A Model for Predicting Significant Hyperbilirubinemia in Neonates From China
OBJECTIVES: To develop and validate a predischarge risk stratification model by using transcutaneous bilirubin (TcB) values and clinical factors to predict significant postdischarge hyperbilirubinemia in healthy term and late preterm Chinese neonates.
METHODS: In a prospective cohort study, 8215 healthy term and late preterm neonates in 8 hospitals in China underwent TcB measurement at <168 hours of age. TcB percentiles were calculated and used to develop an hour-specific nomogram, and 9 empirically weighted items were used to derive a prediction model. A risk stratification model was developed by combining the TcB nomogram with clinical risk scores to predict significant hyperbilirubinemia, defined as a postdischarge bilirubin level that exceeded the hour-specific recommended threshold value for phototherapy. Data from another 13 157 neonates were used to validate the model.
RESULTS: A TcB nomogram for every 12 hours of the studied interval was constructed from the development set. Gestational age, male gender, history of previous neonate who received phototherapy, bruising, feeding mode, weight loss, and early discharge were predictors of postdischarge significant hyperbilirubinemia. The combination of the TcB nomogram and clinical risk score provided the best prediction of significant hyperbilirubinemia with an area under the curve of 0.95 (95% confidence interval: 0.94–0.95) in the development data set and 0.94 (95% confidence interval: 0.93–0.94) in the validation data set. A risk stratification model with 6 distinct risk levels was developed and validated.
CONCLUSIONS: A risk classification model, combining discharge transcutaneous bilirubin values and clinical risk factors, separated term and late preterm Chinese neonates into 6 risk classes for the timely follow-up of postdischarge hyperbilirubinemia detection.
- AAP —
- American Academy of Pediatrics
- AUC —
- area under the curve
- CI —
- confidence interval
- GA —
- gestational age
- LR —
- likelihood ratio
- OR —
- odds ratio
- ROC —
- receiver operating characteristic
- TcB —
- transcutaneous bilirubin
- TSB —
- total serum bilirubin
What’s Known on This Subject:
Guidelines for postdischarge monitoring of hyperbilirubinemia for neonates of white descent are available from the American Academy of Pediatrics; however, such information for healthy term and late preterm Chinese neonates is lacking.
What This Study Adds:
A classification model for predicting the risk of significant hyperbilirubinemia in Chinese neonates was developed that combines a transcutaneous bilirubin–based nomogram with clinical risk factors. It classified newborns into 6 risk groups, which can guide clinicians in planning appropriate follow-up strategies.
Visible jaundice occurs in the majority of neonates in the several days after birth. However, if neonates do not undergo timely monitoring and prompt treatment, they could develop hyperbilirubinemia and bilirubin encephalopathy.1,2 The incidence of chronic kernicterus from the Northern California Kaiser Permanente Medical Care Program is reportedly ∼6 per 1 million, a rate that is similar to that reported from Denmark.3,4 In China, 348 cases of bilirubin encephalopathy were reported from 28 hospitals in 2009.5
Many cases of bilirubin encephalopathy are believed to be preventable; therefore, identification of neonates at risk for developing significant hyperbilirubinemia has become an important aspect of postdischarge monitoring of neonates. The American Academy of Pediatrics (AAP) recommends that, before discharge, all neonates be assessed for the risk of significant hyperbilirubinemia by using predischarge total serum bilirubin (TSB) or transcutaneous bilirubin (TcB) measurements and clinical risk factors.6 The Northern California Kaiser Permanente Medical Care Program implemented the 2004 AAP guidelines and reported that after initiation of a universal bilirubin screening program, there was a 62% reduction in the incidence of TSB levels exceeding the exchange transfusion guideline (0.17% vs 0.45%).7 However, because the impact of race/ethnicity on the hour-specific bilirubin levels and clinical risk factors was not assessed, the AAP recognizes the need for large-scale studies to evaluate the role of such guidelines in nonwhite populations. Researchers from several other countries (Portugal, Greece, and Italy) have developed hour-specific bilirubin nomograms for assessment of clinical risk factors using their own race/ethnicity data.8–10 We previously designed a TcB nomogram from a single tertiary care center; it is unknown whether this nomogram is applicable to a widespread population.11 To improve the generalizability of our findings, a multicenter study was conducted to develop a new hour-specific TcB nomogram.
Study Design and Setting
This report combines data from 2 multicenter prospective cohort studies conducted in 15 hospitals in mainland China (Supplemental Appendix Table 1). Eight hospitals participated in the development cohort, and 13 participated in the validation cohort; 6 participated in both cohorts. The data from the first study were used for the development of a prediction model (from which we constructed a new hour-specific TcB nomogram and clinical risk scores), and data from the second study were used for validation of the model.
Neonates with a gestational age (GA) ≥35 weeks and birth weight ≥2000 g were included in both studies. Neonates who were admitted to the ICU and those who received phototherapy predischarge were excluded. The decision to use phototherapy was made by the attending physicians according to AAP guidelines.6 All newborns whose mothers had blood type “O” or who were Rh-negative were evaluated for ABO and Rh blood type. Newborns who were blood type O or Rh-negative underwent a direct antiglobulin test. Newborns with positive direct Coombs test results were defined as having ABO or Rh incompatibility and were excluded. During this study period, there was no predischarge screening for glucose-6-phosphate dehydrogenase (G6PD) deficiency. No prophylactic intervention for hyperbilirubinemia was used.
Development Data Set
Eight hospitals (Supplemental Appendix Table 1), comprising 2 general hospitals and 6 maternity hospitals, participated in a study between August 2010 and December 2011. The study data have been used to verify the predictive value of the previously constructed single-center TcB nomogram to identify significant hyperbilirubinemia in healthy term and late preterm Chinese neonates.12 This data set was also used to develop a prediction model (which constructed a new hour-specific TcB nomogram and clinical risk scores).
Validation Data Set
Thirteen hospitals (Supplemental Appendix Table 1), comprising 5 general hospitals and 8 maternity hospitals, participated in a study between January 2013 and December 2013. The main purpose of this part of study was to investigate the risk factors of significant hyperbilirubinemia in healthy term and late preterm Chinese infants. This data set was used to validate the developed model.
Measurements of TcB and TSB
In both studies, TcB measurements were performed by using a transcutaneous jaundice meter (model JM-103; Minolta-Osaka, Tokyo, Japan), according to the instructions from the manufacturer and applying standard techniques.13 The methods measurements of TcB were described in a previous study.11 According to previous research, the JM-103 is less accurate at TcB levels >222 μmol/L; these levels were confirmed with a TSB measurement.12 The blood samples (50 μL) were drawn by heel stick, and special care was taken to avoid exposing the collected samples to light. TSB assessment was performed in the clinical chemistry laboratory of each participating unit.
Potential Clinical Risk Factors
All perinatal, postpartum, and demographic data of neonates were recorded in a single database for each unit during the study period. The study coordination group reviewed potential clinical risk factors for neonatal hyperbilirubinemia reported in the literature and identified 9 factors of interest: GA, weight for GA, delivery mode, gender, history of previous neonate who received phototherapy, bruising/cephalohematoma, feeding mode, excessive body weight loss, and early discharge.
GA estimates were based on the last menstrual period and/or early second-trimester ultrasound. A small for GA neonate was defined as birth weight lower than the 10th percentile for GA and gender, according to a Chinese growth chart14; a large for GA neonate was defined as having a birth weight more than the 90th percentile. Excessive weight loss was defined as weight loss ≥10% on the third day after birth. Early discharge was defined as neonates discharged from the hospital within 72 hours after birth.
Follow-up of Studied Neonates
A follow-up TcB evaluation within 24 to 96 hours after discharge was offered to all neonates during the birth hospitalization; the timing of the follow-up evaluation depended on the TcB levels before discharge. Properly trained physicians performed the TcB evaluation; they were blinded as described in a previous study.11
Outcome: Significant Hyperbilirubinemia
Significant hyperbilirubinemia was defined as a postdischarge bilirubin level that exceeded the hour-specific threshold value for phototherapy, according to the guidelines presented by the AAP.6
The demographic characteristics between the development and validation data sets were compared by using Student’s t test and the χ2 test.
Development of Model
The potential clinical risk factors from the development data set were first included in a logistic regression analysis, with significant hyperbilirubinemia as the dependent variable. Variables significant at P < .2 were included in the multiple logistic regression, using stepwise backward elimination. For each significant variable in the multiple logistic regression analysis, a score was calculated by multiplying the regression coefficients by 10. This finding was applied to each neonate to calculate the total score for each neonate. The risk score was categorized into 4 groups (<10, 10–19, 20–29, and ≥30) as described previously to create 4 categories (high-risk, upper–intermediate, lower–intermediate, and low-risk zones). Based on the total score of each neonate in this categorization, the probability and likelihood ratio (LR) for the development of significant hyperbilirubinemia postdischarge were calculated.
TcB data of the development data set were collected and checked for completion, consistency, and accuracy by 2 investigators. The TcB nomogram was developed according to a previously reported method.11
The predictive performance of the TcB nomogram and clinical risk score categorization were used alone and in combination to predict significant hyperbilirubinemia, which was evaluated with respect to the area under the curve (AUC) in a receiver operating characteristic (ROC) curve. The AUCs of 3 predictive models were compared. Model calibration was tested by using the Hosmer-Lemeshow goodness-of-fit statistic.
In addition, the TcB and clinical risk zones were analyzed and classified as distinct groups (16 total groups), in which the test characteristics were assessed. Using the LR of the groups, they were stratified into distinct risk levels of developing significant postdischarge hyperbilirubinemia, which was developed as a risk stratification model.15
Validating the Risk Stratification Model
In the validation data set, a pathway similar to the development set was used. All statistical analyses were performed by using SAS version 9.3 (SAS Institute, Inc, Cary, NC).
The ethics committee of the Nanjing Maternity and Child Health Care Hospital of the Nanjing Medical University approved the 2 studies. It was also approved by each participating center.
The details of eligible patients and enrolled patients in both the development and validation data sets are given in Fig 1. The demographic characteristics of the 2 data sets are shown in Table 1. There was statistically significant difference in mean GA (0.3 week [2 days]) and mean birth weight (53 g); however, these differences were not clinically significant. Postdischarge significant hyperbilirubinemia occurred at 110.31 ± 31.87 hours after birth.
Among the 8215 newborns analyzed in the development data set, the overall incidence of postdischarge significant hyperbilirubinemia was 10.6% (876 of 8215). The results of univariate analyses, multivariable odds ratio (OR) (95% confidence interval [CI]), β-coefficient, and given scores for clinical predictors are reported in Table 2 and Table 3. Of the variables with significant ORs in univariate and multivariable analyses, weight for GA and mode of delivery were not identified as significant predictors and were eliminated from the model and score calculations, separately.
In the scoring system, the minimum possible total score was −17, and the maximum was 59. Scores were grouped in 4 groups, as done previously, and 4 clinical risk zones were created (Table 4). Test characteristics and LRs for the 4 clinical risk zones were calculated, which ranged from 0.17 to 10.05.
For all TcB data points of the development data set for each of the periods (from 12 to 168 hours of age), postnatal age (hours) and TcB (milligrams per deciliter) were plotted on a scatter plot. The hour-specific TcB nomogram was constructed by using the 40th, 75th, and 95th percentile values of TcB (Fig 2); these values were used as risk zone delimiters, and all neonates were classified into 4 risk zones (Table 4).
The ROC curves for different predictor models in the development data set were compared (Fig 3). Combining the TcB nomogram and clinical risk score provided the best prediction of postdischarge significant hyperbilirubinemia (AUC: 0.945 [95% CI: 0.937–0.952]; Hosmer-Lemeshow goodness-of-fit test, P = .65). This outcome was significantly better than with the clinical risk factors alone (AUC: 0.846 [95% CI: 0.831–0.860]; P < .001) or the TcB nomogram alone (AUC: 0.870 [95% CI: 0.860–0.881]; P < .001).
Neonates were classified into 16 groups based on the 4 clinical risk score zones and the 4 TcB value risk zones. The predictive abilities (probability and LR) for a positive test result for the development data set are shown in Supplemental Appendix Table 2. These 16 zones were classified into 6 distinct risk stratification levels for prediction of postdischarge significant hyperbilirubinemia, according to the LR.
A total of 13 157 newborns were analyzed in the validation data set. The overall proportion of significant hyperbilirubinemia was 12.3%. The ROC curves for 3 different models in the validation data set were compared (Fig 3). Combining the TcB nomogram and clinical risk score provided the best prediction of subsequent significant hyperbilirubinemia (AUC: 0.936 [95% CI: 0.930–0.942]), and this outcome was significantly better than the clinical risk factors or the TcB nomogram. There was no significant difference in the AUC between the validation and development data sets. The predictive abilities of the 16 distinct groups in the validation data set are shown in Supplemental Appendix Table 3. Similar results were confirmed. Based on the high predictive characteristics of the combined, clinical, and TcB risk scores, follow-up recommendations were generated (Table 5).
In the development data set, 7130 (86.79%) newborns had a follow-up visit at ≥4 days, and AUC was assessed. There was no significant difference in the AUC between the subgroup data set (AUC: 0.861 [95% CI: 0.849–0.873]), and the all development data set (AUC: 0.870 [95% CI: 0.860–0.881]). A total of 5914 (71.99%) newborns underwent TSB measurement postdischarge, and AUC was assessed. There was no significant difference in the AUC between the subgroup data set (AUC: 0.864 [95% CI: 0.852–0.877]) and the all development data set (AUC: 0.870 [95% CI: 0.860–0.881).
We have developed and validated a quantitative risk stratification model to predict significant hyperbilirubinemia in healthy term and late preterm neonates from China that combines a TcB nomogram with clinical risk scores. The model permits clinicians to group neonates into 6 discrete risk-based subsets that can help plan subsequent follow-up for early detection and intervention, if needed.
The combined use of the TcB nomogram and clinical risk factors was shown to be the best method for predicting subsequent significant hyperbilirubinemia. Our results are similar to those of Keren et al,15 who combined predischarge bilirubin levels and clinical risk factors, and reported equally good predictive abilities in a mixed ethnic group (AUC: 0.954). The timing of follow-up visits based on clinical risk factors and the TSB nomogram has been described by several authors.7,16 Six distinct risk categories were created for follow-up; however, we acknowledge that validation of these recommendations or the ability of these recommendations to avoid any untoward consequences remains to be assessed. Future studies will also need to explore the best timetable for follow-up of neonates after discharge.
In this study, we only included neonatal risk factors and did not include maternal factors (eg, maternal age >25 years). In addition, we developed the predischarge risk stratification model, which is easy to use and does not involve any laboratory testing. The risk factors that involve laboratory testing as reported in the AAP guidelines (eg, albumin level <3 g/dL) were not included in the present model for pragmatic purposes. The clinical risk factors included in our model are similar to other models except for 2 risk factors: excessive weight loss and early discharge.7,17,18 In China, excessive weight loss and early discharge are 2 problems commonly encountered after birth, and they occur in 7.2% and 26.1% of neonates, respectively.12,19 Although these 2 factors were not included in previous models, they have been identified as predictors of severe hyperbilirubinemia in previous studies.17,18 Postpartum hospital stay has decreased dramatically over the past few decades in many parts of the world. The majority of women are discharged within 2 days after vaginal births and within 4 days after cesarean deliveries. However, the natural course of a bilirubin increase in neonates reaches a plateau at ∼72 to 96 hours’ postnatal age. Early discharge before 72 hours of age has been identified as a predictor of subsequent hyperbilirubinemia.20 Thus, our results are concordant with the substantial body of literature which suggests that early discharge is a risk factor and encourages proper guidance for the timely follow-up for hyperbilirubinemia in those neonates who are discharged early.
Other clinical variables in our model have been identified previously as clinical risk factors for hyperbilirubinemia. In our development data set, GA was the strongest predictor for significant hyperbilirubinemia, followed by feeding mode and history of previous neonate who received phototherapy. This risk profile is similar to what is reported in literature.8 The ROC curves for a model using all risk factors and a model using only the strongest predictors (GA) in the development data set were compared. The model using all risk factors provided the better prediction of postdischarge significant hyperbilirubinemia (AUC: 0.846 [95% CI: 0.831–0.860]) compared with the model using only the strongest predictors (AUC: 0.706 [95% CI: 0.687–0.725]; P < .001). We therefore used the combination markers in the study.
Cesarean delivery was a significant factor in the univariate analysis but not in the multivariate analysis. This finding was similar to that of Chen et al,21 who also found an increased risk of significant hyperbilirubinemia (OR: 1.72 [95% CI: 1.04–2.83]) in the univariate analysis. We speculate that delayed discharge in those born via cesarean delivery led to identification of hyperbilirubinemia before discharge, and thus the variable was not retained in the model. In addition, higher rates of formula feeding in neonates born to mothers who had a cesarean delivery may have been an additional factor in reducing the likelihood of subsequent hyperbilirubinemia.22
To the best of our knowledge this report is the first to use the combination of clinical score and TcB to predict significant hyperbilirubinemia in Chinese neonates. This analysis is important because follow-up postdischarge from maternity units for these neonates is inadequate in China. No national guideline is available that is evidence-informed, validated, and developed from local data sets. Currently, in some places, neonates are followed up according to AAP guidelines; in other places, no scheduled follow-up is planned, and it is the responsibility of the parents to seek help. Our results provide high-quality evidence suggesting the potential benefits of a risk stratification model for timely postdischarge follow-up and identification of neonates at risk for developing significant hyperbilirubinemia. In addition, to address if differential verification bias falsely inflated both sensitivity and specificity, we conducted sensitivity analyses and assessed the AUC of newborns who had follow-up visits at ≥4 days or had the TSB measurement conducted postdischarge. We found that the 2 factors did not affect the AUC.
Strengths of our study include its large sample size, single ethnicity population, multicenter collaboration, and development and external (new data set) validation of our combined model. We speculate that our results are possibly also applicable to a large number of Chinese neonates born outside of mainland China; however, we acknowledged that further evaluation will be helpful. Another strength of the study is the use of the second-generation transcutaneous device (JM-103, a 2-wavelength measurement), which is not affected by skin pigmentation. There are also limitations to our study. First, 6.4% of newborns in the development data set and 5.0% in the validation data set were lost to follow-up. Despite our goal to identify all infants with significant hyperbilirubinemia after discharge, the potential exists for differential verification bias in that not all neonates were evaluated according to TcB values after discharge. Second, the acceptability, user-friendliness, and ease of implementation of this risk stratification model in clinical practice have not been evaluated. Third, our nomogram is based on TcB values as opposed to total serum bilirubin levels. There are advantages to TcB monitoring, however. It is a noninvasive test that can be operated with minimal training and supervision, and the results are available immediately. In addition, TcB has been shown to have excellent agreement with the TSB at values ≤250 µmol/L in term and late preterm neonates.23–26 Mohamed et al27 showed that plotting TcB on a TSB nomogram may result in an increased false-negative rate and decreased predictive characteristics.
We developed an hour-specific TcB nomogram according to the data regarding TcB levels, and we plotted TcB values on the hour-specific TcB nomogram to predict neonatal hyperbilirubinemia. Previous reports have also suggested plotting TcB values on an hour-specific TcB nomogram to predict neonatal hyperbilirubinemia.28,29 We have reviewed these TcB nomograms (developed by the United States, India, Italy, and Israel), for which the AUC for the prediction of subsequent significant hyperbilirubinemia ranged from 0.720 to 0.971.30 In the present study, the TcB nomogram exhibited equally good predictive abilities (AUC: 0.870). Thus, we believe this method is advantageous in resource-limited settings such as China where the number of health care providers per patient is small. Finally, our suggested follow-up plan must be monitored to assess its impact on clinical outcomes, which would be the subject of further investigation.
We acknowledge that further steps are necessary to promote and optimize the application of the model. First, the models with different TcB nomograms described by using various percentile risk zones should be assessed, which could theoretically improve the model performance. Second, medical calculation applications need to be developed that could make assessment of clinical scoring systems easier by using patient-specific data.31 This method could be developed as a smartphone-based medical calculation application. The model provides rapid risk information to neonatal clinicians, allowing them to make appropriate decisions. With the possibility that this information is applicable to 15% to 20% of total births in the world, we believe there is a huge potential for this knowledge to be translated into actual clinical practice. Third, a collaborative quality improvement program based on the risk stratification model should be conducted to assess its impact on the incidence of extreme and hazardous hyperbilirubinemia and bilirubin encephalopathy. Fourth, the individual performing the TcB assessment was blinded, which does not lead to differential verification bias. Fifth, neonates with G6PD deficiency were not included in our study, and we were thus unable to include G6PD deficiency as a risk factor in our model. We would like to caution that G6PD deficiency is prevalent in southern parts of China where screening for G6PD deficiency should be considered. Sixth, it must be remembered that newborns classified as low risk were not entirely without risk of readmission for hyperbilirubinemia.32 All newborns, including those at low risk, must be vigilantly observed for subsequent hyperbilirubinemia as standard part of any screening program.
We developed and validated a predischarge risk stratification model by combining clinical risk factors and a TcB nomogram. This method classified term and late preterm Chinese neonates into 6 distinct risks levels and provided timely discharge follow-up suggestions.
The following institutes (city and province or municipality) and investigators participated in the Chinese Multicenter Study Coordination Group for Neonatal Hyperbilirubinemia: Department of Pediatrics, Nanjing Maternity and Child Health Care Hospital, Nanjing Medical University, Nanjing, China (Zhangbin Yu, Shuping Han, Xiaofan Sun, Xiaoyue Dong, Qing Sun, Xiaoqi Gu, and Xirong Guo); Department of Neonatology, Children’s Hospital of Fudan University, Shanghai, China (Jin Wang, Siyuan Jiang, and Chao Chen); Department of Neonatology, Gynecology and Obstetrics Hospital of Fudan University, Shanghai, China (Jimei Wang, Jiale Dai, and Beiqian Qian); Department of Neonatology, Guiyang Maternal and Child Health Hospital, Guiyang, China (Ling Liu and Bizhang Shi); Department of Neonatology, Guangxi Maternal and Child Health Hospital, Nanning, China (Qiufen Wei, Danhua Meng, and Xinnian Pan); Department of Neonatology, The First Affiliated Hospital of Harbin Medical University, Harbin, China (Chunming Jiang and Jiang Guo); Department of Neonatology, Shanxi Provincial Maternal and Child Health Hospital, Xi’an, China (Zhankui Li and Jinzhen Guo); Department of Neonatology, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, China (Mingxia Li and Yanping Zhu); Department of Neonatology, Guangdong Maternal and Children’s Hospital, Guangzhou Medical College, Guangzhou, China (Jie Yang and Kui Li); Department of Neonatology, Jilin Provincial Maternal and Child Health Hospital, Changchun, China (Zhiyong Sun and Yan Guo); Department of Neonatology, Peking University Third Hospital, Beijing, China (Meihua Po, Xiaomei Tong, and Huiqing Liu); Department of Neonatology, Inner Mongolia Maternal and Child Health Care Hospital, Huhehot, China (Hongyun Wang and Jinxia Wu); Department of Neonatology, Sichuan Provincial People’s Hospital, Chengdu, China (Changhui Chen and Maojun Li); Department of Neonatology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China (Xiuyong Cheng and Mengmeng Chen); Department of Pediatrics, The Fifth People’s Hospital of Shenzhen, Shenzhen, China (Jiebo Liu and Jun Long); and Department of Neonatology, Changzhou Maternal and Child Health Care Hospital, Changzhou, China (Huaiyan Wang and Ying Wang).
We thank Dr Yanyu Lyu (School of Public Health, Peking University Health Science Centre) for conducting the statistical analysis and Dr Shoo K. Lee (Maternal-Infant Care Research Center, Mount Sinai Hospital) for providing support for this study.
- Accepted July 21, 2015.
- Address correspondence to Chao Chen, MD, Department of Neonatology, Children’s Hospital of Fudan University, 399 Wanyuan Rd, Shanghai 201102, China. E-mail:
Drs Han, Yu, and Chen conceptualized and designed the study and drafted the initial manuscript; Drs Wang, Liu, and Shah conducted the initial analyses and reviewed and revised the manuscript; Drs Wei, Jiang, Guo, Li, and Yang designed the data collection instruments, coordinated and supervised data collection at their own site, and critically reviewed the manuscript; and all authors approved the final manuscript as submitted and agree to be accountable for all aspects of the work.
FINANCIAL DISCLOSURE: The authors have indicated they have no financial relationships relevant to this article to disclose.
FUNDING: Supported by grants from the Key Medical Personnel Foundation of Jiangsu Province (grant RC2011021), the Medical Youth Personnel Foundation of Nanjing Municipality (grant QRX11107), the Nanjing Municipal Medical Science Development Foundation (grant ZKX12026, YKK13142), and the Maternal and Child Health Foundation of Jiangsu Province (grant F201308).
POTENTIAL CONFLICT OF INTEREST: The authors have indicated they have no potential conflicts of interest to disclose.
- Subspecialty Group of Neonatology, Society of Pediatrics, Chinese Medical Association,
- Chinese Multicenter Study Coordination Group for Neonatal Bilirubin Encephalopathy
- American Academy of Pediatrics Subcommittee on Hyperbilirubinemia
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- Copyright © 2015 by the American Academy of Pediatrics