The Predictive Value of Serum Uric Acid on Acute Kidney Injury following Traumatic Brain Injury

A dynamic model to predict early occurrence of acute kidney injury in ICU hospitalized cirrhotic patients: a MIMIC database analysis

BACKGROUND

This study aimed to develop a tool for predicting the early occurrence of acute kidney injury (AKI) in ICU hospitalized cirrhotic patients.

METHODS

Eligible patients with cirrhosis were identified from the Medical Information Mart for Intensive Care database. Demographic data, laboratory examinations, and interventions were obtained. After splitting the population into training and validation cohorts, the least absolute shrinkage and selection operator regression model was used to select factors and construct the dynamic online nomogram. Calibration and discrimination were used to assess nomogram performance, and clinical utility was evaluated by decision curve analysis (DCA).

RESULTS

A total of 1254 patients were included in the analysis, and 745 developed AKI. The mean arterial pressure, white blood cell count, total bilirubin level, Glasgow Coma Score, creatinine, heart rate, platelet count and albumin level were identified as predictors of AKI. The developed model had a good ability to differentiate AKI from non-AKI, with AUCs of 0.797 and 0.750 in the training and validation cohorts, respectively. Moreover, the nomogram model showed good calibration. DCA showed that the nomogram had a superior overall net benefit within wide and practical ranges of threshold probabilities.

CONCLUSIONS

The dynamic online nomogram can be an easy-to-use tool for predicting the early occurrence of AKI in critically ill patients with cirrhosis.

Classification and Regression Tree Predictive Model for Acute Kidney Injury in Traumatic Brain Injury Patients

Background

Acute kidney injury (AKI) is prevalent in hospitalized patients with traumatic brain injury (TBI), and increases the risk of poor outcomes. We designed this study to develop a visual and convenient decision-tree-based model for predicting AKI in TBI patients.

Methods

A total of 376 patients admitted to the emergency department of the West China Hospital for TBI between January 2015 and June 2019 were included. Demographic information, vital signs on admission, laboratory test results, radiological signs, surgical options, and medications were recorded as variables. AKI was confirmed since the second day after admission, based on the Kidney Disease Improving Global Outcomes criteria. We constructed two predictive models for AKI using least absolute shrinkage and selection operator (LASSO) regression and classification and regression tree (CART), respectively. Receiver operating characteristic (ROC) curves of these two predictive models were drawn, and the area under the ROC curve (AUC) was calculated to compare their predictive accuracy.

Results

The incidence of AKI on the second day after admission was 10.4% among patients with TBI. Lasso regression identified five potent predictive factors for AKI: glucose, serum creatinine, cystatin C, serum uric acid, and fresh frozen plasma transfusions. The CART analysis showed that glucose, serum uric acid, and cystatin C ranked among the top three in terms of the feature importance of the decision tree model. The AUC value of the decision-tree predictive model was 0.892, which was higher than the 0.854 of the LASSO regression model, although the difference was not statistically significant.

Conclusion

The decision tree model is valuable for predicting AKI among patients with TBI. This tree-based flowchart is convenient for physicians to identify patients with TBI who are at high risk of AKI and prompts them to develop suitable therapeutic strategies.

The establishment and validation of a prediction model for traumatic intracranial injury patients: a reliable nomogram

Objective

Traumatic brain injury (TBI) leads to death and disability. This study developed an effective prognostic nomogram for assessing the risk factors for TBI mortality.

Method

Data were extracted from an online database called "Multiparameter Intelligent Monitoring in Intensive Care IV" (MIMIC IV). The ICD code obtained data from 2,551 TBI persons (first ICU stay, >18 years old) from this database. R divided samples into 7:3 training and testing cohorts. The univariate analysis determined whether the two cohorts differed statistically in baseline data. This research used forward stepwise logistic regression after independent prognostic factors for these TBI patients. The optimal variables were selected for the model by the optimal subset method. The optimal feature subsets in pattern recognition improved the model prediction, and the minimum BIC forest of the high-dimensional mixed graph model achieved a better prediction effect. A nomogram-labeled TBI-IHM model containing these risk factors was made by nomology in State software. Least Squares OLS was used to build linear models, and then the Receiver Operating Characteristic (ROC) curve was plotted. The TBI-IHM nomogram model's validity was determined by receiver operating characteristic curves (AUCs), correction curve, Hosmer-Lemeshow test, integrated discrimination improvement (IDI), net reclassification improvement (NRI), and decision-curve analysis (DCA).

Result

The eight features with a minimal BIC model were mannitol use, mechanical ventilation, vasopressor use, international normalized ratio, urea nitrogen, respiratory rate, and cerebrovascular disease. The proposed nomogram (TBI-IHM model) was the best mortality prediction model, with better discrimination and superior model fitting for severely ill TBI patients staying in ICU. The model's receiver operating characteristic curve (ROC) was the best compared to the seven other models. It might be clinically helpful for doctors to make clinical decisions.

Conclusion

The proposed nomogram (TBI-IHM model) has significant potential as a clinical utility in predicting mortality in TBI patients.

Systemic immune-inflammation index may predict the acute kidney injury and prognosis in patients with spontaneous cerebral hemorrhage undergoing craniotomy: a single-center retrospective study

BACKGROUND

The systemic immune-inflammation index (SII) is an emerging prognostic marker of cancer. We aimed to explore the predictive ability of the SII on acute kidney injury (AKI) and prognosis in patients with spontaneous cerebral hemorrhage (SCH) who underwent craniotomy.

METHODS

Patients with SCH who underwent craniotomy between 2014 and 2021 were enrolled in this study. The epidemiology and predictive factors for AKI after SCH were analyzed. The prognostic factors for clinical outcomes in patients with SCH and AKI were further investigated. The prognostic factors were then analyzed using a logistic regression model and a receiver operating characteristic curve.

RESULTS

In total, 305 patients were enrolled in this study. Of these, 129 (42.3%) patients presented with AKI, and 176 (57.7%) patients were unremarkable. The SII (odds ratio [OR], 1.261; 95% confidence interval [CI], 1.036-1.553; P = 0.020) values and serum uric acid levels (OR, 1.004; 95% CI, 1.001-1.007; P = 0.005) were significant predictors of AKI after SCH craniotomy. The SII cutoff value was 1794.43 (area under the curve [AUC], 0.669; 95% CI, 0.608-0.730; P < 0.001; sensitivity, 65.9%; specificity, 65.1%). Of the patients with AKI, 95 and 34 achieved poor and good outcomes, respectively. SII values (OR, 2.667; 95% CI, 1.167-6.095; P = 0.020), systemic inflammation response index values (OR, 1.529; 95% CI, 1.064-2.198; P = 0.022), and Glasgow Coma Scale (GCS) scores on admission (OR, 0.593; 95% CI, 0.437-0.805; P = 0.001) were significant in the multivariate logistic regression analysis. The cutoff SII value was 2053.51 (AUC, 0.886; 95% CI, 0.827-0.946; P < 0.001; sensitivity, 78.9%; specificity, 88.2%).

CONCLUSIONS

The SII may predict AKI in patients with SCH who underwent craniotomy and may also predict the short-term prognosis of these patients.

Association of hyperchloremia and acute kidney injury in pediatric patients with moderate and severe traumatic brain injury

PURPOSE

Acute kidney injury (AKI) is an established complication of adult traumatic brain injury (TBI) and known risk factor for mortality. Evidence demonstrates an association between hyperchloremia and AKI in critically ill adults but studies in children are scarce. Given frequent use of hypertonic saline in the management of pediatric TBI, we believe the incidence of hyperchloremia will be high and hypothesize that it will be associated with development of AKI.

METHODS

Single-center retrospective cohort study was completed at an urban, level 1 pediatric trauma center. Children > 40 weeks corrected gestational age and < 21 years of age with moderate or severe TBI (presenting GCS < 13) admitted between January 2016 and December 2021 were included. Primary study outcome was presence of AKI (defined by pediatric Kidney Disease: Improving Global Outcomes criteria) within 7 days of hospitalization and compared between patients with and without hyperchloremia (serum chloride ≥ 110 mEq/L).

RESULTS

Fifty-two children were included. Mean age was 5.75 (S.D. 5.4) years; 60% were male (31/52); and mean presenting GCS was 6 (S.D. 2.9). Thirty-seven patients (71%) developed hyperchloremia with a mean peak chloride of 125 (S.D. 12.0) mEq/L and mean difference between peak and presenting chloride of 16 (S.D. 12.7) mEq/L. Twenty-three patients (44%) developed AKI; of those with hyperchloremia, 62% (23/37) developed AKI, while among those without hyperchloremia, 0% (0/15) developed AKI (difference 62%, 95% CI 42-82%, p < 0.001). Attributable risk of hyperchloremia leading to AKI was 62.2 (95% CI 46.5-77.8, p = 0.0015).

CONCLUSION

Hyperchloremia is common in the management of pediatric TBI and is associated with development of AKI. Risk appears to be associated with both the height of serum chloride and duration of hyperchloremia.

A Machine Learning Approach for the Prediction of Severe Acute Kidney Injury Following Traumatic Brain Injury

BACKGROUND

Acute kidney injury (AKI), a prevalent non-neurological complication following traumatic brain injury (TBI), is a major clinical issue with an unfavorable prognosis. This study aimed to develop and validate machine learning models to predict severe AKI (stage 3 or greater) incidence in patients with TBI.

METHODS

A retrospective cohort study was conducted by using two public databases: the Medical Information Mart for Intensive Care IV (MIMIC)-IV and the eICU Collaborative Research Database (eICU-CRD). Recursive feature elimination was used to select candidate predictors obtained within 24 h of intensive care unit admission. The area under the curve and decision curve analysis curves were used to determine the discriminatory ability. On the other hand, the calibration curve was employed to evaluate the calibrated performance of the newly developed machine learning models.

RESULTS

In the MIMIC-IV database, there were 808 patients diagnosed with moderate and severe TBI (msTBI) (msTBI is defined as Glasgow Coma Score < 12). Of these, 60 (7.43%) patients experienced severe AKI. External validation in the eICU-CRD indicated that the random forest (RF) model had the highest area under the curve of 0.819 (95% confidence interval 0.783-0.851). Furthermore, in the calibration curve, the RF model was well calibrated (P = 0.795).

CONCLUSIONS

In this study, the RF model demonstrated better discrimination in predicting severe AKI than other models. An online calculator could facilitate its application, potentially improving the early detection of severe AKI and subsequently improving the clinical outcomes among patients with msTBI.