A clinical prediction rule for acute bilirubin encephalopathy in neonates with extreme hyperbilirubinemia: A retrospective cohort study

"Light" on Phototherapy-Complications and Strategies for Shortening Its Duration, A Review of the Literature

Neonatal hyperbilirubinemia is an extremely common metabolic complication of the neonatal period which may be associated with bilirubin encephalopathy and even death. Adverse neurological consequences are preventable if a timely diagnosis and treatment are provided. Phototherapy is usually the preferred option to decrease hyperbilirubinemia. Although considered to be safe, evidence in recent years has shown that this treatment may not be free of side effects and short- and long-term unfavorable outcomes. These are usually mild or rare, but should be decreased or avoided if possible. Many useful complementary measures and treatments have been described that could shorten the duration of exposure to phototherapy. However, there is no current unequivocal recommendation to use any of the methods presented in this review. Our review aims to depict the wide range of possible complementary treatments to phototherapy, and to provide the scientific and clinical evidence available regarding their usefulness. It is essential that, while utilizing the full potential of phototherapy to treat hyperbilirubinemia, caregivers are aware of its side effects and possible inherent dangers, and seek ways to minimize the exposure to phototherapy to what is really needed for the newborn. Further studies are needed to clarify the preferred complementary treatments that could reduce the duration of exposure to phototherapy without impairing its effectiveness.

Establishment and Evaluation of Influencing Factors and Risk Prediction Model of Severe Neonatal Hyperbilirubinemia Complicated with Acute Bilirubin Encephalopathy

Objective

To explore the influencing factors of severe hyperbilirubinemia in neonates complicated with acute bilirubin encephalopathy (ABE) and then build relevant prediction models and evaluate the prediction performance of the models.

Methods

The data of 120 neonates with severe hyperbilirubinemia were collected by retrospective analysis. Univariate and multivariate analysis methods were used to analyze the data of 120 children. R software was used to visualize the results of multivariate analysis, and a nomogram model was obtained. The receiver operating characteristic curve (ROC), calibration curve, and decision-making curve (DC) were used to evaluate the discrimination, accuracy, and clinical net profit rate of the model.

Results

Multivariate analysis showed that nonfull breastfeeding, high-risk symptoms, and pregnancy complications were independent risk factors for ABE in neonates with severe hyperbilirubinemia. At the same time, the risk of ABE in neonates with severe hyperbilirubinemia increased with the increase of B/A and Hb levels. The ROC curve showed that the area under the curve for the model was 0.908 (95% CI: 0.839-0.960). The calibration curve shows that the actual prediction curve of the model is in good agreement with the corrected prediction curve. Using the cutoff value of the ROC curve as the diagnostic criterion, the threshold probability of the model was calculated to be 38%. The decision curve shows that when 38% is used as the basis for judging whether to take measures to intervene, the profit rate is 61%.

Conclusion

The occurrence of ABE in neonates with severe hyperbilirubinemia is affected by many factors, and there is a certain degree of interaction between these factors. Combining multiple factors to construct a risk nomogram model can provide a reference for early clinical detection of high-risk neonates.

Kernicterus Spectrum Disorders Diagnostic Toolkit: validation using retrospective chart review

BACKGROUND

Kernicterus Spectrum Disorders (KSDs) result from hyperbilirubinemia-induced brain injury. We developed a Toolkit (KSD-TK) to predict the likelihood of KSDs. This study aims to validate the KSD-TK by comparing it to clinical diagnoses made by the Kernicterus Clinic in the Division of Neurology.

METHODS

Through retrospective chart review, we completed a KSD-TK for 37 patients evaluated between 2011 and 2019 using highest bilirubin, newborn risk factors, neonatal exam, follow-up exam, auditory testing, tooth enamel, and MRI brain results. KSD-TK results were compared to the clinical diagnoses given by a kernicterus expert (SS).

RESULTS

Of 37 patients, 29 were clinically diagnosed with kernicterus, including 14/14 with KSD-TK scored as "definite", 14/15 "probable", and 1/2 with "possible" kernicterus. None of 6 patients with KSD-TK "not kernicterus" were clinically diagnosed with kernicterus. Combining KSD-TK "definite" and "probable", the KSD-TK has 96.6% sensitivity and 87.5% specificity. Each KSD-TK component had high sensitivity, but only three had specificity ≥0.75: auditory neuropathy spectrum disorder, abnormal movements and/or tone on follow-up exam, and abnormal globus pallidus and/or subthalamic nucleus on MRI.

CONCLUSION

The KSD-TK is a promising screening tool for patients at risk for kernicterus.

IMPACT

This study provides validation of a Kernicterus Spectrum Disorders (KSDs) Toolkit. The toolkit provides screening criteria for predicting KSD diagnosis. Scores of definite or probable have high sensitivity and specificity for KSDs. Abnormal auditory processing, exam, and MRI were most specific for KSDs.

The development and validation of a predictive model for neonatal phototherapy outcome using admission indicators

Delayed exchange transfusion therapy (ETT) after phototherapy failure for newborns with severe hyperbilirubinemia could lead to serious complications such as bilirubin encephalopathy (BE). In this current manuscript we developed and validated a model using admission data for early prediction of phototherapy failure. We retrospectively examined the medical records of 292 newborns with severe hyperbilirubinemia as the training cohort and another 52 neonates as the validation cohort. Logistic regression modeling was employed to create a predictive model with seven significant admission indicators, i.e., age, past medical history, presence of hemolysis, hemoglobin, neutrophil proportion, albumin (ALB), and total serum bilirubin (TSB). To validate the model, two other models with conventional indicators were created, one incorporating the admission indicators and phototherapy failure outcome and the other using TSB decrease after phototherapy failure as a variable and phototherapy outcome as an outcome indicator. The area under the curve (AUC) of the predictive model was 0.958 [95% confidence interval (CI): 0.924-0.993] and 0.961 (95% CI: 0.914-1.000) in the training and validation cohorts, respectively. Compared with the conventional models, the new model had better predictive power and greater value for clinical decision-making by providing a possibly earlier and more accurate prediction of phototherapy failure. More rapid clinical decision-making and interventions may potentially minimize occurrence of serious complications of severe neonatal hyperbilirubinemia.

Detecting neonatal acute bilirubin encephalopathy based on T1-weighted MRI images and learning-based approaches

Background

Neonatal hyperbilirubinemia is a common clinical condition that requires medical attention in newborns, which may develop into acute bilirubin encephalopathy with a significant risk of long-term neurological deficits. The current clinical challenge lies in the separation of acute bilirubin encephalopathy and non-acute bilirubin encephalopathy neonates both with hyperbilirubinemia condition since both of them demonstrated similar T1 hyperintensity and lead to difficulties in clinical diagnosis based on the conventional radiological reading. This study aims to investigate the utility of T1-weighted MRI images for differentiating acute bilirubin encephalopathy and non-acute bilirubin encephalopathy neonates with hyperbilirubinemia.

Methods

3 diagnostic approaches, including a visual inspection, a semi-quantitative method based on normalized the T1-weighted intensities of the globus pallidus and subthalamic nuclei, and a deep learning method with ResNet18 framework were applied to classify 47 acute bilirubin encephalopathy neonates and 32 non-acute bilirubin encephalopathy neonates with hyperbilirubinemia based on T1-weighted images. Chi-squared test and t-test were used to test the significant difference of clinical features between the 2 groups.

Results

The visual inspection got a poor diagnostic accuracy of 53.58 ± 5.71% indicating the difficulty of the challenge in real clinical practice. However, the semi-quantitative approach and ResNet18 achieved a classification accuracy of 62.11 ± 8.03% and 72.15%, respectively, which outperformed visual inspection significantly.

Conclusion

Our study indicates that it is not sufficient to only use T1-weighted MRI images to detect neonates with acute bilirubin encephalopathy. Other more MRI multimodal images combined with T1-weighted MRI images are expected to use to improve the accuracy in future work. However, this study demonstrates that the semi-quantitative measurement based on T1-weighted MRI images is a simple and compromised way to discriminate acute bilirubin encephalopathy and non-acute bilirubin encephalopathy neonates with hyperbilirubinemia, which may be helpful in improving the current manual diagnosis.

Nomogram for Acute Bilirubin Encephalopathy Risk in Newborns With Extreme Hyperbilirubinemia

Background and Objectives: This work aimed to develop a predictive model of neonatal acute bilirubin encephalopathy.

Methods: We retrospectively analyzed the data on extreme hyperbilirubinemia (EHB) newborns hospitalized in the First Hospital of Jilin University from January 1, 2012 to December 31, 2019. The demographic characteristics, pathological information, and admission examination results of newborns were collected to analyze the factors affecting acute bilirubin encephalopathy and to establish a predictive model.

Results: A total of 517 newborns were included in this study, of which 102 (19.7%) had acute bilirubin encephalopathy. T1WI hyperintensity [18.819 (8.838–40.069)], mother's age > 35 years [2.618 (1.096–6.2530)], abnormal white blood cell (WBC) [6.503 (0.226–18.994)], TSB level [1.340 (1.242–1.445)], and albumin level [0.812 (0.726–0.907)] were independently associated with neonatal acute bilirubin encephalopathy (ABE). All independently associated risk factors were used to form an ABE risk estimation nomogram. The bootstrap validation method was used to internally validate the resulting model. The nomogram demonstrated good accuracy in predicting the risk of ABE, with an unadjusted C index of 0.943 (95% CI, 0.919–0.962) and a bootstrap-corrected C index of 0.900.

Conclusion: A nomogram was constructed using five risk factors of ABE. This model can help clinicians determine the best treatment for neonatal hyperbilirubinemia.

Increased Serum Expression of Inflammatory Cytokines may Serve as Potential Diagnostic Biomarker for Bilirubin Encephalopathy

OBJECTIVES

The present study was designed to explore the roles of inflammatory cytokines interleukin-1β (IL-1β) and Tumor growth factor-β (TGF-β) in the diagnosis and treatment of neonate bilirubin encephalopathy (BE).

METHODS

A total of 128 BE neonates and 128 normal neonates were included. The serum samples of the BE children and controls were collected, and the levels of IL-1β and TGF-β were examined. Moreover, the correlation between the level of bilirubin and serum expression of IL-1β or TGF-β in BE patients was analyzed. Finally, receiver operating characteristic (ROC) curves were generated to determine the diagnostic value of the cytokines.

RESULTS

IL-1β and TGF-β levels were higher in the serum of BE patients than those in non-BE patients, and the expression of either IL-1β or TGF-β showed a strong positive correlation with the serum expression of bilirubin in BE patients. Moreover, the results of ROC analysis showed that either IL-1β or TGF-β could distinguish BE patients from healthy controls.

CONCLUSION

IL-1β and TGF-β levels were upregulated in BE and might function as potential biomarkers or therapeutic targets for BE patients.