Warm needling is a type of traditional Chinese medicine (TCM), which uses burned moxa to generate warmth and exerts a therapeutic effect on human skin. Additionally, based on TCM theory, warm needling could improve deficiency-cold syndrome. Research has found that warm needling has analgesic effects as well as effects on the immune system, endocrine system, nervous system, and lower urinary tract symptoms. However, the mechanism of warm needling on the meridians is not fully understood. In modern studies, the electric characteristics of the meridians have been discovered and the combination of semiconductor and TCM theory could explain the meridian phenomena through physics. However, there is no known research exploring the relationship between thermal effect and electric property on the meridians.A total of 30 healthy participants older than 20 years of age will be recruited for this study. The participants would first be administered acupuncture, following which the electric characteristics will be measured using the semiconductor analyzer Agilent B1500A/Agilent 4156C. The visual analog score (VAS) will also be rated. Thereafter, a burned moxa will be added to the acupuncture needles as a method for warm needling. The electric characteristics and VAS will be measured again. We will use the paired t-test and repeated measure two-way ANOVA to compare the electric characteristics and VAS before and after warm needling in each participant.This protocol aims to explore the thermal effect on the electric characteristics of meridians in a warm needling model and provide a scientific explanation of TCM through the aspect of physics. In traditional Chinese medicine (TCM) theory, there are some methods to improve the therapeutic effects of acupuncture, including manual manipulation, electroacupuncture (EA), moxibustion, or warm needling.–4 Warm needling is a combination therapy of acupuncture and moxibustion by placing an ignited moxa on the handle of an acupuncture needle. This could stimulate acupoints by needling, heating, and pharmacological effect of moxa. It is thought that warm needling could warm meridians and improve deficiency-cold syndrome. Modern research has found that warm needling has effects on the immune, nervous, and endocrine systems,8 and that it relieves pain and improves the range of motion of the shoulders, knee osteoarthritis, and lower urinary tract symptoms,. However, how the warm needling works on the meridians has not been identified yet.Acupuncture has been widely used to treat various diseases and relieve pain. Many researchers have been exploring several ways, including biophysics and biochemistry, to elucidate the meridians. Researchers explored meridians and acupoints according to the physical and electric characteristics of the human skin, and the result showed that some specific parts of the skin had the characteristic of higher conductivity, lower resistance, and higher potential.,13 Furthermore, the researchers found that the specific parts of the skin and acupoints on the meridians were matched.–16 Recently, some studies combined semiconductor and TCM theory to discuss the meridian phenomena and the relationship between electric characteristics and meridians.,18 Hung et al introduced the ion current model and compared different kinds of pulses, and found that ions might be the substance delivered by the meridians. Wang et al applied supercritical fluid technology to discriminate the relation between different conductivity of acupuncture needles and the electric properties of the meridians. It was proven that electrical conductivity and the therapeutic effects of the meridian could be improved by supercritical fluid technology. Additionally, the directions of ion transport in the meridians and changing the direction of needling could influence the electricity of the meridians. Moreover, the change of needling direction could also affect the electric current, which implies that the acupuncture manipulations might produce different effects in humans.There is no definitive conclusion regarding what meridians and acupoints essentially are.–20 However, there is no published research on the relationship between temperature and electric characteristics in the meridians. The purpose of our study is to combine the theory of TCM and physics to explore the thermal effect on the electric characteristics of meridians in a warm needling model. We believe that our study can provide more scientific information about the effect of temperature on the meridians in physics.The change in the physical properties of the acupuncture needle could produce different electric characteristics in the meridians and the phenomena could be explained by physics.This is a prospective and observational study that will be conducted by the Department of Chinese Medicine at the Kaohsiung Chang Gung Memorial Hospital and the Department of Physics at National Sun Yat-sen University from March 2022 to December 2022. The participants will be recruited from the students and staff at National Sun Yat-sen University. Our study will enroll 30 healthy participants in 1 group and all of them will receive both interventions, acupuncture and warm needling, and electric measurement. All personal data will be preserved in a locked closet and kept confidential until the end of the experiment when the principal investigator will disclose and analyze the results.The participants will receive acupuncture, following which we will use a semiconductor analyzer to measure the electric characteristics and evaluate the visual analog scale (VAS) score before warm needling. Subsequently, we will place burned moxa on the acupuncture needles as our warm needling method. After the moxa burns out, we will measure the electric characteristics using the same evaluation method. We will analyze 1 meridian at a time and measure 12 meridians sequentially in this study. Therefore, all participants will undergo 12 sessions of measurements. Finally, we will compare individual electric characteristics data, before and after warm needling, on 12 meridians. The measurements will be completed in 1 day and the participants would not have to return for follow-up. The flow chart of the study design is presented in Figure The participants who are healthy, aged over 20 years, and willing to sign the informed consent will be considered eligible for the study. The exclusion criteria for this study are: aged below 20 years, pregnant or breast-feeding women, empty stomach before the study, bleeding tendency due to thrombocytopenia or platelets <20,000/µL or taking antiplatelet medicine, and consuming hypnotics or psychoactive medicines. We intend to recruit 30 participants for this study.All the participants in our study will receive acupuncture and warm needling. The acupuncture needles will be sterile, disposable, and 40 mm in length and 0.3 mm in diameter or 25 mm in length and 0.3 mm in diameter. The acupuncture needles would be obtained from the same manufacturer . We will perform acupuncture on 12 meridians and choose 2 acupoints in each meridian  and Kongzui (LU6), Shousanli (LI10) and Quchi (LI11), Zusanli ST36) and Shangjuxu (ST37), Sanyinjiao (SP6) and yinlingquan (SP9), Shaohai (HT3) and Shenmen (HT7), Yanglao (SI6) and Xiaohai (SI8), Weizhong (BL40) and Chengshan (BL57), Taixi (KI3) and Fuliu (KI7), Quze (PC3) and Neiguan (PC6), Waiguan (SJ5) and Zhigou (SJ6), Yanglingquan (GB34) and Guangming (GB37), and Ligou (LR5) and Zhongdu (LR6). The schematic picture of acupoints lists in Figure 6 and Sha This machine integrates multiple measurements, including fundamental current-voltage (IV), capacitance-voltage, pulsed IV measurement, and analysis capabilities. The IV measurement ranges from 0.1 fA to 1 A and 0.5 µV to 200 V. The maximal pulse forcing is 40 V and the minimal sampling interval is 5 ns. We would adjust the output electrical parameters. Further, the voltage and current will be supplied by the semiconductor analyzer Agilent B1500A/Agilent 4156C and the electric characteristics will be measured immediately as baseline data (before warm needling) .Paired  nausea, sweating, and dizziness. Other complications include pneumothorax and septicemia. Most adverse side effects are minor, and serious complications are rare. The most common adverse events related to warm needling are burns, skin rash, and pain. Warm needling therapy has been reported as a safe method in clinical practices. In our study, acupuncture will be performed by qualified doctors and the acupoints we chose are at the 4 limbs. The procedure will be safe with minimal risk of serious adverse events. The device we use for EA will apply voltage from 1 to 60 V and current under 2 mA. The voltage and current are in the therapeutic range, which means that our study is safe. Additionally, we will perform warm needling, which adds moxa on acupuncture needles and introduces heat stimulation through the needles, and thick cardboard will be placed on the skin surface to prevent burning injury. If serious adverse effects of acupuncture occur, such as fainting or general weakness, the doctor will remove the acupuncture needles and stop the intervention immediately, and we will record the reason for dropping out of the study.Acupuncture, EA, and warm needling are general practices in TCM. Minor adverse side effects of acupuncture and EA include minor bruising, bleeding or pain at needle insertion sites, tumor necrosis factor-α, and transforming growth factor-β1 to inhibit the inflammatory response. Warm needling also inhibited the overexpression of insulin-like growth factor-1 to achieve the effects of improving pathological changes in the tissue. Besides, warm needling modulated nicotinamide adenine dinucleotide phosphate oxidase 2 and superoxide dismutase 2, which are associated with oxidative stress. Chondrocyte cytoskeletal proteins such as Rho-associated protein kinase, phospho-cofilin, and monopherine domain kinase 1 were down-regulated by warm needling, which relieved symptoms of osteoarthritis. Histopathological changes observed using transmission electron microscopy showed an increase in the number of organelles with rearrangement and redistribution of the extracellular matrix after intervention with warm needling. Moreover, warm needling down-regulated matrix metalloproteinase-3, thereby influencing tissue remodeling. However, few studies are present on physical characteristics related to meridians and warm needling. In the present study, we will introduce physical parameters to measure the meridians and explore warm needling through physical perspectives. Some reviews of animal models discussed the mechanism of warm needling; however, most of these investigated molecular biological mechanisms. Warm needling regulated cytokines such as interleukin-1β, This therapeutic technique delivers heat at acupoints to stimulate the circulation of Qi and blood. In TCM theory, this procedure achieves the effect of a warm supplement, which means to activate and restore a decreased function to normal. In the present study, EA, through the semiconductor analyzer Agilent B1500A/Agilent 4156C, applied the same voltage before and after warm needling. Recent meridian studies showed that the meridian has electric characteristics and physical properties. Additionally, improving the conductivity of the meridians could cause higher electric current and lower impedance in the meridians, which could enhance de-qi sensations and increase the therapeutic effect. Since there are different acupuncture manipulations in traditional Chinese theory, a previous study revealed that the electric current measured during acupuncture was significantly larger in supplementation manipulation than in draining manipulation in the meridians. Accordingly, we expected the changing electric characteristics to a higher current after warm needling would be comparable with the current measured before warm needling since warm needling is a supplementation method. There will be new physical discoveries of warm needling, acupuncture plus moxibustion, in our study.According to the World Health Organization International Standard Terminologies On Traditional Medicine In The Western Pacific Region, warm needling is a practice of acupuncture in which the needle is warmed during needling by placing a burned moxa stick on the handle of the needle after insertion.Although the therapeutic effects of warm needling have been affirmed clinically, the mechanism of warm needling has not been widely elucidated. Few studies discussed the association between ionic conductivity and change of temperature in the biophysical field. This is an interdisciplinary study that has not been performed before, whereby TCM professionals cooperated with physics professionals, toward exploring warm needling using new methods and interventions. The results of electric characteristics related to warm needling will give a new perspective on this popular and effective TCM treatment. The electrical intervention and electric characteristics of warm needling are expected to have vast applications in medical treatment.This protocol was approved by the Human Ethics Committee of Chang Gung Medical Foundation Institutional Review Board, IRB No. 202101924A3. The protocol has been registered at ClinicalTrials.gov (Identifier: NCT05249010) and ISRCTN.com (Identifier: ISRCTN82061181). All participants will provide written informed consent before enrollment. Personal information about potential and enrolled participants will be collected, shared, and maintained in an independent closet to protect confidentiality before, during, and after the trial. Data will be available under reasonable request.The authors would like to express our thanks to the other members of the research team of this research protocol: Yung-Fang Tan, Wen-Chung Chen, Chuan-Wei Kuo, and Chia-Chuan Wu.C.H. Lin and T.C. Chang were responsible for the design, conception, and plan of the study. C.H. Hsieh drafted the manuscript. C.H. Lin and Y.C. Hung designed the statistical plan. C.H. Lin, S.T. Tseng, and W.L. Hu participated in the revision of the manuscript and coordination of the study. All authors read, approved the final manuscript, and agreed with the submission.Conceptualization: Shih-Ting Tseng.Investigation: Ting-Chang Chang.Methodology: Yu-Chiang Hung, Ting-Chang Chang.Project administration: Yu-Chiang Hung.Resources: Ting-Chang Chang.Software: Ting-Chang Chang.Supervision: Shih-Ting Tseng, Yu-Chiang Hung, Wen-Long Hu, Chien-Hung Lin.Visualization: Wen-Long Hu.Writing – original draft: Chiao-Hsuan Hsieh.Writing – review & editing: Chien-Hung Lin.


Birth preparedness and Complication Readiness (BPCR) promotes prompt use of skilled delivery and readiness for any complications to reduce delays in the provision of emergency care. Husband’s involvement in preparation for childbirth is one way to mitigate life-threatening delays in providing care during childbirth. The current study assessed the association of the husband’s involvement in birth preparedness and complication readiness with the use of skilled birth attendants.A community-based cross-sectional study was conducted among 1,432 husbands. Study participants were selected from Arbaminch university health and demographic surveillance sites. Data were collected electronically using the Open Data Kit. The data were exported to STATA version 16 for analysis. Descriptive statistics were computed to describe the sociodemographic and reproductive variables of the study. The associations between birth preparedness and complication readiness practice and birth in the presence of a skilled birth attendant were assessed using multiple logistic regressions after adjusting for known confounders. Explanatory variables on bivariate logistic regression analysis were entered into multivariable logistic regression analysis, and a p-value of less than 0.05 was used to ascertain statistical significance.Mean age of respondents was 33.7 (SD ± 6.2) years. Of all the respondents, 140 (10.3%) had made at least three preparations according to birth preparedness and complication readiness. After controlling for confounders through multivariable logistic regression, giving birth in the presence of a skilled birth attendant consistently increased with husbands involved in birth preparedness and complication readiness . Husbands whose wives had complications during previous pregnancy were 33% less likely to have skilled birth attendants . Moreover, husbands whose houses were near the health facilities were more likely to have skilled birth attendants for their wives .Husband’s involvement in birth preparedness and complication readiness is strongly associated with using skilled birth attendants in Ethiopia. It is imperative that when designing husband’s involvement programs targeting birth preparedness and complication readiness, programs and strategies should focus on enhancing activities that are inclusive of husbands in birth preparedness and complication education.The online version contains supplementary material available at 10.1186/s12884-022-05147-3. Birth preparedness and Complication Readiness (BPCR) is conceptualized using six main variables, including a preparing birth kit, identifying a skilled attendant, saving money, knowing where to go in cases of emergency, contacting a blood donor in advance, and preparing transportation in advance . This prThe concept of male involvement in maternal health, which includes the shared responsibility of men to engage in responsible prevention of high-risk pregnancies actively, maternal and child health, family planning, parenthood, and prevention of sexually transmitted diseases, has been recognized in recent years . One of Efforts to understand and improve the extent of men’s responsibilities for their health and that of their partners are growing . FindingIn 2015, the WHO recommended the use of, BPCR stating that “programs that involve BPCR interventions are recommended to increase the use of skilled care at birth and to increase the timely use of facility care for obstetric and newborn complications” (1), and the WHO included it as an integral component of antenatal care (2). A study conducted in southern Ethiopia on husbands’ involvement during antenatal care was proven to be associated with skilled birth attendance (3). Given that majority of maternal deaths and obstetric complications occur during childbirth, skilled birth attendance is the most important intervention (4).A systematic review of interventions conducted in low and middle-income countries highlighted that engaging men in maternal and child health services were associated with improved antenatal care attendance, skilled birth attendance, facility birth, postpartum care, birth and complications preparedness, and maternal nutrition , 5. SimiA higher proportion of male involvement was reported among primiparous women living in developed countries and planned pregnancies . HoweverSeveral factors have been explored to establish the extent of husbands’ involvement in maternity care and BPCR. A study in Tanzania indicated that the number of children, limited access to information, and limited spousal communication decreased male involvement . SimilarIn Ethiopia, to reduce maternal mortality to 199 maternal deaths per 100,000 live births and neonatal mortality to 10 per 1,000 live births by 2020, a series of high-impact interventions have been performed, including prenatal care, skilled birth attendant, and postnatal care . HoweverIn Ethiopia, BPCR planning have been incorporated as essential component in delivering focused antenatal care and have been prioritized as a key strategy for promoting skilled birth attendants . DespiteA community-based cross-sectional study was conducted at Arba Minch Demographic and Health Surveillance Site (HDSS) from October 1, 2020, to December 30, 2020. The surveillance site is found in Arba Minch zuria district with the administrative center of Arba Minch town, which is located 505 km southwest of Addis Ababa, the capital city of Ethiopia, and 275 km southwest of Hawassa, the capital city of Southern Nation Nationalities and Peoples Region (SNNPR). The district has a total of 29 kebeles ; two of which are semi-urban, and the remaining 27 are rural. The Arba Minch HDSS site operates in nine, district kebeles randomly selected after stratifying the districts into highland and lowland climatic zones. The surveillance site tracks demographic changes like death, birth, migration, marital status change, nutrition, reproductive health, morbidity/health-seeking behavior, and health care utilization by permanently recruited data collectors who use the Open Data Kit (ODK) mobile phone-based application for data collection and report monthly to the center located at Arba Minch University , 22.The source population was all men in marital union with a woman  having children less than one year of age and permanent residents of the Arba Minch zuria district. All men in a marital relationship having children less than one year of age and permanent residents of Arba Minch zuria district and selected during the study period were the study population.The sample size for this study was initially calculated using the stat calc menu of Epi-info software version 7  using thAll nine kebeles under Arba Minch HDSS were included. A total of 1652 husbands in a marital relationship with children less than one year of age and permanent residents that fulfill the inclusion criteria were selected. A sampling frame (secondary data was used from the HDSS site database) of these 1652 husbands was used to determine the sample for this study. The sample size for each kebeles was proportionally allocated based on the information obtained. Then, a separate list of husbands was prepared for each kebele, and computer-generated random numbers were used to select the required samples.All the questionnaires were prepared in excel and changed online to extensible markup language (XML) form using files in Excel (Xls). Then, it is uploaded to the Open Data Kit (ODK) Aggregate, which is available in the Arba Minch University Health Demographic and Surveillance System (AMU-HDSS) office, and the form was downloaded to the data collection tablet/phone using ODK collect. The data collection tool was developed by reviewing different works of literature , 27–29. Twelve data collectors with experience in data collection using a tablet/phone and working for AM-HDSS were recruited. Three days  training about the study’s objectives and the procedure to be followed was given to data collectors and supervisors by the investigators and information communication technology (ICT) experts using a tablet before the actual data collection.The participant was asked to mention any danger signs during labor, and delivery spontaneously. Knowledge of at least one obstetric danger sign during the period was coded as Yes or No .A husband of a woman was considered well prepared or involved if he made arrangements for at least three of the six components of BPCR practices  have been trained to be proficient in the skills necessary to manage normal deliveries and diagnose, manage or refer obstetric complications .It is a women-centered community organization that requires the establishment of health development teams of up to 30 households. Their main task is to identify and monitor pregnant women and focus on encouraging their members to use maternal health services .Measured from the husband’s report on the walking hours to the health facilities. This was coded 1 if husbands reported the walking hours to reach the nearby health facility to be ≥ 30 min; otherwise, it was coded 0 .p < 0.25 were entered into multivariable logistic regression analysis, and a p-value of less than 0.05 was used to declare statistical significance.Data were coded and entered through excel and exported to an open data kit (ODK) for data collection. The entered data was exported to STATA version 16 for analysis. Descriptive statistics such as frequency, mean, and standard deviation were computed to describe the variables of the study. The associations between BPCR practice and birth in the presence of a skilled birth attendant were assessed using multiple logistic regressions after adjusting for known confounders . Explanatory variables on bivariate logistic regression analysis with A structured interviewer-administered questionnaire was prepared in English and translated into local languages, then translated back to English by an independent language expert to ensure its consistency. The translators were well-known translators for both languages. The instrument was pre-tested on 5% (72) of the sample size in Mirab Abaya district that was not included in the study and analysis. Amendments were made after the pretest. The training was given to data collectors and supervisors by the principal investigator. Investigators and supervisors checked on the spot and review all the questionnaires to ensure completeness and consistency of the information collected and immediate action was taken accordingly.All participants completed this study’s interview (response rate = 100%). Table As regards birth preparedness and complication readiness, the most common preparation was to save money (38.3%), then identified birth kit (17%), followed by identification of means of transportation (10.9%) and identification of which health facility to visit in case of emergency (10.1%) Few mentioned identification of blood donor. 14% of the respondents had not done any preparations before childbirth. One hundred forty men, 10.3% had made at least three preparations according to the concept of BPCR Table .In the current study, the prevalence of skilled birth attendants is 798 (55.8%) (95% CI: 53.2–58.3).Husband involvement in BPCR, wives’ age, complications during a previous pregnancy, knowledge about danger signs during labour and delivery, and distance from facility associated with a skilled birth attendant in the binary logistic regression. However, in our study, there was no association between having 1–2 children and wives aged 30–34 years with a skilled birth attendant. After controlling for confounders through multivariable logistic regression, husbands who were involved in BPCR were more likely to have their spouse have a skilled birth attendants . Husbands whose wives had complications during previous pregnancy were 33% less likely to have a skilled birth attendant . Moreover, husbands whose houses were proximal to health facilities were predicted to using skilled birth attendants  (Table This paper highlights findings on BPCR among husbands and their association with skilled birth attendance in Southern Ethiopia. Husband involvement has been established to positively impact maternal and infant morbidity and mortality . This stThe husband’s involvement in BPCR, especially within communities in Sub-Saharan Africa, plays a vital role in the ability of women to prepare for birth and respond to obstetric complications . RegardiMoreover, educating men on danger signs during labor and delivery has significantly improved delivery outcomes . This stDistance from the facility was established as a factor for a skilled birth attendant in this study. The findings corroborate with results from a study conducted in Ethiopia, which established a higher proportion of skilled birth attendants among those whose facilities were closer to their residence , 26. In In the current study, husbands whose wives had complications during previous pregnancies were less likely to use skilled birth attendants than those without complications. The finding of our study is supported by Kea et al. . This coThis study shows that husband involvement in BPCR is significantly associated with a skilled birth attendant in Ethiopia. Therefore, it is imperative that when designing husband involvement programs targeting BPCR, clear strategies describing why men should be involved, what men need to do, how they need to be involved, and the extent of involvement based on differences in social and cultural contexts within various areas in Ethiopia should be integrated into programs to increase uptake of husband involvement in BPCR and thereby enhance skilled birth attendant.Below is the link to the electronic supplementary material.Supplementary Material 1Supplementary Material 2Supplementary Material 3


Fetal growth restriction (FGR) is a clinical condition wherein a fetus fails to achieve the expected growth potential. Although FGR is the leading cause of perinatal morbidity and mortality, there is a lack of knowledge about the long-term developmental outcomes of children who had FGR in Japan. Here, we sought to clarify the features of neurodevelopmental outcomes in preterm-born children with severe FGR (sFGR) and identify associated clinical factors. The clinical data of 26 preterm sFGR cases and 26 preterm appropriate for gestational age (AGA) cases with a similar gestational age distribution were reviewed retrospectively. Developmental quotient (DQ) scores assessed during the 1- and 2-year corrected ages using the Kyoto Scale of Psychological Development were analyzed. P = 0.0127). Of the three areas that comprise the DQ , the sFGR group only showed significantly lower DQ scores  in the Language-Social area. Both fetal body weight and fetal body weight Z score at birth significantly correlated with the DQ scores , whereas neither the duration of fetal growth arrest nor the gestational age at birth correlated with the DQ scores . sFGR was diagnosed at 26 (18-34) weeks of gestation, and the gestational age at delivery was 31 (25-36) weeks. The overall DQ scores of children in the sFGR group were significantly lower than those in the AGA group (80 vs. 90.5, Our results indicate that preterm-born children with sFGR have greater neurodevelopmental impairment than preterm-born children without FGR, specifically in terms of the DQ scores for the Language-Social area. It is imperative to encourage continuous long-term follow-up and appropriate interventions after birth.  Generally, FGR is diagnosed when the estimated fetal weight by ultrasound measurement is below the 10th percentile (4). On the other hand, small for gestational age (SGA) refers to an infant with a birth weight below the 10th percentile based on the population standard (4). Although the two are not necessarily synonymous, there is a significant overlap.Fetal growth restriction (FGR) is a clinical condition wherein a fetus fails to achieve the expected growth potential because of several factors, such as placental dysfunction, infections, or chromosomal abnormalities (1). In severe cases, intrauterine chronic hypoxia can cause not only fetal acidosis but also fetal death (1). Nevertheless, there is currently no proven effective treatment for FGR. The only practiced management strategies are the close monitoring of fetal well-being and the termination of pregnancy at the most appropriate time to avoid fetal acidosis and improve neonatal short-term prognosis (5). Importantly, infants who had FGR and were SGA have higher rates of neurodevelopmental delay and poor cognitive outcomes (7). Hence, the optimal timing for the termination of pregnancy for severe FGR (sFGR) cases should be determined not only in terms of short-term fetal prognosis but also in terms of long-term neurodevelopmental outcomes.FGR is one of the leading causes of perinatal morbidity and mortality However, there is a lack of knowledge about the characteristics detected in the neurodevelopmental delay of children who had FGR and were SGA. Therefore, this study aimed to clarify the features of neurodevelopmental outcomes in preterm-born children with sFGR and identify associated clinical factors.In this study, we defined FGR, sFGR, and SGA as follows:th percentile (−1.28 SD).FGR: if the estimated body weight in utero by ultrasonic measurement based on the birth size standards by gestational age for Japanese neonates is below the 10rd percentile (−1.88 SD).sFGR: if the estimated body weight in utero by ultrasonic measurement is below the 3th percentile.SGA: if the body weight at birth is below the 10(8).The estimated fetal body weight was calculated using the following formula 3 + 0.30 × AC (cm)2 × FL (cm)EFW (g) = 1.07 × BPD (cm)EFW: estimated fetal weightBPD: biparietal diameterAC: abdominal circumferenceFL: femur lengthThis retrospective observational study was approved by the Ethics Committee of Kyoto University (R3305). Electronic medical charts were used to identify and enroll patients diagnosed with FGR and babies delivered at Kyoto University Hospital between January 2011 and December 2015. The inclusion criteria were (1) patients diagnosed with sFGR and babies delivered at less than 37 weeks of gestation, and who were in fact SGA at birth, and (2) patients whose developmental quotient (DQ) was assessed between 1- and 2- years of age, corrected for prematurity. The exclusion criteria were chromosomal abnormality, multiple gestation, and congenital fetal anomaly.rd percentile. Among the 47 cases of preterm sFGR, 15 never had their DQ measured and 6 did not have their DQ assessed during the 1- and 2-year corrected ages. Ultimately, 26 patients were included in the present study as the sFGR group. No patients were diagnosed with TORCH syndrome in the sFGR group. Randomly selected 26 cases of gestational age-matched preterm birth with normal fetal growth were defined as the appropriate for gestational age (AGA) group. The sFGR and AGA groups were selected during the same study period.(9). The developmental age for each area is estimated according to the sum score obtained in each of the three sections. An overall developmental age is also obtained. The DQ was calculated by dividing these developmental ages by the child’s chronological age and then multiplying it by 100.The Kyoto Scale of Psychological Development (KSPD) test was performed by trained testers to assess the children’s neurodevelopment. The KSPD is the most standardized developmental test for Japanese children and covers the following three areas: Postural-Motor (P-M: fine and gross motor functions), Cognitive-Adaptive , and Language-Social  U test was used for continuous variables. Spearman’s rank correlation was used to evaluate possible associations. A p-value of <0.05 was considered statistically significant.The data are presented as the median (range). Statistical analyses were performed using GraphPad Prism 8 . Fisher’s exact test was used for categorical variables, whereas the Mann-Whitney In the sFGR group, FGR was recognized at 26 (18-34) weeks of gestation, and once FGR was diagnosed, the diagnostic criteria were met until the time of delivery in all cases. The duration of fetal growth arrest was 21 (0-56) days. Pregnancy was terminated only when there was maternal indication or when one or more of the following findings were observed: repeated deceleration seen in the cardiotocographs, loss of fetal movement, or an absent or reversed a-wave in the fetal ductus venosus Doppler waveform.With regard to neonatal complications, the following conditions were found. In the AGA group, there were cases of intraventricular hemorrhage (n = 5), periventricular leukomalacia , and pervasive developmental disorders (n = 4). In the sFGR group, there were cases of cerebral palsy , pervasive developmental disorders (n = 5), and chronic lung disease (n = 3). There were no cases of necrotizing enterocolitis, sepsis, or blindness in either group.P = 0.0127) (P = 0.0357).The DQ scores during the 1- and 2-year corrected ages of the children in the AGA and sFGR groups were compared. The overall DQ scores of the children in the sFGR group were significantly lower than those in the AGA group  . In P-M and C-A, there was no significant difference in the DQ scores between the sFGR and AGA groups  and 2c. P = 0.0496), the duration of fetal growth arrest and gestational age at birth did not correlate with the DQ scores    , and 3c. 0.0028)  and 3e.(12). To the best of our knowledge, this is the first and sole study wherein the neurodevelopmental outcomes of children with preterm sFGR in Japan was clarified using the KSPD.The present study analyzed the neurodevelopmental outcomes during the 1- and 2-year corrected ages of preterm-born infants in sFGR and AGA cases. Results showed that children diagnosed with sFGR have poorer overall DQ scores in the assessment using the KSPD. Notably, children in the sFGR group only had significantly lower DQ scores in L-S than those in the AGA group among the three areas measured by the KSPD. Moreover, there was a significant correlation between the DQ scores and fetal body weight or fetal body weight Z score at birth in the sFGR group. There have been studies regarding the neurodevelopmental outcomes of very low birth weight infants using the KSPD in Japanese cohorts. However, these studies present heterogeneous groups of children: preterm infants without FGR, preterm infants with FGR, and fullterm infants with FGR (13); however, it is currently unclear whether prematurity or FGR has a stronger effect on the children’s long-term neurodevelopmental outcome. Our data indicated that preterm birth with FGR is associated with worse DQ scores than mere preterm birth. It may also be implied, despite the small number of cases, that the effect of prematurity on the DQ scores may be stronger up to a certain point of gestational age; the influence of FGR may be more significant afterwards.Infants delivered at less than 37 weeks of gestation due to sFGR have two serious concerns: prematurity due to preterm birth and small physique due to FGR and consequent SGA. In contrast to preterm AGA infants, the complex interplay of these two factors in the case of infants born preterm due to FGR determines the short-term life prognosis and long-term neurodevelopmental outcomes. Specifically, there may be difficulties in characterizing the neurodevelopmental outcome of preterm FGR infants due to the many factors that determine it. Preterm birth itself is known to be associated with neurocognitive impairments (6). The study, which included 52,822 children, revealed that cognitive outcomes in childhood were significantly poorer in children with FGR and diagnosed as SGA than in AGA children. Furthermore, the results were similar, regardless of whether the children were born preterm or fullterm. In our case series, there were 41 patients with sFGR born after 37 weeks of gestation, and only 9 of them were followed up long-term for neurodevelopmental outcomes. This may presumably be due to the favorable short-term prognosis for the child’s development. However, there should be long-term monitoring of the neurodevelopment of these children, considering that the neurodevelopment of children with FGR is impaired, even at fullterm (6).Meanwhile, our results are in line with the conclusions drawn from the large systematic review and meta-analysis conducted by Sacchi et al (7), its specific features have not been known. Here, the findings of this study revealed the characterized neurodevelopmental outcomes of children born prematurely with sFGR: lower DQ scores in L-S. In the KSPD, development in the L-S area between the ages of 1 and 2 years is assessed as follows: (a) whether children can play ball with the examiner, (b) point at an indicated object such as a dog or car, and (c) answer the names of objects such as flowers and an umbrella. Our results are in partial concordance with the study results of Mukhopadhyay et al. Their results showed that the prevalence of language and visuomotor developmental delay at the corrected age of 2 years was high in infants whose birthweight was less than 1250 g (14). In addition, SGA infants were at higher risk of this delay (14). The exact mechanism as to why only the DQ scores in L-S are low in preterm infants with FGR is yet to be clarified. However, considering the findings of this study, care for these infants should focus more on helping them develop in the L-S area. FGR infants, whether born preterm or not, should receive follow-up care and appropriate rehabilitative training after birth.Even though previous studies have shown unfavorable neurodevelopmental outcomes in children with low birth weight or FGR (15). Therefore, the results obtained in this study have the potential to be extended to non-Japanese children.We employed the KSPD to assess the developmental levels of children, since it is the most widespread and familiar method of testing in Japan, although it has not been standardized in English. Apart from fine and gross motor function, it is essential to have a method that is rooted in the language and culture that the children are exposed to in order to assess language and social development precisely. Furthermore, it has already been shown that the developmental features on the KSPD in the Japanese cohort are well correlated with those on the Bayley III scale, which is a globally acknowledged method for the assessment of developmental outcomes (5). In practice, however, there are cases in which fetal growth is extremely slow or almost arrested even though fetal well-being is not that bad. In such cases, termination of pregnancy may be an option from the point of view of long-tern neurodevelopmental outcomes, specifically if the Z score of the fetal body weight continuously increases with advancing gestation. Nonetheless, the long-term follow-up of children in the Growth Restriction Intervention Trial showed that there were no clinically significant differences in the neurodevelopmental outcomes between the immediate and delayed delivery groups (16). Meanwhile, fetal circulatory redistribution has been shown to be associated with neurodevelopmental impairment (7), which may make obstetricians hesitant to take a deferred delivery approach. It could also be implied that the optimal timing of delivery may not be the only determinant of favorable long-term neurodevelopmental outcomes. A follow-up after birth may be more important to support the development of preterm children with FGR.The findings of this study have emphasized new implications for the management and optimal timing of delivery in sFGR cases. At present, preterm sFGR cases are managed primarily with the aim of achieving a good short-term perinatal outcome. This means that fetuses are monitored closely using biophysical profile scores, umbilical artery Doppler, ductus venosus Doppler, and cardiotocography and are delivered to preempt serious fetal acidosis or intrauterine fetal death A major limitation of this study is its retrospective nature and small sample size. However, as this was a single-center study, the management of FGR and judgment about the timing of delivery were based on specific uniform criteria. Considering this, the potential impact of different management strategies for FGR on neurodevelopmental outcomes is negligible. Another limitation is that the demographic information of both the sFGR and AGA groups  was not known; therefore, they might not be homogenous. This could be potentially relevant to children’s neurodevelopmental outcomes. Future studies employing larger prospective studies could be conducted to further understand the features of the developmental outcomes of preterm children with FGR.In conclusion, the findings of this study indicate that preterm-born children with sFGR have greater neurodevelopmental impairment than preterm-born children without FGR. A characteristic feature of neurodevelopmental delay is low DQ scores in the L-S area in the KSPD assessment. It is notable that instead of gestational age at birth, the fetal body weight and fetal body weight Z score at birth were significantly correlated with the DQ scores, suggesting that these two factors are important in determining the optimal timing of delivery for sFGR cases. Furthermore, it is imperative that all healthcare providers working with preterm-born infants with FGR are aware of these facts. Moreover, continuous long-term follow-up and appropriate interventions after birth are important for children with sFGR to promote neurodevelopment.Nonewww.editage.com) for English language editing.We would like to thank Editage  and with the 1964 Declaration of Helsinki and its later amendments. Informed consent was obtained from all the patients included in the study. This study was approved by the Ethics Committee of Kyoto University (R3305).


To investigate the correlation between maternal glucose and lipid metabolism indexes and blood-lipid ratio in the first trimester and large-for- gestational-age (LGA) infants.Women in the first trimester of pregnancy who underwent regular obstetric examination in the obstetric outpatient department of the Affiliated Hospital of Chengde Medical College from June 2018 to March 2019 were included according to the standard. Basic information were collected based on questionnaires at the first visit of pregnant women, including early fasting blood glucose (FBG), fasting insulin (FINS), glycated hemoglobin (HbA1c), high-density lipoprotein (HDL), low-density lipoprotein (LDL), triglyceride (TG), total cholesterol (TC), apolipoprotein A1 (APO-A1), apolipoprotein B (APO-B), lipoprotein a (LP(a)), LDL/HDL, TG/HDL, TC/HDL, APO-B/APO-A1 ratio, birth weight of newborns, gestational age at delivery etc.P < 0.05); multivariate stepwise logistic regression analysis indicated that the correlation between maternal age, pre-pregnancy BMI, weight gain during pregnancy, APO-B/APO-A1 level and LGA were statistically significant (P < 0.05); compared with the reference range of APO-B/APO-A1 of 0.46–0.65, values < 0.46 and > 0.65 were protective factor of LGA (P < 0.05). The receiver operating curve(ROC) indicated that the area under the curve (AUC)s for predicting LGA using maternal age, pre-pregnancy BMI, weight gain during pregnancy, and early pregnancy APO-B/APO-A1 were 0.585, 0.606, 0.637, 0.531, respectively. The AUC for a combined prediction model was 0.742, showing greater predictive value than any other factors individually.A total of 418 cases were included for analysis. The incidence rate of LGA infants was 13.88%, and that of small-for-gestational-age (SGA) infants was 4.78%. Univariate analysis revealed that the age, pre-pregnancy body mass index (BMI), weight gain during pregnancy, APO-B/APO-A1 between LGA group and appropriate-for-gestational-age (AGA) group were significantly different (Maternal age, pre-pregnancy BMI, weight gain during pregnancy, and APO-B/APO-A1 levels in first trimester are significant factors influencing the occurrence of LGA infants, and the combination of the four factors would have certain predictive value for LGA. During pregnancy, the mother undergoes many adaptive changes to meet the needs of fetal growth and development, and change in glucose and lipid metabolism is one of the main changes , 2. AlthWomen in the first trimester who underwent regular obstetric examination at the obstetric outpatient department of the Affiliated Hospital of Chengde Medical College from June 2018 to March 2019 and planned to give birth in this hospital were included. All patients included in the study were informed, expressed their willingness to participate, and signed an informed consent form at the first check-up at 6–14 weeks of pregnancy. The approval of the Medical Ethics Committee of this hospital was obtained. Inclusion criteria: permanent residents of Chengde city (2 consecutive years or more), aged 18–45 years, naturally conceived, singleton, gestational age 6–14 weeks, and planned to deliver in this hospital. Exclusion criteria: suffering from type 1 or type 2 diabetes mellitus, chronic hypertension, dyslipidemia detected before pregnancy, cardiovascular disease, etc.; acute or chronic liver, kidney or other organ diseases; smoking and/or drinking history; recent use of drugs that affect glucose and lipid metabolism. This study initially included 510 women in first trimester, and 6 cases with missing laboratory indicators and 11 cases with fetal abortion or fetal malformation were excluded. Another 75 pregnant women who did not deliver in our hospital as planned were excluded due to incomplete information, and there were 418 pregnant women with complete information.2. When the pre-pregnancy BMI is below 18.5, the normal weight gain during-pregnancy is 12.5 to 18 kg. When the BMI is between 18.5–24.9, a normal weight is 7–11.5 kg; in a similar sense, when the BMI is greater than or equal to 30, a normal weight gain is considered to be 5–9 kg. When the during-pregnancy weight gain of a patient is below the weight ranges, it was ruled as insufficient weight gain while a weight gain is above the ranges, it was ruled as excessive xcessive . Other iin = 0.05, α out = 0.10). Using ROC to analyze the predictive value of each indicators for LGA. The significant level for other hypothesis tests is 0.05.SPSS 26.0 software was used for statistical analysis. In this study, all data tested using a Kolmogorv-Smirnov test. the P values are all below 0.05 indicates that data did not follow a normal distribution and the P value greater than 0.05 indicates that data followed a normal distribution. Measurement data subject to normal distribution is expressed as mean ± standard deviation. Independent-samples t test is used for comparison between two groups. For measurement data that does not obey normal distribution is described by M (P25-P75), independent-samples Wilcoxon sank sum test was used for comparison between two groups. Enumeration data were described by composition ratio or rate, and comparison between groups was done by chi-square test or Fisher's exact probability method. Multivariate stepwise logistic regression analysis was performed on the influencing factors of LGA , overweight pregnant women accounted for 25.5% (107/418), and pre-pregnancy obese pregnant women accounted for 9.02% (39/418). Excessive and insufficient weight gain during pregnancy accounted for 59.33% (248/418) and 10.53% (44/418), respectively; while normal weight gain during pregnancy accounted for 30.14% (126/418). The incidence of LGA was 13.88% (58/418), and that of SGA was 4.78% (20/418).Among the 418 pregnant women included in the analysis, the age ranged from 20 to 44 years, with an average age of 29.31 ± 4.24 years. The average pre-pregnancy BMI was 22.71 ± 3.77 kg/mP < 0.05). Other factors including FBG, FINS, HbA1c, HDL, LDL, TG, TC, APO-A1, APO-B, LP(a), LDL/HDL, TG/HDL, TC/HDL, APO-B/APO-A1 levels in the first trimester were comparable between the two groups   as the independent variable. Given that it is believed in the clinical work that the older the gestational age at delivery results in the longer growth of the fetus in the womb and greater the birth weight, the gestational age at delivery is also used as an independent variable. As shown in the Tables P < 0.05); compared with APO-B/APO-A1 in the reference range of 0.46–0.65, both < 0.46 and > 0.65 were protective factors for LGA (P < 0.05).The LGA stepwise logistic regression analysis was performed by taking LGA as the dependent variable , and the variables with P < 0.05). Pre-pregnancy BMI has the highest sensitivity but the lowest specificity, APO-B/APO-A1 has high sensitivity but low specificity, and the sensitivity and specificity of the joint prediction model are relatively high , is responsible for lipid transport. Common lipoproteins include LDL, HDL, etc. LDL transports TC in the liver to the outside, resulting in hypertriglyceridemia, and TC deposits on the blood vessel wall, causing damage to the vascular endothelium. APO-B is responsible for transporting LDL; HDL transports extrahepatic TC to the liver for metabolism to reduce the TC in peripheral blood. APO-A1 mainly exists in HDL, so APO-B/APO-A1 can reflect the levels of LDL and HDL to a certain extent. It has been reported that  lipid-loWith the increasing research on lipids during pregnancy, it has been affirmed that maternal lipid metabolism is closely related to fetal development. At present, it is believed that the levels of triglyceride and cholesterol rise in the 9th to 13th week of pregnancy. The timing of blood collection in this study is from 6 to 14th week of pregnancy, which is in the initial stage of blood lipid changes. Therefore, in this study, the APO-B/ The performance of APO-A1 alone in predicting LGA is not competent (AUC = 0.531), but maternal age, pre-pregnancy BMI, gestational weight gain combined with APO-B/APO-A1 in the first trimester have a relatively high value in predicting the occurrence of LGA (AUC = 0.742), and the predictive performance is significantly better than that of a single variable. The ability of pregnant women to adjust the physiological changes of pregnancy varies, which suggests that the management of women in the first trimester of pregnancy should be strengthened, especially women with high risk factors in the first trimester of pregnancy.In this study, multiple factors that are potentially related to LGA were analyzed; after screening of various mixed factors, it has been demonstrated that early pregnancy APO/APO-A1 ratio, age, pre-pregnancy BMI and during-pregnancy weight gain have significant correlation with LGA. By constructing ROC and calculating the area below the curve, it has been shown that the combination of the four factors has a greater predictive value for LGA than individual factors.First, this study is conducted in only one medical institution, with relatively small sample size and potential bias. Future results would be more convincing if multiple institutions in the region can be combined to formulate a multi-center study. Second, this study is a prospective study, and the participants are pregnant women of 6–14 weeks involving a large range of gestational weeks. While expanding the sample size in the future, the grouping can be further refined to clarify the changes in blood lipids in different stages of early pregnancy, and to provide a powerful reference for formulating diagnostic criteria for hyperlipidemia in pregnancy. Third, this study focuses on the relationship between blood lipid ratio and LGA only in the first trimester, and its predictive value is limited. Longitudinal research can be combined with the results of the second and third trimesters to improve the predictive efficiency.As a result, APO-B/APO-A1 < 0.46 is a protective factor for LGA, however, when APO-B/APO-A1 > 0.65 is reached, possible vascular endothelial injury may hinder the normal growth and development of the fetus. In conclusion, APO-B/APO-A1 in the first trimester is a vital factor for the LGA outcome, and the management of blood lipids in this period should be carefully monitored to avoid adverse pregnancy outcomes. From the analysis of APO-B/APO-A1 ratio in the first trimester of pregnancy, along with ages of pregnant patients, pre-pregnancy BMI and weight gains during pregnancy, it was found that the combination of four has a certain predictive value for the occurrence of LGA. During regular pregnancy check-ups, practitioners should be encouraged to administrate dynamic monitoring to patients in their first trimesters regarding the during-pregnancy weight gains, while improving their APO-B/APO-A1 ratio. Obstetricians should provide guidance concerning dietary, nutritional and exercise-wise choices to pregnant patients with risk factors, which may bring positive influence on preventing from LGA occurrence.


Preterm birth is a global public health problem with a significant burden on the individuals affected. The study aimed to extend current research on preterm birth prognostic model development by developing and internally validating models using machine learning classification algorithms and population-based routinely collected data in Western Australia. The longitudinal retrospective cohort study involved all births in Western Australia between 1980 and 2015, and the analytic sample contains 81,974 (8.6%) preterm births (< 37 weeks of gestation). Prediction models for preterm birth were developed using regularised logistic regression, decision trees, Random Forests, extreme gradient boosting, and multi-layer perceptron (MLP). Predictors included maternal socio-demographics and medical conditions, current and past pregnancy complications, and family history. Class weight was applied to handle imbalanced outcomes and stratified tenfold cross-validation was used to reduce overfitting. Close to half of the preterm births  were correctly classified by the best performing classifier (MLP) for all women when current pregnancy information was available. The sensitivity was boosted to 52.7%  after including past obstetric history in a sub-population of births from multiparous women. Around half of the preterm birth can be identified antenatally at high specificity using population-based routinely collected maternal and pregnancy data. The performance of the prediction models depends on the available predictor pool that is individual and time specific. Approximately 15 million babies were born prematurely worldwide in 2014, and a large majority (81%) occurred in Asia and sub-Saharan Africa10. In Australia, the prevalence of preterm birth was 8.6% (2014), lower than the global and U.S. figures of 10.6% and 9.6%, respectively10. Nevertheless, it fared worse than other countries like Canada (8.2%) and the U.K. (7.0%) in the same period10. According to a report by the Australian Institute of Health and Welfare, the prevalence of preterm birth was 8.6% in 2019, and, despite advances in antenatal care, the occurrence was higher than the trough of 8.3% in 2010 and was barely lower than the peak of 8.7% in 2017–201811. Importantly, Indigenous Australians were disproportionately affected, with a higher prevalence of 13.2%.Preterm birth, defined as birth before 37 completed weeks of pregnancy, is a significant contributor to neonatal mortality and morbidity13, prognostic models have been developed to identify pregnant women with increased risk of preterm birth using prenatal and perinatal predictors. However, findings from these studies were affected by a small sample size, a limited number of predictors, and methodological and reporting shortcomings16. The discrimination capacity, measured using the area under the receiver operating characteristic curve (AUC), of models developed to date, was found to be poor to moderate (58–77%)16. In a recent retrospective study that involved more than 2 million births in the U.S., the AUC of an early pregnancy cumulative risk scoring tool for preterm birth was 59.1% 17. Despite the large sample size, the study neither considered risk factors known to be strongly associated with preterm birth, such as socioeconomic status, nor reported additional prediction performance statistics for comparison purposes. At the other end of the spectrum, a French prospective study conducted in 2018 demonstrated that a nomogram  developed for personalised preterm birth risk evaluation has an AUC of 0.77 (95% CI 0.72–0.81). Nevertheless, this study was limited as only high-risk women were targeted, and some of the risk factors used in the prediction model were not routinely assessed 18. Importantly, among the prognostic models developed, there is a paucity of evidence on preterm birth prediction that are timely and relevant to the Australian population and utilises readily available health information19.Given the profound health impact and the possibility of prevention when detected early20. Using data linkage, the predictive capability of the population-based database can be expanded by bringing in predictors from other health-related data sources21. Linkage can also be applied longitudinally to include historical events and vertically to capture features across generations21. Machine learning, a subset of artificial intelligence, has been used in reproductive medicine to automate pattern recognition22. By applying algorithms to learn and adapt from experience with data without being explicitly instructed, the technology has been adopted as an adjunct to clinicians in the U.S. for interpreting electronic foetal heart monitoring23. In predictive analytics, models developed using machine learning have been explored to tackle the challenge of identifying stillbirth and perinatal morbidities25. Recent advances in the field of machine learning enable the analysis of large-scale health data and improve prediction by generating models that may use novel and complex combinations of features  and weights 26.Population-based research using routinely collected health data coupled with innovative approache for model generation can potentially improve prediction outcomes. Western Australia is the home to the largest and most comprehensive collection of routine perinatal data within the country, that span nearly four decades and has a complete geographic coverageAn effective prognostic model for preterm birth will contribute to the estimation of individual risk and aid in identifying potentially high-risk pregnancies. As a complement to standard antenatal care, the tool can inform surveillance strategies and enable efficient use of resources. Crucially, the resulting prediction is locally relevant and readily translatable to the Australian antenatal care program. The primary aim of this study was to develop models for predicting preterm birth in Western Australia using a suite of machine learning classification algorithms and a comprehensive range of routinely collected risk factors and internally validate these models. The secondary aim of this study was to compare the performance of the classification algorithms in predicting preterm birth.27. Birth records for the period from 1st January 1980 to 31st December 2015 were ascertained from the WA Midwives Notification System, a statutory data collection and encompasses all live births and stillbirths that occur from 20 weeks of gestation or have a birth weight of at least than 400 g20. These records, augmented with those from the WA Births Registrations, formed the preliminary study cohort. The final cohort was established by excluding duplicates as well as births with gestational age that was missing or less than 20 weeks  Data Linkage System maintained by the WA Department of Health28. The values were divided into quintiles for the state of WA, with the first category assigned as the most disadvantaged. The remoteness of residence was categorised as major cities or regional/remote using the Accessibility and Remoteness Index for Australia (ARIA +) score remoteness area categories29. Maternal chronic medical conditions included essential hypertension, diabetes mellitus, asthma, miscarriage, obesity and circulatory system diseases. Characteristics and complications in current and previous pregnancies included gestational diabetes, gestational hypertension, urinary tract infection, cancer registration (before or during pregnancy), pre-eclampsia, threatened miscarriage, placenta praevia, placental abruption, pre-labour rupture of membranes, unspecified antepartum haemorrhage, threatened preterm labour and uterine rupture. Other characteristics of current pregnancy included: parity , year of birth (in five-year periods), plurality , presence of congenital anomalies, and small-for-gestational-age (SGA) birth as a proxy for foetal growth restriction. Congenital anomalies were defined as any birth defect registered in the WA Register of Developmental Anomalies which collects data, including diagnostic information for anomalies, on children with birth defects up until the age of six. We calculated SGA as the lowest 10th percentile of birthweight by sex and gestation within calendar intervals of five years. For past obstetric history, additional predictors included caesarean section delivery, stillbirth, gestational age of the last birth , SGA birth and congenital anomalies. For women with two or more birth records during the study period, we coded the timing of occurrence for past obstetric events, indicating whether the condition/complication occurred in the last birth, not in the last but an earlier birth, no history, or unknown. For a subset of births to mothers and fathers who were born during the study period and whose birth details were included in our cohort, we ascertained whether they were born preterm, small for gestational age, or were diagnosed with congenital anomalies. For these parents, we also recorded the chronic medical conditions and past obstetric history of their mothers.Preterm birth was defined as births before 37 weeks of gestation. Both spontaneous and medically indicated preterm births were included in the definition. Predictors were categorised into six main groups, including (1) maternal socio-demographic factors, (2) maternal chronic medical conditions, (3) current pregnancy characteristics and complications, (4) past obstetric history, (5) parent’s birth outcomes, and (6) grandmothers’ chronic medical conditions and obstetric history. Maternal socio-demographic factors included age , ethnicity , socio-economic status, remoteness of residence, and smoking. Based on the maternal residence at the time of birth, the Index of Relative Socioeconomic Disadvantage (IRSD) at the statistical local area level was used as a proxy for the individual-level socio-economic status30, which targets four aspects of a prediction model  to obtain a context-specific required sample size that minimises the potential for model overfitting while ensuring precise estimates of key parameters. Our sample size far exceeds the required numbers and is thus adequately designed to develop and validate the prognostic models.Descriptive statistics were used to summarise the characteristics of the study cohort and the prediction results. The minimal study size needed was ascertained using the method by Riley et al.We first developed three prediction models with full information on relevant predictor groups for specific target populations. For these models, we considered the inclusion of predictors collected after birth as a proxy for measurements taken during pregnancy . The first model (Model A) encompassed all births in the study population   contained predictors that were collected at the early phase of pregnancy  for both nulliparous and multiparous women   and evaluation .We applied five different supervised machine learning algorithms to solve the binary classification problem of identifying preterm and term births: (1) regularised logistic regression; (2) decision trees via classification and regression trees (CART); (3) Random Forests; (4) extreme gradient boosting; and (5) multi-layer perceptron. These classification algorithms, or classifiers, were chosen because of their widespread application, relative ease of use, and diverse learning capabilities. They also represent a spectrum of classifiers ranging from conventional  to modern .Regularised logistic regression (LR) is an extension of logistic regression that incorporates a penalty term on the cost function. The L1 penalisation used resulted in variable selection by shrinking the regression coefficient of predictors that contribute least to the prediction and thus addressing the issue of overfitting.Decision trees (DT) use the CART algorithm to make predictions by selecting the most differentiating predictors using an impurity criterion (Gini impurity or information gain using entropy) at the root and successive nodes to split the sample into the best homogenous sets of sub-samples. The risk of overfitting was minimised by pruning the depth of the tree and restricting the number of predictors to consider when looking for the best split.Random Forests (RF) employs bootstrap aggregating (or bagging) to build an ensemble of decision trees using random sample subsets and then combine the results by averaging the probabilistic prediction of the trees. This classification approach reduces variance by combining diverse trees thus further lessons the effect of overfitting and improving prediction performance as well.Gradient boosting is a method of converting weak learners  into strong ones by first identifying shortcomings of a weak learner through gradients (or first-order derivatives) of the loss function and then improving upon, or boosting, predictions of the weak learner by adding more weak ones in a gradual, additive, and sequential manner. Extreme gradient boosting (XGBoost [XGB]) is a specific implementation of the gradient boosting method which enhances efficiency through parallelisation and delivers more accurate estimation by using the second-order derivative of the loss function and L1 and L2 regularisations.Multi-layer perceptron (MLP) is an artificial neural network that mimics the learning processes found in the neural network of the human brain. The classifier processes information starting from an input layer, through one or more hidden layers, and ending at an output layer. Learning was facilitated through backpropagation using stochastic gradient descent. Weights were then learnt through backpropagation, and the activation functions were selected as hyperparameters. The nodes of the input layer represent the predictors in the model, and the nodes of the remaining layers represent the non-linear functions of weighted combinations of nodes in the previous layers.1 score, the harmonic mean of positive predictive value and sensitivity, by tenfold cross-validation. Model performance and generalisability were evaluated using internal validation, which was undertaken using cross-validation by first randomly partitioning the data into 10 equal portions with the same prevalence of the outcome in each fold via the stratified k-fold cross-validation technique. The model was then trained using data from the first 9 folds and validated on the 10th fold, and the process was repeated iteratively, resulting in 10 model variants for each classifier. We measured the overall performance of the classifier by averaging the values of each selected evaluation metric.All predictors were categorised as previously described and checked for missing values. For each model, records with unknown values for a subset of predictors were excluded to reduce multicollinearity. Predictors were pre-processed using either one-hot encoding (excluding binary variables) for DT, RF, XGB and MLP, or dummy encoding for LR. Class weight was used in all classifiers except MLP (class weight is not supported in scikit-learn implementation of MLP) to handle imbalanced outcomes. Hyperparameter tuning was carried out using a grid search , or Bayesian optimisation  combined with the asynchronous HyperBand scheduler  over specified parameter search space. The hyperparameters of the classifiers were optimised for F1 score (F1); 4) true positive rate (TPR) ; 5) positive predictive value (PPV) ; 6) negative predictive value (NPV); 7) positive likelihood ratio (LR +); and 8) negative likelihood ratio (LR-). Accuracy was defined as the proportion of correctly classified individuals. For comparison with prior studies, performance results were reported at a false positive rate (FPR) of 5% (95% specificity) and 10% (90% specificity). As the outcome classes are highly imbalanced , accuracy would be erroneously high even if the classifier predicts all term births correctly and misclassifies all preterm births. Therefore, AUC and F1 were mainly used in our reporting of results. The latter was chosen because of its balanced view of TPR and PPV, which we assumed are equally important in preterm birth prediction. Feature importance, a non-directional measure of the individual contribution of the corresponding predictor towards model performance for a specific classifier, was reported. Scoring was determined using a non-standardised absolute beta coefficient for LR and impurity-based importance measure for tree-based classifiers, and ranking was performed in descending order of the value. The top 10 ranked one-hot or dummy encoded predictors were then compared. To enable interpretation of the models generated by non-linear tree-based classifiers, the impact of each encoded predictor on the prediction of preterm birth in the models developed by the best classifier was estimated using SHapley Additive exPlanations (SHAP), a unified framework that extends the classic Shapley values from game theory to explain the output of machine learning model31.The performance of each classifier was based on the ability to discriminate between preterm births and term births and was evaluated using the following classification metrics: 1) area under the receiver operating characteristic curve (AUC); 2) accuracy (ACC); 3) F32.All analyses were conducted using miniconda3 4.10.1–5, python 3.8.9, pandas 1.3.0, numpy 1.19.5, scikit-learn 0.23.2, tune-sklearn 0.4.0 and shap 0.41.0. The results were reported following the Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD) statementThe study was approved by the Human Research Ethics Committees of the Department of Health WA and Curtin University. All research activities were conducted in accordance with corresponding guidelines and regulations. Informed consent was waived by the Department of Health WA Human Research Ethics Committee (PRN RGS0000005148) because the study uses existing and consented registry data and presents minimal harm to participants while offering potential benefits to the public.The characteristics of the 958,729 births, including 81,974 (8.6%) preterm births and 876,755 (91.4%) term births, are shown in Supplementary Table Predictors used in the prediction models and the tuned hyperparameters optimised for F1 by models are shown in Supplementary Tables For model A, which included maternal socio-demographic factors, chronic medical conditions and current pregnancy characteristics and complications-related predictors, the best performing classifier (MLP) achieved an AUC of 84.41% (95% CI 84.21–84.61) and a F1 of 48.55% (95% CI 48.31–48.79) at 5% FPR  achieving an AUC of 58.70% (95% CI 58.52–58.88) and a F1 of 13.38% (95% CI 13.18–13.59) at 5% FPR Table . Model EFeature importance of the six models by classifiers are presented in Supplementary Tables The SHAP value summary plots of the six models developed using the best-performing tree-based classifier (XGB) are shown in Figs. 33, and, therefore, our models attained a PPV that was around 70% of the maximum possible.In a large population-based cohort of Western Australia births with more than 80 thousand preterm births, we were able to correctly classify nearly half (49% at 5% FPR) of the cases using the model generated by multi-layer perceptron and a suite of routinely collected data on maternal socio-demographic, chronic medical conditions and current pregnancy characteristics and complications. Augmenting the pool of predictors with past obstetric history slightly improved the prediction performance to 53% in a sub-population of birth from multiparous women. On the other hand, we found minimal prediction improvement when family history was accounted for in the modelling that limited analysis to only births from locally born parents/grandparents . We also noted that omitting current pregnancy characteristics and complications significantly reduced the prediction performances, suggesting that current pregnancy information is crucial. The positive predictive values of the models generated were between 46–49% (except that of the models with only early pregnancy predictors), indicating that nearly half of the predictions of preterm birth were correct. Nevertheless, the values are influenced by the prevalence of preterm birth and should be interpreted in the context of an upper bound. In our study, given the overall prevalence of 8.6%, the highest possible value of PPV at 100% TPR and 5% FPR was 65%When comparing the performance of the classifier in the prediction of preterm birth, we have observed that XGBoost and multi-layer perceptron consistently outperformed the rest. In the overall sample with full information on current pregnancy history , compared to logistic regression, multi-layer perceptron was able to correctly identify an additional 2659 cases (3.3%) among the 81,578 preterm births. Similarly, the TPR of multi-layer perceptron was around 2% higher than that of logistic regression in the multiparous sample . As a result, a further 782 cases were correctly detected among 37,978 preterm births. Nevertheless, despite the apparent better performance of XGBoost and multi-layer perceptron, the performance differences were small among the algorithms and were much less than the variations among the models developed.34.Consistent with previous findings, our study has identified plurality  and specific events that occurred during current pregnancy  are pertinent risk factors for preterm birth, and having a history of preterm birth was also found to be informative for prediction among multiparous women37. Weber et al. applied a suite of algorithms, including logistic regression and machine learning techniques, with administrative data to assess the prediction of spontaneous early preterm  birth among nulliparous women in California from 2007 to 2011 36. Their optimal model, generated by logistic regression, was able to identify 61% of cases at 36% FPR with an AUC of 0.67 . The model’s TPR, which was approximately 20% at 5% FPR as estimated from the reported ROC curve, was lower than ours . We have postulated that the discrepancy was partially due to differences in the study population  and outcome definition (< 32 weeks of gestation vs. < 37 weeks of gestation). Nevertheless, the data quality, instead of the prediction tools, may have played an important role in the model performance. For example, uncertainty about clinical diagnoses in hospital discharge records, and inadequate life-course risk exposure data could have affected the model's predictability. Using more than 100 maternal and neonatal features from the Iranian Maternal and neonatal (IMAN) registry, Khatibi et al. utilised machine learning algorithms and the Map-Reduce approach to develop a model for preterm birth prediction37. Important predictors identified include maternal age, maternal education level, city of birth, foetal sex, parity, pregnancy risk factors, gestational diabetes, cardiovascular disease, and chronic illnesses. Their best-performing model, an ensemble of decision trees, support vector machines and random forests with weighted voting, can predict preterm birth with an accuracy of 81% and an AUC of 68%. However, no other model performance statistics were presented, so a direct comparison with our study was impossible. In a more recent study, using population-based data from the United States natality datasets that provide demographic and health information on births that occurred between 2013 and 2016 , Koivu and Sairanen employed logistic regression and machine learning methods to develop novel risk models for the prediction of pregnancies with delivery before 37 weeks of gestation 25. A gradient boosting decision tree algorithm provided the best model with a TPR of 31% at 10% FPR and an AUC of 0.67 . The model prediction was subsequently validated by a New York City dataset collected during the years 2014 to 2016 , and the prediction performances were lower with this dataset . Despite the large volume of data involved in the U.S. study, their number of predictors was limited as they were derived from information in the birth certificates only. Moreover, although a handful of past obstetric history predictors  were used, they were simply dichotomised without considering that nulliparous and multiparous women were included in the modelling. These factors might explain why the results of our study  were better than that of their validation sample.Studies have shown that routinely collected population-based maternal and pregnancy data can be used to predict preterm birth38. The study was recently re-evaluated with an expanded cohort , and when both CL and qfFN were incorporated into the model, the AUC for the prediction of sPTB at < 37 weeks’ gestation was 0.746, and the TPR at 5% FPR was estimated to be 25%39. In a separate but related study where the risk-assessment tool was applied to a cohort of high-risk women with symptoms of threatened preterm labour, the AUC for the prediction of the same outcome was 0.73 (95% CI 0.63–0.83) and the TPR at 5% FPR was 20%40. Despite the difference in study design between the QUiPP studies and ours, it is nevertheless noteworthy to mention that the performance of their models was comparable to that of our model  in which only administrative data on socio-demographics, chronic medical conditions and past obstetric history were used. Furthermore, our predictors were not necessarily restricted to be proximal to the birth, and we utilised a more generalisable population.Other studies have employed additional risk factors collected during pregnancy to boost prediction performance. In the U.K., researchers combined proximal predictors, including cervical length (CL) and quantitative foetal fibronectin (qfFN), with other pertinent risk factors to develop a risk assessment tool for the prediction of spontaneous preterm birth (sPTB) in asymptomatic high-risk women. The research was translated into an online platform (QUiPP) for widespread useOur study harnessed the benefits of using professionally curated population data on all births that occurred in Western Australia over three decades. The databases’ extensive time coverage allowed us to study many predictors, including familial factors that are otherwise not feasible. Furthermore, due to the administrative nature of the data, information on medical and family history was objectively collected and systematically recorded, thus avoiding the shortcomings associated with traditional health medical records and keeping missing data to a minimum.There are limitations that need to be considered. Regarding predictors, firstly, their measurement could have changed over time due to shifts in practices. For example, there was a notable increase in the incidence of gestational diabetes in 2012–2013 due to a change in the diagnosis criteria of gestational diabetes by the Australasian Diabetes in Pregnancy Society because of the new consensus guidelines established by the International Association of Diabetes and Pregnancy Study Groups in 2010. Secondly, we do not know when risk factors first emerged, which could affect predictive performance if precise timing is required. Thirdly, as interventions and their indication were not recorded, the resulting predictions could be problematic because the treatment effects could have rendered some of the predictors less relevant. Fourthly, simplicity and interpretability can be gained by categorisation, but the ability to extract predictive information could be diminished as a result. Lastly, it is possible that machine learning was unable to detect the signal from the noise when categorised predictors were used.Regarding the outcome and model development, firstly, spontaneous and medically indicated preterm births were collectively analysed because the study was prognostic instead of aetiologic and prediction performance  benefits from larger case numbers. Secondly, we have used class weight to improve class imbalance, but other methods, such as resampling, might be useful and haven’t yet been explored. Thirdly, the assumption of the equal importance of TPR and PPV in prediction performance is likely conservative, and a different weighting that caters for a specific clinical context might be necessary. Fourthly, the expansive time coverage of the databases allowed us to explore family factors in preterm birth prediction, but the sample size of the family linkage cohort was limited as the ancestors of the birth cohort are mostly migrants, and thus they were not included in the local birth perinatal registry. Lastly, although feature importance on model performance and the contribution of predictors to model prediction was investigated, we did not examine how the predictions using the multi-layer perceptron classifier were generated in this study. The insights gained can help promote acceptance and facilitate future model development.The results of our study are promising, and the evidence contributes to establishing an effective prediction mechanism to identify pregnancies at elevated risk of preterm birth. The decision support system that utilises the proposed prediction algorithm can be used to inform individualised care. It aids clinicians by providing an objective evaluation of the risk of preterm birth based on administrative and clinical predictors already routinely collected in the antenatal system, thus having minimal impact on the workload. Furthermore, the assessment can be repeated when new information becomes available during the antenatal period. The longitudinal data can benefit clinicians and patients in evaluating progress and planning for preventative treatments and future interventions.We demonstrated that around half of the preterm birth could be identified antenatally at high specificity using population-based routinely collected maternal and pregnancy data. We have found that the performance of the prediction models largely depends on the available predictor pool that is individual and timing specific and is less reliant on the algorithms used for model development. Further research is needed to externally validate the models and to improve the performance using additional informative predictors and treatment indicators.Supplementary Information.


Editorial on the Research TopicDevelopmental origins of health and disease: Impact of preterm birthin utero environment that precipitates the early birth. Further, preterm birth has been associated with impaired structural and function development of organ systems in the offspring, thus making preterm birth a unique predisposing factor for adult disease  scores, but late preterm birth mothers had even higher depressive symptoms; both of which may affect the care provided to their respective infants.” Both Motte-Signoret et al. and Gonzales-Garcia et al. studied small for gestational age or intrauterine growth restricted (IUGR) infants. Motte-Signoret measured growth factor (GF), insulin growth factor 1 (IGF1), and insulin resistance in small for gestational age (SGA) infants. SGA infants demonstrated resistance to GF and IGF1 with insulin resistance. These findings could explain initial defects in early catch-up growth, risks for later catch-up growth, and higher prevalence of metabolic syndrome in later life. Gonzales-Garcia compared the Fenton 2013 growth charts to those from the International Fetal and Newborn Growth Consortium for the 21st Century (Intergrowth 21st) Project (IW-21) for assessing to intra-(IUGR) and extrauterine (EUGR) growth restriction in very low birthweight infants. There was concordance between the charts for IUGR growth trajectories but not for EUGR infants. The dynamic IW-21 was more restrictive but better predicted morbidities in EUGR infants.Reports on the negative effects of preterm birth included identifying predictors of severe necrotizing enterocolitis (NEC) and impaired neurodevelopmental outcomes and indicated that early identification of risk factors might ultimately lead to improved outcomes. Lin et al. sought to identify predictive indicators of necrotizing enterocolitis (NEC) by comparing biomarkers between NEC with portal venous gas (PVG) and NEC without PVG. C-reactive protein (CRP), fibrinogen degradation product, and blood glucose demonstrated predictive value for NEC-PVG. Current outcomes of the Neuroprem 2 cohort study were reported by Lugli et al. Out of 502 very preterm infants 9.6% had severe disability and 5.4% had cerebral palsy. Gestational age and periventricular hemorrhage were most highly associated with severe disability. Along these same lines, Wibowo et al. measured bone mineral content in newborns and found it lower in underdeveloped countries, higher in males, and negatively correlated with maternal cigarette usage. Hole et al. utilized a preterm piglet model to study early motor development. Preterm piglets took shorter steps than term piglets in early stages of walking but rapidly adapted with no differences within 3 days. Overall conclusions from the combined studies were that early interventions are needed to prevent later delays.Khasawneh et al. retrospectively analyzed determinates of late hospital discharge. The majority of all preterm births analyzed were late preterm infants. While several parameters such as gestational age and maternal and neonatal morbidities were correlated with length of stay , there was no correlation between length of stay after birth and later readmission.In summary, growth restriction and small birth weight can have permanent consequences on the metabolic health of the infant. Furthermore, the morbidities associated with preterm birth such as NEC, and motor and neurodevelopmental disabilities are likely to contribute to deficits in the quality of life once these infants reach adulthood. Prematurity and SGA should be considered overall risk factors for adult health and quality of life.