DOI: https://doi.org/10.21203/rs.3.rs-154330/v1
BACKGROUND: Evidence regarding the relationship between unbound bilirubin levels and acute bilirubin encephalopathy was limited. Therefore, this study set out to investigate whether the unbound bilirubin level was independently related to acute bilirubin encephalopathy in children who underwent exchange transfusion after adjusting for other covariates.
METHODS: A total of 46 neonates who underwent exchange transfusion were involved in The First People's Hospital Of Changde City in China from 2016-1-1 to 2018-12-31. The target independent variable and the dependent variable were unbound bilirubin levels measured at baseline and acute bilirubin encephalopathy respectively. Covariates involved in this study included sex, age, birth weight, blood glucose, red blood cell, hemolysis, receive phototherapy before exchange transfusion.
RESULTS: The average gestational age of 46 selected participants was 38.6 ± 1.3 weeks old, the average age was 146.5 ± 86.9 hours old, 52.17% of them were male. Result of fully-adjusted binary logistic regression showed unbound bilirubin levels were positively associated with risk of acute bilirubin encephalopathy after adjusting confounders (Odds ratio = 1.41, 95% confidence intervals 1.05-1.91, P value <0.05).
CONCLUSION: Unbound bilirubin levels are associated with neonatal acute bilirubin encephalopathy. The mechanism of unbound bilirubin levels leading to neonatal acute bilirubin encephalopathy needs to be further explored.
This preprint is available for download as a PDF.
Table 1. Characteristics of participants (n=46)
|
ABE |
no |
yes |
P-value |
|
n |
28 |
18 |
|
|
Admission data |
|
|
|
|
Age, mean (SD), hours |
128.7 (94.2) |
174.2 (67.6) |
0.042 |
|
Gestational age, mean (SD), weeks |
38.8 (1.5) |
38.3 (0.9) |
0.205 |
|
Birth weight, mean (SD), kilogram |
3.28 (0.35) |
3.25 (0.45) |
0.825 |
|
Weight, mean (SD), kilogram |
3.14 (0.39) |
3.13 (0.41) |
0.923 |
|
Female, No. (%) |
15 (53.57%) |
7 (38.89%) |
0.331 |
|
Laboratory data |
|
|
|
|
TSB1 , mean (SD), mg/dL |
25.96 (5.53) |
33.77 (5.94) |
<0.001 |
|
UB, mean (SD), mg/dL |
23.74 (5.12) |
30.35 (5.54) |
<0.001 |
|
TSB2 , mean (SD), mg/dL |
11.13 (3.32) |
17.53 (6.27) |
<0.001 |
|
Blood glucose, mean (SD), mmol/L |
5.3 (1.2) |
6.9 (3.0) |
0.017 |
|
White blood cell, mean (SD), *10⋀9/L |
13.2 (5.1) |
14.2 (4.7) |
0.508 |
|
Blood platelet count, mean (SD), *10⋀9/L |
289.7 (125.6) |
264.0 (116.0) |
0.493 |
|
Red blood cell, mean (SD), *10⋀12/L |
4.07 (1.08) |
3.29 (0.72) |
0.010 |
|
Hemoglobin, mean (SD), g/L |
143.2 (35.5) |
116.1 (29.6) |
0.011 |
|
Serum sodium, mean (SD), mmol/L |
142.5 (3.0) |
142.1 (2.6) |
0.584 |
|
Serum calcium, mean (SD), mmol/L |
2.40 (0.25) |
2.38 (0.17) |
0.762 |
|
Serum potassium, mean (SD), mmol/L |
4.62 (0.52) |
4.39 (0.38) |
0.117 |
|
Serum albumin, mean (SD), g/L |
37.45 (2.80) |
36.12 (4.16) |
0.201 |
|
Etiology |
|
|
|
|
Hemorrhage, No. (%) |
6 (21.43%) |
3 (16.67%) |
0.691 |
|
Hemolysis, No. (%) |
16 (57.14%) |
12 (66.67%) |
0.518 |
|
Infection, No. (%) |
11 (39.29%) |
8 (44.44%) |
0.729 |
|
Erythrocytosis, No. (%) |
2 (7.14%) |
0 (0.00%) |
0.246 |
|
Unknown reason, No. (%) |
1 (3.57%) |
3 (16.67%) |
0.124 |
|
Treatment |
|
|
|
|
Iv immunoglobulins before ET, No. (%) |
6 (21.43%) |
7 (38.89%) |
0.199 |
|
Receive phototherapy before ET, No. (%) |
25 (89.29%) |
11 (61.11%) |
0.024 |
TSB1：TSB at starting ET
TSB2：TSB at end of the ET
Hemorrhage: include scalp hematoma, intracranial hemorrhage showed by MRI, and abdominal Haemorrhage showed by ultrasonography.
Hemolysis: include ABO haemolytic, RhD haemolytic, Autoimmune haemolytic and Glucose-6-phosphate Dehydrogenase defificiency.
Infection: include sepsis, pulmonary infection, intracranial infection and umbilical infection.
Table 2. Relationship between UB VS ABE in different models
|
|
OR（95%CI） |
P-value |
|
Non-adjusted |
1.29 (1.10, 1.52) |
0.002 |
|
Adjust I |
1.34 (1.11, 1.63) |
0.003 |
|
Adjust II |
1.41 (1.05, 1.91) |
0.025 |
Result variable: ABE
Exposure variable: UB
Non-adjusted model adjust for: None
Adjust I model adjust for: Sex; Age; Birth weight;
Adjust II model adjust for: Sex; Age; Birth weight; Blood glucose; Red blood cell; Hemolysis; Receive phototherapy before ET;