Development and validation of a clinical features-based nomogram for predicting neonatal cerebral microbleeds

Introduction

Intracranial hemorrhage, a common disease that endangers the life and health of neonates, includes cerebral parenchymal hemorrhage, germinal matrix hemorrhage-intraventricular hemorrhage (GMH-IVH), subdural hemorrhage, and subarachnoid hemorrhage and is correlated with a variety of high-risk factors during the perinatal period. Intracranial hemorrhage can affect the development of the respiratory, circulatory, and nervous systems and even lead to severe complications such as neurological sequelae and death (1,2). Bolisetty et al. (3) found that mild GMH-IVH was independently associated with higher neurological dysfunction and could directly or indirectly affect the surrounding brain parenchyma through subarachnoid hemorrhage, leading to parenchymal hemorrhage, reduced volume, and residual sequelae (4). In another study, subarachnoid hemorrhage in newborns was reported to be caused by trauma or aneurysms and was associated with a reasonably positive prognosis (5). Subdural hemorrhage is often caused by the compression of the birth canal during delivery, resulting in the overlapping of cranial sutures and the displacement of the skull and adjacent brain tissue, which in turn contributes to the rupture of the bridging veins on the surface of the brain (6). The likelihood of neonatal cerebral parenchymal hemorrhage is relatively low, but corresponding symptoms appearing in the early stage of large-scale cerebral parenchymal hemorrhage are indicators for early clinical intervention. Cerebral microbleeds (CMBs) constitute one type of cerebral parenchymal hemorrhage and manifest as round, oval, or punctate low signals with a diameter <10 mm on susceptibility-weighted imaging (SWI) and no surrounding edema. In the diagnosis of CMBs, it is necessary to exclude structures such as enlarged perivascular spaces, calcified lesions, and small veins. However, there is often a lack of abnormal clinical manifestations in the early stages of CMBs, which delays diagnosis and treatment timing (7).

SWI is an imaging technique that exploits the difference in magnetic sensitivity between tissues for gradient echo imaging. It can generate amplitude and phase maps through postprocessing and has the characteristics of three-dimensional acquisition, thin-slice reconstruction, and high resolution. Moreover, SWI can more sensitively and accurately display small blood vessels, bleeding, and calcification, etc. (8,9), and is thus a critical sequence for diagnosing CMBs (10). In addition to the manifestations mentioned above, CMBs are characterized by a distribution in the subcortical area, basal ganglia, thalamus, brainstem, and cerebellum and a pathogenesis of microvascular damage, resulting in blood extravasation and deposition of hemosiderin (11).

Relatively little research has been conducted on neonatal CMBs, with even less on the related risk factors. However, identifying these risk factors and actively preventing microbleeds is particularly critical for clinical management. Therefore, this study aimed to develop and validate a nomogram based on clinical characteristics to predict the occurrence of CMBs in neonates and to provide a critical aid in the early diagnosis, treatment, and improvement of prognosis. We present this article in accordance with the TRIPOD reporting checklist (available at https://qims.amegroups.com/article/view/10.21037/qims-24-1274/rc).

Methods

Participants

This study was conducted in accordance with the Declaration of Helsinki (as revised in 2013) and was approved by the Medical Ethics Committee of The First Hospital of Tsinghua University [No. 2024-012-01(R)]. The need for obtaining written informed consent from participants was waived due to the retrospective nature of the analysis. When clinical physicians suspect or are concerned about the presence of cranial diseases in neonates, magnetic resonance imaging (MRI) examinations may be performed on neonates. Therefore, the MRI data of 230 neonates were retrospectively collected in our hospital from July 2017 to October 2023 according to the following inclusion criteria: (I) age ≤28 days after birth; (II) completion of routine MRI and SWI examination; and (III) complete clinical data. Meanwhile, the exclusion criteria were as follows: (I) a history of genetic or metabolic diseases or congenital structural malformations; (II) a history of severe infections or other serious diseases; and (III) MRI with motion artifacts. Neonates with genetic metabolic diseases, congenital structural abnormalities, or severe infections, such as hypoglycemic encephalopathy, hypoxic-ischemic encephalopathy, and bilirubin encephalopathy, etc., were also excluded, as these render the examination of CMBs diagnostically and therapeutically irrelevant among neonates.

Collection of clinical data

Clinical data were obtained through query of the medical record system and included gender, term or premature birth, mode of delivery, gestational age, days after birth, adjusted gestational age, birth weight, Apgar score, history of asphyxia, neonatal pneumonia, metabolic acidosis, mechanical ventilation, gestational hypertension, and gestational diabetes. All clinical data were obtained prior to the MRI examination.

Image acquisition and analysis

MR images were acquired on a 1.5-T MR system (Ingenia, Philips Healthcare, Best, the Netherlands) with an 8-channel head coil. The neonates were sedated before the scan, a plastic sponge was used to secure the head, and earplugs were used to reduce noise. Four sequences were obtained: T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and SWI. The DWI parameters were as follows: field of view (FOV) =16 cm × 16 cm, repetition time (TR) =3,642 ms, echo time (TE) =102 ms, matrix =128×128, excitation time =4, slice thickness =5 mm, slice spacing =1 mm, and B value =800 s/mm². The SWI parameters were as follows: FOV =16 cm × 16 cm, TR =35 ms, TE =50 ms, flip angle (FA) =15°, matrix =320×224, excitation time =0.75, and slice thickness =2 mm. After SWI, the amplitude map and phase map were obtained. The venography image was reconstructed on the postprocessing workstation using the minimum intensity projection technique.

The imaging results were separately reviewed by two attending physicians, and another deputy chief physician was asked to make a final decision when there was a disagreement. The criteria for detecting CMBs by SWI were as follows: low signal intensity on the SWI sequence and lesion size <10 mm. Moreover, CMBs could be differentiated from calcification, as CMBs show a low signal on the phase map, while calcification shows a high signal. Finally, venules and enlarged perivascular spaces could be excluded by the continuous observation from multiple sequences (T1, T2, DWI, and SWI). The neonates with CMBs were further divided into mild and moderate-to-severe groups according to the number of CMBs: those with ≤4 were defined as mild, and those with >4 were defined as moderate-to-severe (12). Cerebral ventricular microbleeds (CVMBs) include subarachnoid hemorrhage, anterior and posterior horn hemorrhage, and choroidal plexus hemorrhage. The diagnostic method for CVMBs is the same as that for CMBs, with the only difference being localization. CVMBs are located in the ventricular system. Subdural hemorrhage refers to the accumulation of blood between the dura mater and the arachnoid membrane. On MRI, it appears as a crescent-shaped abnormal signal in the subplate of the skull. In the acute phase, T1WI shows a low isosignal while T2WI shows a low signal; during the subacute phase, T1WI shows a high signal while T2WI shows a high or low signal; during the chronic phase, T1WI shows a low signal while T2WI shows a high signal. The ischemic infarction refers to spotted T1WI high signal, T2WI low signal, DWI high or equal signal within the brain parenchyma. Finally, we gathered the following data: CVMBs, subdural hemorrhage, ischemic infarction, CMBs, the number of CMBs, and the grading of CMBs.

Statistical analysis

Data processing and analysis were performed using R version 4.3.0 (2023-04-21; The R Foundation for Statistical Computing) and with the Storm Statistical Platform (www.medsta.cn/software). Independent-samples t-tests and Mann-Whitney tests were used to compare continuous variables, expressed as the mean ± standard deviation or as median (Q₂₅, Q₇₅). The chi-square test or Fisher exact test was used to compare categorical variables, which are described as the number of cases. Multivariate analysis was conducted for variables with a P value <0.2 in the univariate analysis (13), and the significant variables (P<0.05) were screened to construct the nomogram. The diagnostic performance of the models was assessed using the area under the curve (AUC) of the receiver operating characteristic (ROC) curve. The optimal threshold value of the ROC curve was determined through the Youden index. The corresponding specificity and sensitivity values were calculated. The Hosmer-Lemeshow test was performed to estimate the goodness of fit of the model, and the calibration curves were plotted for the model, which was capable of visualizing the consistency of the models. Finally, clinical decision curve analysis was used to evaluate the clinical application value of the model. The model’s performance also was validated using a validation cohort. P<0.05 indicated statistical significance.

Results

Clinical and MRI data

The screening and grouping process for CMBs is shown in Figure 1. A total of 230 neonates were randomly sampled and divided into a training cohort (n=161) and a validation cohort (n=69) at a 7:3 ratio. The clinical characteristics of the neonates with and without CMBs are listed in Table 1. The mode of delivery, days after birth, Apgar score, neonatal pneumonia, metabolic acidosis, and gestational diabetes significantly differed between the two groups in the training cohort (P<0.05). The mode of delivery and Apgar score were significantly different between the two groups in the validation cohort (P<0.05). No significant differences were found for other clinical characteristics between the two groups with and without CMBs in the training or validation cohorts (P>0.05). Except for neonatal pneumonia, there was no significant difference between the training and validation cohorts (P>0.05). The specific MRI findings of mild and moderate-to-severe CMBs are shown in Figure 2.

Figure 1 Flowchart of the screening and grouping for neonates with CMBs. CMBs, cerebral microbleeds.

Figure 2 MRI findings of mild and moderate-to-severe CMBs. (A-D) T1WI, T2WI, amplitude map, and phase map of neonates with moderate-to-severe CMBs. Multiple CMBs (n=5) in both cerebellar hemispheres (blue arrows). (E-H) T1WI, T2WI, amplitude map, and phase map of neonates with mild CMBs. A single cerebral parenchymal microbleed in the left cerebellar vermis (green arrows). MRI, magnetic resonance imaging; CMBs, cerebral microbleeds; T1WI, T1-weighted imaging; T2WI, T2-weighted imaging.

Nomogram construction

Univariate logistic regression analysis revealed differences in birth period (term vs. premature), mode of delivery, days after birth, birth weight, Apgar score, history of asphyxia, neonatal pneumonia, metabolic acidosis, gestational hypertension, and gestational diabetes between neonates with and without CMBs in the training cohort (P<0.2). Multivariate analysis was conducted for the variables with a P value <0.2 in the univariate analysis. According to the multivariate logistic analysis, spontaneous delivery [odds ratio (OR) =7.88; 95% confidence interval (CI): 3.27–19.00; P<0.001], neonatal pneumonia (OR =2.63; 95% CI: 1.16–6.25; P=0.020), gestational hypertension (OR =4.69; 95% CI: 1.35–16.26; P=0.015), and gestational diabetes (OR =3.60; 95% CI: 1.24–10.40; P =0.018) were the independent risk factors for neonatal CMBs (Table 2). The percentage of CMBs in the different groups according to multivariate logistic regression analysis is shown in Figure 3. A nomogram was constructed from the variables with P<0.05 in the multivariate analysis, as shown in Figure 4.

Figure 3 Proportion of different factors in the CMBs and without-CMBs groups according to the multivariate logistic regression analysis. (A) The percentage of cesarean and spontaneous deliveries in the CMBs and without-CMBs groups. (B) The percentage of neonates with and without neonatal pneumonia in the CMBs and without-CMBs groups. (C) The percentage of neonates with and without gestational hypertension in the CMBs and without-CMBs groups. (D) The percentage of neonates with and without gestational diabetes in the CMBs and without-CMBs groups. CMBs, cerebral microbleeds.

Figure 4 A clinical features-based nomogram for predicting CMBs in neonates. Mode of delivery: 0/1= spontaneous delivery/cesarean; neonatal pneumonia: 0/1= no/yes; gestational hypertension: 0/1= no/yes; gestational diabetes: 0/1= no/yes. CMBs, cerebral microbleeds.

Nomogram performance

The ROC curves for the training and validation cohorts are shown in Figure 5A,5B. The AUC of the models for predicting neonates with CMBs was 0.811 (95% CI: 0.746–0.877), and the corresponding optimal threshold, specificity, and sensitivity were 0.630, 0.872, and 0.627 in the training cohort, respectively. The AUC of the models was 0.780 (95% CI: 0.667–0.892), and the corresponding optimal threshold, specificity, and sensitivity were 0.366, 0.649, and 0.875 in the validation cohort, respectively. The calibration curve indicated a good performance in the training and validation cohorts (Figure 5C,5D). The decision curve showed that the nomograms had a favorable net clinical benefit in the training and validation cohorts (Figure 5E,5F).

Figure 5 The ROC curve, calibration curve, and decision curve of the model. (A,B) ROC curves of the training and validation cohorts. (C,D) Calibration curves of the training and validation cohorts. (E,F) Decision curves of the training and validation cohorts. AUC, area under the curve; CI, confidence interval; ROC, receiver operating characteristic.

Neonates with mild and moderate-to-severe CMBs

We also analyzed differences in clinical and MRI characteristics between neonates with mild and moderate-to-severe CMBs. However, because of the small sample size of the moderate-to-severe group, we only established the prediction model and did not validate it. There was no significant difference between the two groups except for the incidence of ischemic infarction. Multivariate analysis revealed that ischemic infarction (OR =5.00; 95% CI: 1.51–16.60; P=0.009) was an independent risk factor for moderate-to-severe CMBs. The AUC was 0.731 (95% CI: 0.574–0.888), and the corresponding optimal threshold, specificity, and sensitivity were 0.187, 0.786, and 0.706, respectively (Table S1 and Figure S1).

Discussion

The clinical manifestations of neonatal CMBs differ due to differences in the location and amount of bleeding. Some patients have no obvious clinical manifestations, and some have symptoms of hyporesponsiveness, paroxysmal respiratory rhythm disorder, and lethargy, etc., but these symptoms are not specific (14). MRI is noisy, time-consuming, and costly, posing a challenge for newborns and their families (15). Therefore, based on the clinical manifestations of neonates and the accessibility of laboratory tests, screening for neonates with high-risk factors and conducting MRI examinations have become the top priorities. However, a scoring model for predicting whether neonates will develop CMBs has not been developed. Therefore, we included items that are required to be examined after birth and developed and validated a risk model based on these characteristics. The results of multivariate analysis indicated that spontaneous delivery, neonatal pneumonia, gestational hypertension, and gestational diabetes could lead to the occurrence of neonatal CMBs. The prediction of CMBs based on these clinical risk factors represents a practical innovation in the field.

Åberg et al. reported that prolonged fetal suction-assisted delivery could increase the risk of intracranial hemorrhage in full-term neonates (16). Another study noted that the risk of intracranial hemorrhage was similar between successful vaginal delivery and transfer to a cesarean section after the failure of vaginal delivery, but both bore a greater risk than did natural vaginal delivery and elective cesarean section (17). Other research suggested that even neonates without intervention via vaginal delivery may experience intracranial hemorrhage (18). Our study also confirmed that spontaneous delivery is a risk factor for neonatal CMBs. These findings collectively indicate that vaginal delivery, whether assisted by suction or forceps or whether a natural delivery, is associated with an increased incidence of intracranial hemorrhage in neonates. We speculate that this might be related to the prolonged passage of the fetal head through the birth canal, and significant squeezing, fetal suction, or delivery forceps may cause greater compression and deformation of the intracranial veins, leading to impaired cerebral blood flow self-regulation and resulting in capillary rupture and bleeding.

One study reported that neonatal pneumonia may induce intracranial hemorrhage, which is consistent with our research (19). Neonatal pneumonia can trigger a series of inflammatory and immune responses, affecting the respiratory and circulatory functions of the child, leading to hypoxemia and hypercapnia and even affecting the development of the nervous system. The most severely damaged area is the periventricular white matter, mainly affecting the immature vascular system and blood-brain barrier (20). This may be attributable to the active division of embryonic germinal stromal cells in the subventricular membrane of the neonatal lateral ventricle, abundant capillaries, loose structure, and a lack of connective tissue support. Pneumonia causes an increase in inflammatory factors and oxygen consumption, resulting in impaired cerebral vascular autoregulation function, increased intravascular pressure, capillary rupture, and ultimately intracranial hemorrhage (21). In addition, hypoxia and hypercapnia secondary to inflammation can lead to a large accumulation of acidic metabolites, damage vascular endothelial cells, increase vascular permeability, and result in intracranial hemorrhage (22,23).

Previous studies have identified gestational hypertension as a risk factor for adverse outcomes in neonates (24-27). Pregnant women with gestational hypertension experience uterine and placental vascular spasm and sclerosis, narrowing of the lumen, and decreased blood flow, leading to insufficient placental blood supply. Fetal hypoxia increases intracranial vascular permeability and causes capillary rupture and bleeding. Therefore, the prevention and treatment of gestational hypertension are crucial for the prevention of neonatal CMBs.

Gestational diabetes is a common perinatal disease, and its incidence rate is increasing annually (28). This disease can worsen the intrauterine environment of the fetus, affecting its growth and development (29). Recent literature (30) indicates that diabetes during pregnancy is related to an increase in intraventricular hemorrhage and other intracranial hemorrhages in newborns but is not related to the severity of hemorrhage, which is highly consistent with the results of our study. Pregnant women, due to factors such as insulin deficiency, increased blood volume, and blood dilution during pregnancy, as well as the antagonistic effect of estrogen and progesterone synthesized by the placenta during pregnancy, experience a significant decrease in the insulin sensitivity of various organs, leading to insulin resistance and abnormal glucose metabolism. A continuous increase in blood glucose can cause acidosis and chronic hypoxia in the fetus, thereby increasing vascular permeability and contributing to capillary rupture and bleeding (31-33).

A study that included 495 premature infants with a gestational age <29 weeks reported that the incidence of intracranial hemorrhage decreased with increasing gestational age (34). However, this study did not find gestational age to be a risk factor for neonatal CMBs. This may be because all neonates included were ≥32 weeks old and were premature or term infants, with extremely premature or ultrapremature infants being excluded. Moreover, there were many term infants, which might have masked the findings of the study. Other research (35,36) suggests that mechanical ventilation increases the incidence of intracranial hemorrhage in newborns after birth, but we did not find this to be the case in our study. This may be related to the small number of newborns who received mechanical ventilation, and there was no statistically significant difference in mechanical ventilation between the CMBs group and the normal newborn group. Yeo et al. analyzed the risk factors for severe intracranial hemorrhage in premature infants with a gestational age <32 weeks and reported that a 5-minute Apgar score <7 was associated with a two times greater risk of intracranial hemorrhage in these infants as compared to controls, which diverges from the results of this study (37). We believe that the Apgar score may vary according to gestational age, birth weight, maternal medication, administration of anesthesia, and other individual factors and is subjective. The evaluation of the response to resuscitation may help but should not be used to infer the results.

Our study involved certain limitations which should be acknowledged. First, the model constructed for grading CMBs in this study revealed that neonatal CMBs were more prone to moderate-to-severe progression when there was an ischemic infarction, and the ROC curve was 0.731. However, this approach has not been validated, and no other significant results were obtained. This may be due to the fact that there were only 17 neonates in the moderate-to-severe CMBs group. In the future, we will expand the sample size to conduct detailed studies on the mild and moderate-to-severe groups. Second, we employed a single-center, retrospective design with a small sample size, and most neonates were lost to follow up. Subsequent multicenter, large-sample studies with follow-up observation and statistical analysis of adverse outcomes of CMBs should be conducted. Finally, this study did not further examine the distribution of CMBs or the relevance of the different parts of CMBs. Additionally, the clinical indicators were not refined, and the sample size will be further expanded for confirmation.

Conclusions

The model constructed based on clinical risk factors in this study provides a noninvasive method for quickly predicting the occurrence of neonatal CMBs. It can facilitate active and early intervention in neonates with risk factors in clinical practice, avoid or reduce iatrogenic treatments or procedures that affect cerebral hemodynamics, and closely monitor laboratory indicators, which is conducive to reducing the occurrence of CMBs and safeguarding the life and health of neonates.

Acknowledgments

We thank the families who generously allowed their children to participate and the medical staff of The First Hospital of Tsinghua University for their generous support.

Funding: None.

Footnote

Reporting Checklist: The authors have completed the TRIPOD reporting checklist. Available at https://qims.amegroups.com/article/view/10.21037/qims-24-1274/rc

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-24-1274/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. This study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). The study was approved by the Medical Ethics Committee of The First Hospital of Tsinghua University [No. 2024-012-01(R)]. The need for obtaining written informed consent from participants was waived due to the nature of the retrospective analysis.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

References

1. Heit JJ, Iv M, Wintermark M. Imaging of Intracranial Hemorrhage. J Stroke 2017;19:11-27. [Crossref] [PubMed]
2. Loth C, Treluyer L, Pierrat V, Ego A, Aubert AM, Debillon T, Zeitlin J, Torchin H, Chevallier MEPICE AND SHIPS RESEARCH GROUP. Variations in neonatal mortality of preterm infants with intraparenchymal haemorrhage in Europe: the EPICE cohort. Arch Dis Child Fetal Neonatal Ed 2024;109:488-94.
3. Bolisetty S, Dhawan A, Abdel-Latif M, Bajuk B, Stack J, Lui KNew South Wales and Australian Capital Territory Neonatal Intensive Care Units' Data Collection. Intraventricular hemorrhage and neurodevelopmental outcomes in extreme preterm infants. Pediatrics 2014;133:55-62. Erratum in: Pediatrics 2019;144:e20192079. [Crossref] [PubMed]
4. Tortora D, Severino M, Sedlacik J, Toselli B, Malova M, Parodi A, Morana G, Fato MM, Ramenghi LA, Rossi A. Quantitative susceptibility map analysis in preterm neonates with germinal matrix-intraventricular hemorrhage. J Magn Reson Imaging 2018;48:1199-207. [Crossref] [PubMed]
5. Barreto ARF, Carrasco M, Dabrowski AK, Sun LR, Tekes A. Subpial Hemorrhage in Neonates: What Radiologists Need to Know. AJR Am J Roentgenol 2021;216:1056-65. [Crossref] [PubMed]
6. Cheshire EC, Biggs MJP, Hollingbury FE, Fitzpatrick-Swallow VL, Prickett TRA, Malcomson RDG. Frequency of macroscopic intradural hemorrhage with and without subdural hemorrhage in early childhood autopsies. Forensic Sci Med Pathol 2019;15:184-90. [Crossref] [PubMed]
7. Gregoire SM, Chaudhary UJ, Brown MM, Yousry TA, Kallis C, Jäger HR, Werring DJ. The Microbleed Anatomical Rating Scale (MARS): reliability of a tool to map brain microbleeds. Neurology 2009;73:1759-66. [Crossref] [PubMed]
8. Haacke EM, Xu Y, Cheng YC, Reichenbach JR. Susceptibility weighted imaging (SWI). Magn Reson Med 2004;52:612-8. [Crossref] [PubMed]
9. Meoded A, Poretti A, Northington FJ, Tekes A, Intrapiromkul J, Huisman TA. Susceptibility weighted imaging of the neonatal brain. Clin Radiol 2012;67:793-801. [Crossref] [PubMed]
10. Luo Y, Gao K, Fawaz M, Wu B, Zhong Y, Zhou Y, Haacke EM, Dai Y, Liu S. Automatic detection of cerebral microbleeds using susceptibility weighted imaging and artificial intelligence. Quant Imaging Med Surg 2024;14:2640-54. [Crossref] [PubMed]
11. Schreiber S, Wilisch-Neumann A, Schreiber F, Assmann A, Scheumann V, Perosa V, Jandke S, Mawrin C, Carare RO, Werring DJ. Invited Review: The spectrum of age-related small vessel diseases: potential overlap and interactions of amyloid and nonamyloid vasculopathies. Neuropathol Appl Neurobiol 2020;46:219-39. [Crossref] [PubMed]
12. Lee SH, Bae HJ, Ko SB, Kim H, Yoon BW, Roh JK. Comparative analysis of the spatial distribution and severity of cerebral microbleeds and old lacunes. J Neurol Neurosurg Psychiatry 2004;75:423-7. [Crossref] [PubMed]
13. Tsujimoto T, Kanayama M, Fukada S, Oha F, Shimamura Y, Hasegawa Y, Hashimoto T, Kakutani K, Yurube T, Takeoka Y, Miyazaki K, Iwasaki N. Predictors of Persistent Postoperative Numbness Following Lumbar Fusion in Patients Older Than 75 Years: A Minimum 2-Year Follow-up. Neurospine 2024;21:596-605. [Crossref] [PubMed]
14. Xu J, Sisniega A, Zbijewski W, Dang H, Stayman JW, Mow M, Wang X, Foos DH, Koliatsos VE, Aygun N, Siewerdsen JH. Technical assessment of a prototype cone-beam CT system for imaging of acute intracranial hemorrhage. Med Phys 2016;43:5745. [Crossref] [PubMed]
15. Kozak BM, Jaimes C, Kirsch J, Gee MS. MRI Techniques to Decrease Imaging Times in Children. Radiographics 2020;40:485-502. [Crossref] [PubMed]
16. Åberg K, Norman M, Pettersson K, Järnbert-Pettersson H, Ekéus C. Protracted vacuum extraction and neonatal intracranial hemorrhage among infants born at term: a nationwide case-control study. Acta Obstet Gynecol Scand 2019;98:523-32. [Crossref] [PubMed]
17. Simonson C, Barlow P, Dehennin N, Sphel M, Toppet V, Murillo D, Rozenberg S. Neonatal complications of vacuum-assisted delivery. Obstet Gynecol 2007;109:626-33. [Crossref] [PubMed]
18. Looney CB, Smith JK, Merck LH, Wolfe HM, Chescheir NC, Hamer RM, Gilmore JH. Intracranial hemorrhage in asymptomatic neonates: prevalence on MR images and relationship to obstetric and neonatal risk factors. Radiology 2007;242:535-41. [Crossref] [PubMed]
19. Rocha G, Soares P, Gonçalves A, Silva AI, Almeida D, Figueiredo S, Pissarra S, Costa S, Soares H, Flôr-de-Lima F, Guimarães H. Respiratory Care for the Ventilated Neonate. Can Respir J 2018;2018:7472964. [Crossref] [PubMed]
20. Atienza-Navarro I, Alves-Martinez P, Lubian-Lopez S, Garcia-Alloza M. Germinal Matrix-Intraventricular Hemorrhage of the Preterm Newborn and Preclinical Models: Inflammatory Considerations. Int J Mol Sci 2020;21:8343. [Crossref] [PubMed]
21. Huang J, Meng J, Choonara I, Xiong T, Wang Y, Wang H, Qu Y, Mu D. Antenatal infection and intraventricular hemorrhage in preterm infants: A meta-analysis. Medicine (Baltimore) 2019;98:e16665. [Crossref] [PubMed]
22. Cheng XR, Xia PG, Shi ZY, Xu QY, Luo CH, Lei MY, Zhang Q. Increased risk of intracranial hemorrhage in preterm infants with OPRM1 gene A118G polymorphism. Ann Transl Med 2019;7:478. [Crossref] [PubMed]
23. Novak CM, Ozen M, Burd I. Perinatal Brain Injury: Mechanisms, Prevention, and Outcomes. Clin Perinatol 2018;45:357-75. [Crossref] [PubMed]
24. Ge G, Zhang Y, Zhang M. Pregnancy-induced hypertension and retinopathy of prematurity: a meta-analysis. Acta Ophthalmol 2021;99:e1263-73. [Crossref] [PubMed]
25. Epstein KN, Kline-Fath BM, Zhang B, Venkatesan C, Habli M, Dowd D, Nagaraj UD. Prenatal Evaluation of Intracranial Hemorrhage on Fetal MRI: A Retrospective Review. AJNR Am J Neuroradiol 2021;42:2222-8. [Crossref] [PubMed]
26. Yusuf K, Alshaikh B, da Silva O, Lodha AK, Wilson RD, Alvaro RE, Lee SK, Shah PSCanadian Neonatal Network Investigators. Neonatal outcomes of extremely preterm infants exposed to maternal hypertension and cigarette smoking. J Perinatol 2018;38:1051-9. [Crossref] [PubMed]
27. Hu J, Li Y, Zhang B, Zheng T, Li J, Peng Y, Zhou A, Buka SL, Liu S, Zhang Y, Shi K, Xia W, Rexrode KM, Xu S. Impact of the 2017 ACC/AHA Guideline for High Blood Pressure on Evaluating Gestational Hypertension-Associated Risks for Newborns and Mothers. Circ Res 2019;125:184-94. [Crossref] [PubMed]
28. Sweeting A, Wong J, Murphy HR, Ross GP. A Clinical Update on Gestational Diabetes Mellitus. Endocr Rev 2022;43:763-93. [Crossref] [PubMed]
29. McIntyre HD, Catalano P, Zhang C, Desoye G, Mathiesen ER, Damm P. Gestational diabetes mellitus. Nat Rev Dis Primers 2019;5:47. [Crossref] [PubMed]
30. Farghaly MAA, Qattea I, Ali MAM, Saker F, Mohamed MA, Aly H. Intracranial hemorrhages in infants of diabetic mothers: A national cohort study. Early Hum Dev 2023;183:105796. [Crossref] [PubMed]
31. Kleinwechter H, Schäfer-Graf U, Bührer C, Hoesli I, Kainer F, Kautzky-Willer A, Pawlowski B, Schunck KU, Somville T, Sorger M. Diabetes and Pregnancy. Exp Clin Endocrinol Diabetes 2021;129:S1-8. [Crossref] [PubMed]
32. Vela-Huerta MM, Amador-Licona M, Medina-Ovando N, Aldana-Valenzuela C. Factors associated with early severe intraventricular haemorrhage in very low birth weight infants. Neuropediatrics 2009;40:224-7. [Crossref] [PubMed]
33. Coucha M, Abdelsaid M, Ward R, Abdul Y, Ergul A. Impact of Metabolic Diseases on Cerebral Circulation: Structural and Functional Consequences. Compr Physiol 2018;8:773-99. [Crossref] [PubMed]
34. Khanafer-Larocque I, Soraisham A, Stritzke A, Al Awad E, Thomas S, Murthy P, Kamaluddeen M, Scott JN, Mohammad K. Intraventricular Hemorrhage: Risk Factors and Association With Patent Ductus Arteriosus Treatment in Extremely Preterm Neonates. Front Pediatr 2019;7:408. [Crossref] [PubMed]
35. Yoon SJ, Lim J, Han JH, Shin JE, Eun HS, Park MS, Park KI, Lee SM. Impact of neonatal resuscitation changes on outcomes of very-low-birth-weight infants. Sci Rep 2021;11:9003. [Crossref] [PubMed]
36. Debay A, Patel S, Wintermark P, Claveau M, Olivier F, Beltempo M. Association of Delivery Room and Neonatal Intensive Care Unit Intubation, and Number of Tracheal Intubation Attempts with Death or Severe Neurological Injury among Preterm Infants. Am J Perinatol 2022;39:776-85. [Crossref] [PubMed]
37. Yeo KT, Thomas R, Chow SS, Bolisetty S, Haslam R, Tarnow-Mordi W, Lui KAustralian and New Zealand Neonatal Network. Improving incidence trends of severe intraventricular haemorrhages in preterm infants <32 weeks gestation: a cohort study. Arch Dis Child Fetal Neonatal Ed 2020;105:145-50. [Crossref] [PubMed]