In the United States, disparities in gestational age at birth by maternal race, ethnicity, and geography are theorized to be related, in part, to differences in individual- and neighborhood-level socioeconomic status (SES). Yet, few studies have examined their combined effects or whether associations vary by maternal race and ethnicity and United States Census region.Race) on gestational weeks at birth using linear regression and on gestational age at birth categories  using multinomial logistic regression.We assembled data from 34 cohorts in the Environmental influences on Child Health Outcomes (ECHO) program representing 10,304 participants who delivered a liveborn, singleton infant from 2000 through 2019. We investigated the combined associations of maternal education level, neighborhood deprivation index (NDI), and Index of Concentration at the Extremes for racial residential segregation , and some college  relative to a master’s degree or higher. Those with a high school diploma or less also had an increased odds of preterm  and early term birth . In adjusted models, NDI quartile and ICERace quartile were not associated with gestational weeks at birth. However, higher NDI quartile (most deprived) associated with an increased odds of early term and late term birth, and lower ICERace quartile  associated with a decreased odds of late or post-term birth. When stratifying by region, gestational weeks at birth was lower among those with a high school education or less and some college only among those living in the Northeast or Midwest. When stratifying by race and ethnicity, gestational weeks at birth was lower among those with a high school education or less only for the non-Hispanic White category.After adjustment for NDI and ICERace.In this study, maternal education was consistently associated with shorter duration of pregnancy and increased odds of preterm birth, including in models adjusted for NDI and ICE It is wGestational age at birth is also an important determinant of cognitive and educational outcomes. Systematic reviews demonstrate that, relative to their full term peers, children born preterm are at increased risk for academic difficulties in reading and math  and thosWithin the United States, there are stark disparities in gestational age at birth, including by maternal race and ethnicity , 13. FroRacial, ethnic, and geographic disparities in gestational age at birth are theorized to be related, in part, to differences in individual-level socioeconomic status (SES) . Those ode facto) via historical and contemporary systemic racism  launched the ECHO program to investigate the influence of early life environmental exposures on child health and development. ECHO supports pediatric cohorts throughout the United States in the sharing of extant data and the collection of new data under a common protocol. This nationwide consortium is able to leverage demographic and geographic heterogeneity and a large sample size to answer important research questions about child health and development , 46. The2.2n = 8 cohorts were dropped). The secondary study population excluded three cohorts (n = 547) that enrolled only preterm births (< 37 weeks) and included 9,757 participants from 31 ECHO cohorts. This secondary study population was utilized to investigate the association between the exposures and gestational age outcome categories.The primary study population consisted of 10,304 participants from 34 ECHO cohorts who delivered a liveborn, singleton infant from 2000 through 2019 and who had the following outcome and exposure information available for analysis: gestational age at birth, maternal prenatal level of education, maternal race and ethnicity, and at least one geocoded residential address during pregnancy . We rest2.3in vitro fertilization or on artificial insemination (0%), obstetrical estimate from first trimester ultrasound (2%), obstetrical estimate from ultrasound taken in the second trimester with fetal biparietal diameter dating within 2 weeks of sure last menstrual period (LMP) (0%), ultrasound taken in the second trimester with unsure or no LMP date (0%), report from obstetrical medical record reporting “consensus” estimated date of delivery with no ultrasound documented during first and second trimester (8%), obstetrical estimate from LMP only (8%), neonatal estimate of gestational age at birth obtained from child medical records (39%), estimate from cohort research encounter (3%), report by mother (27%), and cohort-provided estimated date of delivery without further description (12%)  , 48.continuous measure  and a categorical measure . These categorizations were based on the definitions of the American College of Obstetricians and Gynecologists Committee Opinion  that addresses match to and the algorithms and code that attempt to select the most accurate latitude/longitude location for each address . To asseRace)   and the Race) , 55. We Race) , and preRace) , includiRace) . We chosortality . We calcortality , 57.NDI is a summary measure created through principal components analysis using the following eight census tract measures (as percentages): males in management and professional occupations, crowded housing, households in poverty, female-headed households with dependents, households on public assistance, households earning < $30,000 per year, adults with less than a high school education, and unemployed individuals . The cenRace was utilized to quantify the extent of racial segregation within the census tract and was calculated using the established formula  in the census tract.TRace ranges from −1 to 1; a value of 1 signifies that 100% of the population of the census tract is concentrated in the most racially privileged group, whereas a value of −1 signifies that 100% of the population of the census tract is concentrated in the least racially privileged group. The census tracts were matched to their corresponding ICERace value and categorized into quartiles, with the first quartile (lowest proportion of non-Hispanic White persons) representing those least racially privileged.For this analysis, the most racially privileged category was defined as non-Hispanic White persons and the least racially privileged category was defined as non-Hispanic Black persons. ICE2.6via self-report and then categorized as non-Hispanic White, non-Hispanic Black, non-Hispanic Other Race, and Hispanic. Non-Hispanic Other Race included non-Hispanic persons who identified their race as American Indian or Alaska Native, Native Hawaiian or other Pacific Islander, Asian, multiple races, or other race; these were collapsed into a single category due to small sample size.Maternal race and ethnicity were ascertained 2.7Geographic region was assigned according to United States Census regions  categori2.8Covariates for inclusion in statistical modeling were selected based on their association with SES and gestational age at birth in the literature , 56, 58 Maternal age in years (continuous), child sex , and parity  were based on medical record abstraction or maternal report. Prenatal marital status was based on maternal report and was categorized as married or living with a partner; widowed, separated, or divorced; or single, never married, and partnered not living together.via self-report or medical record abstraction with documentation of high blood pressure or anti-hypertensive medication use during pregnancy.Pre-pregnancy body mass index (BMI) was defined using measured or self-reported height and weight between 12 months prior to conception through the first trimester and was categorized as underweight, healthy weight, overweight or obese according to accepted definitions . GestatiPrenatal substance use was ascertained through self-report or medical record abstraction for substance use during pregnancy. Binary variables (yes/no) for tobacco, alcohol, and marijuana use during pregnancy were created. Nicotine exposure was defined as cigarette smoking, use of Electronic Nicotine Delivery Devices/ENDS , and other forms of tobacco . Alcohol use included the consumption of beer, wine, mixed drinks, spirits, shot liquor, or any other type of alcohol.2.9Race according to maternal race and ethnicity categories and United States Census regions  for continuous variables and the number of observations, missingness, and the percentage of total observations for categorical variables . Additio regions , 3. To e regions . We emplFor both linear and multinomial logistic regression modeling, we evaluated each SES measure of interest  using an unadjusted model (without adjustment for covariates), a co-adjusted model (adjusting the estimate for each SES exposure for the other SES exposures), and an adjusted model .Additionally, we conducted two types of sensitivity analyzes. In the first sensitivity analyzes, we explored the effect of further adjusting linear mixed effect models for maternal health conditions and behaviors previously associated with SES and gestational age at birth, including pre-pregnancy BMI and pregnancy-related conditions  and prenatal substance use , which could potentially confound or be on the causal pathway for any observed SES-gestational age at birth associations. In a second set of sensitivity analyzes, we performed ‘leave one out’ analysis in which we examined the point estimate for each SES measure of interest on completed gestational weeks in linear mixed effect modeling after excluding one cohort at a time.a priori hypotheses that there would be substantial variation by maternal race and ethnicity and United States Census region, adjusted linear models were stratified by maternal race/ethnicity categories  and by United States Census region  to examine differences by strata.We compared measures of association for unadjusted, co-adjusted, and further adjusted models to explore whether individual- and neighborhood-level measures of SES attenuate or potentiate observed associations by maternal health conditions and health behaviors. Based on Race; urbanicity; and Census region, with cohort membership as a classification variable. All statistical models were performed using non-imputed and imputed data. No substantial differences in estimated measures of association were observed when considering models based on non-imputed values  and multiple imputation values; thus, we chose to present models based on imputed data since parameter estimates were more stable. Estimates of association  by chained equations using the fully conditional specification with a discriminant function . Imputatociation –7 combinmice package . The  package  was usedgression , and the package  was used33.1Race values were lower  among mothers with lower educational levels  relative to those with higher educational levels (bachelor’s or master’s degree). Substantial proportions of the study population resided in the Northeast (41.2%) and West (28.0%) United States regions, with smaller proportions from the Midwest (15.9%) and South (14.9%). Overall, 18.4% of participants were Hispanic of any race; 54.5% were non-Hispanic White; 17.3% were non-Hispanic Black; and 9.9% were non-Hispanic Other Race. Participants’ mean age at birth was 30.5 years (SD 5.6) and approximately 35% had not given birth prior to the index delivery. Just over 40% of participants had a healthy BMI (18.5 to less than 25.0 kg/m2), with considerable variability by level of education .The primary sample consisted of 10,304 ECHO participants from 34 ECHO cohorts who delivered a liveborn infant from 2000 through 2019 with available participants’ residential address during the prenatal period . The disRace varied by maternal race and ethnicity  and after including age, marital/cohabitation status, parity, and child sex. In the unadjusted model, a significantly lower gestational age at birth was observed among those with higher levels of neighborhood deprivation (third and fourth quartiles) relative to the first quartile (lowest deprivation), with attenuation of the effect with co-adjustment for the other SES exposure variables  and virtually no change in the effect estimate with further adjustment for age, marital/cohabitation status, parity, and child sex. When considering the principal exposure of ICERace quartile, in the unadjusted model, a significantly lower gestational age at birth was found among those residing in census tracts in the first quartile  relative to the fourth quartile ; with co-adjustment and in the adjusted model, no significant associations were observed between ICERace and gestational age at birth.Gestational age at birth was significantly lower among those with up to a high school diploma or less  and some college  relative to those with a master’s degree or higher across all models , with minimal change in the effect estimate after adjusting for the other principal exposure variables  were associated with gestational age at birth outcomes  for the secondary sample in unadjusted, co-adjusted, and adjusted models . These r3.3.1In the unadjusted model utilizing the secondary study population, having a high school diploma or less vs. a master’s degree or above was associated with a significantly increased odds of preterm birth  and early term birth  and a decreased odds of late or post-term birth . Similarly, in the unadjusted model, having some college was associated with a significantly increased odds of preterm birth  and a significantly decreased odds of late- or post-term birth . With co-adjustment for other principal exposure variables, effect estimates from the unadjusted model were somewhat attenuated. In the models that were additionally adjusted for maternal age, parity, marital/cohabitation status, and child sex, having a high school degree or less vs. a master’s degree or above remained associated with a significantly increased odds of preterm birth  and early term birth . Similarly, in the overall adjusted model, having some college was significantly associated with an increased odds of preterm birth . In the overall adjusted model, having some college vs. a master’s degree or above was also significantly associated with a decreased odds of late or post-term birth .3.3.2For the secondary sample, an increased level of neighborhood deprivation (as indicated by the fourth NDI quartile) was associated with a significantly increased odds of preterm and early term birth in the unadjusted models. This association only remained significant after adjustment for the early term births . In addition, participants with increasing levels of neighborhood deprivation had a significantly increased odds of late or post term birth  in the adjusted model.3.3.3Race quartiles  vs. the fourth quartile  and preterm birth ; however, these findings became non-significant after adjustment for covariates. The unadjusted model showed a significantly increased odds of early term birth among those in the first, second, and third ICERace quartiles, with some attenuation of the effect estimate in the co-adjusted and adjusted models, resulting in some non-significant results. In the unadjusted, co-adjusted, and adjusted models, there was consistently a significantly decreased odds of late or post-term birth among those residing in the first, second, and third ICERace quartiles compared to the fourth quartile .For the secondary sample, the unadjusted model showed a significant association between residence in a census tract in the first and second ICE3.4Race and gestational age at birth after stratification by maternal race/ethnicity.In our primary study population, the association between SES exposure variables and gestational age at birth differed when stratified by maternal race/ethnic category . We obse3.5Race and gestational age at birth after stratification by United States Census Region.Associations between maternal education and gestational age at birth varied by United States Census region . We obse3.6Race with gestational age at birth also did not differ after further adjustment.Estimates that incorporated adjustments for additional covariates related to maternal health conditions and substance use behaviors had a minimal impact on the results regarding the relationship between maternal education and neighborhood deprivation and gestational age at birth . SpecifiRace  may be more salient or may reflect that there were relatively few minority participants in the highest education category. Understanding the association between individual- and neighborhood-level factors with gestational age is critical for the identification and targeting of effective solutions to improve maternal and child health and health equity.Race quartile reflecting a lower proportion of non-Hispanic White individuals in the census tract) had an decreased odds of post term birth.In our analysis of the combined effects of individual- and neighborhood-level measures of SES, neighborhood-level measures of SES were not associated with pregnancy duration overall. However, individuals living in more deprived neighborhoods  had an increased odds of post term birth, and individuals living in higher racial privilege neighborhoods  on gestational age at birth; with the inclusion of these variables in the model, a lower ICERace quartile  associated with a decreased odds of preterm birth and attenuated the association with the increased odds of early-term birth. These findings suggest a complex relationship among pre-pregnancy BMI, pregnancy-related conditions, and racial segregation that warrants further investigation. For example, are neighborhoods with a lower proportion of non-Hispanic White individuals more communal or supportive in some way that influences gestational age at birth?Notably, in statistical models that additionally adjusted for maternal health conditions, including pre-pregnancy BMI and pregnancy-related conditions  and substance use behaviors, little change occurred in the effect estimates for maternal education level and NDI quartile on gestational age at birth. This observation may suggest that these individual- and neighborhood-level measures of SES exert their influence partly through pathways that are not directly related to maternal health status and/or behaviors, which may include pathways mediated by psychosocial factors, social support and stress, environmental exposures, and access to healthy food and recreation , 64, 65.The relationship between a higher level of maternal education and a lower preterm birth risk has been previously documented in the United States , 51. AltPrevious studies employing neighborhood-level measures of deprivation largely support an association between greater deprivation and preterm birth. In one study, NDI was found to associate with preterm birth in four United States between 1995 and 2001 . LikewisRace-Income) on health outcomes found five studies examining the outcome of preterm birth; these studies support a significant association between higher racialized economic segregation and preterm birth  being associated with an increased odds of preterm birth primarily among non-Hispanic Black individuals . A systerm birth . A meta-rm birth . Anotherrm birth . The autA growing body of research has assessed area-based measures of SES in conjunction with environmental exposures to investigate preterm birth from an environmental justice perspective. Such studies have found that exposures to air pollution and water pollution increase the risk for preterm birth, particularly among neighborhoods of low SES . Future 5This study addresses an important gap in the literature, namely the limited data on racial/ethnic and geographic variation in individual- and neighborhood-level measures of SES and gestational age at birth across the United States A strength of the ECHO-wide cohort is its heterogeneity with respect to race/ethnicity, geographic variation covering multiple states and regions, and SES. Another strength of the ECHO cohort data is the availability of maternal prenatal residential addresses, which allowed for geocoding and assignment of neighborhood-level measures of SES at the census tract. Publicly available birth record data does not allow for discernment of geography to the census tract level. Within the published literature, aside from our previously published disseminated meta-analysis , no otheAn additional strength of the ECHO cohort data is the availability and wealth of data on maternal health conditions and pregnancy complications that are linked with gestational age at birth outcomes. This rich data available across the ECHO cohorts offer a unique opportunity to expand on the analyzes of geographic variation in gestational age outcomes based on natality files. Conversely, the heterogeneity of the ECHO cohorts presents limitations. There was considerable variability in the measurement methods of the different variables , thus leading to the potential misclassification of outcomes  and exposures.An important limitation of this study and many other studies is that we were unable to examine the duration of exposure to neighborhood deprivation and racial segregation, which would require the construction of longitudinal residential address data over time. Neighborhoods where a mother has lived over the life course, encompassing childhood, adolescence, and adulthood, may have significant effects on health later in life. Living in a poorer neighborhood as a youth may expose a mother to chronic stress that later increases the risk for preterm birth . A studyA further limitation of the present study is the lack of investigation into the effect of interpersonal racial discrimination on gestational age at birth outcomes in the United States A substantial body of research suggests that racial discrimination is an important stressor, especially for Black Americans, and that physiological responses to chronic stress related to interpersonal and systemic racial discrimination contribute to an excess risk of preterm birth . A syste6The findings from this analysis indicate that both maternal education and neighborhood deprivation, representing individual- and neighborhood-level measures of SES, associate with preterm birth in the United States The findings suggest that interventions aimed at reducing preterm birth should include the promotion of higher educational attainment among women of reproductive age and multi-level community initiatives that foster community development. Additionally, the findings reinforce that for some racial and ethnic groups and geographic regions, factors other than higher educational attainment, such as racism, may be more salient exposures such that higher educational attainment and neighborhood privilege are not protective. Thus, interventions are needed that focus on achieving equitable access to the social determinants of health. In particular, there is a need for innovative social and economic policies and programs that support equal benefits from social resources for racial and ethnic minority individuals, including educational attainment. Such policies should focus on optimizing access to resources and assets and reducing societal and structural barriers that hinder racial and ethnic minority populations, including residential segregation and fewer and lower-quality educational opportunities . The CenSelect de-identified data from the ECHO Program are available through NICHD’s Data and Specimen Hub (DASH) . Information on study data not available on DASH, such as some Indigenous datasets, can be found on the ECHO study DASH webpage . Further enquires can be directed to the corresponding author’s.The studies involving humans were conducted in accordance with the local legislation and institutional requirements. The ECHO study protocol was approved by the local and/or central ECHO Institutional Review Board. Written informed consent was provided by the participants’ legal guardians/next of kin .AD, MB, LD, and MM contributed to conception and design of the study. MB and MM organized the database, performed the statistical analysis, and prepared tables and figures of results. AD and MM wrote the initial drafts of the manuscript. All authors contributed data for analysis and contributed to manuscript revision, read, and approved the submitted version of the manuscript.ECHO Components—Coordinating Center: Benjamin DK, Smith PB, and Newby KL, Duke Clinical Research Institute, Durham, North Carolina; Data Analysis Center: Jacobson LP*, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland; Catellier D and Parker CB*, Research Triangle Institute, Durham, North Carolina; Person-Reported Outcomes Core: Gershon R and Cella D, Northwestern University, Evanston, Illinois.ECHO Awardees and Cohorts—Alshawabkeh AN, Northeastern University, Boston, MA; Aschner J, Albert Einstein College of Medicine, Bronx, New York; Dabelea D. University of Colorado Denver, Denver, CO; Koinis Mitchell D, Deoni S, D’Sa V, Rhode Island Hospital, Providence RI; Duarte CS, New York State Psychiatric Institute, New York, NY; Dunlop AL, Emory University, Atlanta, GA; Elliott AJ, Avera Research Institute, Sioux Falls, SD; Ferrara A, Kaiser Permanente Northern California Division of Research, Oakland, CA; Breton C, University of Southern California, Los Angeles, CA; Hipwell A, University of Pittsburgh, Pittsburgh, PA; Karagas M, Geisel School of Medicine at Dartmouth, Lebanon, NH; Karr C, University of Washington, Department of Environmental and Occupational Health Sciences, Seattle, WA; Leve L, Prevention Science Institute, University of Oregon, Eugene, OR; Ganiban J, George Washington University, Washington, DC; Weiss ST, Brigham and Women’s Hospital, Boston, MA; McEvoy C, Oregon Health and Science University, Portland, OR; Lyall K, AJ Drexel Autism Institute, Philadelphia, PA; Oken E, Harvard Pilgrim Health Care Institute, Boston, MA; O’Shea M, University of North Carolina, Chapel Hill, NC; Kerver JM, Michigan State University, East Lansing, MI; Herbstman J, Columbia University Medical Center, New York, NY; Schantz S, University of Illinois, Beckman Institute, Urbana, IL; Stanford J, University of Utah, Salt Lake City, UT; Wright RJ, Icahn School of Medicine at Mount Sinai, New York, NY; Huddleston K, George Mason University, Fairfax, VA; Sathyanarayana S, Seattle Children’s Research Institute, Seattle, WA; Gern J, University of Wisconsin, Madison WI; Merhar S, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH; Ren C, Indiana University, Riley Hospital for Children, Indianapolis, IN; Reynolds A, University of Buffalo, Jacobson School of Medicine and Biomedical Sciences, Buffalo, NY; Keller R, University of California, San Francisco; Pryhuber G, University of Rochester Medical Center, Rochester, NY; Duncan A, University of Texas Health Sciences Center, Houston, TX; Moore P, Vanderbilt Children’s Hospital, Nashville, TN; Lampland A, Children’s Hospital and Clinic Minneapolis, MN; Wadhawan R, Florida Hospital for Children, Orlando, FL; Wagner C, Medical University of South Carolina, Charleston, SC; Keller R, University of Arkansas for Medical Science; Hudak M, University of Florida College of Medicine, Jacksonville, FL; Mayock D, University of Washington, Seattle, WA; Washburn L, Wake Forest University School of Medicine, Winston Salem, NC; Canino G, University of Puerto Rico, San Juan, PR; Croen L, Kaiser Permanente Northern California Division of Research, Oakland, CA; Detroit, MI and Zoratti E, Henry Ford Health System; Seroogy C and Bendixsen C, Marshfield Clinic Research Institute, Marshfield, WI; Johnson C, Henry Ford Health System, Detroit, MI; Bastain T, Farzan S, and Habre R, University of Southern California, Los Angeles, CA; Mason A, University of Tennessee Health Science Center, Memphis, TN; Women and Infants Hospital of Rhode Island, Providence RI, Lester B; Carter B, Children’s Mercy, Kansas City, MO; Pastyrnak S, Helen DeVos Children’s Hospital, Grand Rapids, MI; Neal C, Kapiolani Medical Center for Women and Children, Providence, RI; Smith L, Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, Los Angeles CA; Helderman J, Wake Forest University School of Medicine, Winston Salem, NC; O’Connor G, Boston University Medical Center, Boston, MA; Zeiger R, Kaiser Permanente, Southern California, San Diego, CA; Bacharier L, Washington University of St. Louis, St Louis, MO; Volk H, Johns Hopkins Bloomberg School of Public Health; O’Connor T, University of Rochester Medical Center Rochester, NY; Simhan H, University of Pittsburgh Medical Center, Magee Women’s Hospital, Pittsburgh, PA; Vaidya R, Baystate Children’s Hospital, Springfield, MA; Obeid R, Beaumont Health Medical Center, Royal Oak, MI; Rollins C, Boston Children’s Hospital, Boston, MA; Bear K, East Carolina University Brody School of Medicine, Greenville, NC; Pastyrnak S, Helen DeVos Children’s Hospital, Grand Rapids, MI; Lenski, M, Michigan State University College of Human Medicine, East Lansing, MI; Msall M, University of Chicago, Chicago IL; Frazier J, University of Massachusetts Medical School, Worcester, MA; Washburn, L, Wake Forest Baptist Health , Winston Salem, NC; Montgomery A, Yale School of Medicine, New Haven, CT; Barone, C, Henry Ford Health System, Detroit, MI; McKane, P, Michigan Department of Health and Human Services, Lansing, MI; Paneth N, Michigan State University, East Lansing, MI; Elliott, M, University of Michigan, Ann Arbor, MI; Woodruff T, University of California, San Francisco, San Francisco, CA; Porucznik C, University of Utah, Salt Lake City, UT; Silver R, University of Utah, Salt Lake City, UT; Trasande L, New York School of Medicine, New York, NY; Bosquet-Enlow M, Boston Children’s Hospital, Boston MA; Bush N, University of California, San Francisco, San Francisco CA; Nguyen R, University of Minnesota, Minneapolis, MN; Rochester, NY and Barrett E, University of Rochester Medical Center; Miller R, Columbia University Medical Center, New York, NY.


BackgroundThe umbilical cord coiling index (UCI) is usually measured sonographically during antenatal follow-up and can be used to determine the fetuses at risk of adverse outcomes.MethodologyUCI measured antenatally and postnatally whose correlation is studied along with the association of abnormal UCI with the adverse outcomes in terms of gestational age, intrauterine growth restriction (IUGR), intra-uterine death, birth weight, sex, neonatal intensive care unit (NICU) admission, the color of the liquor, Amniotic Fluid Index (AFI), Appearance, Pulse, Grimace, Activity, and Respiration (APGAR) score at one min and five mins and mode of delivery. All parameters are tested for significant differences among UCI and a p-value < 0.05 is considered significant. The correlation of UCI measured antenatally and postnatally is tested using the spearman correlation coefficient.Resultss 0.9. The majority of the population had normo coiling. Hyper and hypo coiling are associated risks of emergency lower segment cesarean section (LSCS). Low birth weight is seen in 88.89% of hypo coiled patients with a p-value < 0.01. The coiling index among sex is found to be insignificant with a p-value of 0.81. Meconium-Stained Liquor (MSL) is seen in 78.5% of hyper coiled patients. IUGR is found to be associated with hypo coiling as seen in 59.2% of patients with significant p-value (< 0.01). Age, gestational age, and birth weight are found to be statistically significant between various coiling indexes with p-value < 0.05.A strong correlation is found between antenatal UCI and postnatal UCI with rConclusionAntenatal UCI correlates with postnatal UCI and any abnormal index found can be used as a predictor of adverse perinatal outcomes and help obstetricians to monitor continuously and put the patients at risk on prophylactic measures. The umbilical cord is a fascinating and complex structure that connects the placenta and fetus . The umbTo correlate with possible antenatal and perinatal complications, umbilical cord characteristics such as the number of vessels, amount of Wharton’s jelly, cord length, type of placental insertion, cord thickness, blood flow patterns, cord diameter, and coiling are analyzed . The numUCI below the 10th percentile (under coiling) and UCI above the 90th Percentile (over coiling) are associated with increased risk for adverse perinatal outcomes . UmbilicThe current study is conducted to correlate the umbilical cord index measured antenatally using ultrasonography with that of the umbilical cord index measured postnatally and the association of this index with adverse fetal outcomes.Objectives of the study1. To assess the correlation between the antenatal umbilical coiling index & true umbilical coiling index at birth. 2. To study whether abnormal umbilical cord coiling index detected antenatally by ultrasonography will correlate with true umbilical coiling index at birth. 3. To determine the possibility of adverse pregnancy outcomes . using antenatal umbilical cord coiling index. Study designThe study was designed as a longitudinal analytical study for 18 months. The study was done after obtaining institutional ethical committee clearance. Inclusion criteria consisted of a) booked singleton pregnancy irrespective of parity, b) reliable gestational age between 18 to 23 weeks at the time of sonography, c) normal amniotic fluid index, d) presence of three-vessel umbilical cord, e) planned delivery at this institution. The exclusion criteria consisted of a) multiple pregnancies, b) fetal congenital anomalies, and c) maternal complications that interfere with fetal growth .The sample size was identified to be 200. Qualitative analysis was done, n=4pq/lA2 , q- 100-p (negative character). 1-allowable error (20%). According to the study , the prevalence of abnormal coiling is 34%. So, p=34%, q=66, 1=6.8. n=194ProcedurePregnant women who came to the obstetrics and gynecology (OBG) department of our tertiary care medical college hospital, and met our inclusion & exclusion criteria were explained about the project in detail and informed consent was obtained from the patients who expressed an interest in participating. The study variables were collected from the participants and analyzed. Antenatally measured umbilical cord coiling index is referred to as UCI1 and the umbilical cord coiling index measured postnatally is referred to as UCI2. Confidentiality was ensured by not collecting their personal, social, and economic details.Statistical analysisAntenatal umbilical coiling index (UCII) and postnatal umbilical coiling index (UCI2), were considered as primary outcome variables. Gestational age at delivery, mode of delivery, birth weight (baby), APGAR score, and intrauterine growth restriction (IUGR) were considered secondary outcome variables. For normally distributed quantitative parameters the mean values were compared between study groups using analysis of variance (ANOVA) (more than two groups). Categorical outcomes were compared between study groups using the Chi-square test, p-value < 0.05 was considered statistically significant. Data were analyzed by using CoGuide software  .A total of 200 subjects were included in the final analysis.In postnatal umbilical coiling index with normo coiling, hyper coiling, and hypo coiling, the majority of 140 (96.55%) participants were in normo coiling, 24 (85.71%) were in hyper coiling and 24 (88.89%) were in hypo coiling as per antenatal umbilical coiling index  participants were in < 37 weeks gestational age at delivery. Out of 28 hyper coiling, the majority of 24 (85.71%) participants were at 37 to 40 weeks gestational age at delivery. Out of 27 hypo coiling, the majority of 15 (55.56%) participants were in > 40 weeks gestational age at delivery  and Birth weight (kg) was statistically significant with p-value < 0.001. Out of 27 hypo coiling, one 3.70%) participant had reported intrauterine death , IUGR, neonatal intensive care unit (NICU), and APGAR at 1 min was statistically significant with a p-value < 0.001. Out of 145 normo coiling in the postnatal umbilical coiling index, the majority of 139 95.86%) participants had the clear color of the liquor. Out of 28 hyper coilings, the majority of 22 78.57%) participants had MSL color of the liquor. Out of 27 hypo coiling, the majority of all of them 27 (100.00%) had the clear color of the liquor. Out of 145 normo coiling, the majority of all of them 145 (100.00%) participants were >=7 APGAR score. Out of 28 hyper coilings, the majority of 25 (89.29%) participants were >=7 APGAR score. Out of 27 hypo coiling, the majority of all of them 26 (96.30%) were >=7 APGAR score. (Tables % partici8.57% parThe mean age (years) within normo coiling was 26.47 ± 4.09, it was 26.11 ± 3.39 in hyper coiling and it was 28.78 ± 4.11 in hypo coiling. The mean difference of age (years) in the postnatal umbilical coiling index was statistically significant with a p-value of 0.0166. The mean difference between gestational age at delivery (weeks) and birth weight (kg) in the postnatal umbilical coiling index was statistically significant with a p-value < 0.001  is seen in 78.5% of hyper coiled patients. IUGR is found to be associated with hypo coiling as seen in 59.2% of patients with a significant p-value of < 0.01. Age, gestational age, and birth weight are found to be statistically significant between various coiling indexes with p-value < 0.05.The majority of the population in our study are with normo coiling  which is in line with the study conducted by Chitra et al., where normo coiling was seen predominantly with 78.3% . The samA study conducted by Milani et al. reported insignificant differences among parity of the pregnancy across coiling indexes with a p-value of 0.26 which is in agreement with the current study p-value of 0.31 . The stuOur study results are in line with other similar study results . There iThe longitudinal analytical study conducted to assess the correlation of the umbilical cord coiling index measured antenatally with the umbilical cord coiling index measured at birth reported a strong positive correlation. The association of abnormal umbilical cord coiling index with adverse pregnancy outcomes was found to be significant for low birth weight, oligohydramnios, meconium staining of liquor, preterm labor, IUGR, and IUD. Hence, UCI measured antenatally can be used as a predictor of adverse perinatal outcomes and help obstetricians to monitor continuously and put the patients at risk on prophylactic measures.


How does the Deterioration Index perform in predicting patient clinical deterioration across diverse hospital settings and demographic groups?In this prognostic study involving more than 5 million DTI predictions for 13 737 patients, the DTI had acceptable discrimination at the observation level but poor discrimination at the encounter level. Performance also varied by demographic subgroup, with worse performance for patients identifying as American Indian or Alaska Native and those who chose not to disclose their ethnicity.These findings highlight the need to consider patient demographic characteristics and variations in care practices when implementing predictive models such as the DTI. The Deterioration Index (DTI), used by hospitals for predicting patient deterioration, has not been extensively validated externally, raising concerns about performance and equitable predictions.To locally validate DTI performance and assess its potential for bias in predicting patient clinical deterioration.This retrospective prognostic study included 13 737 patients admitted to 8 heterogenous Midwestern US hospitals varying in size and type, including academic, community, urban, and rural hospitals. Patients were 18 years or older and admitted between January 1 and May 31, 2021.DTI predictions made every 15 minutes.Deterioration, defined as the occurrence of any of the following while hospitalized: mechanical ventilation, intensive care unit transfer, or death. Performance of the DTI was evaluated using area under the receiver operating characteristic curve (AUROC) and area under the precision recall curve (AUPRC). Bias measures were calculated across demographic subgroups.A total of 5 143 513 DTI predictions were made for 13 737 patients across 14 834 hospitalizations. Among 13 918 encounters, the mean (SD) age of patients was 60.3 (19.2) years; 7636 (54.9%) were female, 11 345 (81.5%) were White, and 12 392 (89.0%) were of other ethnicity than Hispanic or Latino. The prevalence of deterioration was 10.3% (n = 1436). The DTI produced AUROCs of 0.759  at the observation level and 0.685  at the encounter level. Corresponding AUPRCs were 0.039  at the observation level and 0.248  at the encounter level. Bias measures varied across demographic subgroups and were 14.0% worse for patients identifying as American Indian or Alaska Native and 19.0% worse for those who chose not to disclose their ethnicity.In this prognostic study, the DTI had modest ability to predict patient deterioration, with varying degrees of performance at the observation and encounter levels and across different demographic groups. Disparate performance across subgroups suggests the need for more transparency in model training data and reinforces the need to locally validate externally developed prediction models. This prognostic study evaluates and validates the performance and potential for bias of a proprietary deterioration index in predicting clinical deterioration among hospitalized adult patients. Only patients who had not chosen to opt out of research during their initial interaction with our health care system, such as a first clinic appointment or emergency department registration, were included in the study. This study followed the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis  within 12 hours or death (defined as discharge time) within 38 hours.11 We excluded predictions made in preoperative, intraoperative, ICU, or labor and delivery locations because surgical procedures were likely to involve planned mechanical intubation, ICU patients had already deteriorated, and labor and delivery patients were excluded from the DTI training data.In 2020, our organization silently turned on the DTI model, which ran in the background of our electronic health record (EHR). Consequently, clinicians did not see or act on the scores generated by the DTI. For this study, we included all patients 18 years or older who were hospitalized at MHealth Fairview  between January 1 and May 31, 2021, and who had at least 1 DTI score recorded. The 8 hospitals in our study were a mix of academic (n = 2) and community (n = 6) hospitals, including 3 rural hospitals .16 Deterioration events occurring after comfort care orders were excluded from analyses. Although the DTI was trained to also detect RRT or code team activations, our EHR did not discretely capture these events; consequently, they could not be incorporated into our definition of deterioration. Nevertheless, to gain insights into the occurrence of such events in patients experiencing deterioration across our hospitals, we conducted a manual records review of 50 cases .We used area under the receiver operating characteristic curve (AUROC) and area under the precision recall curve (AUPRC) to describe the overall performance of the DTI among the patient population. The AUROC is the probability that the model correctly ranks deterioration risk among 2 randomly selected samples from the outcome and nonoutcome groups. The AUPRC is the weighted mean of precisions  achieved at each threshold, in which weight is the increase in recall  from the previous threshold.20  to identify an optimal single score threshold. We report the number needed to evaluate (NNE) at each threshold, calculated as the inverse of PPV. The NNE describes the number of predictions with a score higher than the threshold that must be evaluated to identify 1 case of deterioration.25As an ordinal model, the DTI was designed to predict 2 distinct events: care escalation and death. To accommodate these different outcomes, the vendor allows hospitals to set both a medium- and high-risk score threshold. Accordingly, we evaluated DTI performance at both a medium-risk threshold targeting 50% sensitivity and a high-risk threshold targeting 10% PPV. Hospitals may also prefer a single threshold to trigger an intervention,27 Ethnicity was reduced from more than 100 options , defined as the ratio of the model’s sensitivity in the protected group to its sensitivity in the reference group. Third, we calculated PPV parity , defined as the ratio of the model’s PPV in the protected group to its PPV in the reference group. We studied these 3 measures because a deterioration model must discriminate well (AUROC parity) while identifying the greatest number of patients experiencing deterioration (sensitivity parity) and avoiding false-positive cases (PPV parity). We measured sensitivity and PPV parity at the threshold that maximized F1 score.To measure potential bias, we calculated 3 bias measures29 Differences in model performance across lead times and subgroups should be considered exploratory. All analyses were performed using Stata software, version 16.1 (StataCorp LLC); Python software, version 3.9 (Python Software Foundation); and R software, version 4.2.3 .We abstained from conducting formal statistical tests on model performance measures because our focus was solely on evaluating a single model. We calculated 95% CIs for proportional point estimates using the Wilson score.A total of 13 737 patients from 8 hospitals, representing 14 834 encounters and 5 143 513 DTI predictions, met the inclusion criteria . Once weThrough records review, we identified 2 cardiac arrests among the 50 total cases of deterioration. A rapid response was called on 7 patients (14.0%), all of whom eventually experienced deterioration. The underlying reason for deterioration in 2 patients (4.0%) was clearly COVID-19. Bacterial sepsis was clearly the underlying reason for deterioration in 4 patients (8.0%). In all, 16 of 43 deterioration events (37.2%) were deemed unlikely preventable while 23 (53.5%) were deemed clearly not preventable.At the observation level, the DTI had an AUROC of 0.759  and an AUPRC of 0.039 , suggesting acceptable discrimination. At the encounter level, the DTI had an AUROC of 0.685  and an AUPRC of 0.248 , suggesting poor discrimination. The AUROC and AUPRC plots are shown in eFigure 1 in The ability of the DTI to predict the individual components and composite definition of deterioration across increasing lead times, evaluated at the observation level, is shown in Observation-level performance measures at clinically informed score thresholds are shown in Bias measures for protected subgroups are shown in The purpose of this prognostic study was to comprehensively evaluate the performance of the DTI among a large cohort of patients across 8 heterogenous midwestern US hospitals. We discovered 3 key findings: (1) the DTI had acceptable discrimination in predicting deterioration at the observation level and poor discrimination at the encounter level, with discrimination improving closer to deterioration; (2) achieving clinically appropriate DTI score thresholds is challenging due to large tradeoffs between sensitivity and PPV; and (3) DTI performance varied across certain demographic subgroups.10 In late 2022, Epic switched to an observation-level evaluation method because it “more accurately represents the experience clinicians have when using the model.”10 Epic currently reports a mean AUROC of 0.710 (used with permission),10 which lies between our calculated observation- and encounter-level AUROCs. Compared with the largest existing external validation of the DTI,20 our observation-level AUROC was similar (0.759 vs 0.768-0.780), while our encounter-level AUROC was substantially lower (0.685 vs 0.821-0.863). We found a general pattern of decreasing performance with increasing lead times for mechanical ventilation and ICU transfer, whereas performance for death remained relatively high and stable across lead times. Overall performance was more reliable in the immediate term (within a few hours) and decreased over longer periods. The variable performance of the DTI across different components of deterioration highlights the necessity of context-specific interpretation.Overall DTI performance fluctuated widely by encounter- vs observation-level evaluation methods, as it has in other settings. For example, Epic initially used an encounter-level evaluation method to validate the DTI and reported its mean AUROC as 0.801.Our strategic selection of suggested score thresholds revealed large tradeoffs in sensitivity and PPV. At the medium-risk score threshold, we prioritized sensitivity to ensure that a substantial proportion of patients at risk was identified. However, the high NNE at this threshold could lead to resource strain due to a high number of false-positive cases. By optimizing the model’s maximal F1 score, we balanced sensitivity and PPV to create a single decision threshold. A single-threshold approach is best suited for a well-calibrated model; however, our calibration curves revealed that the DTI does not exhibit a linear association between predicted risk and deterioration. Given the ordinal nature of the model, poor calibration may be expected, which supports the use of 2 distinct score thresholds. Accordingly, we chose our high-risk threshold to prioritize PPV at the expense of identifying a substantially smaller proportion of patients at risk.30 While bias is suggested if parity measure values approach 0.2 with regard to the reference group, the interpretation of such inequality in parity must be contextually aligned with the specific application and potential implications of the observed disparity.31 For example, although our results suggested that the DTI performed worse among American Indian or Alaska Native patients, with an overall parity discrepancy of 14.0%, this performance could be associated with differences in care with respect to our definition of deterioration rather than inherent statistical bias present in the model. Our system might send White patients to the ICU faster and more frequently than American Indian or Alaska Native patients who have the same severity of illness. An external model trained on nonbiased data could accurately predict the same risk of ICU transfer for both groups; however, when comparing their rates for postprediction ICU transfers, the model would appear to overestimate risk for White patients and underestimate risk for American Indian or Alaska Native patients. In contrast, we found that the DTI exhibited performance parity among Black and Hispanic patients, 2 groups traditionally at risk of machine learning bias due to poor representation in training data. Notably, we found the largest parity discrepancy of 19.0% for patients who chose not to disclose their ethnicity, and future research should explore whether this nondisclosure represents a distinct clinical risk profile.Bias in predictive models manifests as a systematic favoritism in the model’s predictions, often because of insufficient data collection, selection, or model training.Our records review revealed no instances of preventable deterioration because most patients were known to be critically ill or had been appropriately transferred to the ICU for closer monitoring. Despite the small sample, our findings raise a pertinent clinical question: how often do DTI predictions provide new and actionable insights for the health care team? Higher risk thresholds may overlook patients who could benefit from early intervention. Consequently, future research is needed to formulate standard measures that could answer this question both retrospectively and prospectively during model implementation.10 and a subsequent external validation16 on patients with COVID-19 revealed comparable if not superior DTI performance among those with COVID-19. For this study, we analyzed the DTI based on the manner in which it was likely to be implemented  to provide an assessment of its performance within the clinical practice setting.This study has several limitations. We did not stratify our analysis by COVID-19 positivity. Because the DTI was developed before the emergence of COVID-19, researchers did not know how it would perform in patients with the disease. However, both an internal validation by the developer32 Although we attempted such an analysis, the exponential increase in the number of subgroups when combining protected features led to many subgroups having no or few observations, making the results unreliable. New techniques to robustly assess intersectional bias are needed.28In our bias assessment, we did not control for multiple protected features simultaneously, which limits our ability to understand how the DTI performs across specific intersectional groups .This prognostic study found that a proprietary deterioration model had poor to acceptable discrimination when applied to patient data from a large midwestern US health care system, and its performance generally decreased over longer lead times. Large tradeoffs were found between sensitivity and PPV as well as variable performance among certain demographic subgroups. These findings highlight the need for health care organizations to thoroughly evaluate the performance and equity of externally developed predictive models.


By fitting a tensor model to quantify the directionality of water diffusion a Diffusion Tensor Image (DTI) can be derived and scalar measures, such as fractional anisotropy (FA), can then be estimated from the DTI to summarise quantitative microstructural information for clinical studies. In particular, FA has been shown to be a useful research metric to identify tissue abnormalities in neurological disease (e.g. decreased anisotropy as a proxy for tissue damage). However, time constraints in clinical practice lead to low angular resolution diffusion imaging (LARDI) acquisitions that can cause inaccurate FA value estimates when compared to those generated from high angular resolution diffusion imaging (HARDI) acquisitions. In this work, we propose High Angular DTI Estimation Network (HADTI-Net) to estimate an enhanced DTI model from LARDI with a set of minimal and evenly distributed diffusion gradient directions. Extensive experiments have been conducted to show the reliability and generalisation of HADTI-Net to generate high angular DTI estimation from any minimal evenly distributed diffusion gradient directions and to explore the feasibility of applying a data-driven method for this task. The code repository of this work and other related works can be found at  Since its inception, several MRI modalities have been proposed to probe specific information about different properties of multiple tissues and organs of interest. On one hand, conventional structural MRI sequences, such as T1- and T2-weighted images, can provide valuable morphological details of the brain and pathological conditions. On the other hand, Diffusion Weighted Imaging (DWI) can measure the water diffusivity within tissues and reveal their microstructure and integrity (Each set of images at different gradient directions can be summarised using a Diffusion Tensor Imaging (DTI) model to uncover microstructural information by describing water’s directionality and its corresponding quantitative anisotropy . The difanalysis , 5. For analysis  usually analysis . Specifianalysis .However, scalar metrics derived from DTI can be unreliable due to clinical imaging constraints. This is usually caused by sub-optimal acquisition protocols, which often lead to a low angular resolution diffusion image (LARDI) . By defimeasures , 11 whicTo address the trade-off between acquisition time and high resolution , image enhancement techniques have played an important role in generating high-quality images from their corresponding low-quality acquisitions . U-Net , a de-faTo this end, we propose to estimate an enhanced DTI model from a minimal acquisition of 6 evenly distributed diffusion gradient directions, by exploiting the corresponding information from high angular resolution diffusion imaging (HARDI) training data using a High Angular DTI Estimation Network (HADTI-Net). For the rest of the paper, we define HARDI as a high angular resolution of 90 diffusion gradient directions and LARDI as a sampled DWI set of 6 evenly distributed gradient directions from all the available 90 directions. The corresponding estimated DTI models for HARDI and LARDI are defined as HAR-DTI and LAR-DTI, respectively. We investigated the feasibility of HADTI-Net to attain FA measurements from LARDI that are comparable to those obtained through HAR-DTI.To the best of our knowledge, this is the first work to estimate a HAR-DTI model from a minimal number of evenly distributed diffusion gradient directions.The proposed High Angular DTI Estimation Network (HADTI-Net) is specifically designed to estimate an enhanced DTI model from LARDI combining the structural information of T1 and b0 with the diffusion gradient directions to mitigate FA disparities when compared to HAR-DTI.Evaluation has been conducted and compared with clinical evidence for various neurological disorders to prove the effectiveness and consistency of the enhanced LAR-DTI to reduce noisy estimates and recover missing clinical information from LARDI.Extensive evaluations have been conducted to prove the robustness of the proposed HADTI-Net to estimate an improved and consistent DTI model from LARDI with significantly lower FA differences from any set of 6 evenly distributed directions.The major contributions of this work are summarised as follows:2.2.1.2.1.1.The Human Connectome Project (HCP) database  includes1 images . The cor1 images . The pre1 images , 23. Fre2.1.2.To simulate a single shell acquisition, the 90 directions from the ch voxel  of all tFinally, to analyse the performance of HADTI-Net on each individual white matter (WM) tract, TractSeg was used to generate 72 white matter tracts on corresponding HARDI for each testing subject as described in their original paper .2.1.3.To sample a set of gradient directions from the HARDI acquisition, two variations of the Kennard-Stone (KS) algorithm , 29 wereDue to the algorithm’s nature, smaller sets of values are always a subset of larger ones (e.g. the 6 evenly distributed directions are part of the 12 evenly distributed ones). Furthermore, KS is a deterministic algorithm that will always provide the same samples given a specific set of values. As a consequence, there would only be one single set of 6 evenly distributed directions if using the original algorithm for the HCP dataset. To increase the variability of sampled directions and guarantee generalisation to unseen minimal acquisitions, we introduce an extension of the KS algorithm (RandomKS). Specifically, for each subject, a random direction gradient is selected instead of the two furthest apart. Afterwards, the regular KS algorithm is applied to select the furthest value from the current sampled set until 6 directions are selected.2.2.Common clinical acquisitions rely on a high-quality T1-weighted image for structural information  and a single or multiple b0 images (images acquired using the same acquisition parameters as the other diffusion volumes but without any diffusion weighting) as a reference to merge structural information from T1-weighted images and help with the DWI pre-processing. The design of our proposed High Angular DTI Estimation Network (HADTI-Net) is shown in 2.3.The 100 preprocessed HCP subjects were split into a training and testing set with a commonly used 80–20% ratio, respectively. The concatenated input had a total of 8 channels which include a channel for T1, a channel for b0, and 6 channels for minimally evenly distributed diffusion gradient directions. The patch size of the input was set to 2.4.To evaluate the differences between two sets of DTI volumes (i.e. LAR-DTI and HAR-DTI), we used the following common similarity metrics: mean absolute error (MAE), structural similarity index (SSIM), and normalised cross-correlation. For each metric, we report the mean and standard deviation values of different regions of interest (depending on the experiment) for all the subjects.Finally, for each set of values, we performed a normalcy test to determine whether the requirements to run 2.5.To validate the proposed algorithm, we performed 4 different experiments, where each one addressing a different research question. These experiments are briefly described as follows:What is the gap between our prediction and HAR-DTI from an image similarity perspective for the whole brain? Section . To address this question, we compute different common image similarity metrics (as described in the previous section) to determine the original gap between the LAR-DTI using KS sampling and the ground truth HAR-DTI and whether that gap was bridged using our enhanced prediction focusing on the whole brain.What is the gap between our prediction and the HAR-DTI metrics on different WM tracts? Section . For this experiment, we focus exclusively on FA which is the most affected metric when using a minimum number of gradient directions. Furthermore, we use the TractSeg segmentations from corresponding HARDI to determine 72 regions of interest.How are the prediction errors relevant in clinical practise when analysing group-wise differences according to relevant literature? Section . First, we performed a literature review for group-wise FA differences in different tracts for neurological disorders. Afterwards, we compare these differences with the margin error obtained by our method to determine whether these group-wise differences could still be observed with the original LAR-DTI model and our enhanced prediction.How important is the sampling of gradient vectors and their order? Section . We propose three different hypotheses that we statistically test to determine the importance of how the diffusion-weighted images are seen by introducing randomness to different aspects during inference.3.3.1.To evaluate the gap between the DTI estimates from LARDI  in the whole brain, we calculated four different DTI-derived scalars commonly used in clinical practice . The results are summarised separately for WM and GM in A clear observation from the results is the overall improvement of the diffusion scalar metrics for the whole brain (even in the GM region which was not supervised by HARDI-Net). Regarding FA measures, the differences can already be apparently observed exclusively at the MAE values. This is not necessarily surprising as FA images have in general higher intensity values leading to a higher range of error values. Nonetheless, enhanced LAR-DTI by HADTI-Net is capable to obtain the highest SSIM  for FA and comparable results for cross-correlation. This is important, as FA is one of the most commonly used metrics for clinical studies as it provides a good proxy to understand anisotropy (even though it is not suitable for regions with crossing fibres). Furthermore, due to its computational complexity , it is the most prone to numerical errors. Therefore, being able to predict a DTI tensor that is closer to a HARDI acquisition (with 90 directions) with a clinical acquisition is important.Finally, while improvements are shown for MD, the differences were not found to be significant. In fact, the cross-correlation values for both LAR-DTI and the network predictions are the highest among all the diffusion scalar measures. These results suggest that the MD information is preserved and is not as sensitive to noise in the acquisition. This is to be expected when taking into account the definition of MD and the limitations of the DTI model. MD is related to the magnitude of diffusivity on the three axes of the tensor. As such, it is not much affected by the direction of the tensor , or its relationship between axes (like FA would). In conclusion, erroneous LARDI MD measurements may be undetected if the sum of the magnitude of each axis is not changed regardless of the changes of the fitted ellipsoid shapes.To better illustrate these results, we visualise the axial, coronal, and sagittal views (and a zoomed-in region) of the diffusion tensors for a testing subject in 3.2.As discussed in the previous experiment, FA is highly sensitive to microstructural changes  and is s3.3.To underscore the potential clinical significance of our work, evidence was gathered from relevant works to carry out further analysis. In particular, different clinical studies have demonstrated that aging-related neurodegeneration can lead to FA reduction , and a sThe figure demonstrates that LAR-DTI has a higher mean and a wider distribution of FA differences, which highlight the limitations of using a LARDI acquisition in clinical studies without further enhancement. For instance, a 0.1 FA difference was observed in the Genu tract (CC_2) between healthy controls and HD patients , where t3.4.We have shown that HADTI-Net can estimate an improved DTI model from LARDI with a fixed set of 6 diffusion gradient directions using the deterministic KS algorithm (referred to as LAR-KS). To further validate the proposed HADTI-Net, we conducted a set of experiments to understand the importance of the 6 sampled gradient directions during inference and whether our random sampling training strategy led to a more general framework for any 6 evenly distributed gradient directions. Specifically, we defined 3 hypotheses to statistically test. Similarly to Section 3.2design and data preparation for this section are shown in 3.4.1.For this experiment, we define two sets of testing images. On the one hand, we have the LAR-KS images previously analysed in Sections Since these sets of images cannot be paired (the FA of different random samplings are not measurements of the same property) and due to the lack of normalcy for the LAR-Random measurements we designed a subject-wise statistical test based on the Wilcoxon signed test. Specifically, for each subject, we designated the LAR-KS MAE as the “ground truth” estimate and subtracted it from the LAR-Random estimates. This gave us a distribution of the differences between LAR-KS and LAR-Random. Therefore, we could formulate our null hypothesis as the distribution of these values to be symmetrical around 0 (meaning that LAR-Random does not obtain lower or higher errors). After testing for each subject independently and correcting for multiple comparisons (20 subjects), no significant difference was found with a significance value after correction of 3.4.2.While the previous experiment proved that the sampling is not important, it did not necessarily prove that different orderings on the input vector would not lead to different results. While our modified KS algorithm introduces an element of randomness (by choosing the first gradient direction), the sampling is still deterministic for the other 5 gradient directions. Furthermore, the ordering of the directions is not relevant and the network should not try to learn that. To test this hypothesis, we created a new testing dataset called LAR-Shuffled, where we conducted 5 random shufflings for the 6 gradient directions in each DWI image within the 10 sets of LAR-Random, leading to 50 different sets of input images. Similarly to the previous experiment, the images were combined with the T1-weighted and b0 images and fed to HADTI-Net to obtain an enhanced DTI and the MAE of FA values was calculated.Once again, samples could not be paired and for each subject we compared the distributions between the set of LAR-Random and LAR-Shuffle errors. In that case, we formulated our null hypothesis as both sets have the same distribution of error values. Due to the lack of normalcy for both sets, we performed a Mann-Whitney 3.4.3.While we have proven that the sampled directions are not important as long as they are evenly distributed , the question still remains of whether the network exploits the relationship between b0 and the diffusion-weighted information . To test this hypothesis, we need to assume that positional information in the input is important (i.e. the order of the given channels). Unlike self-attention mechanisms or graph convolutions that do not rely on positional information in a sequence, convolutional and linear layers have different weights for different channels. We have proven with the previous experiment, that this is not the case, at least for the last 6 channels. However, that does not prove that the first two channels do not have specific weights . Therefore, assuming that the position of b0 is important, we now create a new set of files once again based on the LAR-Random set of images. Specifically, for each image in the LAR-Random set we randomly place b0 as one of the diffusion-weighted images (refer to as LAR-b0). T1 was then combined as the first image again and the input was fed to the network to estimate the enhanced DTI and calculate the FA MAE.Similarly to the previous experiment, two sets of unpaired errors were obtained per subject. Therefore, we could reformulate our hypothesis as the distributions of errors for LAR-b0 and LAR-random being equal and perform a Mann-Whitney 4.In this work, we have proposed HADTI-Net, a deep learning network to estimate the enhanced DTI model from LARDI with a set of minimal evenly distributed diffusion gradient directions. We conducted extensive experiments on subjects from the HCP dataset to validate the feasibility of applying HADTI-Net to generate enhanced DTI volumes and to evaluate the randomised training strategy for generalisation to any set of 6 directions. These results have shown comparable DTI scalar measures when compared to those from HARDI on the whole WM region and a large improvement for each WM tract independently. Furthermore, our quantitative analysis of WM tracts from a clinical perspective demonstrated that HADTI-Net has clinical significance by mitigating the FA errors brought by a set of minimal diffusion gradient directions. Finally, a set of statistical tests have proven that the network could generalise to any set of evenly distributed directions while highlighting the ability to distinguish between b0 and diffusion-weighted information. In conclusion, HADTI-Net can be used as a post-scanning technique to allow clinical studies with a set of minimal evenly distributed diffusion gradient directions while achieving reliable DTI metrics, comparable to a HARDI acquisition.


This research aimed to analyze whether ewes' total reproductive performanceup to the fourth year of life (RP4) can be predicted based on the dataavailable at an early stage of a ewe's productive life. The RP4 of 133Romanov ewes was measured in terms of the total number of lambs born per ewe(TNLE) and total birth weight of lambs per ewe (TBLE). Multiple regressionwas used to analyze whether early reproductive performance indicators , ewe birth rank, dam age, and dam birth rank can be used as thepredictors of RP4. Predicted  Frequently usedindicators of the reproductive performance of sheep include fertility,prolificacy, sexual maturity, age at first lambing, lambing interval,conception rates, and lambs weaned/ewe/year . Total reproductive performance measured in terms of the totalnumber of lambs born per ewe over a long period or in a lifetime is usedless frequently but is one of the best indicators of overall sheepproductivity and value .In annual lambing systems, total reproductive performance depends on thecomponent traits such as fertility, fecundity, and lamb survival. In theaccelerated lambing systems, ewes may lamb more than once per year, makingthe lambing frequency  an essential component trait.Performance records are often used as one of the primary sources ofinformation for selecting replacements and culling ewes . Total reproductive performance usually contributes little to theselection or culling decisions due to late availability. However, if itcould be predicted using the reproductive indicators recorded earlier in thesheep's life, total reproductive performance could be an important factor inmaking that decision. In predictive modeling, the imperative is to produceprecise and unbiased predictions, and predictive models usually requireinexpensive and measurable data . In that sense, earlyreproductive performance indicators  are promising candidate predictors for thetotal reproductive performance of ewes. Also, dam age and ewe birth rank, asinvestigated by Pettigrew et al. (2019), might have a significant influenceon ewe total reproductive performance and thus be used as its predictors.Generating the prediction values of reproductive traits in sheep can beperformed using various statistical procedures, including a regressionapproach  and data miningalgorithms such as neural networks and decision trees . So far, the regression approach was more common in determining thefavorable predictors of the lifetime performance of ewes. However, theprecision of prediction based on the This research aimed to assess the influence and predictive value of severalewe early reproductive performance indicators, ewe birth rank, dam age, anddam birth rank on the total reproductive performance of sheep up to thefourth year of life (RP4).22.1The study was conducted between 2015 and 2020 at one commercialsemi-intensive sheep farm in Croatia located at 452.2Initially, a total of 148 ewes were included in the study. Excludingcriteria were missing data, age at first lambing longer than 600 d, andlambing interval longer than 500 d. At the end of the study period, 15 ewes were excluded from the analyses. Measurements ended after ewe reached4 years of life. A 4-year threshold was set because most of theRomanov ewes reached and passed their maximum productivity during this time. Records on ewes included their birth rank (size of the litterin which ewe was born), date of birth, date of first and second lambings,litter sizes, and birth weight of lambs. Dam birth rank (size of the litterin which dam was born) and dam age (ewe lamb or mature ewe) were alsocollected. A young female dam giving birth for the first time was considereda ewe lamb, while mature ewes (dams) lambed two or more times. Lambs wereweighed within 12 h after birth. Age at first lambing and first lambinginterval were calculated using the obtained records. Based on the age atfirst lambing, ewes were divided into groups as follows: early  wascalculated as the total number of live-born lambs except first lambing, andthe total birth weight of lambs per ewe (TBLE1) as the sum of allbirth weights of lambs, except those from first lambing. The total number oflambs per ewe 2 (TNLE2) was determined as the total number of live-bornlambs per ewe and the total birth weight of lambs per ewe 2 (TBLE2) by addingup all birth weights of lambs.2.3Statistical analysis was performed in the Minitab statistical program. Two separate multivariate regression models were built.In the first model, TNLE1 and TBLE1 were evaluated based on the age at firstlambing class, first litter size, and first lambing interval class as fixedfactors. The equation of the model was as follows:The second model was built to analyze TNLE2 and TBLE2 and included the ageof the dam, dam birth rank, and ewes' birth rank as fixed factors. The modelwritten in mathematical form was as follows:ANOVA procedure was used to test for the significant effect of fixedfactors, and the Tukey post hoc test for an unequal number of samples wasused to test the significance of differences between groups within the samefixed factor. The significance level was set at Akaike information criterion (AIC) was used to determine the best model forthe prediction of RP4. After the formation of the optimal models, predicted 33.1During the 4-year period investigated, ewes produced, on average, 11.84 lambs, with an average total lamb weight of 37.96 kg (Table 1). Thecoefficient of variation suggests relative homogeneity of the data.3.2The proportion of variation explained by the model with early reproductiveperformance indicators was 15.4 %  or its stayability .In our research, the total number of lambs per ewe and the total birth weightof lambs per ewe were analyzed. Early reproductive performance indicators were significantly associatedwith ewe RP4. This result was expected given that the litter size is theindicator most closely connected to the total reproductive performance.Indeed, Lee and Atkins (1996) found that, in the commercial Merino flock,early life fecundity was indicative of fertility, fecundity, and the rearingability of ewes in later life. Based on the results from our research, eweswith short first lambing intervals had significantly higher production interms of the number of lambs and birth weight of lambs than ewes whose firstlambing interval was long. This may be because some of the variations inlambing intervals are repeatable, so ewes with short first lambing intervalslambed more frequently than ewes with long first lambing interval. Areb etal. (2021) found the repeatability of the lambing interval in Bonga sheep tobe as high as 0.57. They concluded that selection decisions could be madebased on the early reproductive performance indicators. However, thissuggestion should be the topic of further and more extensive research, giventhat the repeatability of lambing intervals is usually below 0.10 .Although all ewes in our research were initially joined with rams at thesame time, the age at first lambing varied by 3 months. However,differences in RP4 were not significant, meaning that the Romanov ewes inthe continuous mating system can be mated at a relatively large age spanwithout hindering their reproductive efficiency in later life. This seemslike valuable information, given that the age at first mating/lambing isconsidered one of the main determinants of ewes' productive life  and has a negative genetic correlationwith lifetime reproduction . Indeed, it was shownthat age at first lambing affects stayability  andlifetime reproductive performance of ewes in pelt ,dairy , and meat  productionsystems. It should be pointed out that although continuous mating providessimple reproductive management, it also has several disadvantages .Several authors  suggestedusing early reproductive performance indicators to predict the totalreproductive performance of ewes. Based on the results obtained in ourresearch, first litter size and first lambing interval seem to be likelypredictors of RP4, but a precise prediction was not possible. Even thoughobserved and predicted means were very similar in most litter size-lambinginterval combination groups, the low value of the The second model demonstrated that dam and ewe birth rank significantlyaffect the RP4. If ewes and their dams were from multiple litters, ewes' RP4tended to be higher in terms of lamb production and birth weight. Age of thedam did not significantly influence the RP4. Reports on the influence of dambirth rank, ewe birth rank, and age of dam on the reproductive performanceof ewe are scarce. Loureiro et al. (2012) investigated whether being born toa ewe lamb or adult dam affected ewes' live weight and reproductiveperformance of ewes. Authors found that ewes born to ewe lamb dams weresignificantly lighter at birth and 12 months of age than ewes born to adultdams, but no significant difference regarding reproductive performance wasobserved. In a recent experiment, Pettigrew et al. (2019) investigated thecombined effect of the ewe's birth rank and the dam's age on severallifetime reproductive performance indicators of ewes. None of the effectswas significant for the number of lambs born. However, ewes born to maturedams had significantly lighter litters at birth, and twin-born ewes born toewe lamb dams had lighter litters than single-born ewes. Higher RP4 ofmultiple litter-born ewes from multiple litter-born dams might be partiallyattributed to the genetic variation in reproductive potential among ewes. However, further research on the geneticstructure of reproductive traits, including the candidate genes approach, isnecessary to draw valid conclusions.The main limitation of this study was the relatively small sample size.However, intensive and semi-intensive sheep farming in Croatia is mainlyorganized in small farms containing from 100 to 200 reproductive sheep, andour intention was to investigate sheep on one farm and under the samemanagement practices in order to minimize environmental variations. A studyon the larger number of sheep might produce more reliable predictions.5This study confirmed the association between early reproductive performanceindicators , the birth rank ofewe and birth rank of dam, and total reproductive performance measured interms of the total number of lambs born per ewe and total birth weight oflambs per ewe. However, these indicators were of limited value as predictorsof ewe total reproductive performance, mainly because they generatedimprecise estimates. Including additional predictors and increasing thesample size might improve the precision of the prediction.


Neonatal necrotizing enterocolitis (NEC) is a severe gastrointestinal disease that primarily affects preterm and very low birth weight infants, with high morbidity and mortality. We aim to build a reliable prediction model to predict the risk of NEC in preterm and very low birth weight infants.We conducted a retrospective analysis of medical data from infants  admitted to Maternal and Child Health Hospital of Guangxi Zhuang Autonomous Region. We collected clinical data, randomly dividing it into an 8:2 ratio for training and testing. Multivariate logistic regression was employed to identify significant predictors for NEC. Principal component analysis was used for dimensionality reduction of numerical variables. The prediction model was constructed through logistic regression, incorporating all relevant variables. Subsequently, we calculated performance evaluation metrics, including Receiver Operating Characteristic (ROC) curves and confusion matrices. Additionally, we conducted model performance comparisons with common machine learning models to establish its superiority.n = 58) randomly selected for external validation. Multivariate logistic regression revealed the significance of four predictors for NEC in preterm and very low birth weight infants: temperature (P = 0.003), Apgar score at 5 min (P = 0.004), formula feeding (P = 0.007), and gestational diabetes mellitus . The model achieved an accuracy of 82.46% in the test set with an F1 score of 0.90, outperforming other machine learning models .A total of 292 infants were included, with 20% (Our logistic regression model effectively predicts NEC risk in preterm and very low birth weight infants, as confirmed by external validation. Key predictors include temperature, Apgar score at 5 min, formula feeding, and GDM. This study provides a vital tool for NEC risk assessment in this population, potentially improving early interventions and child survival. However, clinical validation and further research are necessary for practical application. NEC is In summary, NEC is a life-threatening intestinal inflammation that typically occurs in preterm or low birth weight infants . Until n2.2.1.The present retrospective study was performed by reviewing infant's medical records. It was approved by the Medical Ethics Committee of Maternal and Child Health Hospital of Guangxi Zhuang Autonomous Region. Informed consent was waived considering the retrospective study design.The present retrospective study was performed by reviewing infant's medical records. We conducted a retrospective study of infants admitted to the Department of Neonatology of Maternity and Child Health Care of Guangxi Zhuang Autonomous Region between September 2019 and September 2021. Our inclusion criteria were as follows: (1) infants <32 weeks of gestation and birth weight <1,500 g; (2) transferred to the Department of Neonatology within 24 h after birth. From September 2019 to September 2021, a total of 308 infants were enrolled. In this study, we excluded 11 infants with birth defects, and 5 infants missing information. Finally, 292 infants were included in the study. This sample was used to build the prediction model.2.2.Clinical information of selected infants was collected from the electronic medical record system and included the clinical information on data within 24 h after birth , as well as maternal perinatal information .In this study, we diagnosed NEC in newborns using clinical and radiological examination results and classified its severity according to the modified Bell's criteria. Only infants with NEC at stage 2 or above according to Bell's staging were included in this study . The Apg2.3.Random assignment was conducted following an 8:2 ratio based on international standard training and test sets. The validation set is not processed in any way and is only used for the validation of the experimental results, and for the training set, we perform logistic regression using all variables.To develop and evaluate predictive models for NEC in preterm and very low birth weight infants, all potential factors were first analyzed using univariate logistic regression to determine their individual significance in relation to NEC. In previous studies, any factor found to be significant or associated with NEC was included in multivariate logistic regression models to identify independent predictors of NEC. Furthermore, for comprehensive information extraction from the data, all variables were initially considered during predictive modeling. Recognizing that some variables may have a minimal mathematical impact on NEC but are not devoid of influence, we applied principal component analysis to extract valid information while reducing dimensionality. This approach aimed to minimize factor redundancy in model prediction.p features be denoted as:Let the multivariate sample of Multiple regression model form:i-th principal component of the overall i-th principal component. With n samples, each with p indicator variables, the sample can be represented as If j-th variable, respectively.To normalize the original data:Using the standardized data, the standardized matrix is obtained as follows:Calculate the correlation coefficients of the sample matrices:R to obtain P eigenvalues:Solve the characteristic equation for the sample correlation coefficient matrix The final result of the principal component analysis equation was obtained as follows:P, the logistic regression model between it and the independent variable isTaking the probability of a positive NEC as The probability of the relative negativity is:In order to make the best prediction of the model, the log-likelihood function is constructed and the maximum value of the function is sought by finding the first-order partial derivative to obtain the estimated value of the parameters.2.4.P < 0.05 was considered statistically significant. R and the MATLAB classification model tool were used to predict the sample. Through MATLAB code, including confusion chart, stats of measure, etc., calculate the F 1 score, confusion matrix, Area Under Curve (AUC) and importance level of each indicator.Categorical variables were reported as a percentage, and continuous data as means and standard deviations (SD). A two-tailed value of 3.3.1.In our study, 292 infants were enrolled in this study . The obs3.2.The correlation graph describes the correlation between several variables, namely gestational age weeks, birth weight, head circumference, maternal age, and temperature. According to , there iHowever, the correlations between maternal age and temperature and the other three variables were weak. This suggests that changes in maternal age or temperature do not have a significant effect on fetal or neonatal gestational age, birth weight or head circumference. It is important to note that correlation does not necessarily imply causation. The fact that two variables are correlated does not mean that one variable causes a change in the other variable. There may be other factors contributing to the observed correlation that require further investigation to determine causality. Nonetheless, understanding the associations between these variables is useful for predicting outcomes, identifying risk factors, and informing medical decisions.3.3.To reduce the dimensionality of the numerical variables, we performed a principal component analysis with the criterion of a maximum variance explained of 75% or more. This resulted in a reduction of the numerical variables to three dimensions.The dimensions are represented by axes, with the length of the axes representing the variance explained. The first dimension has more than 40% of the variance explained and the second dimension has more than 20%. As shown in The numerical variables were then plotted on a two-dimensional plane. As shown in Mapping the sample points on the two-dimensional space consisting of the first and second principal components, it can be seen that the cosine value of the sample points in the middle is close to 0, which is closer to the horizontal plane; meanwhile, the cosine value of the surrounding sample points is close to 1, which is closer to the vertical plane. In addition, the sample points contain some extreme values .3.4.The model was trained and tested using two separate datasets. The training data represents 80% of the total sample and we derived the model results based on this dataset. To select the most relevant features for the model, we performed a feature selection process for numerical variables based on the results of logistic regression analysis. We also reduced the dimensionality of the variables using principal components analysis, with the maxi-mum explained variance criterion set at 75%. Finally, we selected the model with the best performance indicators based on the results obtained from the above process.3.5.P < 0.05) (P = 0.003), Apgar score at 5 min (P = 0.004), formula feeding (P = 0.007) and GDM (P = 0.033) in the final model were statistically significantly correlated to NEC.In univariate analysis, there were statistically significant differences among temperature, formula feeding and GDM ( < 0.05) . In prev3.6.To evaluate the performance of the logistic regression model, we created a ROC curve based on the actual and predicted classifications of the training set. The curve was created to determine the ability of the model to discriminate the presence of NEC in preterm infants, where 1 represents a positive classification and 0 represents a negative classification. The logistic regression model performed better than other models such as random forest and support vector machine in terms of prediction accuracy, AUC and other performance metrics. These results demonstrate the potential of the logistic regression model as a reliable and effective tool for predicting NEC in preterm and very low birth weight infants.3.7.The logistic regression model built using the above parameters achieved an accuracy of 82.46% on the test set with an area under the curve of 0.70. Analysis of the mixture matrix also showed that the model has good predictive performance. These results suggest that the prediction model has great potential to aid in clinical decision making, as shown in Furthermore, it is important to note that logistic regression provides a probability estimate of NEC occurrence, ranging from 0 to 1. The model classifies samples with a probability less than or equal to 0.5 as negative predictions, indicating that those samples are not predicted to have NEC. Conversely, samples with a probability greater than 0.5 receive positive predictions, signifying the likelihood of NEC. Both Accuracy and AUC calculations consider the combination of predicted and true outcomes.3.8.The logistic regression model ranked first in terms of accuracy and AUC when compared to the results of support vector machines and random forests. This result confirms the choice of logistic regression as the appropriate classifier for this study, as shown in 4.Our study developed a model for predicting the occurrence of NEC using potential risk factors. This study revealed that Apgar score at 5 min, temperature, formula feeding and GDM were more important predictors of NEC among infants born <32 weeks of gestation and whose birth weight is <1,500 g. Good results and interpretability were shown by the ROC curve on the test set. The accuracy and AUC for the test set were 82.46% and 0.70, respectively. We external validated the model and found it to have high accuracy and robustness.In the present study, Apgar score at 5 min between 0 points and 7 points was considered an important predictor of NEC. Previous studies have shown that an Apgar score of less than 7 points is one of the most important indicators for the diagnosis of neonatal asphyxia and is an important risk factor for neonatal asphyxia –21. AccoNewborns have a lower efficiency in regulating their body temperature compared to adults, and are prone to both hypothermia and hyperthermia. These conditions can affect the growth and health of newborns. Previous studies have reported that hypothermia in low birth weight premature infants is significantly associated with increased in-hospital mortality and NEC –29. SimiAs we know, GDM is a common pregnancy complication linked to adverse newborn outcomes , 31. TheFormula feeding was also an independent risk factor for NEC in our study. This is consistent with previous research . Chen etIntestinal flora imbalance is recognized as a significant factor contributing to NEC development , 40. ConIn this study, we used multiple logistic regression, combining clinical data within 24 h after birth and maternal perinatal data, to construct a clinical prediction model for NEC and compare it with other commonly used machine models . External validation showed that the prediction model had good accuracy and calibration. However, our study has the following limitations. First, only infants with GA <32 weeks and birth weight <1,500 g were included in our study. The reason for this is that infants with GA <32 weeks and birth weight <1,500 g are prone to NEC . Second,5.In conclusion, this study developed a clinical prediction model using logistic regression to estimate the risk of neonatal necrotizing enterocolitis (NEC) in preterm and very low birth weight infants. The model demonstrated outstanding predictive performance in external validation. Key predictors included temperature, Apgar score at 5 min, formula feeding, and a history of gestational diabetes mellitus (GDM). This study provides a vital tool for predicting NEC risk in preterm and very low birth weight infants and is expected to enhance early clinical interventions, ultimately improving the survival and quality of life of affected children. However, further validation and additional studies are warranted to ensure the feasibility of its clinical application.