Literature DB >> 27552225

Blood Reference Intervals for Preterm Low-Birth-Weight Infants: A Multicenter Cohort Study in Japan.

Masayuki Ochiai1,2, Yuki Matsushita1, Hirosuke Inoue1,2, Takeshi Kusuda1, Dongchon Kang3, Kiyoshi Ichihara4, Naoki Nakashima5, Kenji Ihara6, Shouichi Ohga6,7, Toshiro Hara1.   

Abstract

Preterm low-birth-weight infants remain difficult to manage based on adequate laboratory tests. The aim of this study was to establish blood reference intervals (RIs) in those newborns who were admitted to and survived in the neonatal intensive care unit (NICU). A multicenter prospective study was conducted among all infants admitted to 11 affiliated NICUs from 2010 to 2013. The clinical information and laboratory data were registered in a network database designed for this study. The RIs for 26 items were derived using the parametric method after applying the latent abnormal values exclusion method. The influence of birth weight (BW) and gestational age (GA) on the test results was expressed in terms of the standard deviation ratio (SDR), as SDRBW and SDRGA, respectively. A total of 3189 infants were admitted during the study period; 246 were excluded due to a lack of blood sampling data, and 234 were excluded for chromosomal abnormalities (n = 108), congenital anomalies requiring treatment with surgical procedures (n = 76), and death or transfer to another hospital (n = 50). As a result, 2709 infants were enrolled in this study. Both the SDRGA and SDRBW were above 0.4 in the test results for total protein (TP), albumin (ALB), alanine aminotransferase (ALT), and red blood cells (RBC); their values increased in proportion to the BW and GA. We derived 26 blood RIs for infants who were admitted to NICUs. These RIs should help in the performance of proper clinical assessments and research in the field of perinatal-neonatal medicine.

Entities:  

Mesh:

Substances:

Year:  2016        PMID: 27552225      PMCID: PMC4994999          DOI: 10.1371/journal.pone.0161439

Source DB:  PubMed          Journal:  PLoS One        ISSN: 1932-6203            Impact factor:   3.240


Introduction

The prognosis of preterm low-birth-weight infants has improved dramatically with advances in perinatal medicine. The Neonatal Research Network of Japan revealed that more than 80% of infants delivered at 24 weeks of gestational age (GA) survived in neonatal intensive care units (NICUs) [1], and the survival rates of infants born at GA 22 and 23 weeks were also improved compared with those in previous studies [2]. However, the treatment of these vulnerable newborns and the associated clinical research remain great challenges. It is essential to properly assess these infants based on adequate physiological data [3] as well as laboratory tests. Due to the nature of the physiological growth and development of infants and children, many efforts have been made to establish pediatric reference intervals (RIs) for routinely-measured laboratory parameters[4] [5]. Christensen et al. published RIs for the complete blood cell (CBC) counts in term and preterm infants using the large database of a health care system [6] [7] [8] [9]. The Canadian Laboratory Initiative on Pediatric Reference Interval Database established RIs for blood chemistry data in healthy and multiethnic child populations [10] [5]. In contrast, few studies have been published regarding the RIs of blood chemistry elements for preterm low-birth-weight infants[11] [12] [13]. The “Kyushu University High-Risk Neonatal Clinical Research Network” Project is a collaborative study conducted among NICUs across the northern part of Kyushu Island in Japan from April 2010 to March 2013. For this project, a prospective observational study was performed in the 11 affiliated hospitals in order to establish blood RIs in preterm low-birth-weight infants within 24 h after birth who were admitted to multiple perinatal care centers and who survived until hospital discharge.

Materials and Methods

Multicenter Prospective Study

All infants admitted to any of the 11 affiliated NICUs on the first day of life were enrolled in this study, with the following exclusion criteria: chromosomal abnormalities, congenital anomalies requiring surgical procedures during the neonatal period, and death or transfer to other hospitals prior to discharge. Data acquisition was carried out using a web-based electronic medical software program that stores laboratory data and clinical information (Hitachi Solutions, Ltd., Tokyo, Japan). The data were classified into three subgroups based on either by BW or GA according to the classification of the World Health Organization (WHO) for neonates. The study protocol was approved by the Institutional Review Board (#22–131; Kyushu University Hospital) at each institution and registered as a prospective observational study with the University Hospital Medical Information Network clinical trial registration system in Japan (UMIN000008763) in April 2010. Written informed consent was obtained from all of the caretakers of the patients prior to their enrollment in this study.

Target Test Items and Standardization of Measurements

The Japan Society of Clinical Chemistry established common RIs for use nationwide in Japan for 40 commonly tested laboratory tests [14]. Annual external quality controls have been done for the major analytes among the affiliated medical facilities in the northern region of Kyushu Island[15], and we confirmed the standardized status of all the assays[16]. The CBC count and differential white blood cell counts were measured using automated Beckman Coulter Hematology Analyzers (Beckman Coulter Inc., FL, USA). The following 16 biochemical and 10 hematological test items were chosen as analytes: total protein (TP), albumin (ALB), blood urea nitrogen (BUN), creatinine (CRE), total bilirubin (T-BIL), direct bilirubin (D-BIL), sodium (Na), potassium (K), chlorine (CL), calcium (Ca), C-reactive protein (CRP), aspartate aminotransferase (AST), alanine aminotransferase (ALT), lactate dehydrogenase (LDH), alkaline phosphatase (ALP), creatine kinase (CK), white blood cells (WBC), red blood cells (RBC), hemoglobin (HGB), hematocrit (HCT), platelets (PLT), neutrophils (NEUT), lymphocytes (LYMP), monocytes (MONO), eosinophils (EOS) and basophils (BASO).

Statistical Analysis

The following items, which may be affected by a pathological state, were excluded depending on the international classification of diseases-10 (ICD-10) code: LDH, AST, and CK in infants with a disease code of P20-29; respiratory and cardiovascular disorders specific to the perinatal period and CRP in infants with P35-39; infections specific to the perinatal period, K, and T-BIL in infants with P50-61; and hemorrhagic and hematological disorders specific to fetuses or newborns. In order to exclude inappropriate infants with multiple abnormal results, a multivariate iterative method called latent abnormal values exclusion (LAVE) [14] [15] [16] was applied for simultaneous derivation of RIs for multiple test items. In this study, the LAVE method was used for the values of WBC, HGB, HCT, TP, BUN, CK, K, LDH, ALT, and CRP with eight iterations and an allowance of up to one result outside the RI and up to one missing result in the reference test items. The parametric method was used for computing the RIs after transforming the distribution of the reference values into a Gaussian form using a modified Box-Cox transformation [14]. The 90% confidence intervals (CIs) for the upper (UL) and lower limits (LL) of the RIs were estimated by the bootstrap method to avoid any abnormal results in the reference test [17, 18]. The need to partition the reference values by sex, GA, and BW was judged based on the SD ratio (SDR) introduced by Ichihara [14]; the SDR for sex (SDRSEX) is expressed as the SD representing the sex difference divided by the SD of the RI (SDRI, or 1/4th of RI). Similarly, the SDRs for GA and BW (SDRGA, SDRBW) were computed as a ratio of the SD representing the between-GA and between-BW subgroup differences divided by the SDRI, respectively. We adopted an SDR cutoff value of 0.4 by consensus among the collaborators [17, 19, 20].

Results

Outline of the Prospective Study

Fig 1 shows an outline for the recruitment of preterm low-birth-weight infants in this prospective study. A total of 3,189 infants were hospitalized on the first day of life at the 11 NICUs between April 2010 and March 2013; 246 did not undergo laboratory testing on admission, 210 were recognized to be healthy and did not require blood testing, 9 were transferred to other hospitals, and 27 died within 24 hours after birth. We also excluded 234 infants who received a diagnosis of congenital abnormalities (n = 210) or congenital abnormalities requiring surgical procedures within 28 days after birth (n = 76) or who died in the hospital or were transferred to other facilities (n = 50). Therefore, the final study group for analyzing the blood RIs comprised 2,709 infants who did not meet the exclusion criteria and survived until hospital discharge.
Fig 1

Overview of the prospective study of blood reference intervals for preterm low-birth-weight infants.

ICD-10

Table 1 shows the number of subjects in the final group classified based on the ICD-10 code. We diagnosed 1,555 low-birth-weight infants (91.6%) (Total <2,500 g) and 1,340 preterm birth infants (89.9%) (total <37 w) with P05-08: disorders related to the length of gestation and fetal growth. S1 Table displays the ICD-10 diagnostic information for the excluded infants.
Table 1

Number of preterm low-birth-weight infants classified in the ICD-10 (n = 2709).

ICD-10 codesTotal <2500gTotal <37w
Classified by BWClassified by GA
<1000g1000-1500g1500-2500g<28w28-32w32-37w
P00-P04 Fetus and newborn affected by maternal factors and by complications of pregnancy, labour and delivery50059009
P05-P08 Disorders related to length of gestation and fetal growth1555148225118213401242151001
P10-P15 Birth trauma00000000
P20-P29 Respiratory and cardiovascular disorders specific to the perinatal period740272880187
P35-P39 Infections specific to the perinatal period80086006
P50-P61 Haemorrhagic and haematological disorders of fetus and newborn1410136105
P70-P74 Transitory endocrine and metabolic disorders specific to fetus and newborn340331310130
P75-P78 Digestive system disorders of fetus and newborn30035005
P80-P83 Conditions involving the integument and temperature regulation of fetus and newborn00000000
P90-P96 Other disorders originating in the perinatal period11001100
Q00-Q07 Congenital malformations of the nervous system00000000
Q10-Q18 Congenital malformations of eye, ear, face and neck10011001
Q20-Q28 Congenital malformations of the circulatory system00000000
Q30-Q34 Congenital malformations of the respiratory system00000000
Q35-Q37 Cleft lip and cleft palate10010000
Q38-Q45 Other congenital malformations of the digestive system10012002
Q50-Q56 Congenital malformations of genital organs00000000
Q60-Q64 Congenital malformations of the urinary system00000000
Q65-Q79 Congenital malformations and deformations of the musculoskeletal system00000000
Q80-Q89 Other congenital malformations00000000
Q90-Q99 Chromosomal abnormalities, not elsewhere classified00000000
Total1697150230131714891262171146

BW; birth weight, GA; gestational age, According with the World Health Organization classification of BW and GA, the data were collected for sub-stratified by six BW and GA groups: low-birth-weight infant as a BW of <2500g, very low-birth-weight infant as <1500g, extremely low-birth-weight infant as <1000g, and moderate to late preterm infant as GA of <37w, very preterm infant as <32w, extremely preterm infant as <28w, respectively.

BW; birth weight, GA; gestational age, According with the World Health Organization classification of BW and GA, the data were collected for sub-stratified by six BW and GA groups: low-birth-weight infant as a BW of <2500g, very low-birth-weight infant as <1500g, extremely low-birth-weight infant as <1000g, and moderate to late preterm infant as GA of <37w, very preterm infant as <32w, extremely preterm infant as <28w, respectively.

GA- and BW-specific Blood RIs

Table 2 displays the GA-specific RIs of hematology and blood chemistry (international units). The items with SDRs of more than 0.4 were subgrouped based on the GA classification. The SDRGA values of TP, ALB, CRE, Na, ALT, WBC, RBC, NEUT and MONO were significant. The BW-specific RIs for low-birth-weight infants (international units) are shown in Table 3. The SDRBW values of TP, ALB, ALT, RBCs, and EOS were significant. The values of TP, ALB, ALT and RBC were significant in both SDRGA and SDRBW, with values of more than 0.4. These levels increased in proportion to the BW and GA (Fig 2). S2 and S3 Tables show the RIs converted from international to conventional units.
Table 2

GA-specific RIs of blood chemistry and hematology for preterm infants (International Unit).

Total <37wClassified by GA
<28w28-32w32-37w
LL90%CIRIUL90%CILL90%CIRIUL90%CILL90%CIRIUL90%CILL90%CIRIUL90%CI
AnalyteSI unitSDRGAnLLULLLULLLULnLLULLLULLLULnLLULLLULLLULnLLULLLULLLUL
TPg/L1.021132530274844521943335345251541164404140636264
ALBg/L0.95961922203230341612526253534361052282828403940
BUNmmol/L0.1414761.381.571.445.985.656.41
CREμmol/L0.4411224.829.227.479.772.686.719224.831.030.179.769.985.0114935.437.236.385.081.487.6
T-BILμmol/L0.36143220.521.921.057.855.661.2
D-BILμmol/L0.008685.65.85.819.218.320.2
Nammol/L0.40941281301291421411441621311331311421411431050133134134142142143
Kmmol/L0.0012723.63.73.76.16.06.2
CLmmol/L0.001314100101101111111112
Cammol/L0.1814662.02.02.02.72.62.7
CRPμg/L0.1413560.000.000.000.800.207.20
ASTIU/L0.281281151616706578
ALTIU/L0.6610511165919311197111149222121113
LDHIU/L0.111297238263245787742958
ALPIU/L0.16462351397372108210221152
CKIU/L0.448822604591658312631733876535524556949887911391707644778
WBC109/L0.571022.553.873.0431.022.539.21792.584.073.4019.916.423.511005.766.756.0321.220.322.6
RBC1012/L0.591032.632.982.764.654.514.821783.203.413.285.074.915.2710843.593.653.625.525.465.57
HGBg/L0.331406125129127203201206
HCT/L0.3114080.370.380.380.600.600.61
PLT109/L0.201344107120114375367382
NEUT109/L0.79460.170.810.6417.011.727.6910.150.680.379.587.3612.64900.931.541.1912.710.714.3
LYMP109/L0.307541.882.362.069.729.2110.3
MONO106/L0.645230152571623127021851182911064114710051323578150217181141312861523
EOS106/L0.327456368741693797
BASO106/L0.10748122270241300

TP; total protein, ALB; albumin, BUN; blood urea nitrogen, CRE; creatinine, T-BIL; total bilirubin, D-BIL; direct bilirubin, Na; sodium, K; potassium, CL; chlorine, Ca; calcium, CRP; C-reactive protein, AST; aspartate aminotransferase, ALT; alanine aminotransferase, LDH; lactate dehydrogenase, ALP; alkaline phosphatase, CK; creatine kinase, WBC; white blood cell, RBC; red blood cell, HGB; hemoglobin, HCT; hematocrit, PLT; platelet, NEUT; neutrophil, LYMP; lymphocyte, MONO; monocyte, EOS; eosinophil, BASO; basophil, GA; gestational age, RI; reference interval, LL; lower limit of the RI, UL; upper limit of the RI, CI; confidential interval (90%) of LLs and ULs were estimated by the bootstrap method. The results were excluded data of LDH, AST and CK in the infants with P20-29; Respiratory and cardiovascular disorders specific to the perinatal period, CRP with P35-39; Infections specific to the perinatal period, and K and T-Bil with P50-61; Hemorrhagic and homological disorders specific to fetus and newborn, respectively. A multivariate iterative method called latent abnormal value exclusion (LAVE) was applied to nine analytes (TP, BUN, K, LDH, ALT, WBC, CRP, HGB and HCT) which deemed to be adversely affected by hemolysis and inflammation. By use of 3-level nested ANOVA, the influence of GA on test results was expressed in terms of standard deviation (SD) ratio (SDR), as SDRGA. SDR> = 0.4 were used as a criteria for the need of partition by the factor.

Table 3

BW-specific RIs of blood chemistry and hematology for low-birth-weight infants (International Unit)

Total <2500gClassified by BW
<1000g1000-1500g1500-2500g
LL90%CIRIUL90%CILL90%CIRIUL90%CILL90%CIRIUL90%CILL90%CIRIUL90%CI
AnalyteSI unitSDRBWnLLULLLULLLULnLLULLLULLLULnLLULLLULLLULnLLULLLULLLUL
TPg/L0.941322631295148572023335346058631178404140646365
ALBg/L0.891121922213533371692525253837391010282828404041
BUNmmol/L0.0015321.381.591.466.125.786.51
CREμmol/L0.32152831.035.431.985.081.492.0
T-BILμmol/L0.11147420.522.121.260.758.064.6
D-BILμmol/L0.009175.55.85.620.019.021.0
Nammol/L0.381315132133133143142143
Kmmol/L0.0012873.63.73.76.16.06.2
CLmmol/L0.131315100101101111111112
Cammol/L0.1015192.02.02.02.72.62.7
CRPμg/L0.0015210.00.00.05.10.37.7
ASTIU/L0.001268151615747082
ALTIU/L0.491201117612202111139171161222121113
LDHIU/L0.001281235261243835767984
ALPIU/L0.00463352393372109710511181
CKIU/L0.341258587364685641744
WBC109/L0.2214374.134.914.4224.023.025.6
RBC1012/L0.591152.683.002.864.614.484.791893.233.473.295.385.225.6510883.623.703.655.585.525.66
HGBg/L0.361450125129127204203206
HCT/L0.2914540.370.390.380.610.600.61
PLT109/L0.211392108121108375365383
NEUT109/L0.277480.741.070.9017.515.919.1
LYMP109/L0.008771.672.151.849.378.939.78
MONO106/L0.218751161641431599
EOS106/L0.42621245411289552112060555462755596457359867778932
BASO106/L0.09823132284249313

TP; total protein, ALB; albumin, BUN; blood urea nitrogen, CRE; creatinine, T-BIL; total bilirubin, D-BIL; direct bilirubin, Na; sodium, K; potassium, CL; chlorine, Ca; calcium, CRP; C-reactive protein, AST; aspartate aminotransferase, ALT; alanine aminotransferase, LDH; lactate dehydrogenase, ALP; alkaline phosphatase, CK; creatine kinase, WBC; white blood cell, RBC; red blood cell, HGB; hemoglobin, HCT; hematocrit, PLT; platelet, NEUT; neutrophil, LYMP; lymphocyte, MONO; monocyte, EOS; eosinophil, BASO; basophil, GA; gestational age, RI; reference interval, LL; lower limit of the RI, UL; upper limit of the RI, CI; confidential interval (90%) of LLs and ULs were estimated by the bootstrap method. The results were excluded data of LDH, AST and CK in the infants with P20-29; Respiratory and cardiovascular disorders specific to the perinatal period, CRP with P35-39; Infections specific to the perinatal period, and K and T-Bil with P50-61; Hemorrhagic and homological disorders specific to fetus and newborn, respectively. A multivariate iterative method called latent abnormal value exclusion (LAVE) was applied to nine analytes (TP, BUN, K, LDH, ALT, WBC, CRP, HGB and HCT) which deemed to be adversely affected by hemolysis and inflammation. By use of 3-level nested ANOVA, the influence of BW on test results was expressed in terms of standard deviation (SD) ratio (SDR), as SDRBW. SDR> = 0.4 were used as a criteria for the need of partition by the factor.

Fig 2

Scatter plots of the items that were significant in both the SDRGA and SDRBW.

The horizontal axis shows the birth weight (BW). The vertical axis shows the measured values of A. Total protein (g/dL), B. Albumin (g/dL), C. Alanine aminotransferace (ALT) (IU/L), and D. Red blood cells (106/μl). The data were classified into three subgroups based on gestational age (GA): GA of 32–37 weeks, cross marks (×); 28–32 weeks, open circles (○); 22–28 weeks, filled circles (●).

Scatter plots of the items that were significant in both the SDRGA and SDRBW.

The horizontal axis shows the birth weight (BW). The vertical axis shows the measured values of A. Total protein (g/dL), B. Albumin (g/dL), C. Alanine aminotransferace (ALT) (IU/L), and D. Red blood cells (106/μl). The data were classified into three subgroups based on gestational age (GA): GA of 32–37 weeks, cross marks (×); 28–32 weeks, open circles (○); 22–28 weeks, filled circles (●). TP; total protein, ALB; albumin, BUN; blood urea nitrogen, CRE; creatinine, T-BIL; total bilirubin, D-BIL; direct bilirubin, Na; sodium, K; potassium, CL; chlorine, Ca; calcium, CRP; C-reactive protein, AST; aspartate aminotransferase, ALT; alanine aminotransferase, LDH; lactate dehydrogenase, ALP; alkaline phosphatase, CK; creatine kinase, WBC; white blood cell, RBC; red blood cell, HGB; hemoglobin, HCT; hematocrit, PLT; platelet, NEUT; neutrophil, LYMP; lymphocyte, MONO; monocyte, EOS; eosinophil, BASO; basophil, GA; gestational age, RI; reference interval, LL; lower limit of the RI, UL; upper limit of the RI, CI; confidential interval (90%) of LLs and ULs were estimated by the bootstrap method. The results were excluded data of LDH, AST and CK in the infants with P20-29; Respiratory and cardiovascular disorders specific to the perinatal period, CRP with P35-39; Infections specific to the perinatal period, and K and T-Bil with P50-61; Hemorrhagic and homological disorders specific to fetus and newborn, respectively. A multivariate iterative method called latent abnormal value exclusion (LAVE) was applied to nine analytes (TP, BUN, K, LDH, ALT, WBC, CRP, HGB and HCT) which deemed to be adversely affected by hemolysis and inflammation. By use of 3-level nested ANOVA, the influence of GA on test results was expressed in terms of standard deviation (SD) ratio (SDR), as SDRGA. SDR> = 0.4 were used as a criteria for the need of partition by the factor. TP; total protein, ALB; albumin, BUN; blood urea nitrogen, CRE; creatinine, T-BIL; total bilirubin, D-BIL; direct bilirubin, Na; sodium, K; potassium, CL; chlorine, Ca; calcium, CRP; C-reactive protein, AST; aspartate aminotransferase, ALT; alanine aminotransferase, LDH; lactate dehydrogenase, ALP; alkaline phosphatase, CK; creatine kinase, WBC; white blood cell, RBC; red blood cell, HGB; hemoglobin, HCT; hematocrit, PLT; platelet, NEUT; neutrophil, LYMP; lymphocyte, MONO; monocyte, EOS; eosinophil, BASO; basophil, GA; gestational age, RI; reference interval, LL; lower limit of the RI, UL; upper limit of the RI, CI; confidential interval (90%) of LLs and ULs were estimated by the bootstrap method. The results were excluded data of LDH, AST and CK in the infants with P20-29; Respiratory and cardiovascular disorders specific to the perinatal period, CRP with P35-39; Infections specific to the perinatal period, and K and T-Bil with P50-61; Hemorrhagic and homological disorders specific to fetus and newborn, respectively. A multivariate iterative method called latent abnormal value exclusion (LAVE) was applied to nine analytes (TP, BUN, K, LDH, ALT, WBC, CRP, HGB and HCT) which deemed to be adversely affected by hemolysis and inflammation. By use of 3-level nested ANOVA, the influence of BW on test results was expressed in terms of standard deviation (SD) ratio (SDR), as SDRBW. SDR> = 0.4 were used as a criteria for the need of partition by the factor.

Discussion

The Kyushu University High-Risk Neonatal Clinical Research Network Project recently established a university initiative to accumulate information for all infants admitted to the affiliated NICUs using a web-based electronic medical software program. As part of this initiative, our study’s main purpose was to establish RIs for hematology and blood chemistry analytes in preterm low-birth-weight infants who were admitted to perinatal-neonatal care centers in Japan and who survived until discharge. We were able to obtain a sufficient sample size (2709 infants) during the three-year study period. The practically attainable target sample size for each analyte was set at a minimum of 250 or more, which is greater than twice the minimum number (120 or more) [19]. We excluded outliers based on the clinical diagnosis (ICD-10) and the results of the multivariate iterative method (LAVE) [19]. The LAVE features the simultaneous setting of RIs for multiple test items that are mutually related and the rigid exclusion of individuals with abnormal values for other test items [20]. As a result, the reference individuals were “considered to be normal” preterm low-birth-weight infants based on the ICD-10 code (Table 1). The Ichihara method utilizes information for the SDR attributable to each source of variation and can be applied in situations in which more than two subgroups are categorized according to the factors [15] [18]. In our study, we presented each RI classified into six subgroups by the BW and GA according to the SDRBW and SDRGA, respectively. The SDRSEX of each item were zero for all analytes for the age groups (data not shown). Therefore, sex-specific RIs were not presented in this study. In our study, the following tended to increase in proportion to both the GA and BW: TP, Alb, and ALT (Tables 2 and 3 and Fig 2). The TP is made up of Alb and globulin. Alb is synthesized in the liver, and a low serum Alb may result from immaturity of the liver function. ALT is an important transaminase enzyme in various tissue, especially the liver; therefore, the blood ALT level is used clinically as a biomarker for the liver function. In small-for-gestational-age infants, the AST and ALT serum activities were correlated with BW and GA [21]. Our data confirmed the parallel upward trend in these values as the organ function matured. In contrast, extremely premature infants had high plasma enzyme activities compared to babies at a later corrected GA [22], possibly due to suffering more severe illness immediately after birth. Significant SDRGA and SDRBW values (>0.4) were observed for WBC, RBC, NEUT, and MONO (Table 2); and RBC and EO (Table 3), respectively. When the test results of those analytes were compared among the groups, the RBC count was found to have a tendency to increase in proportion to both the BW and GA (Fig 2). The RIs of HCT and HGB in extremely preterm patients were reported to be lower than those in later preterm and term infants [23]. Several reports have shown the same gradual upward tendency in hematological data [9] [23]. The PLT counts increased for GAs of 22 to 42 weeks using a huge data system [8]. In contrast, an abnormal lymphocyte count at birth is associated with adverse outcomes, including early-onset sepsis, intraventricular hemorrhaging and retinopathy of prematurity [24]. The onset of neutropenia in the first days of life is sometimes noted in SGA infants or those born to mothers with persistent maternal hypertension or early-onset bacterial infection [25]. These reports suggest that the GA and BW, as well as potentially pathogenic maternal and neonatal variables, should be considered when developing RIs. We recognize various limitations and pitfalls that should be considered when applying these RIs in practice. First, preterm or low-birth-weight babies are considered to be in a clinically pathological or unhealthy state, and many require medical management. Therefore, “normal range” is not a suitable term for the blood chemistry and hematology data for these infants. We therefore used the term “reference interval” in this project and discarded data confirmed to be unacceptable based on the ICD-10 code and LAVE method. A second limitation is that the source of blood specimens (capillary, venous, or arterial) was not taken into account. Some hematological and chemical test values are somewhat higher in capillary samples than in venous or arterial samples. The third limitation is that we did not analyze the trends in the values after birth [23]. The values of analytes may change after several postnatal days depending on the clinical course of the infant.

Conclusions

Our project provides 26 blood RIs in preterm low-birth-weight infants requiring neonatal intensive care in Japan. These RIs should help researchers in the field of perinatal-neonatal medicine perform proper assessments in routine clinical work and research. Further evaluations are needed to determine whether these RIs are representative of the physiological data for those infants.

The ICD-10 of the excluded infants

(DOCX) Click here for additional data file.

GA-specific RIs of blood chemistry and hematology for preterm infants (Conventional Unit)

(DOCX) Click here for additional data file.

BW-specific RIs of blood chemistry and hematology for low birth weight infants (Conventional Unit)

(DOCX) Click here for additional data file.
  25 in total

1.  Reference ranges for lymphocyte counts of neonates: associations between abnormal counts and outcomes.

Authors:  Robert D Christensen; Vickie L Baer; Philip V Gordon; Erick Henry; Cody Whitaker; Robert L Andres; Sterling T Bennett
Journal:  Pediatrics       Date:  2012-04-16       Impact factor: 7.124

Review 2.  An appraisal of statistical procedures used in derivation of reference intervals.

Authors:  Kiyoshi Ichihara; James C Boyd
Journal:  Clin Chem Lab Med       Date:  2010-11       Impact factor: 3.694

3.  The erythrocyte indices of neonates, defined using data from over 12,000 patients in a multihospital health care system.

Authors:  R D Christensen; J Jopling; E Henry; S E Wiedmeier
Journal:  J Perinatol       Date:  2007-11-01       Impact factor: 2.521

Review 4.  The CBC: reference ranges for neonates.

Authors:  Robert D Christensen; Erick Henry; Jeff Jopling; Susan E Wiedmeier
Journal:  Semin Perinatol       Date:  2009-02       Impact factor: 3.300

5.  Closing the gaps in pediatric laboratory reference intervals: a CALIPER database of 40 biochemical markers in a healthy and multiethnic population of children.

Authors:  David A Colantonio; Lianna Kyriakopoulou; Man Khun Chan; Caitlin H Daly; Davor Brinc; Allison A Venner; Maria D Pasic; David Armbruster; Khosrow Adeli
Journal:  Clin Chem       Date:  2012-02-27       Impact factor: 8.327

6.  Statistical considerations for harmonization of the global multicenter study on reference values.

Authors:  Kiyoshi Ichihara
Journal:  Clin Chim Acta       Date:  2014-02-08       Impact factor: 3.786

7.  Glomerular filtration rate reference values in very preterm infants.

Authors:  Rachel Vieux; Jean-Michel Hascoet; Dana Merdariu; Jeanne Fresson; Francis Guillemin
Journal:  Pediatrics       Date:  2010-04-05       Impact factor: 7.124

8.  Platelet reference ranges for neonates, defined using data from over 47,000 patients in a multihospital healthcare system.

Authors:  S E Wiedmeier; E Henry; M C Sola-Visner; R D Christensen
Journal:  J Perinatol       Date:  2008-09-25       Impact factor: 2.521

9.  Plasma aminotransferase concentrations in preterm infants.

Authors:  S Victor; H Dickinson; M A Turner
Journal:  Arch Dis Child Fetal Neonatal Ed       Date:  2009-07-01       Impact factor: 5.747

Review 10.  Normal ranges of heart rate and respiratory rate in children from birth to 18 years of age: a systematic review of observational studies.

Authors:  Susannah Fleming; Matthew Thompson; Richard Stevens; Carl Heneghan; Annette Plüddemann; Ian Maconochie; Lionel Tarassenko; David Mant
Journal:  Lancet       Date:  2011-03-19       Impact factor: 79.321

View more
  4 in total

1.  Machine learning guided postnatal gestational age assessment using new-born screening metabolomic data in South Asia and sub-Saharan Africa.

Authors:  Sunil Sazawal; Kelli K Ryckman; Sayan Das; Abdullah H Baqui; Fyezah Jehan; Usha Dhingra; Rajiv Bahl; Rasheda Khanam; Imran Nisar; Elizabeth Jasper; Arup Dutta; Sayedur Rahman; Usma Mehmood; Bruce Bedell; Saikat Deb; Nabidul Haque Chowdhury; Amina Barkat; Harshita Mittal; Salahuddin Ahmed; Farah Khalid; Rubhana Raqib; Alexander Manu; Sachiyo Yoshida; Muhammad Ilyas; Ambreen Nizar; Said Mohammed Ali
Journal:  BMC Pregnancy Childbirth       Date:  2021-09-07       Impact factor: 3.105

2.  Reference intervals for 26 common biochemical analytes in term neonates in Jilin Province, China.

Authors:  Kaijin Wang; Xuetong Zhu; Qi Zhou; Jiancheng Xu
Journal:  BMC Pediatr       Date:  2021-03-31       Impact factor: 2.125

3.  Machine learning prediction of gestational age from metabolic screening markers resistant to ambient temperature transportation: Facilitating use of this technology in low resource settings of South Asia and East Africa.

Authors:  Sunil Sazawal; Sayan Das; Kelli K Ryckman; Rasheda Khanam; Imran Nisar; Saikat Deb; Elizabeth A Jasper; Sayedur Rahman; Usma Mehmood; Arup Dutta; Nabidul Haque Chowdhury; Amina Barkat; Harshita Mittal; Salahuddin Ahmed; Farah Khalid; Said Mohammed Ali; Rubhana Raqib; Muhammad Ilyas; Ambreen Nizar; Alexander Manu; Donna Russell; Sachiyo Yoshida; Abdullah H Baqui; Fyezah Jehan; Usha Dhingra; Rajiv Bahl
Journal:  J Glob Health       Date:  2022-04-23       Impact factor: 4.413

Review 4.  Vitamin K Deficiency Bleeding in Infancy.

Authors:  Shunsuke Araki; Akira Shirahata
Journal:  Nutrients       Date:  2020-03-16       Impact factor: 5.717

  4 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.