Re-evaluating the timing of sequential cranial ultrasound screening in very preterm infants for predicting neurodevelopmental outcomes: A Population Study

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Sequential cranial ultrasound (CUS) is the standard of care for imaging evaluation. There is no consensus on the timing and frequency of CUS screening. At our institution four time points CUS are performed for screening. We hypothesize that the 2-week CUS is not necessary for NDI prognostication. Materials and methods: In this retrospective, multi-center, population-based cohort, we included all liveborn VPI born 22 0 -30 6 weeks gestation between January 2004 and December 2018 who had a neurodevelopmental assessment at 36 months corrected age. A model with and without 2-week CUS was compared to a reference model including gestational age, infant sex, and 6-week CUS. Results: Out of 786 preterm babies born during the study period, 656 survivors were included in the analysis. 30% of our cohort has NDI as per clinical assessment. The mean gestational age was 27.8 weeks with the mean birth weight being 1133 grams, and 55% being male infants. One in three survivors developed NDI when assessed at 36 months of corrected age. There was no strong evidence that the addition of the 2-weeks CUS meaningfully contributes to the risk prediction of any NDI or major NDI. Models with and without the 2-week CUS showed nearly identical performance across a broad set of metrics. Conclusion: The comparison of two methods of sequential CUS screening showed reliable performance of the 3-time points model to predict NDI at 36 months of corrected age. Cranial ultrasound Cerebral Palsy Intraventricular hemorrhage Prematurity Periventricular leukomalacia White matter brain injury Figures Figure 1 Figure 2 Figure 3 Figure 4 Clinical relevance statement The need for a two-week cranial ultrasound (CUS) to predict neurodevelopmental impairment (NDI) can be eliminated if no significant abnormalities are detected during the initial one-week CUS, promoting prudent resource allocation and stewardship in healthcare. Key points 1 – Sequential CUS is used to identify very preterm infants at increased risk of neurodevelopmental impairment. 2 – No consensus exists on the timing and frequency of cranial ultrasound screening in very preterm infants. 3 – 3 time point CUS is reliable for prognostication of neurodevelopmental impairment at 36 months corrected age. Introduction Preterm births remain a significant public health concern globally, with infants born prematurely facing increased risks of neurodevelopmental impairment (NDI) including cerebral palsy (CP), cognitive and language delay and behavioral problems ( 1 , 2 ). Among the myriad challenges in caring for preterm infants, the accurate prediction and early detection of NDI stands as a crucial endeavor for healthcare providers ( 3 – 5 ). The two universally established imaging risk factors for adverse neurodevelopmental outcomes are intraventricular hemorrhage (IVH) and periventricular leukomalacia (PVL) ( 3 , 5 ). Numerous investigative studies have established not only the utility of cranial ultrasound (CUS) in detecting intracranial abnormalities but also its ability to predict neurodevelopmental outcomes in preterm infants ( 6 – 9 ). Several studies focused on comparing the diagnostic performance of CUS to brain magnetic resonance imaging (MRI) ( 4 , 10 – 12 ). While MRI is superior in assessing cortical development, non-cystic white matter injury, and punctate cerebellar lesions, its limited availability, need for transport, sedation, and high-cost limit its widespread use. Unlike MRI, CUS can be done at the bedside and can be repeated as needed. Recent literature has delved deeper to suggest that the timing and frequency of CUS screening play a crucial role in identifying preterm infants at risk for NDI later on in life ( 3 , 13 – 15 ). Studies have demonstrated that while early CUS screening (within the first week of life) provides valuable information about initial brain injury, which is usually an IVH, sequential CUS screening allows for (i) detection of evolving abnormalities, such as the post-hemorrhagic ventricular dilatation, (ii) diagnosis of the latter occurring white matter injury (PVL, porencephaly, any sequela of ischemic changes), and (iii) assessment of brain growth and maturation. Consensus exists to perform early routine CUS during the 1st week of life and a late CUS at term equivalent-age ultrasound (TEA) around 36–40 weeks post menstrual age ( 3 , 5 , 8 , 9 , 14 ). However, there is no consensus on the timing for routine imaging between these time points, particularly with the absence of abnormalities on initial screening. The updated 2021 Canadian Pediatric Society (CPS) guidelines advise routine CUS between 4–7 days, a repeat CUS between 4–6 weeks, and only routine TEA scans in children born before 26 weeks’ gestation age ( 16 ). While the 2021 Canadian Neonatal Network consensus guidelines recommended performing three-time points CUS within 4–7 days of life in VPI < 32 weeks’ gestation and if this is normal, their proposed algorithm suggests performing a late (4-6-week) CUS and a TEA (36–40 week) CUS ( 9 ). Similarly, in 2020 the American Academy of Pediatrics recommended the first screening CUS before 7–10 days after birth and repeat CUS between 4–6 weeks and at TEA or before hospital discharge ( 3 ). However, the European Ultrasound Brain Group advises a far more extensive screening protocol in stable preterm infants born after 28 weeks of gestation, with the frequency of serial CUS being days 1, 3, 7, 14, 28, at 6 weeks, and at TEA ( 14 ). Despite the updated North American recommendations, centres like ours, which is considered one of the major tertiary referral centres at the Canadian Easy Coast are still hesitant to adopt the latest guidelines with the reduced number of CUS scanning. At IWK CUS is routinely performed on all VPI (< 31 weeks’ gestation) admitted to the neonatal intensive care unit (NICU) at four time points; within the first week after birth, at two and 6 weeks of age, and at or near TEA following the prior 2001 CPS guidelines ( 17 ). The practice of performing four-time point CUS places a huge burden on the Radiology department, especially in a high-volume tertiary referral center like IWK Children’s Hospital. There is also increased anxiety among the parents surrounding each study performed. The current study question asks, is the performance of both the 2-week and 6-week routine CUS necessary? Or does the 6-week CUS alone suffice in identifying the infants at risk of NDI. The hypothesis is that the 2-week CUS is not impacting NDI prognostication without a significant abnormality on the 1st week CUS, mandating a short-term interval follow-up. Therefore, we conducted this retrospective population-based cohort study aimed to explore the yield of routine four time points sequential CUS screening in VPI, to evaluate the existing evidence regarding its predictive utility, and to provide stewardship into the optimal approach for clinicians caring for this vulnerable population. The study predominantly serves as proof of the principle of the appropriateness of the North American Guidelines in reducing the frequency of screening CUS in this population to support the change in practice in our institution and similar other centers. Materials and Methods IWK REB approval was received for protocol number 1025476. The parents or guardian of the child signs a consent form for participation in PFUP and inclusion of their child’s data in database for use in research is obtained at the first visit to Provincial Perinatal Follow-up Program (PFUP). 1- Study Population and Data Source The Nova Scotia PFUP administers neurodevelopmental assessment to all VPI whose mothers reside in the province. The program’s AC Allen Research Database captures the maternal, perinatal, neonatal and neurodevelopmental data, up to 36 months corrected gestational age (CGA) in addition to the worst radiological grading of brain injury. Infants included in the database were cared for in one of two tertiary care hospitals: the IWK Health and the Cape Breton Regional Hospital. This retrospective population-based study included all liveborn VPI 22 0 -30 6 weeks gestation born between January 2004 and December 2018 who had neurodevelopmental assessments at 36 months CGA. Infants with congenital anomalies, those receiving palliation at birth and NICU deaths were excluded. 2- Cranial ultrasound protocol and review All CUS are routinely analysed by paediatric trained Radiologists, and these issued reports were used to test the current hypothesis. Routine CUS are performed within the first week of life, at two and six-weeks of chronologic age and at or near TEA. The fourth time point is occasionally excluded when the 6-week CUS and TEA CUS coincide. The CUS reporting data was retrieved from the provincial Picture Archiving and Communications System (PACS). Imaging features were divided into three categories in accordance with the Canadian consensus guidelines for grading of brain injury related to prematurity (9): 1) Normal 2) Mild abnormality: Grade 1 or 2 IVH, grade 1 non cystic PVL, focal non-specific abnormality/ unilateral parenchymal echogenicity (haemorrhagic or ischemic). 3) Moderate to severe abnormality: Grade 3 IVH, periventricular parenchymal haemorrhagic venous infarction (formerly called grade 4 IVH), cystic PVL and non-punctate cerebellar lesions. 3- Neurodevelopmental outcome All VPI included in the study received regular assessments in the PFUP (a multidisciplinary team) to assess neurodevelopmental status at 4, 8, 18 and 36-months CGA. This included a neuromotor physical examination, the Bayley Scales of Infant and Toddler Development (BSITD) at 18 and 36-months CGA (version III) (18), and sensorineural screening (ophthalmologic and auditory assessments). Based on this neurodevelopmental assessment at 36 months CGA, VPI are categized into 2 broad groups: 1) those with or without NDI; or 2) those with or without major NDI (mNDI). Any NDI is defined as one or more of the following: a) any severity of CP, gross motor functional classification system (GMFCS) 1-5 (19); b) BSITD scores < 85 which represents more than 1 standard deviation (SD) below the mean in any domain (cognitive, motor or language); c) visual impairment with vision in the best eye < 20/200; or d) hearing impairment requiring hearing aids). mNDI is defined as one or more of the following: a) Severe CP (³ grade 3 GMFCS)(19) b) BSITD scores < 70 which represents more than 2 SD below the mean in any domain c) Visual impairment with vision in the best eye < 20/200; or d) Hearing impairment requiring aids. Data analysis The data selection included maternal, perinatal and neonatal candidate predictors. The maternal/prenatal factors included maternal age, single parent, gravidity, history of smoking, drug abuse, psychiatric disorder, gestational hypertension, maternal diabetes, prolonged rupture of membranes > 18 hours and chorioamnionitis. The perinatal factors included delivery factors (maternal receipt of antenatal steroids, intrapartum magnesium sulphate and mode of delivery ) and infant factors (gestational age, sex, birthweight z scores, 5 minutes Apgar score and delivery room resuscitation). The neonatal factors included respiratory distress syndrome, bronchopulmonary dysplasia, sepsis (positive blood or CSF culture), necrotising enterocolitis (³ Bell’s stage 2), retinopathy of prematurity, brain injury of prematurity (as defined above) and length of hospital stay. Missing data were imputed through a single imputation using fully conditional specification as implemented in the R package mice (20). Model development Multiple clinically significant variables were assessed for inclusion in prediction models. However, pmsampsize R package was (21) used to determine the number of variables that could be included was ~ 3 (sex, gestational age, and birthweight) and these were chosen based on clinical rationale. Additional variables were assessed in a sensitivity analysis. Model selection Given the need to evaluate multiple models based on predictive performance, a reference model approach was used for model selection (19, 22). Akaike Information Criterion (AIC) approach was chosen because leave one out cross-validation provides an unbiased estimate of out of sample error when the number of models being compared is small (23). Models compared included: 1. The reference model including gestational age, infant sex, 2-week ultrasound grading, and 6-week ultrasound grading. 2. A model dropping 2-week ultrasound grading. Discrimination and Calibration In addition to estimates of out of sample predictive error, models performance were further evaluated based on discrimination and calibration. All models were evaluated on the training data by plotting receiver operating characteristic curves and calculating the area underneath them (AUC-ROC) with 95% confidence interval (CI). The AUC can be interpreted as the probability that a randomly chosen case will have a higher predicted risk than a randomly chosen control (24). Values of AUC of 0.5 indicate that the model performs as well as random guessing. Higher values indicate that the model predicts the outcome, with values greater than 0.7 generally considered good and those higher than 0.9 indicate strong discrimination. The process of calibration determines if infants with a predicted likelihood of experiencing an event actually encounter it at that likelihood. In this study, calibration was assessed using functions from the runway package (25). This involves categorizing patients into bins based on their predicted risk levels and comparing this to the observed risk along with its confidence interval in the respective bin. Additional model diagnostic measures included sensitivity, specificity, negative predictive value, and positive predictive value as assessed in the training dataset. Decision Curve Analysis Ultimately whether a given prediction tool is useful depends on the balance of benefits and harms, or net benefit. A convenient way to visualize this trade-off is through the use of a decision curve, which plots the net benefit of treatment against the risk threshold used for decision making. The net benefit is calculated based on a combination of the potential patients that could benefit (prevalence) minus a penalty for false positives that is implicitly weighted by the decision maker’s threshold for risk (25). The primary benefit of decision curve analysis is that it only relies on the data used to fit the model, with the value of true/false positives made implicit through the decision threshold. Decision curves were fit for all proposed models in order to help guide the trade-off between accuracy and potential uptake. Results Out of 786 VPI born during the study period, 656 were included in the analysis representing 83% of the eligible cohort (Figure 1). Infants were excluded for missing outcome data only (n=23), lost to follow-up (n=28), and those who died before 36 months CGA (n=79). The mean gestational age for this cohort was 27.8 weeks (SD) with a mean birth weight of 1133 grams (SD) and 55% being male infants (Table 1). In this cohort, 31% had abnormal CUS findings at 2 weeks compared to 28% at 6 weeks chronological age. At 36 months CGA, 439/656 (66.9%) had no NDI and 217/656 (33.1%) developed any NDI. Eighty-six of 656 (13.1%) developed major NDI (mNDI), representing 39.6% of those with any NDI (Figure 1). The detailed maternal, perinatal and neonatal characteristics of the study population comparing those with no NDI to those with any NDI and significant NDI are shown in table 2. Table 3 compares the CUS findings at the 2-week and 6-week time points and neurodevelopmental outcomes. The study showed that 76% and 81% of infants who had normal 2-week and 6-week CUS, respectively, demonstrated no NDI. Whereas 24% and 19% of children without NDI demonstrated some abnormality at their 2-week and 6-week CUS, respectively. Children with an abnormal 2-week CUS resulted in 45% with any NDI, of which 57% represented mNDI. Similarly, out of those with abnormal 6-week CUS, 46% developed any NDI, of which 58% represented mNDI (Table 3). The details of the ultrasound findings at 2-week and 6-week screening are provided in Table 4. Generally, severe abnormality on CUS (grade 3 IVH, periventricular parenchymal hemorrhagic venous infarction, cerebellar parenchymal injury, and cystic PVL) were associated with NDI in this cohort (Table 4). The performance of the prediction model was assessed using AUC (with 95% confidence interval (CI). The AUC of the prediction models are presented in (table 5) specifically; 1) 2-week CUS AUC=0.66 (95% CI 0.61-0.70); 2) 6-week CUS AUC=0.68 (95% CI 0.63-0.72); and 3) combined 2 and 6-week CUS AUC=0.68 (95% CI 0.63-0.72). The diagnostic properties (sensitivity, specificity, positive predictive value, negative predictive value, and accuracy) of the 3 models at 2 and 6 weeks as well as the combined models show almost identical curves at all thresholds (Figure 2). The prediction models comparison (Figure 3) and decision curve analysis (Figure 4) further support this with almost identical performance of the 6-week and the combined 2 and 6-week models that are outperforming the 2-week model. Discussion In this population-based cohort of VPI, one-third developed NDI, and almost one in eight developed mNDI. Over the 14-year study period, the majority (76–81%) of VPI with normal 2-week and 6-week CUS did not go on to develop NDI, underscoring the negative predictive value of CUS screening. Models with and without the 2-week CUS showed near identical performance across a broad set of metrics including information criteria, significance of coefficients, AUC, sensitivity, specificity, NPV, PPV, calibration, and decision curves. This emphasizes that re-evaluating the frequency and timing of routine sequential CUS for VPI is valuable to reduce the burden on the radiology department, while not detrimentally affecting the diagnostic and prognostic role CUS plays. In our cohort, 24% of VPI with normal 2-weeks’ CUS and 19% of those with normal 6-weeks’ CUS subsequently developed NDI at 36 months CGA. This finding is in agreement with what was reported in the literature. Similarly, Broitman ( 26 ) reported that 39% of extremely low birth weight children with NDI having normal CUS and Kuban ( 27 ) noted that half of the VPI with CP at two years CGA were not preceded by CUS abnormalities. The prediction of NDI in VPI is indeed complex and best assessed with a multidisciplinary and multimodality approach. Several studies showed that the combined clinical assessment and the imaging findings provides the highest yield in predicting neurodevelopmental outcomes in children born preterm ( 26 , 28 – 30 ). Sequential CUS remains the standard of care for radiological screening of brain injury in VPI ( 3 , 9 , 14 , 16 ). However, the lack of consensus agreements between the major Canadian, American and European bodies on the best timing to acquire the CUS during the neonatal period add to the strains of healthcare systems and management decisions of this vulnerable population ( 3 , 9 , 14 , 16 ). Our study demonstrates that a 3 time point approach is reliable and is comparable to 4 points approach. The AUC of a combined 2 + 6 weeks model was identical to the 6 week CUS alone with overlapping 95% CI. This finding is congruent with the findings of Van Wezel-Meijler ( 15 ) who showed that increasing the frequency of CUS did not improve its diagnostic performance for white matter injury. Overall, the addition of the 2-weeks’ CUS does not meaningfully contributes to the risk prediction of any NDI or major NDI when making predictions at the time of hospital discharge. This supports the hypothesis that the routine 2-week CUS is not necessary in NDI prognostication and did not add any weight to the routine 6-weeks CUS. It is, therefore, proposed that a 3 time point CUS screening be adopted (namely 3–7 days, 6 weeks and TEA). This study has major strengths including its population-based design, the low loss to follow up rates and the standardized CUS protocols and neurodevelopmental assessments over the 14 years of the study period. We also reported the predictive performance (AUC and calibration plots) and the diagnostic properties of different models and used innovative decision-curve analysis to guide the best approach. This study is not without limitations. First, with the CUS being an operator dependant modality, the difference in quality of the CUS images over the study period in relation to the sonographer’s skills, machine models or probes resolution could not be assessed. Second, the lack of interobserver reliability; relying on CUS reports in the data collection with multiple radiologists interpreting the CUS over the 14 years study period, and no dual reading of the images. However, all studies were originally read by trained pediatric radiologists with multiple years of experience in paediatric neuroimaging. Thirdly, the interpretation of the 6-week scans is likely partially influenced by the availability of the reports of the 2-week CUS for the radiologist at time of reporting. While this could be considered a limitation, it also more accurately mimics the daily practice of the scan interpretation. Finally, while no cost benefit analysis has been performed, the proposed sequential CUS does reduce the cost burden on the institutions caring for these VPI and promote resources stewardship. Importantly, this helps in guiding the management decisions and conversation with families. Conclusion In this population-based cohort, two methods of sequential CUS screening were compared (three and four time-points) and showed reliable performance of the 3-time points model (first week, 6 weeks and TEA) to predict NDI at 36 months of CGA. Abbreviations - NICU - Neonatal Intensive Care Unit Declarations Disclosures Funding The project described was supported by RSNA Research and Education Foundation, through Philips/RSNA Seed Grant 2021 and IWK Project Grant 2021. The content is solely the responsibility of the authors and does not necessarily represent the official views of the RSNA R&E foundation or IWK Health Compliance with Ethical Standards 1. Guarantor: The scientific guarantor of this publication is Dr Tahani Ahmad. 2. Conflict of Interest: The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article. 3. Statistics and Biometry: Tim Disher (co-author) provided statistical analysis for this manuscript. 4. Informed Consent: The parents or guardian of the child signs a consent form for participation in PFUP and inclusion of their child’s data in database for use in research is obtained at the first visit to PFUP. 5. Ethical Approval: Institutional Review Board approval was obtained. 6. Methodology Methodology: · Retrospective design · Observational study · Multi-centre Author contributions SR was involved in the data collection, data analysis, and manuscript writing. TD was involved in statistical analysis and manuscript review. MV provided and managed the clinical database and performed manuscript review. JA and TA were both involved in the study design, data curation and analysis, and manuscript review. Data Sharing De-identified data analyzed during the study are available from the corresponding author by request. 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Tables Tables 1 to 5 are available in the Supplementary Files section Additional Declarations No competing interests reported. Supplementary Files TablesTimingofCUS.docx Cite Share Download PDF Status: Published Journal Publication published 04 Dec, 2024 Read the published version in Pediatric Radiology → Version 1 posted Editorial decision: Revision requested 10 Sep, 2024 Reviews received at journal 04 Sep, 2024 Reviews received at journal 25 Aug, 2024 Reviewers agreed at journal 15 Aug, 2024 Reviewers agreed at journal 14 Aug, 2024 Reviewers invited by journal 13 Aug, 2024 Editor assigned by journal 13 Aug, 2024 Submission checks completed at journal 13 Aug, 2024 First submitted to journal 11 Aug, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4896738","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":351555905,"identity":"88444486-df92-42c1-9c53-62872b928dac","order_by":0,"name":"Sunaina Ramdass","email":"","orcid":"","institution":"Dalhousie University","correspondingAuthor":false,"prefix":"","firstName":"Sunaina","middleName":"","lastName":"Ramdass","suffix":""},{"id":351555906,"identity":"955671fb-6e17-4bde-a2e4-9280fc0d0673","order_by":1,"name":"Tim Disher","email":"","orcid":"","institution":"Dalhousie University","correspondingAuthor":false,"prefix":"","firstName":"Tim","middleName":"","lastName":"Disher","suffix":""},{"id":351555907,"identity":"8f61e42f-7630-41ca-897b-e3b15e06fa59","order_by":2,"name":"Michael Vincer","email":"","orcid":"","institution":"Dalhousie University","correspondingAuthor":false,"prefix":"","firstName":"Michael","middleName":"","lastName":"Vincer","suffix":""},{"id":351555908,"identity":"f6a87909-161b-492f-9dbc-c9f6f843cde4","order_by":3,"name":"Jehier Afifi","email":"","orcid":"","institution":"Dalhousie University","correspondingAuthor":false,"prefix":"","firstName":"Jehier","middleName":"","lastName":"Afifi","suffix":""},{"id":351555909,"identity":"6f2d4523-0a99-4908-baed-6608ae1ef267","order_by":4,"name":"Tahani Ahmad","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3klEQVRIiWNgGAWjYFACHiA2kKjn528+wMDYQJQGsBaLBMkZxxJI0cJQkWBwIMeAOC327GcPfi4okMhjOHDmm8TPHTZyDOyHj27AawtPXrL0DAOJYsbm3m2SvWfSjBl40tJu4HdYjoE0j4EEYzPD2W0SvG2HExskeMzwa+F/Y/wbpKWNIeeZ5F+itEjkmIFsSexhyGGTJs6WG2/MrIFajCUkjhlby7alGbMR8gt7f47xbZ4/dXL255sf3nzbZiPHz374GF4tyIBFAkSyEascBJg/kKJ6FIyCUTAKRg4AAJMrRNYEKT+kAAAAAElFTkSuQmCC","orcid":"","institution":"Dalhousie University","correspondingAuthor":true,"prefix":"","firstName":"Tahani","middleName":"","lastName":"Ahmad","suffix":""}],"badges":[],"createdAt":"2024-08-11 22:55:40","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4896738/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4896738/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00247-024-06105-1","type":"published","date":"2024-12-04T15:57:37+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":66328886,"identity":"dba65f4d-6e48-4a89-988e-ed442dc1f435","added_by":"auto","created_at":"2024-10-10 13:09:14","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":41607,"visible":true,"origin":"","legend":"\u003cp\u003ePopulation flow chart\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4896738/v1/9017d823ca99696010b294dc.png"},{"id":66328883,"identity":"46b0d5c8-25c1-4bbc-a746-3b769d34fff0","added_by":"auto","created_at":"2024-10-10 13:09:14","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":80607,"visible":true,"origin":"","legend":"\u003cp\u003eDiagnostic properties of prediction models of NDI\u003c/p\u003e\n\u003cp\u003e-\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; Chart illustration of sensitivity, specificity, and positive and negative predictive values of the 2nd-week model, compared to the 6th-week model and the combined 2\u003csup\u003end\u003c/sup\u003e and 6th-week model.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4896738/v1/0825e3261002429825972e38.png"},{"id":66329536,"identity":"017c397e-2adb-4e11-af63-f11a03043d72","added_by":"auto","created_at":"2024-10-10 13:17:14","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":61845,"visible":true,"origin":"","legend":"\u003cp\u003eDiscriminative ability (AUC) of prediction models of NDI\u003c/p\u003e\n\u003cp\u003e- Area under the curve of the 2-week model, compared to the 6-week model and to the combined 2- and 6-week model\u003c/p\u003e\n\u003cp\u003e- Identical AUC of 6-week and combined models, both outer performing the 2-week Model.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4896738/v1/87e7137a1151273b32e17f66.png"},{"id":66328881,"identity":"bf2d7a53-5314-4025-a2e0-09bc31332e10","added_by":"auto","created_at":"2024-10-10 13:09:14","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":39850,"visible":true,"origin":"","legend":"\u003cp\u003eDecision curve of prediction models of NDI\u003c/p\u003e\n\u003cp\u003e- This graph represents the net benefit of the prediction models at different threshold probability thresholds.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4896738/v1/684d160050ef72e47a19455a.png"},{"id":70964744,"identity":"542f27f9-e881-4299-ae7d-cde6dfe25069","added_by":"auto","created_at":"2024-12-09 16:15:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":535833,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4896738/v1/b6a28aee-3b7b-4900-b7fd-b4ced011e41f.pdf"},{"id":66328885,"identity":"e1f78667-2b1b-4591-8c4d-290c64c90b75","added_by":"auto","created_at":"2024-10-10 13:09:14","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":300533,"visible":true,"origin":"","legend":"","description":"","filename":"TablesTimingofCUS.docx","url":"https://assets-eu.researchsquare.com/files/rs-4896738/v1/77f78285565fb493c288e7c2.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Re-evaluating the timing of sequential cranial ultrasound screening in very preterm infants for predicting neurodevelopmental outcomes: A Population Study","fulltext":[{"header":"Clinical relevance statement","content":"\u003cp\u003eThe need for a two-week cranial ultrasound (CUS) to predict neurodevelopmental impairment (NDI) can be eliminated if no significant abnormalities are detected during the initial one-week CUS, promoting prudent resource allocation and stewardship in healthcare.\u003c/p\u003e"},{"header":"Key points","content":"\u003cp\u003e1 – Sequential CUS is used to identify very preterm infants at increased risk of neurodevelopmental impairment.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e2 – No consensus exists on the timing and frequency of cranial ultrasound screening in very preterm infants.\u003c/p\u003e\n\u003cp\u003e3 – 3 time point CUS is reliable for prognostication of neurodevelopmental impairment at 36 months corrected age.\u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003ePreterm births remain a significant public health concern globally, with infants born prematurely facing increased risks of neurodevelopmental impairment (NDI) including cerebral palsy (CP), cognitive and language delay and behavioral problems (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Among the myriad challenges in caring for preterm infants, the accurate prediction and early detection of NDI stands as a crucial endeavor for healthcare providers (\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). The two universally established imaging risk factors for adverse neurodevelopmental outcomes are intraventricular hemorrhage (IVH) and periventricular leukomalacia (PVL) (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNumerous investigative studies have established not only the utility of cranial ultrasound (CUS) in detecting intracranial abnormalities but also its ability to predict neurodevelopmental outcomes in preterm infants (\u003cspan additionalcitationids=\"CR7 CR8\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Several studies focused on comparing the diagnostic performance of CUS to brain magnetic resonance imaging (MRI) (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). While MRI is superior in assessing cortical development, non-cystic white matter injury, and punctate cerebellar lesions, its limited availability, need for transport, sedation, and high-cost limit its widespread use. Unlike MRI, CUS can be done at the bedside and can be repeated as needed.\u003c/p\u003e \u003cp\u003eRecent literature has delved deeper to suggest that the timing and frequency of CUS screening play a crucial role in identifying preterm infants at risk for NDI later on in life (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Studies have demonstrated that while early CUS screening (within the first week of life) provides valuable information about initial brain injury, which is usually an IVH, sequential CUS screening allows for (i) detection of evolving abnormalities, such as the post-hemorrhagic ventricular dilatation, (ii) diagnosis of the latter occurring white matter injury (PVL, porencephaly, any sequela of ischemic changes), and (iii) assessment of brain growth and maturation.\u003c/p\u003e \u003cp\u003eConsensus exists to perform early routine CUS during the 1st week of life and a late CUS at term equivalent-age ultrasound (TEA) around 36\u0026ndash;40 weeks post menstrual age (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). However, there is no consensus on the timing for routine imaging between these time points, particularly with the absence of abnormalities on initial screening. The updated 2021 Canadian Pediatric Society (CPS) guidelines advise routine CUS between 4\u0026ndash;7 days, a repeat CUS between 4\u0026ndash;6 weeks, and only routine TEA scans in children born before 26 weeks\u0026rsquo; gestation age (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). While the 2021 Canadian Neonatal Network consensus guidelines recommended performing three-time points CUS within 4\u0026ndash;7 days of life in VPI\u0026thinsp;\u0026lt;\u0026thinsp;32 weeks\u0026rsquo; gestation and if this is normal, their proposed algorithm suggests performing a late (4-6-week) CUS and a TEA (36\u0026ndash;40 week) CUS (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Similarly, in 2020 the American Academy of Pediatrics recommended the first screening CUS before 7\u0026ndash;10 days after birth and repeat CUS between 4\u0026ndash;6 weeks and at TEA or before hospital discharge (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). However, the European Ultrasound Brain Group advises a far more extensive screening protocol in stable preterm infants born after 28 weeks of gestation, with the frequency of serial CUS being days 1, 3, 7, 14, 28, at 6 weeks, and at TEA (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e Despite the updated North American recommendations, centres like ours, which is considered one of the major tertiary referral centres at the Canadian Easy Coast are still hesitant to adopt the latest guidelines with the reduced number of CUS scanning. At IWK CUS is routinely performed on all VPI (\u0026lt;\u0026thinsp;31 weeks\u0026rsquo; gestation) admitted to the neonatal intensive care unit (NICU) at four time points; within the first week after birth, at two and 6 weeks of age, and at or near TEA following the prior 2001 CPS guidelines (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). The practice of performing four-time point CUS places a huge burden on the Radiology department, especially in a high-volume tertiary referral center like IWK Children\u0026rsquo;s Hospital. There is also increased anxiety among the parents surrounding each study performed.\u003c/p\u003e \u003cp\u003eThe current study question asks, is the performance of both the 2-week and 6-week routine CUS necessary? Or does the 6-week CUS alone suffice in identifying the infants at risk of NDI. The hypothesis is that the 2-week CUS is not impacting NDI prognostication without a significant abnormality on the 1st week CUS, mandating a short-term interval follow-up.\u003c/p\u003e \u003cp\u003eTherefore, we conducted this retrospective population-based cohort study aimed to explore the yield of routine four time points sequential CUS screening in VPI, to evaluate the existing evidence regarding its predictive utility, and to provide stewardship into the optimal approach for clinicians caring for this vulnerable population. The study predominantly serves as proof of the principle of the appropriateness of the North American Guidelines in reducing the frequency of screening CUS in this population to support the change in practice in our institution and similar other centers.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003eIWK REB approval was received for protocol number 1025476. The parents or guardian of the child signs a consent form for participation in PFUP and inclusion of their child’s data in database for use in research is obtained at the first visit to Provincial Perinatal Follow-up Program (PFUP).\u003c/p\u003e\n\u003cp\u003e1-\u0026nbsp; \u0026nbsp;Study Population and Data Source\u003c/p\u003e\n\u003cp\u003eThe Nova Scotia PFUP administers neurodevelopmental assessment to all VPI whose mothers reside in the province. The program’s AC Allen Research Database captures the maternal, perinatal, neonatal and neurodevelopmental data, up to 36 months corrected gestational age (CGA) in addition to the worst radiological grading of brain injury. Infants included in the database were cared for in one of two tertiary care hospitals: the IWK Health and the Cape Breton Regional Hospital.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis retrospective population-based study included all liveborn VPI 22\u003csup\u003e0\u003c/sup\u003e-30\u003csup\u003e6\u003c/sup\u003e weeks gestation born between January 2004 and December 2018 who had neurodevelopmental assessments at 36 months CGA. Infants with congenital anomalies, those receiving palliation at birth and NICU deaths were excluded. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e2-\u0026nbsp; \u0026nbsp;Cranial ultrasound protocol and review\u003c/p\u003e\n\u003cp\u003eAll CUS are routinely analysed by paediatric trained Radiologists, and these issued reports were used to test the current hypothesis. \u0026nbsp;Routine CUS are performed within the first week of life, at two and six-weeks of chronologic age and at or near TEA. The fourth time point is occasionally excluded when the 6-week CUS and TEA CUS coincide. The CUS reporting data was retrieved from the provincial Picture Archiving and Communications System (PACS).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eImaging features were divided into three categories in accordance with the Canadian consensus guidelines for grading of brain injury related to prematurity\u0026nbsp;(9):\u003c/p\u003e\n\u003cp\u003e1)\u0026nbsp;\u0026nbsp;Normal\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e2)\u0026nbsp;\u0026nbsp;Mild abnormality: Grade 1 or 2 IVH, grade 1 non cystic PVL, focal non-specific abnormality/ unilateral parenchymal echogenicity (haemorrhagic or ischemic).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e3)\u0026nbsp;\u0026nbsp;Moderate to severe abnormality: Grade 3 IVH, periventricular parenchymal haemorrhagic venous infarction (formerly called grade 4 IVH), cystic PVL and non-punctate cerebellar lesions.\u003c/p\u003e\n\u003cp\u003e3-\u0026nbsp; \u0026nbsp;Neurodevelopmental outcome\u003c/p\u003e\n\u003cp\u003eAll VPI included in the study received regular assessments in the PFUP (a multidisciplinary team) to assess neurodevelopmental status at 4, 8, 18 and 36-months CGA. This included a neuromotor physical examination, the Bayley Scales of Infant and Toddler Development (BSITD) at 18 and 36-months CGA (version III)\u0026nbsp;(18), and sensorineural screening (ophthalmologic and auditory assessments). Based on this neurodevelopmental assessment at 36 months CGA, VPI are categized into 2 broad groups: 1) those with or without NDI; or 2) those with or without major NDI (mNDI).\u003c/p\u003e\n\u003cp\u003eAny NDI is defined as one or more of the following:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ea)\u0026nbsp;\u0026nbsp;any severity of CP, gross motor functional classification system (GMFCS) 1-5\u0026nbsp;(19);\u003c/p\u003e\n\u003cp\u003eb)\u0026nbsp;\u0026nbsp; BSITD scores \u0026lt; 85 which represents more than 1 standard deviation \u0026nbsp; (SD) below the mean in any domain (cognitive, motor or language);\u003c/p\u003e\n\u003cp\u003ec)\u0026nbsp;\u0026nbsp;visual impairment with vision in the best eye \u0026lt; 20/200; or\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ed)\u0026nbsp;\u0026nbsp;hearing impairment requiring hearing aids).\u003c/p\u003e\n\u003cp\u003emNDI is defined as one or more of the following:\u003c/p\u003e\n\u003cp\u003ea)\u0026nbsp;\u0026nbsp;Severe CP (³\u0026nbsp;grade 3 GMFCS)(19)\u003c/p\u003e\n\u003cp\u003eb)\u0026nbsp;\u0026nbsp;BSITD scores \u0026lt; 70 which represents more than 2 SD below the mean in any domain\u003c/p\u003e\n\u003cp\u003ec)\u0026nbsp;\u0026nbsp;Visual impairment with vision in the best eye \u0026lt; 20/200; or\u003c/p\u003e\n\u003cp\u003ed)\u0026nbsp;\u0026nbsp;Hearing impairment requiring aids.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eData analysis\u003c/p\u003e\n\u003cp\u003eThe data selection included maternal, perinatal and neonatal candidate predictors.\u0026nbsp;The maternal/prenatal factors included maternal age, single parent, gravidity, history of smoking, drug abuse, psychiatric disorder, gestational hypertension, maternal diabetes, prolonged rupture of membranes \u0026gt; 18 hours and chorioamnionitis. The perinatal factors included \u0026nbsp;delivery factors (maternal receipt of antenatal steroids, intrapartum magnesium sulphate and mode of delivery ) and infant factors (gestational age, sex, birthweight z scores, 5 minutes Apgar score and delivery room resuscitation). \u0026nbsp;The neonatal factors included respiratory distress syndrome, bronchopulmonary dysplasia, sepsis (positive blood or CSF culture), necrotising enterocolitis (³\u0026nbsp;Bell’s stage 2), retinopathy of prematurity, brain injury of prematurity (as defined above) and length of hospital stay.\u003c/p\u003e\n\u003cp\u003eMissing data were imputed through a single imputation using fully conditional specification as implemented in the R package mice\u0026nbsp;(20).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eModel development\u003c/p\u003e\n\u003cp\u003eMultiple clinically significant variables were assessed for inclusion in prediction models.\u0026nbsp;However,\u0026nbsp;pmsampsize R package was\u0026nbsp;(21)\u0026nbsp;used to determine the number of variables that could be included was ~ 3 (sex, gestational age, and birthweight) and these were chosen based on clinical rationale. Additional variables were assessed in a sensitivity analysis.\u003c/p\u003e\n\u003cp\u003eModel selection\u003c/p\u003e\n\u003cp\u003eGiven the need to evaluate multiple models based on predictive performance, a reference model approach was used for model selection\u0026nbsp;(19, 22). Akaike Information Criterion (AIC) approach was chosen because leave one out cross-validation provides an unbiased estimate of out of sample error when the number of models being compared is small\u0026nbsp;(23).\u003c/p\u003e\n\u003cp\u003eModels compared included:\u003c/p\u003e\n\u003cp\u003e1.\u0026nbsp; \u0026nbsp; \u0026nbsp;The reference model including gestational age, infant sex, 2-week ultrasound grading, and 6-week ultrasound grading.\u003c/p\u003e\n\u003cp\u003e2.\u0026nbsp; \u0026nbsp; \u0026nbsp;A model dropping 2-week ultrasound grading.\u003c/p\u003e\n\u003cp\u003eDiscrimination and Calibration\u003c/p\u003e\n\u003cp\u003eIn addition to estimates of out of sample predictive error, models performance were further evaluated based on discrimination and calibration. All models were evaluated on the training data by plotting receiver operating characteristic curves and calculating the area underneath them (AUC-ROC)\u0026nbsp;with 95% confidence interval (CI). The AUC can be interpreted as the probability that a randomly chosen case will have a higher predicted risk than a randomly chosen control\u0026nbsp;(24). Values of AUC of 0.5 indicate that the model performs as well as random guessing. Higher values indicate that the model predicts the outcome, with values greater than 0.7 generally considered good and those higher than 0.9 indicate strong discrimination.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u0026nbsp;The process of calibration determines if infants with a predicted likelihood of experiencing an event actually encounter it at that likelihood. In this study, calibration was assessed using functions from the runway package\u0026nbsp;(25). This involves categorizing patients into bins based on their predicted risk levels and comparing this to the observed risk along with its confidence interval in the respective bin. Additional model diagnostic measures included sensitivity, specificity, negative predictive value, and positive predictive value as assessed in the training dataset.\u003c/p\u003e\n\u003cp\u003eDecision Curve Analysis\u003c/p\u003e\n\u003cp\u003eUltimately whether a given prediction tool is useful depends on the balance of benefits and harms, or net benefit. A convenient way to visualize this trade-off is through the use of a decision curve, which plots the net benefit of treatment against the risk threshold used for decision making. The net benefit is calculated based on a combination of the potential patients that could benefit (prevalence) minus a penalty for false positives that is implicitly weighted by the decision maker’s threshold for risk (25). The primary benefit of decision curve analysis is that it only relies on the data used to fit the model, with the value of true/false positives made implicit through the decision threshold. Decision curves were fit for all proposed models in order to help guide the trade-off between accuracy and potential uptake.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eOut of 786 VPI born during the study period, 656 were included in the analysis representing 83% of the eligible cohort (Figure 1). Infants were excluded for missing outcome data only (n=23), lost to follow-up (n=28), and those who died before 36 months CGA (n=79). The mean gestational age for this cohort was 27.8 weeks (SD) with a mean birth weight of 1133 grams (SD) and 55% being male infants (Table 1).\u003c/p\u003e\n\u003cp\u003eIn this cohort, 31% had abnormal CUS findings at 2 weeks compared to 28% at 6 weeks chronological age. At 36 months CGA, 439/656 (66.9%) had no NDI and 217/656 (33.1%) developed any NDI. Eighty-six of 656 (13.1%) developed major NDI (mNDI), representing 39.6% of those with any NDI (Figure 1).\u003c/p\u003e\n\u003cp\u003eThe detailed maternal, perinatal and neonatal characteristics of the study population comparing those with no NDI to those with any NDI and significant NDI are shown in table 2.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 3 compares the CUS findings at the 2-week and 6-week time points and neurodevelopmental outcomes. The study showed that\u0026nbsp;76% and 81% of infants who had normal \u0026nbsp;2-week and 6-week CUS, respectively, demonstrated no NDI. Whereas 24% and 19% of children without NDI demonstrated some abnormality at their 2-week and 6-week CUS, respectively. Children with an abnormal 2-week CUS resulted in 45% with any NDI, of which 57% represented mNDI. Similarly, out of those with abnormal 6-week CUS, 46% developed any NDI, of which 58% represented mNDI (Table 3).\u003c/p\u003e\n\u003cp\u003eThe details of the ultrasound findings at 2-week and 6-week screening are provided in Table 4.\u0026nbsp;Generally, severe abnormality on CUS (grade 3 IVH, periventricular parenchymal hemorrhagic venous infarction, cerebellar parenchymal injury, and cystic PVL) were associated with NDI in this cohort (Table 4).\u003c/p\u003e\n\u003cp\u003eThe performance of the prediction model was assessed using AUC (with 95% confidence interval (CI). The AUC of the prediction models are presented in (table 5) specifically;\u003c/p\u003e\n\u003cp\u003e1)\u0026nbsp;\u0026nbsp;2-week CUS AUC=0.66 (95% CI 0.61-0.70);\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e2)\u0026nbsp;\u0026nbsp;6-week CUS AUC=0.68 (95% CI 0.63-0.72); and\u003c/p\u003e\n\u003cp\u003e3)\u0026nbsp;\u0026nbsp;combined 2 and 6-week CUS AUC=0.68 (95% CI 0.63-0.72).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe diagnostic properties (sensitivity, specificity, positive predictive value, negative predictive value, and accuracy) of the 3 models at 2 and 6 weeks as well as the combined models show almost identical curves at all thresholds (Figure 2). \u0026nbsp;The prediction models comparison (Figure 3) and decision curve analysis (Figure 4) further support this with almost identical performance of the 6-week and the combined 2 and 6-week models that are outperforming the 2-week model.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this population-based cohort of VPI, one-third developed NDI, and almost one in eight developed mNDI. Over the 14-year study period, the majority (76\u0026ndash;81%) of VPI with normal 2-week and 6-week CUS did not go on to develop NDI, underscoring the negative predictive value of CUS screening. Models with and without the 2-week CUS showed near identical performance across a broad set of metrics including information criteria, significance of coefficients, AUC, sensitivity, specificity, NPV, PPV, calibration, and decision curves. This emphasizes that re-evaluating the frequency and timing of routine sequential CUS for VPI is valuable to reduce the burden on the radiology department, while not detrimentally affecting the diagnostic and prognostic role CUS plays.\u003c/p\u003e \u003cp\u003eIn our cohort, 24% of VPI with normal 2-weeks\u0026rsquo; CUS and 19% of those with normal 6-weeks\u0026rsquo; CUS subsequently developed NDI at 36 months CGA. This finding is in agreement with what was reported in the literature. Similarly, Broitman (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e) reported that 39% of extremely low birth weight children with NDI having normal CUS and Kuban (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e) noted that half of the VPI with CP at two years CGA were not preceded by CUS abnormalities.\u003c/p\u003e \u003cp\u003eThe prediction of NDI in VPI is indeed complex and best assessed with a multidisciplinary and multimodality approach. Several studies showed that the combined clinical assessment and the imaging findings provides the highest yield in predicting neurodevelopmental outcomes in children born preterm (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). Sequential CUS remains the standard of care for radiological screening of brain injury in VPI (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). However, the lack of consensus agreements between the major Canadian, American and European bodies on the best timing to acquire the CUS during the neonatal period add to the strains of healthcare systems and management decisions of this vulnerable population (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOur study demonstrates that a 3 time point approach is reliable and is comparable to 4 points approach. The AUC of a combined 2\u0026thinsp;+\u0026thinsp;6 weeks model was identical to the 6 week CUS alone with overlapping 95% CI. This finding is congruent with the findings of Van Wezel-Meijler (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e) who showed that increasing the frequency of CUS did not improve its diagnostic performance for white matter injury.\u003c/p\u003e \u003cp\u003eOverall, the addition of the 2-weeks\u0026rsquo; CUS does not meaningfully contributes to the risk prediction of any NDI or major NDI when making predictions at the time of hospital discharge. This supports the hypothesis that the routine 2-week CUS is not necessary in NDI prognostication and did not add any weight to the routine 6-weeks CUS. It is, therefore, proposed that a 3 time point CUS screening be adopted (namely 3\u0026ndash;7 days, 6 weeks and TEA).\u003c/p\u003e \u003cp\u003eThis study has major strengths including its population-based design, the low loss to follow up rates and the standardized CUS protocols and neurodevelopmental assessments over the 14 years of the study period. We also reported the predictive performance (AUC and calibration plots) and the diagnostic properties of different models and used innovative decision-curve analysis to guide the best approach.\u003c/p\u003e \u003cp\u003eThis study is not without limitations. First, with the CUS being an operator dependant modality, the difference in quality of the CUS images over the study period in relation to the sonographer\u0026rsquo;s skills, machine models or probes resolution could not be assessed. Second, the lack of interobserver reliability; relying on CUS reports in the data collection with multiple radiologists interpreting the CUS over the 14 years study period, and no dual reading of the images. However, all studies were originally read by trained pediatric radiologists with multiple years of experience in paediatric neuroimaging. Thirdly, the interpretation of the 6-week scans is likely partially influenced by the availability of the reports of the 2-week CUS for the radiologist at time of reporting. While this could be considered a limitation, it also more accurately mimics the daily practice of the scan interpretation. Finally, while no cost benefit analysis has been performed, the proposed sequential CUS does reduce the cost burden on the institutions caring for these VPI and promote resources stewardship. Importantly, this helps in guiding the management decisions and conversation with families.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn this population-based cohort, two methods of sequential CUS screening were compared (three and four time-points) and showed reliable performance of the 3-time points model (first week, 6 weeks and TEA) to predict NDI at 36 months of CGA.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e- NICU\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e- Neonatal Intensive Care Unit\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003eDisclosures\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThe project described was supported by RSNA Research and Education Foundation, through\u0026nbsp;Philips/RSNA Seed Grant 2021\u0026nbsp;and\u0026nbsp;IWK Project Grant 2021. The content is solely the responsibility of the authors and does not necessarily represent the official views of the RSNA R\u0026amp;E foundation or IWK Health\u003c/p\u003e\n\u003cp\u003eCompliance with Ethical Standards\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e1.\u0026nbsp; \u0026nbsp;Guarantor:\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe scientific guarantor of this publication is Dr Tahani Ahmad.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.\u0026nbsp; \u0026nbsp;Conflict of Interest:\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e3.\u0026nbsp; \u0026nbsp;Statistics and Biometry:\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTim Disher (co-author) provided statistical analysis for this manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e4.\u0026nbsp; \u0026nbsp;Informed Consent:\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe parents or guardian of the child signs a consent form for participation in PFUP and inclusion of their child’s data in database for use in research is obtained at the first visit to PFUP.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e5.\u0026nbsp; \u0026nbsp;Ethical Approval:\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eInstitutional Review Board approval was obtained.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e6.\u0026nbsp; \u0026nbsp;Methodology\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eMethodology:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e·\u0026nbsp; \u0026nbsp;\u0026nbsp;Retrospective design\u003c/p\u003e\n\u003cp\u003e·\u0026nbsp; \u0026nbsp;\u0026nbsp;Observational study\u003c/p\u003e\n\u003cp\u003e·\u0026nbsp; \u0026nbsp;\u0026nbsp;Multi-centre\u003c/p\u003e\n\u003cp\u003eAuthor contributions\u003c/p\u003e\n\u003cp\u003eSR was involved in the data collection, data analysis, and manuscript writing.\u003c/p\u003e\n\u003cp\u003eTD was involved in statistical analysis and manuscript review.\u003c/p\u003e\n\u003cp\u003eMV provided and managed the clinical database and performed manuscript review.\u003c/p\u003e\n\u003cp\u003eJA and TA were both involved in the study design, data curation and analysis, and manuscript review.\u003c/p\u003e\n\u003cp\u003eData Sharing\u003c/p\u003e\n\u003cp\u003eDe-identified data analyzed during the study are available from the corresponding author by request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eMedina-Alva P, Duque KR, Zea-Vera A, Bellomo S, Carcamo C, Guillen-Pinto D, et al. Combined predictors of neurodevelopment in very low birth weight preterm infants. Early Hum Dev. 2019;130:109-15.\u003c/li\u003e\n \u003cli\u003eChung EH, Chou J, Brown KA. Neurodevelopmental outcomes of preterm infants: a recent literature review. Translational Pediatrics. 2019:S3-S8.\u003c/li\u003e\n \u003cli\u003eHand IL, Shellhaas RA, Milla SS, FETUS CO, NEWBORN SON, SECTION ON RADIOLOGY, Cummings JJ, et al. Routine Neuroimaging of the Preterm Brain. Pediatrics. 2020;146(5).\u003c/li\u003e\n \u003cli\u003eMirmiran M, Barnes PD, Keller K, Constantinou JC, Fleisher BE, Hintz SR, et al. Neonatal brain magnetic resonance imaging before discharge is better than serial cranial ultrasound in predicting cerebral palsy in very low birth weight preterm infants. Pediatrics. 2004;114(4):992-8.\u003c/li\u003e\n \u003cli\u003eZhang XH, Chen WJ, Gao XR, Li Y, Cao J, Qiu SJ. Predicting the developmental outcomes of very premature infants via ultrasound classification: A CONSORT - clinical study. Medicine (Baltimore). 2021;100(15):e25421.\u003c/li\u003e\n \u003cli\u003eMcLean G, Ditchfield M, Paul E, Malhotra A, Lombardo P. Evaluation of a Cranial Ultrasound Screening Protocol for Very Preterm Infants. J Ultrasound Med. 2023;42(5):1081-91.\u003c/li\u003e\n \u003cli\u003eBeunders VAA, Roelants JA, Suurland J, Dudink J, Govaert P, Swarte RMC, et al. Early Ultrasonic Monitoring of Brain Growth and Later Neurodevelopmental Outcome in Very Preterm Infants. AJNR Am J Neuroradiol. 2022;43(4):639-44.\u003c/li\u003e\n \u003cli\u003eGarg S, Lal N, Lal M. A standardized approach to routine cranial ultrasonography in preterm infants: Improved neuromotor outcome predictability at 2 years and communication with parents. Journal of Clinical Neonatology. 2021;10(3).\u003c/li\u003e\n \u003cli\u003eMohammad K, Scott JN, Leijser LM, Zein H, Afifi J, Piedboeuf B, et al. Consensus Approach for Standardizing the Screening and Classification of Preterm Brain Injury Diagnosed With Cranial Ultrasound: A Canadian Perspective. Frontiers in Pediatrics. 2021;9.\u003c/li\u003e\n \u003cli\u003eLeijser LM, Liauw L, Veen S, de Boer IP, Walther FJ, van Wezel-Meijler G. Comparing brain white matter on sequential cranial ultrasound and MRI in very preterm infants. Neuroradiology. 2008;50(9):799-811.\u003c/li\u003e\n \u003cli\u003eHintz SR, Barnes PD, Bulas D, Slovis TL, Finer NN, Wrage LA, et al. Neuroimaging and Neurodevelopmental Outcome in Extremely Preterm Infants. Pediatrics. 2015;135(1):e32-e42.\u003c/li\u003e\n \u003cli\u003eSkiold B, Hallberg B, Vollmer B, Aden U, Blennow M, Horsch S. A Novel Scoring System for Term-Equivalent-Age Cranial Ultrasound in Extremely Preterm Infants. Ultrasound Med Biol. 2019;45(3):786-94.\u003c/li\u003e\n \u003cli\u003eLaw JB, Wood TR, Gogcu S, Comstock BA, Dighe M, Perez K, et al. Intracranial Hemorrhage and 2-Year Neurodevelopmental Outcomes in Infants Born Extremely Preterm. J Pediatr. 2021;238:124-34 e10.\u003c/li\u003e\n \u003cli\u003eDudink J, Steggerda S, Horsch S, Agut T, Alarcon A, arena r, et al. State of the art neonatal cerebral ultrasound: technique and reporting. 2020.\u003c/li\u003e\n \u003cli\u003evan Wezel-Meijler G, De Bruine FT, Steggerda SJ, Van den Berg-Huysmans A, Zeilemaker S, Leijser LM, et al. Ultrasound detection of white matter injury in very preterm neonates: practical implications. Dev Med Child Neurol. 2011;53 Suppl 4:29-34.\u003c/li\u003e\n \u003cli\u003eGuillot M, Chau V, Lemyre B. Routine imaging of the preterm neonatal brain. Paediatr Child Health. 2020;25(4):249-62.\u003c/li\u003e\n \u003cli\u003eRoutine screening cranial ultrasound examinations for the prediction of long term neurodevelopmental outcomes in preterm infants. Paediatr Child Health. 2001;6(1):39-52.\u003c/li\u003e\n \u003cli\u003eAlbers CA, Grieve AJ. Test Review: Bayley, N. (2006). Bayley Scales of Infant and Toddler Development\u0026ndash; Third Edition. San Antonio, TX: Harcourt Assessment. Journal of Psychoeducational Assessment. 2007;25(2):180-90.\u003c/li\u003e\n \u003cli\u003ePiironen J, Vehtari A. Comparison of Bayesian predictive methods for model selection. Statistics and Computing. 2017;27(3):711-35.\u003c/li\u003e\n \u003cli\u003evan Buuren S, Groothuis-Oudshoorn K. mice: Multivariate Imputation by Chained Equations in R. Journal of Statistical Software. 2011;45(3):1 - 67.\u003c/li\u003e\n \u003cli\u003eRiley RD, Snell KIE, Ensor J, Burke DL, Harrell FE, Jr., Moons KGM, et al. Minimum sample size for developing a multivariable prediction model: Part I - Continuous outcomes. Stat Med. 2019;38(7):1262-75.\u003c/li\u003e\n \u003cli\u003eVehtari A, Ojanen J. Errata: A survey of Bayesian predictive methods for model assessment, selection and comparison. Statistics Surveys. 2014;6.\u003c/li\u003e\n \u003cli\u003eVehtari A, Gelman A, Gabry J. Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. Statistics and Computing. 2017;27.\u003c/li\u003e\n \u003cli\u003eFawcett T. Introduction to ROC analysis. Pattern Recognition Letters. 2006;27:861-74.\u003c/li\u003e\n \u003cli\u003eK S. Visualizing Prediction Model Performance. version 0.1.0 ed2024. p. R package.\u003c/li\u003e\n \u003cli\u003eBroitman E, Ambalavanan N, Higgins RD, Vohr BR, Das A, Bhaskar B, et al. Clinical data predict neurodevelopmental outcome better than head ultrasound in extremely low birth weight infants. J Pediatr. 2007;151(5):500-5, 5 e1-2.\u003c/li\u003e\n \u003cli\u003eKuban KC, Allred EN, O\u0026apos;Shea TM, Paneth N, Pagano M, Dammann O, et al. Cranial ultrasound lesions in the NICU predict cerebral palsy at age 2 years in children born at extremely low gestational age. J Child Neurol. 2009;24(1):63-72.\u003c/li\u003e\n \u003cli\u003eHimpens E, Oostra A, Franki I, Vansteelandt S, Vanhaesebrouck P, den Broeck CV. Predictability of cerebral palsy in a high-risk NICU population. Early Hum Dev. 2010;86(7):413-7.\u003c/li\u003e\n \u003cli\u003eMaas YG, Mirmiran M, Hart AA, Koppe JG, Ariagno RL, Spekreijse H. Predictive value of neonatal neurological tests for developmental outcome of preterm infants. J Pediatr. 2000;137(1):100-6.\u003c/li\u003e\n \u003cli\u003eSeme-Ciglenecki P. Predictive values of cranial ultrasound and assessment of general movements for neurological development of preterm infants in the Maribor region of Slovenia. Wien Klin Wochenschr. 2007;119(15-16):490-6.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 1 to 5 are available in the Supplementary Files section\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"pediatric-radiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"prad","sideBox":"Learn more about [Pediatric Radiology](http://link.springer.com/journal/247)","snPcode":"247","submissionUrl":"https://submission.nature.com/new-submission/247/3","title":"Pediatric Radiology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Cranial ultrasound, Cerebral Palsy, Intraventricular hemorrhage, Prematurity, Periventricular leukomalacia, White matter brain injury","lastPublishedDoi":"10.21203/rs.3.rs-4896738/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4896738/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cu\u003eObjective:\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eThe accurate prediction and early detection of neurodevelopmental impairment (NDI) is a crucial endeavor in caring for very preterm infants (VPI). Sequential cranial ultrasound (CUS) is the standard of care for imaging evaluation. There is no consensus on the timing and frequency of CUS screening. At our institution four time points CUS are performed for screening. We hypothesize that the 2-week CUS is not necessary for NDI prognostication.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eMaterials and methods:\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eIn this retrospective, multi-center, population-based cohort, we included all liveborn VPI born 22\u003csup\u003e0\u003c/sup\u003e-30\u003csup\u003e6\u003c/sup\u003e weeks gestation between January 2004 and December 2018 who had a neurodevelopmental assessment at 36 months corrected age.\u0026nbsp;\u0026nbsp; A model with and without 2-week CUS was compared to a reference model including gestational age, infant sex, and 6-week CUS.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eResults:\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eOut of 786 preterm babies born during the study period, 656 survivors were included in the analysis. 30% of our cohort has NDI as per clinical assessment. The mean gestational age was 27.8 weeks with the mean birth weight being 1133 grams, and 55% being male infants. One in three survivors developed NDI when assessed at 36 months of corrected age.\u003c/p\u003e\n\u003cp\u003eThere was no strong evidence that the addition of the 2-weeks CUS meaningfully contributes to the risk prediction of any NDI or major NDI. Models with and without the 2-week CUS showed nearly identical performance across a broad set of metrics.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eConclusion:\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eThe comparison of two methods of sequential CUS screening showed reliable performance of the 3-time points model to predict NDI at 36 months of corrected age.\u003c/p\u003e","manuscriptTitle":"Re-evaluating the timing of sequential cranial ultrasound screening in very preterm infants for predicting neurodevelopmental outcomes: A Population Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-10 13:09:09","doi":"10.21203/rs.3.rs-4896738/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-09-10T14:37:18+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-04T17:44:36+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-26T03:32:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"296861493258868242063086324405828525931","date":"2024-08-15T05:08:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"140986865479117163569009080115034198328","date":"2024-08-14T13:00:26+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-08-13T21:36:16+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-08-13T04:06:18+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-08-13T04:05:38+00:00","index":"","fulltext":""},{"type":"submitted","content":"Pediatric Radiology","date":"2024-08-11T22:54:17+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"pediatric-radiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"prad","sideBox":"Learn more about [Pediatric Radiology](http://link.springer.com/journal/247)","snPcode":"247","submissionUrl":"https://submission.nature.com/new-submission/247/3","title":"Pediatric Radiology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"ee2fa2a4-5e69-4ede-8d49-cf51bdb2dac1","owner":[],"postedDate":"October 10th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-12-09T16:02:52+00:00","versionOfRecord":{"articleIdentity":"rs-4896738","link":"https://doi.org/10.1007/s00247-024-06105-1","journal":{"identity":"pediatric-radiology","isVorOnly":false,"title":"Pediatric Radiology"},"publishedOn":"2024-12-04 15:57:37","publishedOnDateReadable":"December 4th, 2024"},"versionCreatedAt":"2024-10-10 13:09:09","video":"","vorDoi":"10.1007/s00247-024-06105-1","vorDoiUrl":"https://doi.org/10.1007/s00247-024-06105-1","workflowStages":[]},"version":"v1","identity":"rs-4896738","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4896738","identity":"rs-4896738","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}