A nomogram for predicting adverse perinatal outcome in patients with fetal growth restriction: A prospective observational study

doi:10.21203/rs.3.rs-5131955/v1

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Research Article

A nomogram for predicting adverse perinatal outcome in patients with fetal growth restriction: A prospective observational study

https://doi.org/10.21203/rs.3.rs-5131955/v1

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Background

Fetal growth restriction (FGR) is a major determinant of perinatal morbidity and mortality. Our study aimed to develop a prediction model for the risk of FGR developing adverse perinatal outcome (APO) combining ultrasonic and maternal serum biochemical indicators.

Methods

A total of 122 patients diagnosed with FGR were recruited for our prospective observational cohort study, who were randomly divided into a training and validation cohort at a ratio of 1:1. The primary composite APO comprised one or more of: birth weight below the 3rd percentile, perinatal death, impaired consciousness, asphyxia, seizures, assisted ventilation, septicemia, meningitis, bronchopulmonary dysplasia, intraventricular hemorrhage, cystic periventricular leukomalacia, and necrotizing enterocolitis. The stepwise regression based on the Akaike information criterion minimum was used to select variables for inclusion in the nomogram model. The discrimination and calibration of the nomogram were evaluated using receiver operating characteristic curve (AUC) and calibration plots. The net benefits of the nomogram at different threshold probabilities were quantified via decision curve analysis (DCA). Kaplan-Meier survival curves were constructed to analyze the primary outcome for risk stratifications.

Results

Three variables of abnormal umbilical artery (UA) Doppler, abnormal middle cerebral artery (MCA) Doppler, and log10(sFlt-1/PlGF) were selected to establish a nomogram. The C-index value of 0.79 and 0.75 in the training and validation cohort respectively, indicated satisfactory discriminative ability of the nomogram. The calibration plots showed favorable consistency between the nomogram’s predictions and actual observations in both cohorts. DCA manifested that the nomogram was clinically applicable, for it produced a better discriminative ability to identify those who carry a potentially high risk of APO.

Conclusions

A prognostic nomogram was developed and validated to possess the promising capacity of assisting clinicians in evaluating the prognosis of FGR patients.

fetal growth restriction

prognostic model

nomogram

umbilical artery Doppler

middle cerebral artery Doppler

placental growth factor

Fetal growth restriction (FGR), defined as the fetus failing to reach its genetically predetermined growth potential[1], affects 20% of pregnancies worldwide[2]. FGR is well known to increase the risk of perinatal mortality and lifelong morbidity for survivors especially neurodevelopment[3–5]. Traditionally, the diagnostic methods of identifying FGR are serial symphysis-fundal height (SFH) and sonographic estimated fetal weight (EFW) measurements. Of note is the evidence that neither the use of SFH nor the EFW perform well in isolation for the risk prediction of adverse perinatal outcomes (APO)[6, 7], as both approaches are merely a snapshot of fetal size, incapable of distinguishing the fetus who is healthy small-for-gestational-age (SGA) from the one who has FGR due to inadequate placental function. This ambiguity leads to repeated hospitalizations for antenatal monitoring, increased maternal anxiety and health resource waste. For obstetricians, therefore, the current challenge is how to accurately predict the risk of APO in FGR-afflicted newborns, as the risk can be significantly reduced via close monitoring and appropriate timing of delivery if the condition is identified prenatally[8].

The most common underlying pathophysiological mechanism of FGR is abnormal trophoblastic invasion and consequent placental insufficiency[9]. Doppler velocimetry through evaluation of the uterine and umbilical arteries has been reported to reflect uterine placental insufficiency and/or fetal cardiovascular adaptation to hypoxemia[10–12], while multiple ultrasounds represent more frequent prenatal visits and a healthcare burden. Soluble fms-like tyrosine kinase-1 (sFlt-1) and placental growth factor (PlGF) are angiogenic-related biomarkers of placental dysfunction[13], and positively correlated with both FGR severity and disease progression[14, 15]. While these markers alone cannot differentiate fully between different clinical presentations of placental dysfunction, such as preeclampsia and FGR. There are several published prediction models for FGR based on combinations of maternal characteristics, biomarkers and ultrasound features. While the models have moderate value in the prediction of delivery of SGA neonates instead of adverse outcomes[16, 17]. Thus, these models are not sufficient to be used as the sole method of determining which women should have frequent surveillance. There is a dearth of literature on the approach to the assistance with APO prediction. Therefore, it is imperative that better tools be developed to predict the prognosis of FGR.

Nomograms, which are widely used for cancer prognosis, have the capacity to generate an individual probability by integrating diverse determinant variables. Therefore, nomograms can meet our desire for a clinically integrated model which facilitates our construction of personalized medicine. In this prospective observational cohort study, we aimed to develop and validate a nomogram by incorporating ultrasonic and maternal serum biochemical indicators for risk prediction of ultrasonically diagnosed FGR to develop APO.

2.1 Study design and participants

We conducted a prospective observational cohort study from January 2022 to June 2023 at Obstetrics and Gynecology Hospital of Fudan University, the protocol of which was granted approval by the Ethics Committee. Each subject was required to sign informed consent prior to participating in the study.

The inclusion criteria were set as follows: women aged 18 or older, single pregnancies presented with FGR. The exclusion criteria were referred to as fetal chromosomal abnormalities, congenital defects, multiple pregnancies and incomplete delivery information. FGR was defined as an ultrasonographic EFW or abdominal circumference (AC) below the 10th percentile of its gestational age according to the 2021 ACOG (The American College of Obstetricians and Gynaecologists) guidelines. EFW was calculated based on the Hadlock formula I with biparietal diameter, head circumference, abdominal circumference, and femur length and adjusted according to gestational age against NICHD-Asian growth standards. Gestational age was determined by fetal crown-rump length measurement at 11^+ 0-14^+ 0 weeks of gestation[18]. The individual information was obtained through Hospital Electronic Case System including maternal age, gravidity and parity history, BMI at the first visit, aspirin intake, smoking, chronic disease conditions, biochemical index, gestational weeks and mode of delivery.

The participants were divided into the training and validation cohorts with a ratio of 1:1 using the R function “createDataPartition” to ensure that the outcomes were randomly distributed between the two cohorts. The training cohort was used to screen variables for model construction, and the validation cohort to validate the results from the training cohort.

2.2 Ultrasound

The women diagnosed with FGR by two independent and experienced sonographers were recruited in our cohort. Meanwhile, fetoplacental circulation was assessed by Doppler ultrasound consisting of the measurement of uterine artery (UtA) pulsatility index (PI), umbilical artery (UA) PI, middle cerebral artery (MCA) PI and ductus venosus (DV) PI and diastolic flow (a-wave present, absent, or reversed). The proximal uterine arteries were located in a standard manner using color flow imaging at the crossover point with the external iliac artery; a pulsed Doppler gate was placed just above this crossover point to obtain the PI. The left and right UtA-PI were measured to calculate the mean UtA-PI. MCA Doppler measurements were performed by the sphenoid wings, close to the internal carotid artery origin. UA waveforms were obtained in free loops of cord by pulsed Doppler to derive PI values and record absent end-diastolic velocity (AEDV) or reversed end-diastolic velocity (REDV).

2.3 Sample collection

Blood samples of 5 mL were collected by skilled nurses into vacuum polypropylene tubes from the cubital vein. The serum samples were immediately separated by centrifugation at 1400 rpm for 10 min at room temperature, coded and stored at -80℃ for further analysis. Maternal serum concentrations of PlGF and sFlt-1 in pg/mL were analyzed at the time of diagnosis by electrochemiluminescence with sFlt-1 and PlGF kits of iMAGIN 1800 (Aucheer, Ningbo, China) to be applied to the calculation of the sFlt-1/PlGF ratio.

2.4 Study outcomes

The primary outcome was a composite of APO up to seven days after birth, defined as one or more of the following[19]:

1. Birth weight below the 3rd percentile according to gestational age and gender based on National Health Standard of the People’s Republic of China (WS/T 80-2022)[20];

2. Perinatal death occurring from 28 weeks of gestation until one week after birth;

3. Impaired consciousness such as coma, stupor, or decreased response to pain;

4. Asphyxia, defined as cord blood arterial base excess of < -12 mmol;

5. Seizures on two or more occasions within 72 hours after birth;

6. Assisted ventilation > 24 hours via endotracheal tube initiated within 72 hours of birth;

7. Septicemia ascertained by blood culture;

8. Meningitis ascertained by cerebrospinal fluid culture;

9. Bronchopulmonary dysplasia, requiring oxygen at 36 weeks postmenstrual age in a preterm neonate with radiographic evidence;

10. Intraventricular hemorrhage of any grade, diagnosed by ultrasound or autopsy;

11. Cystic periventricular leukomalacia ascertained by ultrasonography;

12. Necrotizing enterocolitis diagnosed by radiography, surgery, or autopsy.

2.5 Statistical Analysis

The continuous data were expressed as the means ± standard deviation (SD); the categorical data as n (%); and the non-normal variables as the medians (25th and 75th ). P value for the differences was calculated using unpaired Student’s t-test for the continuous variables with normal distribution and Mann-Whitney U test for the continuous variables with non-normal distribution between the two groups. The Chi-square test was used for the categorical variables. Furthermore, we used restricted cubic splines with 3 knots at the 10th, 50th and 90th percentiles of the log10-transformed sFlt-1/PlGF ratio, with the reference value (RR = 1) set at the 50th percentile. Multivariate logistic analysis were performed to identify clinically relevant variables[18] associated with APO in the whole training cohort, and variables with P < 0.05 were identified as independent risk factors. The stepwise regression based on the Akaike information criterion minimum was used to select variables for inclusion in the nomogram, which was to estimate the APO probabilities.

The area under the receiver operating characteristic (ROC) curve (AUC) calculated by bootstrapping were used to evaluate discriminative ability. AUC values greater than 0.7 suggest a reasonable estimation. Calibration plots were used to evaluate calibrating ability. Decision curve analysis (DCA), a method for evaluating the clinical benefit of alternative model[21], was applied to nomograms by quantifying net benefits at different threshold probabilities. The curves of the treat-all-patients scheme (representing the highest clinical costs) and the treat-none scheme (representing no clinical benefit) were plotted as two references. Kaplan-Meier survival curves were constructed for the analysis of primary outcome for risk stratifications using median of total points as cut-off value.

Statistical analyses were performed with RStudio (version 4.2.0) and SPSS Statistics for Windows, Version 20.0 (IBM Corp, NY, USA). P < 0.05 was considered statistically significant.

3.1 General characteristics

A total of 128 women with FGR were enrolled. After exclusion, the analysis included 122 eligible participants who could be evaluate. The incidence of APO according to the protocol-defined criteria was 31.2% (19/61) in the training cohort and 27.9% (17/61) in the validation cohort (Fig. 1). The baseline characteristics of the study population are summarized in Table 1. Age, BMI at the first visit, aspirin intake, primiparous condition, smoking status and gestational age at diagnosis did not differ significantly between those who developed APO and those who did not. In the APO group, the median sFlt-1/PlGF ratio was elevated and the proportion of UA Doppler were higher.

Table 1

Baseline characteristics of the study population
Characteristic	Training Cohort		Validation Cohort
	With APO (n = 19)	No APO (n = 42)	With APO (n = 17)	No APO (n = 44)
Demographic characteristics
Age (yrs), mean ± SD	30.68 ± 3.59	29.95 ± 3.61	31.82 ± 4.87	30.64 ± 4.04
BMI at the first visit (kg/m²), mean ± SD	22.65 ± 4.41	21.17 ± 3.03	22.92 ± 3.25	21.41 ± 2.92
Overweight or obesity, n (%)	2 (10.53%)	3 (7.14%)	4 (23.53%)	8 (18.18%)
Primiparous, n (%)	16 (84.21%)	38 (90.48%)	13 (76.47%)	38 (86.36%)
Miscarriage, median (IQR)	0 (0–1)	0 (0–1)	0 (0–1)	0 (0–1)
Aspirin intake, n (%)	5 (26.32%)	13 (30.95%)	6 (35.29%)	9 (20.45%)
Smoking, n (%)	0	0	0	0
Gestational age at diagnosis (weeks), mean ± SD	30.98 ± 5.14	32.04 ± 4.59	31.31 ± 5.11	32.46 ± 5.01
Clinical characteristics
Chronic hypertension, n (%)	1 (5.26%)	2 (4.76%)	0	2 (4.55%)
Pregestational diabetes mellitus, n (%)	1 (5.26%)	0	0	1 (2.27%)
Chronic kidney disease, n (%)	1 (5.26%)	0	0	0
Immunological disease, n (%)	0	0	0	1 (2.27%)
Prior FGR, n (%)	1 (5.26%)	0	0	1 (2.27%)
Prior preeclampsia, n (%)	1 (5.26%)	1 (2.38%)	0	0
Biochemical index
sFlt-1 (pg/mL), median (IQR)	5635 (2663–8989)	2009** (1259–3589)	3849 (1645–9277)	1750 (1377–4225)
PlGF (pg/mL), median (IQR)	54.43 (23.63-131.45)	146.90* (88.18–263.70)	70.45 (26.59-140.18)	233.80** (109.80-524.40)
sFlt-1/PlGF, median (IQR)	59.36 (20.18-351.42)	14.22** (4.50-45.57)	68.03 (15.18-277.59)	10.46* (2.65–26.44)
Maximal SBP (mmHg), mean ± SD	127.16 ± 10.93	119.60 ± 15.17	124.59 ± 18.80	120.72 ± 13.93
Maximal DBP (mmHg), mean ± SD	82.26 ± 7.27	78.33 ± 8.69	85.12 ± 9.75	80.07 ± 11.07
Proteinuria (g/24 h), median (IQR)	1.08 (0.39–3.29)	0.41 (0.30–0.62)*	0.37 (0.23–2.22)	0.29 (0.16–0.48)
Serum creatinine (umol/L), mean ± SD	48.78 ± 9.10	44.57 ± 6.92	47.17 ± 10.50	46.75 ± 7.14
Serum uric acid (umol/L), mean ± SD	343.89 ± 96.12	268.64 ± 79.68**	316.18 ± 103.66	292.43 ± 67.10
Ultrasonic characteristics
Abnormal UA Doppler, n (%)^†	6 (31.58%)	2 (4.76%)**	6 (35.29%)	5 (11.36%)*
Abnormal MCA Doppler, n (%)^‡	5 (26.32%)	2 (4.76%)*	1 (5.88%)	2 (4.55%)
Abnormal UtA Doppler, n (%)^§	8 (42.11%)	6 (14.29%)*	7 (41.18%)	11 (25.00%)
Abnormal DV Doppler, n (%)^¶	0	0	0	0
Delivery characteristics
Gestational weeks at delivery, mean ± SD	34.83 ± 4.98	38.50 ± 1.37**	36.41 ± 2.84	38.23 ± 1.70*
Delivery at < 34 weeks, n (%)	5 (26.32%)	0**	4 (23.53%)	1 (2.27%)**
Delivery at < 37 weeks, n (%)	7 (36.84%)	5 (11.90%)*	6 (35.29%)	5 (11.36%)*
Neonatal weight (g), mean ± SD	1801 ± 526	2712 ± 333**	2006 ± 437	2679 ± 389**
Mode of delivery
Vaginal delivery, n (%)	7 (36.84%)	21 (50.00%)	5 (29.41%)	20 (45.45%)
Caesarean section, n (%)	12 (63.16%)	21(50.00%)	12 (70.59%)	24 (54.55%)
Caesarean for non-reassuring CTG, n (%)	4 (21.05%)	3 (7.14%)	5 (29.41%)	5 (11.36%)
* P<0.05 for the comparison with participants in whom APO did not develop.
**P<0.01 for the comparison with participants in whom APO did not develop.
† Including umbilical artery PI>95th percentile, AEDV, REDV
‡ Including middle cerebral artery PI<5th percentile, or cerebro-placental ratio<5th percentile
§ Including uterine artery PI>95th percentile
¶ Including absent or reversed a waves of ductus venosus
APO: adverse perinatal outcome; AEDV: absent end-diastolic velocity; CTG: cardiotocography; DV: ductus venosus; MCA: middle cerebral artery; PI: pulsatility index; PlGF: placental growth factor; REDV: reversed end-diastolic velocity; sFlt-1: soluble fms-like tyrosine kinase-1; UA: umbilical artery; UtA: uterine artery.

3.2 Nomogram construction and validation

We used the restricted cubic splines to show the significant log10-linear relations between sFlt-1/PlGF ratio and APO risk (Supplementary Fig. 1). From the univariable and multivariate logistic regression analysis, log10(sFlt-1/PlGF), abnormal UA Doppler, and abnormal MCA Doppler were identified as independent prognostic risk factors for developing APO (Table 2). We constructed a nomogram for APO according to the variables screened. Figure 2 shows an example of using the nomogram to predict APO risk of a given patient. The total score was determined according to the individual scores calculated using the nomogram.

Table 2

Univariate and multivariate logistics regression analyses on variables for the prediction of adverse prenatal outcome
Variable	Univariable		Multivariable
Variable	OR (95% CI)	P	OR (95% CI)	P
Log10 (sFlt-1/PlGF)	4.32 (1.585–11.787)	0.004	4.846 (1.696–13.845)	0.003
PlGF (pg/mL)	0.999 (0.997–1.002)	0.516	1.002 (0.999–1.006)	0.191
Abnormal UA Doppler	9.231 (1.656–51.461)	0.011	8.538 (1.444–50.492)	0.018
Abnormal MCA Doppler	7.143 (1.242–41.068)	0.028	6.471 (1.024–40.899)	0.047
Abnormal UtA Doppler	4.364 (1.243–15.315)	0.021	1.566 (0.297–8.266)	0.597
MCA: middle cerebral artery; PlGF: placental growth factor; sFlt-1: soluble fms-like tyrosine kinase-1; UA: umbilical artery; UtA: uterine artery.

In the training cohort, the area under the ROC curve (AUC) was 0.79 (95% CI 0.65–0.93) in screening by abnormal UA Doppler, abnormal MCA Doppler and log10(sFlt-1/PlGF), 0.70 (95% CI 0.57–0.82) by abnormal UA and MCA Doppler, and 0.63 (95% CI 0.52–0.75) by abnormal UA Doppler. The AUC values were 0.75, 0.63 and 0.62 respectively in the validation dataset. The AUC was > 0.7 for the prediction of APO risk in both the training and validation cohorts (Fig. 3A-B), indicating favorable discrimination by the nomogram. Additionally, its calibration curves showed high consistencies between the predicted and observed risks in both the training and validation cohorts (Fig. 3C-D). In summary, our FGR nomogram had considerable discriminative and calibrating abilities. The clinical benefits of the nomogram were compared with those of abnormal UA and MCA Doppler, and abnormal UA Doppler alone. DCA curves showed that the nomogram could better predict the APO risk, as it added more net benefits for almost all threshold probabilities in both the training and validation cohorts, and with both the treat-all-patients scheme and the treat-none scheme (Fig. 3E-F).

3.3 Risk stratification based on the nomogram

We finally made a risk stratification based on the total points calculated using the nomogram. Patients with FGR were divided into two risk groups: low risk of total points ≤ 5.01 and high risk of total points > 5.01, between which the Kaplan-Meier curves showed significant discrimination (Fig. 4).

In this prospective observational cohort study, we pioneered the use of a nomogram to predict the prognosis of patients with FGR. The nomogram, based on abnormal UA Doppler, abnormal MCA Doppler and log10(sFlt-1/PlGF), was verified to have a discriminatory ability (AUC) of 0.79 (95% CI 0.65–0.93) in predicting APO development. Furthermore, the nomogram was validated to have good capability of calibration and clinical application. The results showed that combining ultrasonic and maternal serum biochemical indicators could effectively predict the APO development in pregnancies complicated by FGR, facilitating clinicians to ensure personalized and precise counseling, efficient allocation of resources and optimization of clinical management.

Earlier studies of FGR, retrospective and prospective, have also attempted to look for predictive indexes of APO. There was only one retrospective study which reported multiple factors involving high-risk maternal factors and ultrasound indicators[22]. By comparison, our study was characterized by the prospective design and dataset of perinatal registry data and hospital records. Thanks to this, the information of predictors was acquired at the time of FGR diagnosis, which in our cohort was on average three weeks prior to delivery.

In general, a single factor such as biometric measurement or growth trajectory alone, is not adequately identify the risk of adverse perinatal outcomes. Previous studies suggested that cerebroplacental (CPR) and umbilicocerebral (UCR) ratios on their own are poor prognostic predictors of APO (AUC 0.44 and 0.56, respectively)[23, 24]. Using a slow growth trajectory as stand-alone criterion for FGR was reported not to be associated with a higher risk of APO in a low-risk population[25]. The large Pregnancy Outcome Prediction (POP) study found that EFW < 10th percentile was associated with the risk of neonatal morbidity only when the AC growth velocity was in the lowest decile[26]. In this context, the sFlt-1/PlGF ratio could be used to complement ultrasound examinations. As reported by a small size study, a sFlt-1/PlGF ratio ≥ 86.2 resulted in maximum detection of pregnancies at risk of APO in a cohort of 34 women with FGR diagnosed at < 34 weeks[27]. In the present study, the analysis of only FGR cases provided an opportunity to reconsider the factors that could be integrated into the prognostic nomogram. The nomogram integrates multiple factors, including ultrasonic and serum markers, into a quantitative model and has been shown to perform better (AUC 0.79 and 0.75 in the training and validation cohorts respectively) than some conventional indexes, such as abnormal UA and MCA Doppler.

Previous studies have applied screening models of multiple factors to the general population and demonstrated the ability to identify small-for-gestational-age (SGA) neonates, without much success at delineating those at risk of APO[16, 17, 28]. For example, a competing-risks model, based on maternal factors and biophysical (UtA-PI) and biochemical (PlGF) markers at 11–13 weeks’ gestation, was reported to be effective for prediction of FGR (defined as birth weight < 3rd percentile) in women with preeclampsia[28]. For obstetricians, it is more important to identify the constitutionally small fetus from one who is pathologically growth restricted and at risk for postnatal complications. In our study, the main outcome of APO was the same as the previously reported in the large prospective study[19]. Therefore, it is necessary to provide individualized consultation and surveillance for pregnant women with a nomogram total score > 5.01, as their fetuses may run a higher risk of developing APO in the future.

Our study has important clinical implications as it has demonstrated promising potential value in predicting FGR patients’ prognosis. The nomogram has shown good performance for detecting FGR pregnancies at a higher risk of APO, which require more frequent scans, but also cases at a lower risk that could benefit from a less strict surveillance protocol. As suggested by our results, FGR patients with total points > 5.01 should be closely monitored and appropriate timing of delivery. In pregnancies with total points < 5.01, on the contrary, scan intervals could be extended so that the number of fetal ultrasounds could be reduced to lower parental anxiety and lessen the burden on the healthcare system. Due to our observational study is preliminary, further randomized clinical trials are required to establish a more efficient predictive model for guiding FGR management.

We acknowledge some limitations of this study. First, our study is based on an observational cohort, so the Doppler measurements and biomarker testing were performed as part of routine clinical care rather than as blinded randomized controlled trials (RCTs), which could increase the potential for bias. Second, the small number of cases could introduce some uncertainty in the screening factors, which was illustrated by 95% CI, thus being likely to limit the external validity of our findings. Larger cohorts or RCTs are expected in the future. Finally, some perinatal outcomes had a low incidence, such as bronchopulmonary dysplasia and cystic periventricular leukomalacia, and only 27 cases were severe FGR (ultrasonographic EFW or AC below the 3rd percentile), so the results should be interpreted with caution.

In conclusion, the newly developed nomogram is simple to use and capable of predicting the prognosis in patients with FGR. The combination of ultrasonic and maternal serum biochemical indicators can be applied to identifying FGR with a high probability of APO development, which facilitates patients to benefit from early triage and more intensive monitoring. More well-designed randomized clinical trials are required.

AC	abdominal circumference
AEDV	absent end-diastolic velocity
APO	adverse perinatal outcome
AUC	receiver operating characteristic curve
CPR	cerebroplacental ratio
DCA	decision curve analysis
DV	ductus venosus
EFW	estimated fetal weight
FGR	fetal growth restriction
MCA	middle cerebral artery
PI	pulsatility index
PlGF	placental growth factor
REDV	reversed end-diastolic velocity
SFH	symphysis-fundal height
SGA	small-for-gestational-age
sFlt-1	soluble fms-like tyrosine kinase-1
UA	umbilical artery
UtA	uterine artery
UCR	umbilicocerebral ratio

Ethics approval and consent to participate

The Ethics Committee of the Gynecology and Obstetrics Hospital of Fudan University reviewed and approved all the study procedures (approval number 2022-18). All participants were fully informed of the content and purpose of the study and provided written informed consent.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Availability of data and materials

All data generated or analyzed during this study are included in this published article and its supplementary information files.

Funding

This work was supported by the National Science Fund of Shanghai, China (No. 22ZR1409000), and Medical Innovation Research Program of Shanghai, China (No. 21Y11908000).

Authors’ contributions

YZ designed the study, performed all the data analysis, and drafted the original manuscript; LX collected the clinical data and samples, and carried out the experiments; PA and JZZ collected the clinical data and samples; JZ provided statistical methodology support; SPL and QJZ supervised the study, and interpreted the results; XTL and YX conceived the study, directed the experiments, interpreted the results, and revised the manuscript. All authors read and approved the final manuscript.

Acknowledgements

We thank all the authors and study participants for their contributions. We thank Prof. Liang Zhengliu (Foreign Languages Department, Shanghai Medical College Fudan University) for polishing the manuscript.

Authors’ information

¹ Obstetrics and Gynecology Hospital, Fudan University, Shanghai, China. ² Chang Ning Maternity & Infant Health Hospital, Shanghai, China. ³ Shenzhen Maternity & Child Healthcare Hospital, Southern Medical University, Shenzhen, China.

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No competing interests reported.

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A nomogram for predicting adverse perinatal outcome in patients with fetal growth restriction: A prospective observational study

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Version 1

Abstract

Background

Methods

Results

Conclusions

Figures

Background

Methods

2.1 Study design and participants

2.2 Ultrasound

2.3 Sample collection

2.4 Study outcomes

2.5 Statistical Analysis

Results

3.1 General characteristics

3.2 Nomogram construction and validation

3.3 Risk stratification based on the nomogram

Discussion

Abbreviations

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1