A New Time-Window Prediction Model For Traumatic Hemorrhagic Shock Based on Interpretable Machine Learning

ABSTRACT

Early warning prediction of traumatic hemorrhagic shock (THS) can greatly reduce patient mortality and morbidity. We aimed to develop and validate models with different stepped feature sets to predict THS in advance. From the PLA General Hospital Emergency Rescue Database and Medical Information Mart for Intensive Care III, we identified 604 and 1,614 patients, respectively. Two popular machine learning algorithms (i.e., extreme gradient boosting [XGBoost] and logistic regression) were applied. The area under the receiver operating characteristic curve (AUROC) was used to evaluate the performance of the models. By analyzing the feature importance based on XGBoost, we found that features in vital signs (VS), routine blood (RB), and blood gas analysis (BG) were the most relevant to THS (0.292, 0.249, and 0.225, respectively). Thus, the stepped relationships existing in them were revealed. Furthermore, the three stepped feature sets (i.e., VS, VS + RB, and VS + RB + sBG) were passed to the two machine learning algorithms to predict THS in the subsequent T hours (where T = 3, 2, 1, or 0.5), respectively. Results showed that the XGBoost model performance was significantly better than the logistic regression. The model using vital signs alone achieved good performance at the half-hour time window (AUROC = 0.935), and the performance was increased when laboratory results were added, especially when the time window was 1 h (AUROC = 0.950 and 0.968, respectively). These good-performing interpretable models demonstrated acceptable generalization ability in external validation, which could flexibly and rollingly predict THS T hours (where T = 0.5, 1) prior to clinical recognition. A prospective study is necessary to determine the clinical utility of the proposed THS prediction models.

Implications of nonlinearity, confounding, and interactions for estimating exposure concentration-response functions in quantitative risk analysis

Recent advances in understanding of biological mechanisms and adverse outcome pathways for many exposure-related diseases show that certain common mechanisms involve thresholds and nonlinearities in biological exposure concentration-response (C-R) functions. These range from ultrasensitive molecular switches in signaling pathways, to assembly and activation of inflammasomes, to rupture of lysosomes and pyroptosis of cells. Realistic dose-response modeling and risk analysis must confront the reality of nonlinear C-R functions. This paper reviews several challenges for traditional statistical regression modeling of C-R functions with thresholds and nonlinearities, together with methods for overcoming them. Statistically significantly positive exposure-response regression coefficients can arise from many non-causal sources such as model specification errors, incompletely controlled confounding, exposure estimation errors, attribution of interactions to factors, associations among explanatory variables, or coincident historical trends. If so, the unadjusted regression coefficients do not necessarily predict how or whether reducing exposure would reduce risk. We discuss statistical options for controlling for such threats, and advocate causal Bayesian networks and dynamic simulation models as potentially valuable complements to nonparametric regression modeling for assessing causally interpretable nonlinear C-R functions and understanding how time patterns of exposures affect risk. We conclude that these approaches are promising for extending the great advances made in statistical C-R modeling methods in recent decades to clarify how to design regulations that are more causally effective in protecting human health.

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.