Blood Urea Nitrogen-to-Albumin Ratio May Predict Mortality in Patients with Traumatic Brain Injury from the MIMIC Database: A Retrospective Study

Abstract

Traumatic brain injury (TBI), a major global health burden, disrupts the neurological system due to accidents and other incidents. While the Glasgow coma scale (GCS) gauges neurological function, it falls short as the sole predictor of overall mortality in TBI patients. This highlights the need for comprehensive outcome prediction, considering not just neurological but also systemic factors. Existing approaches relying on newly developed biomolecules face challenges in clinical implementation. Therefore, we investigated the potential of readily available clinical indicators, like the blood urea nitrogen-to-albumin ratio (BAR), for improved mortality prediction in TBI. In this study, we investigated the significance of the BAR in predicting all-cause mortality in TBI patients. In terms of research methodologies, we gave preference to machine learning methods due to their exceptional performance in clinical support in recent years. Initially, we obtained data on TBI patients from the Medical Information Mart for Intensive Care database. A total of 2602 patients were included, of whom 2260 survived and 342 died in hospital. Subsequently, we performed data cleaning and utilized machine learning techniques to develop prediction models. We employed a ten-fold cross-validation method to obtain models with enhanced accuracy and area under the curve (AUC) (Light Gradient Boost Classifier accuracy, 0.905 ± 0.016, and AUC, 0.888; Extreme Gradient Boost Classifier accuracy, 0.903 ± 0.016, and AUC, 0.895; Gradient Boost Classifier accuracy, 0.898 ± 0.021, and AUC, 0.872). Simultaneously, we derived the importance ranking of the variable BAR among the included variables (in Light Gradient Boost Classifier, the BAR ranked fourth; in Extreme Gradient Boost Classifier, the BAR ranked sixth; in Gradient Boost Classifier, the BAR ranked fifth). To further evaluate the clinical utility of BAR, we divided patients into three groups based on their BAR values: Group 1 (BAR < 4.9 mg/g), Group 2 (BAR ≥ 4.9 and ≤10.5 mg/g), and Group 3 (BAR ≥ 10.5 mg/g). This stratification revealed significant differences in mortality across all time points: in-hospital mortality (7.61% vs. 15.16% vs. 31.63%), as well as one-month (8.51% vs. 17.46% vs. 36.39%), three-month (9.55% vs. 20.14% vs. 41.84%), and one-year mortality (11.57% vs. 23.76% vs. 46.60%). Building on this observation, we employed the Cox proportional hazards regression model to assess the impact of BAR segmentation on survival. Compared to Group 1, Groups 2 and 3 had significantly higher hazard ratios (95% confidence interval (CI)) for one-month mortality: 1.77 (1.37-2.30) and 3.17 (2.17-4.62), respectively. To further underscore the clinical potential of BAR as a standalone measure, we compared its performance to established clinical scores, like sequential organ failure assessment (SOFA), GCS, and acute physiology score III(APS-III), using receiver operator characteristic curve (ROC) analysis. Notably, the AUC values (95%CI) of the BAR were 0.67 (0.64-0.70), 0.68 (0.65-0.70), and 0.68 (0.65-0.70) for one-month mortality, three-month mortality, and one-year mortality. The AUC value of the SOFA did not significantly differ from that of the BAR. In conclusion, the BAR is a highly influential factor in predicting mortality in TBI patients and should be given careful consideration in future TBI prediction research. The blood urea nitrogen-to-albumin ratio may predict mortality in TBI patients.

Keywords: blood urea nitrogen-to-albumin ratio; machine learning; the MIMIC database; traumatic brain injury.

Figure 1

Flowchart showing inclusion and exclusion criteria.

Figure 2

Three machine learning models and ranking of feature importance: (a) Light Gradient Boost Classifier model; (b) Extreme Gradient Boost (XGBoost) Classifier model; (c) Gradient Boost Classifier model. RDW, red blood cell distribution width; BAR, blood urea nitrogen-to-albumin ratio; INR, International normalized ratio; PT, prothrombin time; APTT, activated partial thromboplastin time; GCS, Glasgow coma scale; APS-III, acute physiology score III; SOFA, sequential organ failure assessment; RBC, red blood cell; PLT, platelet; MBP, average arterial pressure; T, temperature; RR, respiratory rate; HR, heart rate; AG, anion gap; SB, bicarbonate.

Figure 2

Three machine learning models and ranking of feature importance: (a) Light Gradient Boost Classifier model; (b) Extreme Gradient Boost (XGBoost) Classifier model; (c) Gradient Boost Classifier model. RDW, red blood cell distribution width; BAR, blood urea nitrogen-to-albumin ratio; INR, International normalized ratio; PT, prothrombin time; APTT, activated partial thromboplastin time; GCS, Glasgow coma scale; APS-III, acute physiology score III; SOFA, sequential organ failure assessment; RBC, red blood cell; PLT, platelet; MBP, average arterial pressure; T, temperature; RR, respiratory rate; HR, heart rate; AG, anion gap; SB, bicarbonate.

Figure 3

The relationship between BAR and all-cause mortality in patients with TBI was described using Kaplan-Meier survival curves to characterize the survival probability for patients with different BAR levels, p-values were acquired using the log-rank test, and graphs were drawn to show (a) survival curves for patients at one month; (b) three months; (c) one year.

Figure 4

The receiver operating characteristic (ROC) curves were used to assess the predictive value of BAR and compare them to other predictors. ROC curve for (a) 1-month; (b) 3-month and (c) 1-year all-cause mortality; APS-III, acute physiology score III; SOFA, sequential organ failure assessment; GCS, Glasgow coma scale; BAR, Blood Urea Nitrogen-to-Albumin Ratio.