placental Growth Factor and Gestational Age. Journal of Personalized Medicine. 2024; 14(10):1059. https://doi.org/10.3390/jpm14101059
Chicago/Turabian StyleNovillo-Del Álamo, Blanca, Alicia Martínez-Varea, Carmen Sánchez-Arco, Elisa Simarro-Suárez, Iker González-Blanco, Mar Nieto-Tous, and José Morales-Roselló. 2024. "Prediction of Fetal Death in Preterm Preeclampsia Using Fetal Sex, Placental Growth Factor and Gestational Age" Journal of Personalized Medicine 14, no. 10: 1059. https://doi.org/10.3390/jpm14101059
APA StyleNovillo-Del Álamo, B., Martínez-Varea, A., Sánchez-Arco, C., Simarro-Suárez, E., González-Blanco, I., Nieto-Tous, M., & Morales-Roselló, J. (2024). Prediction of Fetal Death in Preterm Preeclampsia Using Fetal Sex, Placental Growth Factor and Gestational Age. Journal of Personalized Medicine, 14(10), 1059. https://doi.org/10.3390/jpm14101059
Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.
