Clinical performance of the new glucometer in the nursery and neonatal intensive care unit

Nuntnarumit, Pracha; Chittamma, Anchalee; Pongmee, Pharuhad; Tangnoo, Archara; Goonthon, Somying

doi:10.1111/j.1442-200x.2010.03214.x

Cited by 16 publications

(11 citation statements)

References 25 publications

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“…Apgar score at 5 min after delivery was calculated according to an accepted method for reporting the status of the neonate immediately after birth. Neonatal hypoglycemia was defined as a glucose level < 45 mg/dL. Mechanical ventilation was defined as when a ventilation machine was used to improve the exchange of air between the lungs and atmosphere.…”

Section: Methodsmentioning

confidence: 99%

Prediction of adverse perinatal outcome by fetal biometry: comparison of customized and population‐based standards

Kabiri

Romero²,

Gudicha

et al. 2020

Ultrasound in Obstet & Gyne

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Objective To compare the predictive performance of estimated fetal weight (EFW) percentiles, according to eight growth standards, to detect fetuses at risk for adverse perinatal outcome. Methods This was a retrospective cohort study of 3437 African‐American women. Population‐based (Hadlock, INTERGROWTH‐21st, World Health Organization (WHO), Fetal Medicine Foundation (FMF)), ethnicity‐specific (Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD)), customized (Gestation‐Related Optimal Weight (GROW)) and African‐American customized (Perinatology Research Branch (PRB)/NICHD) growth standards were used to calculate EFW percentiles from the last available scan prior to delivery. Prediction performance indices and relative risk (RR) were calculated for EFW < 10th and > 90th percentiles, according to each standard, for individual and composite adverse perinatal outcomes. Sensitivity at a fixed (10%) false‐positive rate (FPR) and partial (FPR < 10%) and full areas under the receiver‐operating‐characteristics curves (AUC) were compared between the standards. Results Ten percent (341/3437) of neonates were classified as small‐for‐gestational age (SGA) at birth, and of these 16.4% (56/341) had at least one adverse perinatal outcome. SGA neonates had a 1.5‐fold increased risk of any adverse perinatal outcome (P < 0.05). The screen‐positive rate of EFW < 10th percentile varied from 6.8% (NICHD) to 24.4% (FMF). EFW < 10th percentile, according to all standards, was associated with an increased risk for each of the adverse perinatal outcomes considered (P < 0.05 for all). The highest RRs associated with EFW < 10th percentile for each adverse outcome were 5.1 (95% CI, 2.1–12.3) for perinatal mortality (WHO); 5.0 (95% CI, 3.2–7.8) for perinatal hypoglycemia (NICHD); 3.4 (95% CI, 2.4–4.7) for mechanical ventilation (NICHD); 2.9 (95% CI, 1.8–4.6) for 5‐min Apgar score < 7 (GROW); 2.7 (95% CI, 2.0–3.6) for neonatal intensive care unit (NICU) admission (NICHD); and 2.5 (95% CI, 1.9–3.1) for composite adverse perinatal outcome (NICHD). Although the RR CIs overlapped among all standards for each individual outcome, the RR of composite adverse perinatal outcome in pregnancies with EFW < 10th percentile was higher according to the NICHD (2.46; 95% CI, 1.9–3.1) than the FMF (1.47; 95% CI, 1.2–1.8) standard. The sensitivity for composite adverse perinatal outcome varied substantially between standards, ranging from 15% for NICHD to 32% for FMF, due mostly to differences in FPR; this variation subsided when the FPR was set to the same value (10%). Analysis of AUC revealed significantly better performance for the prediction of perinatal mortality by the PRB/NICHD standard (AUC = 0.70) compared with the Hadlock (AUC = 0.66) and FMF (AUC = 0.64) standards. Evaluation of partial AUC (FPR < 10%) demonstrated that the INTERGROWTH‐21st standard performed better than the Hadlock standard for the prediction of NICU admission and mechanical ventilation (P < 0.05 for both). Although fetuses with EFW > 90t...

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Section: Methodsmentioning

confidence: 99%

Prediction of adverse perinatal outcome by fetal biometry: comparison of customized and population‐based standards

Kabiri

Romero²,

Gudicha

et al. 2020

Ultrasound in Obstet & Gyne

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show abstract

“…These results are consistent with a study done by Nuntnarumit et al which evaluated StatStrip in comparison with the Accu-Check monitor in neonates. The study did not find significant differences between these two glucose monitors even though StatStrip did not show meaningful hematocrit dependence [ 27 ]. Taken together, neither the PXP nor the StatStrip can be reliably used as a diagnostic tool because of the unacceptably high level of missed diagnoses.…”

Section: Discussionmentioning

confidence: 95%

Hematocrit correction does not improve glucose monitor accuracy in the assessment of neonatal hypoglycemia

Wang

Sievenpiper

Souza

et al. 2013

Clinical Chemistry and Laboratory Medicine (CCLM)

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“…Hypoglycemia is one of the most common metabolic disorders in infants. Persistent hypoglycemia can cause abnormalities in neuronal development 1 2 3 4) . The clinical signs of hypoglycemia in the neonate are not specific to alterations in glucose concentration.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of three glucometers for whole blood glucose measurements at the point of care in preterm or low-birth-weight infants

et al. 2015

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PurposeWe evaluated three blood glucose self-monitoring for measuring whole blood glucose levels in preterm and low-birth-weight infants.MethodsBetween December 1, 2012 and March 31, 2013, 230 blood samples were collected from 50 newborns, who weighed, ≤2,300 g or were ≤36 weeks old, in the the neonatal intensive care unit of Eulji University Hospital. Three blood glucose self-monitoring (A: Precision Pcx, Abbott; B: One-Touch Verio, Johnson & Johnson; C: LifeScan SureStep Flexx, Johnson & Johnson) were used for the blood glucose measurements. The results were compared to those obtained using laboratory equipment (D: Advia chemical analyzer, Siemens Healthcare Diagnostics Inc.).ResultsThe correlation coefficients between laboratory equipment and the three blood glucose self-monitoring (A, B, and C) were found to be 0.888, 0.884, and 0.900, respectively. For glucose levels≤60 mg/dL, the correlation coefficients were 0.674, 0.687, and 0.679, respectively. For glucose levels>60 mg/dL, the correlation coefficients were 0.822, 0.819, and 0.839, respectively. All correlation coefficients were statistically significant. And the values from the blood glucose self-monitoring were not significantly different from the value of the laboratory equipment , after correcting for each device's average value (P>0.05). When using laboratory equipment (blood glucose ≤60 mg/dL), each device had a sensitivity of 0.458, 0.604, and 0.688 and a specificity of 0.995, 0.989, and 0.989, respectively.ConclusionSignificant difference is not found between three blood glucose self-monitoring and laboratory equipment. But correlation between the measured values from blood glucose self-monitoring and laboratory equipment is lower in preterm or low-birth-weight infants than adults.

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Clinical performance of the new glucometer in the nursery and neonatal intensive care unit

Abstract: The new glucometer (Nova-Statstrip(®) ) could be used for point-of-care blood glucose measurement in neonates as it showed a narrow margin of error and had no hematocrit or bilirubin interference.

Cited by 16 publications

References 25 publications

Prediction of adverse perinatal outcome by fetal biometry: comparison of customized and population‐based standards

Prediction of adverse perinatal outcome by fetal biometry: comparison of customized and population‐based standards

Hematocrit correction does not improve glucose monitor accuracy in the assessment of neonatal hypoglycemia

Evaluation of three glucometers for whole blood glucose measurements at the point of care in preterm or low-birth-weight infants

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