The predictive value of the Oxford Acute Severity of Illness Score for clinical outcomes in patients with acute kidney injury

Association of hydration status and in-hospital mortality in critically ill patients with ischemic stroke: Data from the MIMIC-IV database

BACKGROUND

Hydration plays a critical role in the pathophysiological progression of ischemic stroke. However, the impact of extreme hydration on the mortality of critically ill patients with ischemic stroke remains unclear. Therefore, our objective was to evaluate the association between hydration, as indicated by the blood urea nitrogen to creatinine ratio (UCR), and in-hospital mortality in critically ill patients with ischemic stroke.

METHODS

Data from the Medical Information Mart for Intensive Care (MIMIC-IV) database were utilized. Patients with ischemic stroke admitted to the Intensive Care Unit (ICU) for the first time were identified. The exposure variable was the hydration state represented by the UCR. The study outcome measure was in-hospital mortality. The primary analytical approach involved multivariate Cox regression analysis. Kaplan-Meier curves were constructed, and subgroup analyses with interaction were performed.

RESULTS

A total of 1539 patients, with a mean age of 69.9 years, were included in the study. Kaplan-Meier curves illustrated that patients in higher UCR tertiles exhibited increased in-hospital mortality. Accordingly, the risk of in-hospital mortality significantly rose by 29 % with every 10 units increase in UCR. Subgroup analysis indicated a robust association between UCR and in-hospital mortality in each subgroup, with no statistically significant interactions observed.

CONCLUSION

Hydration status is significantly associated with in-hospital all-cause mortality in critically ill patients with ischemic stroke. This finding underscores the importance of closely monitoring critically ill patients for adequate hydration and implementing appropriate rehydration strategies.

Association of serum ferritin and all-cause mortality in AKI patients: a retrospective cohort study

Background

Serum ferritin (SF) is clinically found to be elevated in many disease conditions, and our research examines serum ferritin in patients with acute kidney injury (AKI) and its implication on the risk of short-term mortality in AKI.

Methods

Data were extracted from the Medical Information Mart for Intensive Care IV 2.2 (MIMIC-IV 2.2) database. Adult patients with AKI who had serum ferritin tested on the first day of ICU admission were included. The primary outcome was 28-day mortality. Kaplan-Meier survival curves and Cox proportional hazards models were used to test the relationship between SF and clinical outcomes. Subgroup analyses based on the Cox model were further conducted.

Results

Kaplan-Meier survival curves showed that a higher SF value was significantly associated with an enhanced risk of 28-day mortality, 90-day mortality, ICU mortality and hospital mortality (log-rank test: p < 0.001 for all clinical outcomes). In multivariate Cox regression analysis, high level of SF with mortality was significantly positive in all four outcome events (all p < 0.001). This result remains robust after adjusting for all variables. Subgroup analysis of SF with 28-day mortality based on Cox model-4 showed that high level of SF was associated with high risk of 28-day mortality in patients regardless of the presence or absence of sepsis (p for interaction = 0.730). Positive correlations of SF and 28-day mortality were confirmed in all other subgroups (p for interaction>0.05).

Conclusion

High level of SF is an independent prognostic predictor of 28-day mortality in patients with AKI.

Association between white blood cell-to-haemoglobin ratio and 30 day mortality in heart failure in intensive care unit

AIMS

The short-term mortality of heart failure (HF) patients admitted to the intensive care unit (ICU) is reported to be high. This study aims to explore the association between white blood cell-to-haemoglobin ratio (WHR) and 30 day mortality from the admission to the ICU.

METHODS AND RESULTS

This retrospective cohort study was performed based on the Medical Information Mart for Intensive Care III (MIMIC-III) database (2001-12) and MIMIC-IV database (2008-19). Covariables were selected using the least absolute shrinkage and selection operator regression. Based on the optimal cutoff point selected using the survminer package, WHR was divided into high-ratio group (≥1.6) and low-ratio group (<1.6). The association between WHR and the risk of 30 day mortality was explored using univariate and multivariable Cox regression models. The area under the receiver operating characteristic curve (AUC) was calculated to evaluate the prediction performance of WHR. A total of 13 702 patients were included. After adjusting the potential covariates, high WHR was associated with a greater risk of 30 day mortality compared with low WHR [hazard ratio = 1.16, 95% confidence interval (CI): 1.07-1.27, P < 0.001]. WHR also showed a good performance for the prediction of risk of 30 day mortality (AUC = 0.751, 95% CI: 0.746-0.756).

CONCLUSIONS

WHR was positively associated with and performed well to predict 30 day mortality, indicating that WHR may be a reliable index to assess the prognosis of HF patients admitted to the ICU.

Urine output for predicting in-hospital mortality of intensive care patients with cardiogenic shock

Background

The role of urine output (UO) in the first 24 h of admission in the clinical management of cardiogenic shock (CS) patients has not been elucidated.

Methods

This study retrospectively analyzed intensive care CS patients in the MIMIC-IV database. Binomial logistic regression analysis was conducted to evaluate whether UO was an independent risk factor for in-hospital mortality in CS patients. The performance of UO in predicting mortality was evaluated by the receiver operating characteristic (ROC) curve and compared with the Oxford Acute Severity of Illness Score (OASIS). The clinical net benefit of UO in predicting mortality was determined using the decision curve analysis (DCA). Survival analysis was performed with Kaplan-Meier curves.

Results

After adjusting for confounding factors including diuretic use and acute kidney injury (AKI), UO remained an independent risk factor for in-hospital mortality in CS patients. The areas under the ROC curves (AUCs) of UO for predicting in-hospital mortality were 0.712 (UO, ml/day) and 0.701 (UO, ml/kg/h), which were comparable to OASIS (AUC = 0.695). In terms of clinical net benefit, UO was comparable to OASIS, with different degrees of benefit at different threshold probabilities. Survival analysis showed that the risk of in-hospital death in the low-UO (≤857 ml/day) group was 3.0143 times that of the high-UO (>857 ml/day) group.

Conclusions

UO in the first 24 h of admission is an independent risk factor for in-hospital mortality in intensive care CS patients and has moderate predictive value in predicting in-hospital mortality.

Association and prediction of red blood cell distribution width to albumin ratio in all-cause mortality of acute kidney injury in critically ill patients

Aim

The progression of acute kidney injury (AKI) might be associated with systemic inflammation. Our study aims to explore the association and predictive value of the red blood cell distribution width (RDW) to human serum albumin (ALB) ratio (RDW/ALB ratio), an inflammation-related indicator, in the risk of all-cause mortality and renal replacement therapy (RRT) in AKI patients admitted in intensive care units (ICU).

Methods

A retrospective cohort study was designed, and data were extracted from the Medical Information Mart for Intensive Care III (MIMIC-III). The primary outcome was the risk of all-cause mortality (1-month, 3-month, and 12-month), and the secondary outcome was the risk of RRT. The association between the RDW/ALB ratio and the risk of all-cause mortality and RRT was assessed using the Cox regression analysis, with results shown as hazard ratio (HR) and 95% confidence intervals (CIs). The relationship between the RDW/ALB ratio and crude probability of all-cause mortality or RRT was assessed using restricted cubic splines (RCS). The concordance index (C-index) was used to assess the discrimination of the prediction model.

Results

A total of 13,856 patients were included in our study. In the fully adjusted Cox regression model, we found that a high RDW/ALB ratio was associated with an increased risk of 1-month, 3-month, and 12-month all-cause mortality and RRT (all p &lt; 0.05). Moreover, RCS curves showed the linear relationship between the RDW/ALB ratio and the probability of all-cause mortality and RRT, and the probability was elevated with the increase of the ratio. In addition, the RDW/ALB ratio showed a good predictive performance in the risk of 1-month all-cause mortality, 3-month all-cause mortality, 12-month all-cause mortality, and RRT, with a C-index of 0.728 (95%CI: 0.719–0.737), 0.728 (95%CI: 0.721–0.735), 0.719 (95%CI: 0.713–0.725), and 0.883 (95%CI: 0.876–0.890), respectively.

Conclusion

The RDW/ALB ratio performed well to predict the risk of all-cause mortality and RRT in critically ill patients with AKI, indicating that this combined inflammatory indicator might be effective in clinical practice.

Effect of sex on the association between arterial partial pressure of oxygen and in-hospital mortality in ICU patients with cardiogenic shock: a retrospective cohort study

Background

Maintaining tissue perfusion and oxygen supply are essential for cardiogenic shock (CS) treatment. Sex has been reported to be associated with mortality and oxygen use in patients with CS. Males and females respond differently to hypoxia. We designed this cohort study to evaluate the effects of sex on the association between the arterial partial pressure of oxygen (PaO₂) and in-hospital mortality.

Methods

We used the Medical Information Mart for Intensive Care (MIMIC) IV database for this cohort study. The outcome was in-hospital mortality. The relationship between the PaO₂ and in-hospital mortality was compared with sex (via an interaction test) using multivariable Cox regression models. Presence of interaction between PaO₂ and sex was tested by using inter interaction terms.

Results

A total of 1,772 patients with CS were enrolled in this study. The association between PaO₂ and in-hospital mortality appeared to differ between males and females [hazard ratio (HR): 0.997, 95% confidence interval (CI): 0.995-0.999 vs. HR: 1.002, 95% CI: 0.999-1.003, P for interaction =0.002]. We repeated the analyses, based on different PaO₂ category (PaO₂ <60 mmHg; PaO₂ 60-100 mmHg; PaO₂ >100 mmHg) and the results remained stable, P for interaction =0.008.

Conclusions

Sex affects the relationship between PaO₂ and in-hospital mortality in CS patients. Our findings may lead to the development of individualized therapies that focus on the use of different target oxygen partial pressures in different sexes to treat patients with CS.

Dynamic nomogram for predicting acute kidney injury in patients with acute ischemic stroke: A retrospective study

Background

This study sought to develop and validate a dynamic nomogram chart to assess the risk of acute kidney injury (AKI) in patients with acute ischemic stroke (AIS).

Methods

These data were drawn from the Medical Information Mart for Intensive Care III (MIMIC-III) database, which collects 47 clinical indicators of patients after admission to the hospital. The primary outcome indicator was the occurrence of AKI within 48 h of intensive care unit (ICU) admission. Independent risk factors for AKI were screened from the training set using univariate and multifactorial logistic regression analyses. Multiple logistic regression models were developed, and nomograms were plotted and validated in an internal validation set. Based on the receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) to estimate the performance of this nomogram.

Results

Nomogram indicators include blood urea nitrogen (BUN), creatinine, red blood cell distribution width (RDW), heart rate (HR), Oxford Acute Severity of Illness Score (OASIS), the history of congestive heart failure (CHF), the use of vancomycin, contrast agent, and mannitol. The predictive model displayed well discrimination with the area under the ROC curve values of 0.8529 and 0.8598 for the training set and the validator, respectively. Calibration curves revealed favorable concordance between the actual and predicted incidence of AKI (p > 0.05). DCA indicates the excellent net clinical benefit of nomogram in predicting AKI.

Conclusion

In summary, we explored the incidence of AKI in patients with AIS during ICU stay and developed a predictive model to help clinical decision-making.

No sex differences in the incidence, risk factors and clinical impact of acute kidney injury in critically ill patients with sepsis

Background

Sex-stratified medicine is an important aspect of precision medicine. We aimed to compare the incidence and risk factors of acute kidney injury (AKI) for critically ill men and women with sepsis. Furthermore, the short-term mortality was compared between men and women with sepsis associated acute kidney injury (SA-AKI).

Method

This was a retrospective study based on the Medical Information Mart for Intensive Care IV database. We used the multivariable logistic regression analysis to evaluate the independent effect of sex on the incidence of SA-AKI. We further applied three machine learning methods (decision tree, random forest and extreme gradient boosting) to screen for the risk factors associated with SA-AKI in the total, men and women groups. We finally compared the intensive care unit (ICU) and hospital mortality between men and women with SA-AKI using propensity score matching.

Results

A total of 6463 patients were included in our study, including 3673 men and 2790 women. The incidence of SA-AKI was 83.8% for men and 82.1% for women. After adjustment for confounders, no significant association was observed between sex and the incidence of SA-AKI (odds ratio (OR), 1.137; 95% confidence interval (CI), 0.949-1.361; p=0.163). The machine learning results revealed that body mass index, Oxford Acute Severity of Illness Score, diuretic, Acute Physiology Score III and age were the most important risk factors of SA-AKI, irrespective of sex. After propensity score matching, men had similar ICU and hospital mortality to women.

Conclusions

The incidence and associated risk factors of SA-AKI are similar between men and women, and men and women with SA-AKI experience comparable rates of ICU and hospital mortality. Therefore, sex-related effects may play a minor role in developing SA-AKI. Our study helps to contribute to the knowledge gap between sex and SA-AKI.

Which model is superior in predicting ICU survival: artificial intelligence versus conventional approaches

Background

A disease severity classification system is widely used to predict the survival of patients admitted to the intensive care unit with different diagnoses. In the present study, conventional severity classification systems were compared with artificial intelligence predictive models (Artificial Neural Network and Decision Tree) in terms of the prediction of the survival rate of the patients admitted to the intensive care unit.

Methods

This retrospective cohort study was performed on the data of the patients admitted to the ICU of Ghaemshahr’s Razi Teaching Care Center from March 20th, 2017, to September 22nd, 2019. The required data for calculating conventional severity classification models (SOFA, SAPS II, APACHE II, and APACHE IV) were collected from the patients’ medical records. Subsequently, the score of each model was calculated. Artificial intelligence predictive models (Artificial Neural Network and Decision Tree) were developed in the next step. Lastly, the performance of each model in predicting the survival of the patients admitted to the intensive care unit was evaluated using the criteria of sensitivity, specificity, accuracy, F-measure, and area under the ROC curve. Also, each model was validated externally. The R program, version 4.1, was used to create the artificial intelligence models, and SPSS Statistics Software, version 21, was utilized to perform statistical analysis.

Results

The area under the ROC curve of SOFA, SAPS II, APACHE II, APACHE IV, multilayer perceptron artificial neural network, and CART decision tree were 76.0, 77.1, 80.3, 78.5, 84.1, and 80.0, respectively.

Conclusion

The results showed that although the APACHE II model had better results than other conventional models in predicting the survival rate of the patients admitted to the intensive care unit, the other conventional models provided acceptable results too. Moreover, the findings showed that the artificial neural network model had the best performance among all the studied models, indicating the discrimination power of this model in predicting patient survival compared to the other models.