RESEARCH ARTICLE |
https://doi.org/10.5005/jp-journals-10016-1343 |
Role of Cerebro-placental-uterine Ratio in Prediction of Late-onset Fetal Growth Restriction: A Prospective Observational Study
1-3Department of Fetal Medicine, Fernandez Hospital, Hyderabad, Telangana, India
4Division of Evidence Synthesis, coGuide Academy, Bengaluru, Karnataka, India
Corresponding Author: Geya Gottipati, Department of Fetal Medicine, Fernandez Hospital, Hyderabad, Telangana, India, Phone: +91 9845509774, e-mail: geya.gottipati@gmail.com
Received on: 15 January 2024; Revised on: 05 April 2024; Published on: 28 October 2024
ABSTRACT
Background: Late-onset fetal growth restriction (FGR) is the failure to achieve growth potential by the fetus and is diagnosed after 32 weeks of gestation. Majorly, it is associated with stillbirths. Hence, fetuses at risk need to be identified.
Objectives: To evaluate association of cerebro-placental-uterine ratio (CPUR) in predicting late-onset FGR.
Materials and methods: A prospective study was carried out including low-risk nulliparous singleton women after a 32-week period of gestation. Analysis of pulsatility indices of umbilical artery (UA), uterine arteries (UtA), middle cerebral artery (MCA), and estimated fetal weight (EFW) was conducted. The cerebroplacental ratio (CPR) and CPUR were calculated. After adjusting for gestational age, EFW and Doppler indices were converted into centiles and multiples of the median (MoM), respectively. Receiver operating characteristic (ROC) analysis was used to assess predictive value of all Doppler parameters for birth weight <10th, <5th, and <3rd centile as per Gestation Related Optimal Weight (GROW) chart. At ~90% specificity, sensitivity, positive and negative predictive value, and odds ratios (OR) were presented.
Results: A: total of 587 participants were included, 169 had birth weight <10th centile and 418 had ≥10th centile. For birth weight <5th and <3rd centiles, the area under the curve (AUC) of CPUR was 0.677 and 0.734, respectively. Low CPUR MoM was associated with birth weight <10th, <5th, and <3rd centile with OR of 3.1, 3.2, and 4.08, respectively, p < 0.001. Higher association with meconium-stained liquor and neonatal intensive care unit (NICU) admission, p < 0.001 was found with low CPUR MoM.
Conclusion: A strong association of uteroplacental insufficiency with CPUR, a novel Doppler ratio, was observed and has higher AUC and OR for identifying low-birth-weight compared to existing Doppler parameters.
How to cite this article: Gottipati G, Singh A, Sattoju N, et al. Role of Cerebro-placental-uterine Ratio in Prediction of Late-onset Fetal Growth Restriction: A Prospective Observational Study. Int J Infertil Fetal Med 2024;15(2):93-100.
Source of support: Nil
Conflict of interest: None
Keywords: Cerebroplacental ratio, Cerebro-placental-uterine ratio, Doppler parameters, Late-onset fetal growth restriction, Stillbirth.
INTRODUCTION
Late-onset fetal growth restriction (FGR) is often defined as the failure of the fetus to achieve growth potential as diagnosed after 32 weeks of gestational age.1 FGR affects approximately 3–7% of all pregnancies which is seen sixfolds higher in underdeveloped countries than developed countries.2 FGR prevalence in India is about 26% with late-onset FGR contributing for about 70–80%.3 About 75% of FGR are missed for identification at early stages of gestation since the measurement of symphysio-fundal height becomes challenging.4 Late-onset FGR results in small for gestational age (SGA) babies, fetal distress, need for emergency cesarean section, neonatal acidosis, and need for NICU admission.5 Late-onset FGR is predominantly due to uteroplacental insufficiency and associated with about 50% of stillbirths, especially after 34 weeks, contributing to 30% of all stillbirths.6,7 Hence, in order to reduce the stillbirth rate, it is essential to identify the fetuses at risk of late-onset FGR.8 Late-onset FGR is mainly due to uteroplacental insufficiency secondary to abnormal trophoblastic invasion, leading to altered blood flow resistance in the uterine, placental, and fetal vasculature, which can be detected using various Doppler parameters. For example, reduced middle cerebral artery (MCA) pulsatility index (PI) reflects vasodilatation of the fetal cerebral vessels. This is seen due to fetal adaptations by redistributing blood flow to essential organs when umbilical artery (UA) and maternal uterine arteries (UtAs) demonstrate increased resistance.9 On comparison to UA-PI, cerebral vasodilatation is a more sensitive indicator of placental insufficiency.
In case of late-onset FGR, due to long-term hypoxic conditions, vascular resistance decreases with increase in brain perfusion, known as “brain sparing effect.” With the fact that FGR impacts different regions, ratios of these would help in improving the diagnosis. Cerebroplacental ratio (CPR) was identified as an ultrasound marker in identifying changes in placental blood flow more accurately than MCA-PI and UA-PI alone. However, varied results from different studies made controversy on its clinical utility.10,11 Another ratio, uteroplacental ratio (UPR), inversion of CPR, was reported to be of greater sensitive in predicting the adverse outcomes.10 Standard diagnosis of FGR is not available, but smallness of the fetus (SGA) is often considered the key factor in identification.4 Depending on the risk of SGA fetuses, FGR risk is estimated. Assessing fetal size only may not be able to detect fetuses that are with placental insufficiency since it can be seen in fetuses that are not small. Relying only on the SGA parameters leads to missed diagnosis of FGR. Hence, combining the clinical and ultrasound parameters in detecting FGR is recommended. Several combinations of ultrasound indices as described earlier are known so far and are in clinical utility. However, the search for more reliable and sensitive novel marker in early identification of late-onset FGR is still ongoing. Studies comparing the various ultrasound markers in detection of late-onset FGR are sparse. In order to overcome the current lag and pitfalls in detecting late-onset FGR, we need to come up with newer modalities with a better prediction. The current study is designed to determine the utility of Doppler parameter, cerebro-placental-uterine ratio (CPUR), in predicting the late-onset FGR.
The current prospective observational study was carried out to determine the predictive validity of CPUR and compare it with other Doppler parameters in detecting late-onset FGR.
OBJECTIVES
Primary Objective
To determine the predictive validity of CPUR in detecting late-onset FGR.
Secondary Objective
To compare the predictive validity of CPUR with other Doppler parameters in detecting late-onset FGR.
MATERIALS AND METHODS
A prospective observational study was carried out at Fernandez Hospital, Hyderabad, Telangana, India from August 2020 to January 2022 after approval from the Institutional Ethics Committee (IEC Ref No. 06_2020). Nulliparous women with singleton pregnancies who crossed 32 weeks of gestation were included. Multiparous women with the first scan before 32 weeks showing estimated fetal weight (EFW) <10th percentile, detected fetal infections, and anomalies were excluded.
The sample size was calculated assuming the expected proportion/prevalence of low-birth-weight, that is, <10th centile as 11.2%, expected sensitivity and specificity of Doppler parameters as 20.5 and 90%, respectively as per study by MacDonald et al.12 The other parameters considered for sample size calculation were 95% confidence level and a 10% precision for sensitivity, and a 5% precision for specificity, respectively. The final calculated sample size was found to be 559. To account for nonparticipation rate of about 5%, another 28 subjects were added to the sample size making a total of 587 subjects.
All the women who presented to the fetal medicine unit at our hospital and met our inclusion and exclusion criteria were recruited after explaining about the study objective and answering all their queries. An informed consent was obtained before including the women into the study. After 32 weeks, ultrasound examination was performed to obtain fetal biometry and Doppler by experienced operators with same level of competence in order to maintain uniformity in attaining values. All examinations were performed using 2–7 MHz linear curved-array transducer by GE Voluson P6-8 and E6,8-10 ultrasound machines. If more than one Doppler assessment was done, last examination prior to delivery was considered. Mean values of UtA-PI and UA-PI were measured by using transabdominal color and pulsed-wave Doppler. During periods of fetal inactivity and apnea, at 1–2 cm from the circle of Willis with an angle of insonation close to zero, MCA-PI was taken. Away from the cord insertion site in a free loop UA-PI was measured. In order to obtain maternal mean UtA-PI, probe was placed in both iliac fossae and the waveform was recorded within 1 cm of the point where UtA crosses the external iliac artery. The mean was calculated after measuring all PI values in triplicate, and for the paired structures like maternal UtAs, overall mean was calculated by obtaining PI values in both the right and the left vessels, and the average was calculated. Mean MCA-PI divided by mean UA-PI was used to calculate CPR. CPUR was calculated as CPR/UtA-PI. Umbilical–cerebral ratio (UCR) was calculated as UA-PI/MCA-PI. If normally distributed, the gestational age-specific centile for each of the PI values was determined, and if not normally distributed, multiples of the median (MoM) was determined. Postnatally, birth weights were plotted on customized Gestation Related Optimal Weight (GROW) chart. Primary outcome variables that were studied include birth weight <10th, <5th, and <3rd centile plotted on GROW chart and secondary outcome variables that were studied include number of NICU admission, stillbirths, and meconium-stained liquor. All the variables were collected as per structured predesigned data collection sheet, entered into excel and analyzed as per statistical plan.
Between those delivering a newborn with birth weight <10th centile and a newborn with birth weight ≥10th centile, maternal characteristics and birth outcomes were compared. Descriptive analysis for quantitative variables was carried out by mean and standard deviation, and for categorical variables, frequency and proportion were measured. All quantitative variables by using visual inspection of histograms and normality Q–Q plots were checked for normal distribution within each category. To assess normal distribution Shapiro–Wilk test was also conducted and as normal distribution p-value of >0.05 was considered. Independent sample t-test (two groups) was used to compare the mean values between study groups for normally distributed quantitative parameters. Mann–Whitney U test (two groups) was used to compare medians and interquartile range (IQR) between study groups for nonnormally distributed quantitative parameters. Chi-squared test was used to compare categorical outcomes between study groups. For detection of birth weight <10th centile, cutoffs of Doppler parameters that corresponded most closely to 90% specificity were determined. To facilitate comparison of the detection rates of various Doppler parameters, cutoff values for a standard specificity were used. Once the cutoff values were determined, odds ratios (OR) were calculated along with 95% confidence interval (CI) to determine the association of abnormal values of each of the Doppler parameters with birth weight <10th, <5th and <3rd centiles. For late-onset FGR for each of the predictors, receiver operating characteristic (ROC) curve was derived. Predictive values, diagnostic accuracy, sensitivity, and specificity of the screening test with the decided cutoff values along with their 95% CI were determined. Spearman correlation coefficient was used to assess the association between quantitative explanatory and outcome variables. p-value < 0.05 was considered statistically significant. Statistical analysis was done by IBM Statistical Package for the Social Sciences (SPSS) version 22.13 Each of the proposed parameters were compared by logistic regression analysis.
RESULTS
A total of 587 participants had ultrasound and Doppler examination after 32 weeks period of gestation. Out of which, 169 participants had birth weight <10th centile and 418 had ≥10th centile on GROW curve. All the maternal characteristics are compared between the two groups in below Table 1.
| Estimated birth weight (GROW) | p-value | ||
|---|---|---|---|
| Characteristic | <10th centile (N = 169) | ≥10th centile (N = 418) | |
| Age | 29 (25, 30) | 28 (26, 31) | 0.805 |
| Body mass index (BMI) | 28.9 (26.02, 31.8) | 27.4 (24.15, 30.43) | <0.001 |
| Gestational age at delivery | 37.2 (37, 38.5) | 38.3 (37.2, 39.2) | <0.001 |
| Birth weight of baby in kilogram | 2.25 ± 0.24 | 2.66 ± 0.27 | <0.001 |
| Mode of labor onset | |||
| Induction of labor | 121 (71.6%) | 234 (55.98%) | <0.001 |
| Spontaneous labor | 20 (11.83%) | 124 (29.6%) | <0.001 |
| Mode of delivery | |||
| Assisted vaginal birth | 19 (11.24%) | 74 (17.7%) | 0.022 |
| Spontaneous vaginal birth | 60 (35.5%) | 170 (40.67%) | |
| Lower segment cesarean section | 90 (53.25%) | 174 (41.63%) | |
In the whole study population of 587 babies, none were seen with macrosomia and an APGAR score <7 at 10 minutes. All the neonatal outcomes seen in the study population are tabulated in Table 2.
| Neonatal outcome | Number (n) | Percentage (%) |
|---|---|---|
| APGAR score <7 at 1 minute | 20 | 3.4 |
| APGAR score <7 at 5 minutes | 3 | 0.5 |
| APGAR score <7 at 10 minutes | 0 | 0 |
| Meconium staining | 75 | 12.7 |
| NICU admission | 49 | 8.3 |
Table 3 provides the comparison of the predictive validity of all the Doppler parameters in case of estimated birth weight <10th, <5th, and <3rd centiles. The determined cutoff of CPUR has the highest predictive validity in comparison to other Doppler parameters at <10th and <5th centiles as per GROW chart. In the case of estimated birth weight of <3rd centile, UA-PI was found to be the better predictive marker.
| Birth weight | AUC | Sensitivity (95% CI) | Specificity (95% CI) | Positive predictive value (PPV) (95% CI) | Negative predictive value (NPV) (95% CI) |
|---|---|---|---|---|---|
| Birth weight <10th centile (GROW) | |||||
| UA-PI MoM >1.105 | 0.625 | 21.30 (15.39–28.25) | 89.95 (86.66–92.66) | 46.15 (34.79–57.82) | 73.87 (69.82–77.64) |
| UtA-PI MoM >1.055 | 0.598 | 18.34 (12.82–25.01) | 90.19 (86.93–92.87) | 43.06 (31.43–55.27) | 73.20 (69.16–76.98) |
| CPR MoM <1.235 | 0.65 | 21.89 (15.91–28.89) | 89.71 (86.39–92.45) | 46.25 (35.03–57.76) | 73.96 (69.91–77.73) |
| CPUR MoM <1.338 | 0.646 | 21.89 (15.91–28.89) | 91.87 (88.82–94.03) | 52.11 (39.92–64.12) | 74.42 (70.42–78.13) |
| Birth weight <5th centile (GROW) | |||||
| UA-PI MoM >1.115 | 0.684 | 22.68 (14.79–32.30) | 90.20 (87.22–92.69) | 31.43 (20.85–43.63) | 85.49 (82.16–88.42) |
| UtA-PI MoM >1.067 | 0.625 | 20.62 (13.07–30.03) | 90 (87–92.51) | 28.99 (18.69–41.16) | 85.14 (81.78–88.09) |
| CPR MoM <1.165 | 0.672 | 19.59 (12.22–28.89) | 91.22 (88.36–93.58) | 30.65 (19.56–43.65) | 85.14 (81.81–88.08) |
| CPUR MoM <1.415 | 0.677 | 28.87 (20.11–38.95) | 88.98 (85.87–91.61) | 34.15 (24.03–45.45) | 86.34 (83.03–89.21) |
| Birth weight <3rd centile (GROW) | |||||
| UA-PI MoM >1.115 | 0.745 | 34.69 (21.67–49.64) | 90.15 (87.31–92.53) | 24.29 (14.83–36.01) | 93.81 (91.37–95.73) |
| UtA-PI MoM >1.076 | 0.686 | 24.49 (13.34–38.87) | 90.33 (87.52–92.70) | 18.75 (10.08–30.46) | 92.93 (90.38–94.97) |
| CPR MoM <1.185 | 0.688 | 24.49 (13.34–38.87) | 89.22 (86.29–91.71) | 17.14 (9.18–28.03) | 92.84 (90.27–94.91) |
| CPUR MoM <1.295 | 0.734 | 28.57 (16.58–43.26) | 91.08 (88.34–93.35) | 22.58 (12.93–34.97) | 93.33 (90.85–95.31) |
The OR was calculated for all the Doppler parameters with determined cutoff values that reported CPUR with the highest OR of 3.166 and 3.276 at <10th and <5th centile, respectively which was statistically significant with p-value < 0.001. In the case of estimated birth weight <3rd centile, UA-PI was found to have the highest OR of 4.861 which was statistically significant with p-value < 0.001. Table 4 provides OR of all the Doppler parameters in predicting the low-birth-weight fetuses in case of <10th, <5th, and <3rd centile.
| Doppler variables | OR (95% CI) | p-value |
|---|---|---|
| Birth weight <10th centile (GROW) | ||
| UA-PI MoM >1.105 | 2.423 (1.489–3.944) | <0.001 |
| UtA-PI MoM >1.055 | 2.066 (1.246–3.425) | 0.005 |
| CPR MoM <1.235 | 2.445 (1.509–3.959) | <0.001 |
| CPUR MoM <1.3385 | 3.166 (1.909–5.250) | <0.001 |
| Birth weight <5th centile (GROW) | ||
| UA-PI MoM >1.115 | 2.701 (1.542–4.733) | 0.001 |
| UtA-PI MoM >1.0675 | 2.338 (1.317–4.149) | 0.004 |
| CPR MoM <1.165 | 2.532 (1.402–4.573) | 0.002 |
| CPUR MoM <1.415 | 3.276 (1.943–5.524) | <0.001 |
| Birth weight <3rd centile (GROW) | ||
| UA-PI MoM >1.115 | 4.861 (2.530–9.340) | <0.001 |
| UtA PI MoM >1.0765 | 3.031 (1.489–6.172) | 0.002 |
| CPR MoM <1.185 | 2.684 (1.325–5.437) | 0.006 |
| CPUR MoM <1.295 | 4.083 (2.054–8.117) | <0.001 |
The ROC curve was plotted for all the Doppler variables that reported an area under the curve (AUC) for UA-PI, UtA-PI, CPR, and CPUR of 0.625, 0.598, 0.650, and 0.646, respectively in case of estimated birth weight <10th centile, 0.684, 0.625, 0.672, and 0.677 in case of <5th centile, and 0.745, 0.686, 0.688, and 0.734 in case of <3rd centile. Greater AUC was seen for UA-PI in case of <5 and <3rd centile and for CPR in case of <10th centile. Figures 1 to 3 in the supplementary file represents the ROC curves of Doppler parameters in predicting late-onset FGR in case of estimated birth weight of <10th, <5th, and <3rd centiles.
Figs 1A to D: Receiver operator characteristic curves of various Doppler variables for birth weight <10th centile plotted on GROW chart; (A) UA-PI MoM; (B) Mean UtA-PI MoM; (C) CPR MoM; (D) CPUR MoM
Figs 2A to D: Receiver operator characteristic curves of various Doppler variables for birth weight <5th centile plotted on GROW chart; (A) UA-PI MoM; (B) Mean UtA-PI MoM; (C) CPR MoM; (D) CPUR MoM
Figs 3A to D: Receiver operator characteristic curves of various Doppler variables for birth weight <3rd centile plotted on GROW chart; (A) UA-PI MoM; (B) Mean UtA-PI MoM; (C) CPR MoM; (D) CPUR MoM
Correlation between of UA-PI, UtA-PI, CPR, and CPUR with birth weight plotted as per GROW chart was tested using Spearman correlation coefficient of that reported weak negative and positive correlation with rs value of –0.239, –0.174, 0.250, and 0.257, respectively which was statistically significant with p-values < 0.001 (Table 5).
| Birth weight <10th centile on GROW | Spearman correlation | p-value |
|---|---|---|
| UA-PI vs birth weight | –0.239 | <0.001 |
| UtA-PI vs birth weight | –0.174 | <0.001 |
| CPR vs birth weight | 0.250 | <0.001 |
| CPUR vs birth weight | 0.257 | <0.001 |
DISCUSSION
The current prospective observational study carried out to determine the predictive validity of CPUR in detecting late-onset FGR and comparing it with the existing Doppler parameters has included a total of 587 women who underwent ultrasound Doppler examination after 32 weeks of gestation and recorded all the desired variables for the study as detailed in the methodology section. Doppler examination revealed that 169 (28.79%) women had fetuses with an estimated birth weight of <10th centile as per the GROW chart. CPUR was found to have with highest predictive validity in comparison with others at <10th and <5th centiles but not at <3rd centiles. CPUR was with the highest OR of 3.166 and 3.276 at <10th and <5th centile, respectively which was statistically significant with a p-value < 0.001 which is not seen at <3rd centile. In the case of <3rd centile, UA-PI was found to have good predictive validity and the highest OR of 4.861 which was statistically significant at <3rd centile. ROC analysis reported UA-PI with greater AUC in predicting low-birth-weight at <5th and <3rd centiles, 0.684 and 0.745, respectively, and CPR at <10th centile, 0.650, which were statistically significant. Despite the better predictive value of CPUR and high OR at <10th and <5th centiles, AUC was lower in comparison to other Doppler parameters.
At a specificity of 90%, CPUR had higher sensitivity than other Doppler parameters at <10th and <5th centile which is in line with the study findings of MacDonald et al. However, their study reported CPUR with higher sensitivity even at <3rd centile, which does not hold to the current study findings. This difference can be interlined with the difference seen in the OR of CPUR which is 15-fold higher in the study of MacDonald et al. than that of our study.12 Differences in the population are assumed to be the main basis of these differences that indicate the predictivity of late-onset FGR with the EFW of <3rd centile would vary greatly across the population at different geographic and ethnic groups.
A prospective observational study carried out by Lobmaier et al. reported that 28% of women with SGA fetuses <10th centile had operative deliveries, which is half of that reported in our population, 53%. This huge difference is to be evaluated. However, the same study has reported a similar AUC of CPR which is 0.63.14 A study conducted in New Zealand by Anderson et al. has found 25% of women with late-onset FGR with estimated fetal birth weight <10th centile which is in line with the current study findings.15 A study conducted by Graupner et al. evaluate the efficacy of Doppler parameter, CPUR, in predicting operative deliveries and composite perinatal adverse outcomes among women presenting intrapartum fetal compromise/FGR that reported an AUC of 0.64 and 0.67, respectively. This further demonstrates the importance of CPUR as a novel marker in routine clinical practice in identifying the risk of late-onset FGR along with associated adverse outcomes.16 Another study conducted by Rizzo et al. reported the significant association of CPR and UtA-PI with adverse perinatal outcomes on multivariate regression analysis.1 A retrospective study conducted by Coenen et al. reported the OR of CPR and UCR for various neonatal outcomes, which reported UCR with the highest odds.10 Hence, in order to prevent the adverse outcomes associated with FGR, it is important to identify the fetuses at risk and manage with appropriate clinical care to improve the outcomes. This extends to the previous statement lightening the importance of the identification of fetuses at risk of FGR.
Coenen et al. concluded that CPR and UCR can be used as predictive markers for identifying fetuses at risk of late-onset FGR. The current study added another marker, CPUR, in predicting the same.10 A study conducted by Macdonald et al. in Australia concluded CPUR as a novel Doppler marker in detecting the late-onset of FGR, which is in line with the current study conclusions. However, the cutoff value and OR of the CPUR varies greatly with cutoff MoM at <10th centile being <0.71, which is almost half of the current study and OR being 9.1 that is almost thrice of our study finding.12 Holding the fact that use of different GROW curves results differently in identification of fetuses at risk of restricted growth as per the study carried out by Hocquette et al.17 The difference in the assignment of fetuses between the groups and determining the cutoff of the CPUR implicated the difference between Indian and Australian studies. Our study findings fall in agreement with a prospective cohort study conducted by Niroomanesh et al. in Iran that concluded CPUR as a Doppler parameter which is with better predictive validity than CPR and UCR but with higher AUC (0.78) than that of ours.18
Despite variations in the values, all the studies discussed above has proved the superiority of CPUR over other Doppler parameters in predicting the late-onset FGR. These differences in the AUC, sensitivity and OR of the Doppler parameters emphasizes the importance to validate the role of CPUR in different nations to determine cutoff value as per their national GROW chart. The current study proves CPUR as an important marker in identification of fetuses at risk of late onset FGR in the Indian population (Appendix 1).
| Section/topic | Item no. | Recommendation | Reported on page no. |
|---|---|---|---|
| Title and abstract | 1 | Indicate the study’s design with a commonly used term in the title or the abstract | 1 |
| Provide in the abstract an informative and balanced summary of what was done and what was found | 1 | ||
| Introduction | |||
| Background/rationale | 2 | Explain the scientific background and rationale for the investigation being reported | 2 and 3 |
| Objectives | 3 | State specific objectives, including any prespecified hypotheses | 3 |
| Methods | |||
| Study design | 4 | Present key elements of study design early in the paper | 3 |
| Setting | 5 | Describe the setting, locations, and relevant dates, including periods of recruitment, exposure, follow-up, and data collection | 3 |
| Participants | 6 | Cohort study—give the eligibility criteria and the sources and methods of selection of participants. Describe methods of follow-up Case-control study—give the eligibility criteria and the sources and methods of case ascertainment and control selection. Give the rationale for the choice of cases and controls Cross-sectional study—give the eligibility criteria and the sources and methods of selection of participants |
3 |
| Cohort study—For matched studies, give matching criteria and number of exposed and unexposed Case-control study—For matched studies, give matching criteria and the number of controls per case |
- | ||
| Variables | 7 | Clearly define all outcomes, exposures, predictors, potential confounders, and effect modifiers. Give diagnostic criteria, if applicable | 4 |
| Data sources/measurement | 8* | For each variable of interest, give sources of data and details of methods of assessment (measurement). Describe comparability of assessment methods if there is more than one group | 4 |
| Bias | 9 | Describe any efforts to address potential sources of bias | 5 |
| Study size | 10 | Explain how the study size was arrived at | 3 and 4 |
| Quantitative variables | 11 | Explain how quantitative variables were handled in the analyses. If applicable, describe which groupings were chosen and why | 5 |
| Statistical methods | 12 | Describe all statistical methods, including those used to control for confounding | 5 |
| Describe any methods used to examine subgroups and interactions | 5 | ||
| Explain how missing data were addressed | – | ||
| Cohort study—if applicable, explain how loss to follow-up was addressed Case-control study—if applicable, explain how matching of cases and controls was addressed Cross-sectional study—if applicable, describe analytical methods taking account of sampling strategy |
– | ||
| Describe any sensitivity analyses | 5 | ||
| Results | |||
| Participants | 13* | Report numbers of individuals at each stage of study—for example, numbers potentially eligible, examined for eligibility, confirmed eligible, included in the study, completing follow-up, and analyzed | – |
| Give reasons for nonparticipation at each stage | – | ||
| Consider use of a flow diagram | – | ||
| Descriptive data | 14* | Give characteristics of study participants (e.g., demographic, clinical, social) and information on exposures and potential confounders | 5 and 6 |
| Indicate number of participants with missing data for each variable of interest | – | ||
| Cohort study—summarize follow-up time (e.g., average and total amount) | – | ||
| Outcome data | 15* | Cohort study—report numbers of outcome events or summary measures over time | – |
| Case-control study—report numbers in each exposure category, or summary measures of exposure | – | ||
| Cross-sectional study—report numbers of outcome events or summary measures | 6 | ||
| Main results | 16 | Give unadjusted estimates and, if applicable, confounder-adjusted estimates and their precision (e.g., 95% CI). Make clear which confounders were adjusted for and why they were included | 6 |
| Report category boundaries when continuous variables were categorized | – | ||
| If relevant, consider translating estimates of relative risk into absolute risk for a meaningful time period | – | ||
| Other analyses | 17 | Report other analyses done—for example, analyses of subgroups and interactions, and sensitivity analyses | 6 |
| Discussion | |||
| Key results | 18 | Summarize key results with reference to study objectives | 7 |
| Limitations | 19 | Discuss limitations of the study, taking into account sources of potential bias or imprecision. Discuss both direction and magnitude of any potential bias | – |
| Interpretation | 20 | Give a cautious overall interpretation of results considering objectives, limitations, multiplicity of analyses, results from similar studies, and other relevant evidence | 8 and 9 |
| Generalizability | 21 | Discuss the generalizability (external validity) of the study results | – |
| Other information | |||
| Funding | 22 | Give the source of funding and the role of the funders for the present study and, if applicable, for the original study on which the present article is based | Financial disclosure form |
*Give information separately for cases and controls in case-control studies and, if applicable, for exposed and unexposed groups in cohort and cross-sectional studies; an explanation and elaboration article discusses each checklist item and gives methodological background and published examples of transparent reporting; the STROBE checklist is best used in conjunction with this article (freely available on the Web sites of PLoS Medicine at http://www.plosmedicine.org/, Annals of Internal Medicine at http://www.annals.org/, and Epidemiology at http://www.epidem.com/); information on the STROBE initiative is available at www.strobe-statement.org
CONCLUSION
The current prospective observational study carried out to determine the predictive validity of novel Doppler marker CPUR in predicting the late-onset FGR concludes that CPUR can be used as a predictive marker in third-trimester ultrasound. In comparison with other Doppler markers, CPUR has better predictive validity in terms of sensitivity, specificity, and positive and negative predictive values in case of estimated birth weight of <10th and <5th centile fetuses. However, in the case of <3rd centile fetuses and AUC comparison, its superiority was not seen. CPUR also had the highest OR at <10th and <5th centiles while not in the case of <3rd centiles. Hence, we propose the combination of fetal biometry and Doppler parameters including CPUR in the third trimester as a better predictive tool in identifying the fetuses at risk of late-onset FGR.
Clinical Significance
The CPUR has been proved to be a better diagnostic marker in detecting the late-onset FGR in the case of fetuses with estimated birth weight of <10th and <5th centiles. With this evidence, the novel ultrasound marker CPUR can be deployed in detecting the late-onset FGR and managed accordingly to avoid adverse pregnancy and fetal outcomes. To further improve the detection rate, this can be combined with the fetal biometry parameters.
ORCID
Arati Singh https://orcid.org/0000-0002-7230-9627
Nithish Sattoju https://orcid.org/0000-0002-0502-7689
Murali Mohan R Gopireddy https://orcid.org/0000-0003-0138-853X
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