Clinical characteristics of neonatal cholestasis in a tertiary hospital and the development of a novel prediction model for mortality

Choi, Ho Jung; Kim, Inki; Lee, Hye-Jin; Oh, Hyun Ju; Ahn, Mi Kyoung; Baek, Woo Im; Kim, Yeong Eun; Oh, Seak Hee; Lee, Byong Sop; Kim, Kyung Mo; Namgoong, Jung‐Man; Kim, Dae Yeon; Shim, Jung Ok; Ko, Jae Sung

doi:10.1016/j.ebiom.2022.103890

Cited by 6 publications

(6 citation statements)

References 51 publications

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“…The inotropes represented circulatory instability and it was a major cause of arrhythmia and leaded to some life-threatening complications such as central nervous bleeding and intestinal ischemia ( 58 ). Furthermore, high serum creatinine levels indicated worsening acute kidney injury associated with poor outcomes ( 59 , 60 ), which confirmed our conclusion.…”

Section: Discussionsupporting

confidence: 88%

A nomogram to predict 28-day mortality in neonates with sepsis: a retrospective study based on the MIMIC-III database

Liang,

Zhao,

Huang

et al. 2023

Transl Pediatr

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Background Sepsis is the second-leading cause of death in neonates. We established a predictive nomogram to identify critically ill neonates early and reduce the time to treatment. Methods It is a retrospective case-control study based on the MIMIC-III database. The study population comprised 924 neonates diagnosed with sepsis. Results Neonates with sepsis included in the MIMIC-III database were enrolled, including 880 surviving neonates and 44 neonates who died. In the derivation dataset, stepwise regression and the Lasso algorithm were employed to select predictive variables, and the neonatal sequential organ failure assessment score (nSOFA) was calculated simultaneously. Bootstrap resampling was utilized to perform internal validation. The results indicated that the Lasso algorithm displayed superior discrimination, sensitivity, and specificity relative to stepwise regression and nSOFA scores. After 500 bootstrap resampling tests, the area under the receiver operating characteristic curve (AUC) of the Lasso algorithm was 0.912 (95% CI: 0.870–0.977). The nomogram based on the Lasso algorithm outperformed stepwise regression and nSOFA scores in terms of calibration and the clinical net benefit. This nomogram can assist in prognosticating neonatal severe sepsis and aid in guiding clinical practice while concurrently improving patient outcomes. Conclusions The established nomogram revealed that jaundice, corticosteroid use, weight, serum calcium, inotropes and base excess are all important predictors of 28-day mortality in neonates with sepsis. This nomogram can facilitate the early identification of neonates with severe sepsis. However, it still requires further modiﬁcation and external validation to make it widely available.

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Section: Discussionsupporting

confidence: 88%

A nomogram to predict 28-day mortality in neonates with sepsis: a retrospective study based on the MIMIC-III database

Liang,

Zhao,

Huang

et al. 2023

Transl Pediatr

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“…It is a visualization tool to identify whether the number of training samples of a given model is sufficient for its convergence by showing how the errors or accuracies of the training and validation sets change as the number of training samples increases [ 36 ]. We used the SHapley Additive explanation (SHAP) method, which is a visual tool that can calculate the contribution of each input variable to the decision of a machine learning model [ 37 ], to interpret the output of the machine learning model, and reveal the relationship between the radiomic features of the image and KRAS/NRAS/BRAF mutations.…”

Section: Methodsmentioning

confidence: 99%

Using tumor habitat-derived radiomic analysis during pretreatment 18F-FDG PET for predicting KRAS/NRAS/BRAF mutations in colorectal cancer

Zhao,

Su,

Wang

et al. 2024

Cancer Imaging

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Background To investigate the association between Kirsten rat sarcoma viral oncogene homolog (KRAS) / neuroblastoma rat sarcoma viral oncogene homolog (NRAS) /v-raf murine sarcoma viral oncogene homolog B (BRAF) mutations and the tumor habitat-derived radiomic features obtained during pretreatment 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) in patients with colorectal cancer (CRC). Methods We retrospectively enrolled 62 patients with CRC who had undergone 18F-FDG PET/computed tomography from January 2017 to July 2022 before the initiation of therapy. The patients were randomly split into training and validation cohorts with a ratio of 6:4. The whole tumor region radiomic features, habitat-derived radiomic features, and metabolic parameters were extracted from 18F-FDG PET images. After reducing the feature dimension and selecting meaningful features, we constructed a hierarchical model of KRAS/NRAS/BRAF mutations by using the support vector machine. The convergence of the model was evaluated by using learning curve, and its performance was assessed based on the area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis. The SHapley Additive exPlanation was used to interpret the contributions of various features to predictions of the model. Results The model constructed by using habitat-derived radiomic features had adequate predictive power with respect to KRAS/NRAS/BRAF mutations, with an AUC of 0.759 (95% CI: 0.585–0.909) on the training cohort and that of 0.701 (95% CI: 0.468–0.916) on the validation cohort. The model exhibited good convergence, suitable calibration, and clinical application value. The results of the SHapley Additive explanation showed that the peritumoral habitat and a high_metabolism habitat had the greatest impact on predictions of the model. No meaningful whole tumor region radiomic features or metabolic parameters were retained during feature selection. Conclusion The habitat-derived radiomic features were found to be helpful in stratifying the status of KRAS/NRAS/BRAF in CRC patients. The approach proposed here has significant implications for adjuvant treatment decisions in patients with CRC, and needs to be further validated on a larger prospective cohort.

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“…Cholestasis is defined as a direct serum bilirubin level above 1 mg/dL at a total bilirubin value of up to 5 mg/dL or when the direct fraction is higher than 20% at total bilirubin over 5 mg/dL [ 5 ]. In clinical practice, direct bilirubin serum levels of 1 mg/dL or more are sufficiently accurate to indicate NC [ 6 , 7 , 8 ]. In the first 5 days of life, even direct or conjugated bilirubin serum levels as low as 0.3–0.4 mg/dl and 10% of the total bilirubin are suggestive of cholestasis [ 9 ].…”

Section: Definitionmentioning

confidence: 99%

Protocols of Investigation of Neonatal Cholestasis—A Critical Appraisal

et al. 2022

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Neonatal cholestasis (NC) starts during the first three months of life and comprises extrahepatic and intrahepatic groups of diseases, some of which have high morbimortality rates if not timely identified and treated. Prolonged jaundice, clay-colored or acholic stools, and choluria in an infant indicate the urgent need to investigate the presence of NC, and thenceforth the differential diagnosis of extra- and intrahepatic causes of NC. The differential diagnosis of NC is a laborious process demanding the accurate exclusion of a wide range of diseases, through the skillful use and interpretation of several diagnostic tests. A wise integration of clinical-laboratory, histopathological, molecular, and genetic evaluations is imperative, employing extensive knowledge about each evaluated disease as well as the pitfalls of each diagnostic test. Here, we review the difficulties involved in correctly diagnosing the cause of cholestasis in an affected infant.

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Clinical characteristics of neonatal cholestasis in a tertiary hospital and the development of a novel prediction model for mortality

Cited by 6 publications

References 51 publications

A nomogram to predict 28-day mortality in neonates with sepsis: a retrospective study based on the MIMIC-III database

A nomogram to predict 28-day mortality in neonates with sepsis: a retrospective study based on the MIMIC-III database

Using tumor habitat-derived radiomic analysis during pretreatment 18F-FDG PET for predicting KRAS/NRAS/BRAF mutations in colorectal cancer

Protocols of Investigation of Neonatal Cholestasis—A Critical Appraisal

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