Blood creatinine and urea nitrogen at ICU admission and the risk of in-hospital death and 1-year mortality in patients with intracranial hemorrhage

Factors Influencing Plasma Concentrations of Valproic Acid in Pediatric Patients With Epilepsy and the Clinical Significance of CYP2C9 Genotypes in Personalized Valproic Acid Therapy

BACKGROUND

The aim of this study was to investigate the factors affecting plasma valproic acid (VPA) concentration in pediatric patients with epilepsy and the clinical significance of CYP2C9 gene polymorphisms in personalized dosing using therapeutic drug monitoring and pharmacogenetic testing.

METHODS

The medical records of children with epilepsy who underwent therapeutic drug monitoring at our institution between July 2022 and July 2023 and met the inclusion criteria were reviewed. Statistical analysis was performed to determine whether age, sex, blood ammonia, liver function, kidney function, and other characteristics affected the concentration-to-dose ratio of VPA (CDRV) in these patients. To investigate the effect of CYP2C9 polymorphisms on CDRV, DNA samples were collected from patients and the CYP2C9 genotypes were identified using real-time quantitative PCR.

RESULTS

The mean age of 208 pediatric patients with epilepsy was 5.50 ± 3.50 years. Among these patients, 182 had the CYP2C9 *1/*1 genotype, with a mean CDRV (mcg.kg/mL.mg) of 2.64 ± 1.46, 24 had the CYP2C9 *1/*3 genotype, with a mean CDRV of 3.28 ± 1.74, and 2 had the CYP2C9 *3/*3 genotype, with a mean CDRV of 6.46 ± 3.33. There were statistical differences among these 3 genotypes ( P < 0.05). The CDRV in these patients were significantly influenced by age, aspartate aminotransferase, total bilirubin, direct bilirubin, globulin, albumin/globulin ratio, prealbumin, creatinine, and CYP2C9 polymorphisms. In addition, multivariate linear regression analysis identified total bilirubin, direct bilirubin, and CYP2C9 polymorphisms as independent risk factors for high CDRV.

CONCLUSIONS

Liver problems and mutations in the CYP2C9 gene increase VPA levels. This underscores the importance of considering these factors when prescribing VPA to children with epilepsy, thereby enhancing the safety and efficacy of the therapy.

The long-term effects of blood urea nitrogen levels on cardiovascular disease and all-cause mortality in diabetes: a prospective cohort study

BACKGROUND

The long-term effects of blood urea nitrogen(BUN) in patients with diabetes remain unknown. Current studies reporting the target BUN level in patients with diabetes are also limited. Hence, this prospective study aimed to explore the relationship of BUN with all-cause and cardiovascular mortalities in patients with diabetes.

METHODS

In total, 10,507 participants with diabetes from the National Health and Nutrition Examination Survey (1999-2018) were enrolled. The causes and numbers of deaths were determined based on the National Death Index mortality data from the date of NHANES interview until follow-up (December 31, 2019). Multivariate Cox proportional hazard regression models were used to calculate the hazard ratios (HRs) and 95% confidence interval (CIs) of mortality.

RESULTS

Of the adult participants with diabetes, 4963 (47.2%) were female. The median (interquartile range) BUN level of participants was 5 (3.93-6.43) mmol/L. After 86,601 person-years of follow-up, 2,441 deaths were documented. After adjusting for variables, the HRs of cardiovascular disease (CVD) and all-cause mortality in the highest BUN level group were 1.52 and 1.35, respectively, compared with those in the lowest BUN level group. With a one-unit increment in BUN levels, the HRs of all-cause and CVD mortality rates were 1.07 and 1.08, respectively. The results remained robust when several sensitivity and stratified analyses were performed. Moreover, BUN showed a nonlinear association with all-cause and CVD mortality. Their curves all showed that the inflection points were close to the BUN level of 5 mmol/L.

CONCLUSION

BUN had a nonlinear association with all-cause and CVD mortality in patients with diabetes. The inflection point was at 5 mmol/L.

Predicting ICU readmission risks in intracerebral hemorrhage patients: Insights from machine learning models using MIMIC databases

BACKGROUND

Intracerebral hemorrhage (ICH) is a stroke subtype characterized by high mortality and complex post-event complications. Research has extensively covered the acute phase of ICH; however, ICU readmission determinants remain less explored. Utilizing the MIMIC-III and MIMIC-IV databases, this investigation develops machine learning (ML) models to anticipate ICU readmissions in ICH patients.

METHODS

Retrospective data from 2242 ICH patients were evaluated using ICD-9 codes. Recursive feature elimination with cross-validation (RFECV) discerned significant predictors of ICU readmissions. Four ML models-AdaBoost, RandomForest, LightGBM, and XGBoost-underwent development and rigorous validation. SHapley Additive exPlanations (SHAP) elucidated the effect of distinct features on model outcomes.

RESULTS

ICU readmission rates were 9.6% for MIMIC-III and 10.6% for MIMIC-IV. The LightGBM model, with an AUC of 0.736 (95% CI: 0.668-0.801), surpassed other models in validation datasets. SHAP analysis revealed hydrocephalus, sex, neutrophils, Glasgow Coma Scale (GCS), specific oxygen saturation (SpO2) levels, and creatinine as significant predictors of readmission.

CONCLUSION

The LightGBM model demonstrates considerable potential in predicting ICU readmissions for ICH patients, highlighting the importance of certain clinical predictors. This research contributes to optimizing patient care and ICU resource management. Further prospective studies are warranted to corroborate and enhance these predictive insights for clinical utilization.

Prediction of prognosis in patients with nontraumatic intracranial hemorrhage using blood urea nitrogen-to-creatinine ratio on admission: a retrospective cohort study based on data from the medical information Mart for intensive care-IV database

Background

The blood urea nitrogen-to-creatinine ratio (BUNCR) has been proposed as a potential biomarker for critical illness-induced catabolism. However, its specific relevance and significance in the context of non-traumatic intracranial hemorrhage (NTIH) remains unclear. As such, the primary objective of this study was to determine the role of BUNCR in the prognosis of patients with NTIH.

Materials and methods

All data were sourced from the Medical Information Mart for Intensive Care-IV 2.0 (MIMIC-IV) database. Study outcomes included 30-day and 1-year mortality rates. Univariate and multivariate logistic regression analyses were used to calculate adjusted odds ratio with corresponding 95% confidence interval, and generalized additive model were used to identify both linear and non-linear relationships between BUNCR and mortality rates. A two-piecewise regression model was performed to calculate the saturation effect. Subgroup analyses were performed to evaluate outcome stability in various groups.

Results

A retrospective study of 3,069 patients with NTIH revealed a U-shaped relationship between BUNCR levels and 30-day/1-year mortality. The two-piecewise regression model showed that the inflection points for 30-day and 1-year mortality were 10.455 and 16.25, respectively. On the left side of the inflection point, the 30-day and 1-year mortality rate decreased by 17.7% (OR = 0.823, 95%CI: 0.705-0.960; p = 0.013) and 5.3% (OR = 0.947, 95%CI: 0.899-0.999; p = 0.046), respectively, per 1 unit increment of BUNCR. On the right side of the inflection point, the 30-day and 1-year mortality rate increased by 1.6% (OR = 1.016, 95%CI: 1.000-1.031; p = 0.046) and 3.6% (OR = 1.036, 95%CI:1.019-1.054; p < 0.001) per 1 unit decrement of BUNCR. Subgroup analyses revealed consistent results across different strata.

Conclusion

This study identified a nonlinear relationship between BUNCR and mortality in patients with NTIH, indicating that BUNCR may be valuable prognostic marker for early identification and proactive management.

A comparison of random survival forest and Cox regression for prediction of mortality in patients with hemorrhagic stroke

OBJECTIVE

To evaluate RSF and Cox models for mortality prediction of hemorrhagic stroke (HS) patients in intensive care unit (ICU).

METHODS

In the training set, the optimal models were selected using five-fold cross-validation and grid search method. In the test set, the bootstrap method was used to validate. The area under the curve(AUC) was used for discrimination, Brier Score (BS) was used for calibration, positive predictive value(PPV), negative predictive value(NPV), and F1 score were combined to compare.

RESULTS

A total of 2,990 HS patients were included. For predicting the 7-day mortality, the mean AUCs for RSF and Cox regression were 0.875 and 0.761, while the mean BS were 0.083 and 0.108. For predicting the 28-day mortality, the mean AUCs for RSF and Cox regression were 0.794 and 0.649, while the mean BS were 0.129 and 0.174. The mean AUCs of RSF and Cox versus conventional scores for predicting patients' 7-day mortality were 0.875 (RSF), 0.761 (COX), 0.736 (SAPS II), 0.723 (OASIS), 0.632 (SIRS), and 0.596 (SOFA), respectively.

CONCLUSIONS

RSF provided a better clinical reference than Cox. Creatine, temperature, anion gap and sodium were important variables in both models.