Gamma-glutamyl transpeptidase has different efficacy on biliary atresia diagnosis in different hospital patient groups: an application of machine learning approach

Predictive modeling for early detection of biliary atresia in infants with cholestasis: Insights from a machine learning study

Cholestasis, characterized by the obstruction of bile flow, poses a significant concern in neonates and infants. It can result in jaundice, inadequate weight gain, and liver dysfunction. However, distinguishing between biliary atresia (BA) and non-biliary atresia in these young patients presenting with cholestasis poses a formidable challenge, given the similarity in their clinical manifestations. To this end, our study endeavors to construct a screening model aimed at prognosticating outcomes in cases of BA. Within this study, we introduce a wrapper feature selection model denoted as bWFMVO-SVM-FS, which amalgamates the water flow-based multi-verse optimizer (WFMVO) and support vector machine (SVM) technology. Initially, WFMVO is benchmarked against eleven state-of-the-art algorithms, with its efficiency in searching for optimized feature subsets within the model validated on IEEE CEC 2017 and IEEE CEC 2022 benchmark functions. Subsequently, the developed bWFMVO-SVM-FS model is employed to analyze a cohort of 870 consecutively registered cases of neonates and infants with cholestasis (diagnosed as either BA or non-BA) from Xinhua Hospital and Shanghai Children's Hospital, both affiliated with Shanghai Jiao Tong University. The results underscore the remarkable predictive capacity of the model, achieving an accuracy of 92.639 % and specificity of 88.865 %. Gamma-glutamyl transferase, triangular cord sign, weight, abnormal gallbladder, and stool color emerge as highly correlated with early symptoms in BA infants. Furthermore, leveraging these five significant features enhances the interpretability of the machine learning model's performance outcomes for medical professionals, thereby facilitating more effective clinical decision-making.

Postoperative morbidity after simultaneous versus staged resection of synchronous colorectal liver metastases: Impact of hepatic tumor burden

BACKGROUND

We sought to characterize the risk of postoperative complications relative to the surgical approach and overall synchronous colorectal liver metastases tumor burden score.

METHODS

Patients with synchronous colorectal liver metastases who underwent curative-intent resection between 2000 and 2020 were identified from an international multi-institutional database. Propensity score matching was employed to control for heterogeneity between the 2 groups. A virtual twins analysis was performed to identify potential subgroups of patients who might benefit more from staged versus simultaneous resection.

RESULTS

Among 976 patients who underwent liver resection for synchronous colorectal liver metastases, 589 patients (60.3%) had a staged approach, whereas 387 (39.7%) patients underwent simultaneous resection of the primary tumor and synchronous colorectal liver metastases. After propensity score matching, 295 patients who underwent each surgical approach were analyzed. Overall, the incidence of postoperative complications was 34.1% (n = 201). Among patients with high tumor burden scores, the surgical approach was associated with a higher incidence of postoperative complications; in contrast, among patients with low or medium tumor burden scores, the likelihood of complications did not differ based on the surgical approach. Virtual twins analysis demonstrated that preoperative tumor burden score was important to identify which subgroup of patients benefited most from staged versus simultaneous resection. Simultaneous resection was associated with better outcomes among patients with a tumor burden score <9 and a node-negative right-sided primary tumor; in contrast, staged resection was associated with better outcomes among patients with node-positive left-sided primary tumors and higher tumor burden score.

CONCLUSION

Among patients with high tumor burden scores, simultaneous resection of the primary tumor and liver metastases was associated with an increased incidence of postoperative complications.

Establishment of Biliary Atresia Prognostic Classification System via Survival-Based Forward Clustering - A New Biliary Atresia Classification

OBJECTIVES

To develop a machine learning algorithm with prognosis data to identify different clinical phenotypes of biliary atresia (BA) and provide instructions for choosing treatment schemes.

METHODS

Six hundred thirty-nine cases of type III BA were retrospectively collected from the Children's Hospital of Fudan University from Jan 1st, 2017 to Dec 1st, 2019 as a training dataset, and a survival-based forward clustering method, which can also be used to predict the subtype of a new patient was developed to identify BA subtypes.

RESULTS

A total of 2 clusters were identified (cluster 1 = 324 and cluster 2 = 315), where cluster 2 had a lower 2 y native liver survival post-Kasai rate. The infant patients in cluster 2 have higher weight, liver, and spleen volume, wider portal vein width, and older operative age; worse coagulation and liver function results; higher grade of liver fibrosis and detection rate of hepatic portal fibrous mass, and higher recent infection detection rate of herpes simplex virus type I. With the proposed prognostic classification system, the authors predicted the subtypes of the 187 cases of type III BA in a testing dataset collected from the whole year of 2020. The p-value computed from the log-rank testing for the Kaplan-Meier survival curves of the predicted two testing groups was 0.0113.

CONCLUSIONS

This classification system would be a convenient tool to choose appropriate treatment and accelerate the choice-making between clinicians and infant patients.