https://orcid.org/0000-0002-0782-4462
Figures
Abstract
Preterm delivery is greatly associated with perinatal mortality and morbidity, while there is no objective way to identify high-risk newborns currently. This study aimed at discovering the risk factor for Apgar score less than 7 at 1 minute of preterm neonates born with vaginal delivery. A retrospective study was performed in single pregnancy women with a vaginal delivery before 37 weeks of gestation. All the preterm infants were categorized into three types: very preterm birth (28 to 32 weeks), moderate preterm birth (32 to 34 weeks) and late preterm birth (34 to 37 weeks). Risk factors were identified through logistic regression analysis in every category of newborns mentioned above. And the receiver operating characteristic analysis was used in continuous variables to determine the best threshold of the outcome. On the basis of the selected factors, the predicting models are created and its prognosticating ability is compared by the area under the curve. A nomogram was established for the proved best model. A total of 981 cases were investigated, of whom 55 were found with 1 min Apgar scores less than 7. The nomogram was set for the predicting model with larger area under the receiver operating characteristic curve, of which is 0.742(95% confidence interval = 0.670–0.805) in very preterm birth, with the variables of first and second labor stage(> = 1.6 hours), birthweight and MgSO4(magnesium sulfate), and is 0.807(95% confidence interval = 0.776–0.837) in late preterm birth, with the variables of second labor stage(> = 1.23 hours), birthweight, a history of previous cesarean delivery, fetal distress and placental abruption. The combination of first and second labor stage, newborn weight and MgSO4 use can predict 74.2% of 1 minute Apgar score < 7 in very preterm neonates. And 80.7% high-risk infants can be identified when second labor stage, newborn weight, VBAC (vaginal birth after cesarean) and the occur of placental abruption and fetal distress were combined in the predicting model for late preterm birth. These predicting models would bring out great assistance towards obstetricians and reduce unnecessary adverse fetal outcomes.
Citation: Lin X-S, Peng X-Y, Yang M-M, Ning L-l, Shao Y-W, Jiang Y, et al. (2022) The single pregnancy predicting model of 1 minute Apgar score less than 7 after preterm birth: A retrospective study. PLoS ONE 17(12): e0279385. https://doi.org/10.1371/journal.pone.0279385
Editor: Claudio Romero Farias Marinho, Universidade de Sao Paulo Instituto de Ciencias Biomedicas, BRAZIL
Received: May 15, 2022; Accepted: December 6, 2022; Published: December 22, 2022
Copyright: © 2022 Lin et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All relevant data are within the paper and its Supporting Information files.
Funding: This work was supported by the Key Subjects Group of Reproductive Medicine, School of Medicine, Zhejiang University - Scientific Research Foundation of the National Health Commission (WKJ-ZJ-2126). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Abbreviations: ROC, the receiver operating characteristic; OR, odds ratio; SPSS, statistical package for social science; SD, standard deviation; CI, confidence interval; AUC, the area under the ROC curve; BMI, prenatal body mass index; HDP, hypertension disorders; GDM, gestational diabetes mellitus; ICP, intrahepatic cholestasis of pregnancy; VBAC, vaginal birth after cesarean; PPROM, preterm premature rupture of membranes; PPH, postpartum hemorrhage; MgSO4, magnesium sulfate; NICU, the admission to the neonatal intensive care unit
Introduction
The prevalence of preterm delivery appears to be showing an increasing trend globally [1–3]. It could be due to advanced maternal age and to widespread use of assisted reproductive technology [4–7]. Besides, it had been demonstrated that preterm delivery greatly contribute to perinatal mortality and morbidity [8, 9]. More than 1 million infants had been witnessed to die before the age of 5 every year because of prematurity birth [10]. Premature infants also encountered higher risk of short-term complications, like respiratory syndromes and intraventricular hemorrhage, and long-term morbidity, including neurologic disabilities and delayed school performances [11, 12].
Gestational age at delivery is crucial to neonatal survival and morbidity [13]. So preterm birth generally be classified into three categories: very preterm birth (28 to 32 weeks), moderate preterm birth (32 to 34 weeks) and late preterm birth (34 to 37 weeks) [14]. Although moderate to late preterm birth take over a majority part of all preterm birth [9, 15], very preterm delivery comprised over half of infant death [16]. It is of great importance to enhance the identification of high-risk infants among preterm deliveries, so that timely intervention can be put into action to improve perinatal outcomes.
The Apgar score is generally used as a quantitative standard for assessing newborns status nowadays [17]. It presents the neonates status at birth and the probability of death [18]. The score value underneath 7 had been proved to be associated with various short-term complications including hypoxic ischemic encephalopathy, hypoglycemia and aspiration complications [19]. It also connected with long-term complications like sensorineural hearing loss and pediatric ophthalmic morbidity [19–21]. Hence it is widely-used to detect high-risk newborns [22, 23].
Previous studies had found several risk factors about Apgar score less than 7, involving the duration of labor stage [24], maternal BMI(body mass index) and the history of caesarean section [25–27]. Gestational age had also been noted as an influencing factor [23, 28]. Additionally, preterm birth had been documented to be a prominent relevant factor for Apgar score <7 [28]. However, to the best of our knowledge, study on discriminating the risk of low Apgar score targeting solely at preterm delivery is scarce.
Consequently, our study aimed at identifying risk factors related to a low Apgar score specializing in premature deliveries, and further establishing a predicting model.
Methods
Study design and population
To identify the risk factors for 1 minute Apgar score less than 7 amongst preterm neonates delivered through the vaginal route. A retrospective study using clinical data was carried out in three Chinese maternity units. Between 1 Jun 2016 and 30 Jun 2019, women who had preterm birth at three hospital centers (Women’s Hospital, Zhejiang University School of Medicine, Changxing Maternal and Child Health Hospital and Fuyang Maternal and Child Health Hospital) were enrolled in this retrospective study. This study was approved by the Clinical Research Ethics Committee of Women’s Hospital Zhejiang University School of Medicine. The inclusion criteria of our research were: (1) singleton pregnancy; (2) the accessibility of integrated medical records; (3) giving birth at cephalic position; (4) delivery time between 28–37 week. And the exclusion criteria were as follows: (1) abnormal fetal positions, such as breech or shoulder presentation; (2) patients complicated by the following complications: pre-gestational diabetes, hyperthyroidism; (4) uterine malformation like unicornuate uterus, uterus duplex; (5) cervical surgery history: cervical cerclage, cervical conization.
Data resource
All the demographic and obstetric data were obtained from medical records in three centers. The collected demographic data included gestational week at delivery, maternal age, preconception and prenatal body mass index (BMI), obstetrics history including cesarean delivery. We also retrieved all the information about maternal complications: hypertension disorders (HDP), gestational diabetes mellitus (GDM), intrahepatic cholestasis of pregnancy (ICP), abnormal amniotic fluid, placenta abruption, preterm premature rupture of membranes (PPROM), intrauterine infection, fetal distress (judged by the abnormalities in cardiotocography), postpartum hemorrhage (PPH). Besides, we summarized medical administration for every patient, which contained the use of magnesium sulfate (MgSO4), dexamethasone before delivery. Finally, the collected neonatal data were as follows: Apgar score after 1 and 5 minutes, delivery records, birthweight and the admission to the neonatal intensive care unit (NICU).
Statistical analysis
Statistical analyses were performed using R software (Version 3.6.0; https://www.R-project.org) and IBM SPSS statistics software version 26(IBM, Armonk, NY). We used mean and standard deviation (SD) to represent numerical variables, and percentages to represent qualitative variables. Using an analysis of variance (ANOVA) or non-parametric test (such as Mann-Whitney U test) to compare differences in numerical variables. Comparison of qualitative variables between case-control groups was performed using a Chi-square test. The best threshold of qualitative variables was predicted from the best sensitivity and specificity by the receiver operating characteristic (ROC) analysis. The DeLong’s test was used to examine the statistical differences of ROC curves. The prediction models were established based on the results of logistic regression analyses. The nomogram of predicting models was formulated by R software. Discrimination of the prediction models were assessed by the area under the ROC curve (AUC). Calibration of the predicting models were adopted by calibration curves.
Ethics approval and consent to participate
Our study was designed in accordance with the Declaration of Helsinki. The study protocol was approved by the Clinical Research Ethics Committee of Women’s Hospital Zhejiang University School of Medicine (IRB-20210239-R). And the exemption from informed consent was approved by the committee for that the nature of the study was extracting and analyzing the existing clinical data, which had minimal influence in the study population. Also, we maintained confidentiality through excluding names or any other personal identifiers during data collection.
Results
Patients’ characteristics and univariable analysis
There were 981 cases enrolled in our study. Among them, 55 were found with 1 min Apgar scores less than 7, while 14 with 5 min Apgar scores less than 7. According to this, the population were divided into LOW-APGAR group and NORMAL-APGAR group. From 18 study variables presented in Table 1, 12 variables selected in the LOW-APGAR group exhibited statistically significant difference compared with the normal one (P<0.05), which included pre-conception BMI, vaginal birth after cesarean (VBAC), PPROM, placental abruption, intrauterine infection, fetal distress, MgSO4 use, dexamethasone use, first labor stage, second labor stage, first and second stage of labor (the sum of the first and second labor stage), newborn weight. There were no significant differences in the following variables: primiparas, maternal age, prenatal BMI and gestational complications like GDM, HDP, ICP. But the occurrence of PPH seemed to be increased among those women in LOW-APGAR group.
ROC analysis for continuous predictors
We performed the ROC analysis for the 4 selected continuous variables to determine the optimal threshold. The results for 1 min Apgar scores of no more than 7 were presented in Table 2. We did stratified analysis by gestational week. The optimal cut-off of first labor stage in the divided groups were 2.67, 5.33 and 7.4 hours, respectively. For newborn weight, the best threshold was 1370, 2120, 2350 grams, respectively. As for low 5-min Apgar score group, no meaningful threshold can be found, which may be due to lacking positive cases. So further analysis was only performed for the low 1-min Apgar score group.
Multivariate analysis
In the multivariate analyses for 1 min Apgar score of no more than 7 in 28- to 32-week-old infants (Table 3), we found that when first labor stage prolonged more than 1.6 hours, the risk of low Apgar score increased by 7.298 times (95% CI = 0.953–55.872; P = 0.011). Compared with those who administrated MgSO4 before delivery, the risk of low Apgar score enhanced by 6.390 times in infants without this history (95% CI = 0.832–49.053; P = 0.020).
Also, in the results of multivariate analysis of neonates born with 34- to 36+6-gestational-age (Table 3), we found that second labor stage, newborn weight, VBAC, fetal distress, placental abruption and PPROM were independent risk factors for the incidence of 1 min Apgar score beneath 7 (P<0.05). Among them, when the second labor stage lasted longer than 1.23 hours, the risk was increased by 4.132-fold (95% CI = 1.644–10.385; P = 0.003).
And for moderate preterm delivery, no independent meaningful factor was identified through multivariate analysis(P>0.05). So further analyses were only performed for very preterm delivery and late preterm delivery group.
Development of predictive nomogram
With multivariable logistic analyses, we establish 4 models for very preterm delivery group. The discrimination of models was evaluated by ROC curve and its AUC value (Table 4). In model 4, we combined first and second labor stage, newborn weight and MgSO4, the AUC of it was largest (0.742, 0.670–0.805). DeLong’s test showed that there was statistically significant difference between model 2 and model 4 (P<0.0001), indicating that model4 could be a better assessment model in very preterm delivery than model 2.
According to this, we created a nomogram prediction model for very preterm delivery with three predictors (Fig 1A). For example, for a 2000-gram (0 point) newborn whose mother didn’t administrate MgSO4 before delivery (about 84 points) and took 3 hours (about 100 points) in the first and second stages of labor, the total points are 184 points, and the likelihood of 1 min Apgar score less than 7 was about 70%-80%. The calibration curves of the prediction nomogram for the risk of 1 min Apgar score less than 7 in the patients demonstrated a good agreement (Fig 1B).
A: Nomogram of Model 4 for 1-min Apgar score less than 7 in 28- to 31+6-week-old infants; B: Calibration of Model 4 in 28- to 31+6-week-old infants; C: Nomogram of Model 1 of 1 min Apgar scores of no more than 7 in 34- to 36+6-week-old infants; D: Calibration of Model 1; MgSO4: magnesium sulfate, First and second stage of labor: the sum of the first and second labor stage(hours), Newborn weight (grams); VBAC: vaginal birth after cesarean, Second stage of labor(hours), Newborn weight(grams).
For late preterm delivery, eight models were established based on the outcomes of multivariate analyses (Table 4). And the AUC of model 1 to model 8 were as follows: 0.807, 0.754, 0.769, 0.765, 0.784, 0.798, 0.773 and 0.801 respectively. Among them, we found that the predictive value of model1 was better than model2, and the difference was statistically significant. In all forecasting models, model 1 had the highest AUC (0.807, 0.776–0.837).
Therefore, we constructed the nomogram of the prediction model with largest AUC (Fig 1C). The working instructions were the same as the above one in Fig 1A. The calibration curves of this nomogram for the risk of 1 min Apgar scores no more than 7 in 34- to 36+6-week-old infants were represented in Fig 1D.
Discussion
The critical finding of our study was that we established the predicting model of 1 min Apgar score lower than 7 in preterm delivery. We discovered that preconception BMI, PPORM, VBAC, placental abruption, abnormal amniotic fluid, intrauterine infection, fetal distress, medicine use, labor stage and birthweight may be the potentially related factors for a low Apgar score. While taken the first and second stage of labor, newborn weight and the use of MgSO4 into consideration, the AUC of the model is 0.742, suggesting that it can predict 74.2% of 1 min Apgar score less than 7 in newborns with 28 to<32 gestational weeks. As for those born during 34 to <37 weeks, 80.7% can be identified with second labor stage, newborn weight, VBAC, placental abruption and fetal distress added to the predicting model.
A prolonged stage of labor had been reported to increase adverse neonatal outcomes like low Apgar score at 1 min or 5 min, arterial cord PH less than 7 and the chance of admission to the neonatal intensive unit in many former studies [23, 24, 29–31]. It had been demonstrated that nulliparous women with a first labor stage greater than 30 hours were more likely to have a neonate whose 5-minute Apgar score less than 7 [24]. While Wang et al found that the risk of low Apgar score increased by almost 3-fold in multiparous women whose first labor stage lasted longer than 6 hours. Additionally, as to the second stage of labor, the risk of Apgar score beneath 7 was seen to increase by 14.76 times in the subgroup longer than 3 hours in this study [29]. Another retrospective study also found that a second labor stage longer than 3 hours was correlated with a low Apgar score [32].
On the other hand, Haper found no association between the length of first labor stage and low Apgar score [30]. And the other one study figured that even the incidence of 5-min Apgar score <7 slowly increase as second labor stage prolonged, it declined greatly when it reached 4 hours [33]. Besides, there was a multiple cohort study suggesting that second labor stage may not be concerned with low Apgar score [34]. All the above study targeted at term delivery. Hence the determination of abnormal labor stage based on low Apgar score in term birth remained controversial.
While for women who delivered beyond 34 weeks, Janni W had reported that low 1-min Apgar score was associated with a second stage labor longer than 2 hours [35]. Despite that, no specific criteria of normal labor stage had been set for preterm delivery yet. While our study confirmed that a second labor stage greater than 1.23 hours in late preterm delivery may result in an over 4-fold elevated low Apgar score risk. But we found no significant difference on first stage labor between the comparison of lower Apgar score group and the normal one in very and moderate preterm delivery, whereas we could see a statistically significant difference in the combined length of first and second labor stage in this comparison(P = 0.011). Hence, we set a duration of 1.6-hour in first and second labor stage as a cutoff with a specificity of 23.8% and a sensitivity of 96.3%. With a length greater than this cutoff of labor stage, the risk of low Apgar score rose by 7.298 times.
Previous studies had described many other possible risk factors for low Apgar score at 1 min [23]. Among them, low birth weight had been regarded to concern with newborns asphyxia. Thomas Hegyi declared that birth weight can be linearly associated with Apgar score in premature newborns [36]. The universal committee for low birth weight was 2500g, while our study discovered a threshold of 1370g for very preterm delivery, and 2350g for late preterm delivery. In comparison, those who were born with a heavier birthweight may lower the risk of low Apgar score.
Another risk factor about Apgar score less than 7 in 5 minutes declared by former study was nulliparity [37], while our study witnessed no difference in primiparas and multiparas both for 1 minute Apgar score and 5 minutes Apgar score. Except for that, some precedent studies show that low Apgar score were connected to preeclampsia [38, 39]. Early diagnosis of preeclampsia (<34 weeks) and low platelet levels (HELLP) syndrome, which was defined as preeclampsia with platelet count <100,000/mm3 and serum transaminases double the averages of normal, was proved to be independent risk factor of low Apgar score at 1 minute [39]. Still, there was no difference in women with or without HDP in our study. This might be due to that we didn’t go into the further classification of hypertension disorders, which required further investigation and more detailed medical information.
Strengths and limitations
One of the strengths of our study is that no such a visualized graph of the predictive model for 1 min Apgar score beneath 7 had been built exclusively for preterm delivery in China yet. Besides, the implication of the nomogram had been proved to be quite simple, intuitive, and practical in previous study. In addition, we still acquired a sufficient sample size with a pretty strict inclusion and exclusion committee, therefore the statistical effectiveness can be ensured. Consequently, we can achieve a more reliable result possible. And all the risk factors for the predicting models were not difficult to obtain during clinical practice, so this model should be easy to be promote in any obstetric department.
The main limitation of this study lied in the fact that the prediction value of our model had only been assessed by calibration curves and Delong’s test, external validation is still required in future research. The main predicting factor in our model is the length of labor stage. However, the definition of it is often quite subjective. It also requires professional competence and demands accumulation of clinical experience, for that those clinical parameters used in the definition, such as the effacement and dilatation of cervix can be pretty difficult to judge. Besides, due to the deficiency of moderate preterm birth cases, we fail to find independent meaningful indicators for low Apgar score in this category, further research and data collection is required in this aspect.
Conclusion
Our study figured that a first and second stage of labor greater than 1.6 hours, birth weight smaller than 1370g and MgSO4 use history can be the optimal combination to assess the adverse outcome in very preterm neonates. While for late preterm newborns, the optimal prediction was based on the second labor stage longer than 1.23 hours, newborn weight lower than 2350 grams, VBAC, fetal distress and placental abruption. By the combination, we proposed the predicting models by the nomogram, providing the obstetrician with visible possibility of low 1-min Apgar score in preterm newborns.
Acknowledgments
The authors thank the Staff at women’s hospital, Zhejiang University for technical assistances and facility supports.
References
- 1. Slattery M.M. and Morrison J.J., Preterm delivery. The Lancet, 2002. 360(9344): p. 1489–1497.
- 2. Chawanpaiboon S., et al., Global, regional, and national estimates of levels of preterm birth in 2014: a systematic review and modelling analysis. The Lancet Global Health, 2019. 7(1): p. e37–e46.
- 3. Blencowe H., et al., National, regional, and worldwide estimates of preterm birth rates in the year 2010 with time trends since 1990 for selected countries: a systematic analysis and implications. The Lancet, 2012. 379(9832): p. 2162–2172.
- 4. Hsieh T.T., et al., Advanced maternal age and adverse perinatal outcomes in an Asian population. Eur J Obstet Gynecol Reprod Biol, 2010. 148(1): p. 21–6.
- 5. Laopaiboon M., et al., Advanced maternal age and pregnancy outcomes: a multicountry assessment. BJOG, 2014. 121 Suppl 1: p. 49–56.
- 6. Keirse M.J., Hanssens M., and Devlieger H., Trends in preterm births in Flanders, Belgium, from 1991 to 2002. Paediatr Perinat Epidemiol, 2009. 23(6): p. 522–32.
- 7. Langhoff-Roos J., et al., Spontaneous preterm delivery in primiparous women at low risk in Denmark: population based study. BMJ, 2006. 332(7547): p. 937–9.
- 8. Liu L., et al., Global, regional, and national causes of child mortality in 2000–13, with projections to inform post-2015 priorities: an updated systematic analysis. 2015. 385(9966): p. 430–440.
- 9. Morken N.-H., Preterm birth: new data on a global health priority. The Lancet, 2012. 379(9832): p. 2128–2130.
- 10. Walani S.R., Global burden of preterm birth. Int J Gynaecol Obstet, 2020. 150(1): p. 31–33.
- 11. Marlow N., et al., Neurologic and developmental disability at six years of age after extremely preterm birth. 2005. 352(1): p. 9–19.
- 12. Teune M.J., et al., A systematic review of severe morbidity in infants born late preterm. Am J Obstet Gynecol, 2011. 205(4): p. 374 e1–9.
- 13. Manuck T.A., et al., Preterm neonatal morbidity and mortality by gestational age: a contemporary cohort. Am J Obstet Gynecol, 2016. 215(1): p. 103 e1–103 e14.
- 14. Moutquin J., Classification and heterogeneity of preterm birth. BJOG: An International Journal of Obstetrics and Gynaecology, 2003. 110: p. 30–33.
- 15. Moser K., et al., Introducing new data on gestation-specific infant mortality among babies born in 2005 in England and Wales. Health Stat Q, 2007(35): p. 13–27.
- 16.
User guide to the 2015 period linked birth/infant death public use file. Available from: ftp://ftp.cdc.gov/pub/Health_Statistics/NCHS/Dataset_Documentation/DVS/periodlinked/LinkPE15Guide.pdf.
- 17. Fetus C.o.O.P.A.A.o.P.-C.o., N.J. Obstetrics, and Gynecology, Committee Opinion No. 644: The Apgar Score. 2015. 126(4): p. 52–5.
- 18. Genzel-Boroviczeny O., et al., Predictive value of the 1-min Apgar score for survival at 23–26 weeks gestational age. Acta Paediatr, 2010. 99(12): p. 1790–4.
- 19. Ondoa-Onama C. and Tumwine J.J.E.A.m.j., Immediate outcome of babies with low Apgar score in Mulago Hospital, Uganda. 2003. 80(1): p. 22–29.
- 20. Li F., et al., The apgar score and infant mortality. PLoS One, 2013. 8(7): p. e69072.
- 21. Gutbir Y., et al., Low Apgar score in term newborns and long-term infectious morbidity: a population-based cohort study with up to 18 years of follow-up. Eur J Pediatr, 2020. 179(6): p. 959–971.
- 22. Lee H.C., Subeh M., and Gould J.B.J.A.P., Low Apgar score and mortality in extremely preterm neonates born in the United States. 2010. 99(12): p. 1785–1789.
- 23. Yang C., et al., Retrospective analysis of risk factors for low 1-minute Apgar scores in term neonates. Braz J Med Biol Res, 2019. 52(12): p. e9093.
- 24. Cheng Y.W., et al., Length of the first stage of labor and associated perinatal outcomes in nulliparous women. 2010. 116(5): p. 1127–1135.
- 25. Rouse D.J., et al., Second-stage labor duration in nulliparous women: relationship to maternal and perinatal outcomes. Am J Obstet Gynecol, 2009. 201(4): p. 357 e1–7.
- 26. Allen V.M., et al., Maternal and perinatal outcomes with increasing duration of the second stage of labor. 2009. 113(6): p. 1248–1258.
- 27. Sandström A., et al., Durations of second stage of labor and pushing, and adverse neonatal outcomes: a population-based cohort study. Journal of Perinatology, 2017. 37(3): p. 236–242.
- 28. Svenvik M., Brudin L., and Blomberg M., Preterm Birth: A Prominent Risk Factor for Low Apgar Scores. BioMed Research International, 2015. 2015: p. 978079.
- 29. Wang L., et al., The impact of stage of labor on adverse maternal and neonatal outcomes in multiparous women: a retrospective cohort study. BMC Pregnancy Childbirth, 2020. 20(1): p. 596.
- 30. Harper L.M., et al., Defining an abnormal first stage of labor based on maternal and neonatal outcomes. Am J Obstet Gynecol, 2014. 210(6): p. 536 e1–7.
- 31. Laughon S.K., et al., Neonatal and maternal outcomes with prolonged second stage of labor. Obstet Gynecol, 2014. 124(1): p. 57–67.
- 32. Altman M., et al., Prolonged second stage of labor is associated with low Apgar score. Eur J Epidemiol, 2015. 30(11): p. 1209–15.
- 33. Cheng Y.W., Hopkins L.M., and Caughey A.B., How long is too long: Does a prolonged second stage of labor in nulliparous women affect maternal and neonatal outcomes? Am J Obstet Gynecol, 2004. 191(3): p. 933–8.
- 34. Cheng Y.W. and Caughey A.B., Defining and Managing Normal and Abnormal Second Stage of Labor. Obstet Gynecol Clin North Am, 2017. 44(4): p. 547–566.
- 35. Janni W S.B., Peschers U, Huber S, Strobl B, Hantschmann P, et al., The prognostic impact of a prolonged second stage of labor on maternal and fetal outcome. Acta Obstet Gynecol Scand. 2002;81(3):214–21., 2002. pmid:11966477
- 36. Hegyi T., et al., The Apgar Score and Its Components in the Preterm Infant. Pediatrics, 1998. 101(1): p. 77.
- 37. Lai S., Flatley C., and Kumar S., Perinatal risk factors for low and moderate five-minute Apgar scores at term. Eur J Obstet Gynecol Reprod Biol, 2017. 210: p. 251–256.
- 38. Chappell L.C., et al., Adverse perinatal outcomes and risk factors for preeclampsia in women with chronic hypertension: a prospective study. Hypertension, 2008. 51(4): p. 1002–9.
- 39. Susilo S.A., et al., Determinants of low APGAR score among preeclamptic deliveries in Cipto Mangunkusumo Hospital: a retrospective cohort study in 2014. Medical Journal of Indonesia, 2015. 24(3): p. 183–9.