Acute kidney injury-attributable mortality in critically ill patients with sepsis

Construction and evaluation of a mortality prediction model for patients with acute kidney injury undergoing continuous renal replacement therapy based on machine learning algorithms

BACKGROUND

To construct and evaluate a predictive model for in-hospital mortality among critically ill patients with acute kidney injury (AKI) undergoing continuous renal replacement therapy (CRRT), based on nine machine learning (ML) algorithm.

METHODS

The study retrospectively included patients with AKI who underwent CRRT during their initial hospitalization in the United States using the medical information mart for intensive care (MIMIC) database IV (version 2.0), as well as in the intensive care unit (ICU) of Huzhou Central Hospital. Patients from the MIMIC database were used as the training cohort to construct the models (from 2008 to 2019, n = 1068). Patients from Huzhou Central Hospital were utilized as the external validation cohort to evaluate the models (from June 2019 to December 2022, n = 327). In the training cohort, least absolute shrinkage and selection operator (LASSO) regression with cross-validation was employed to select features for constructing the model and subsequently established nine ML predictive models. The performance of these nine models on the external validation cohort dataset was comprehensively evaluated based on the area under the receiver operating characteristic curve (AUROC) and the optimal model was selected. A static nomogram and a web-based dynamic nomogram were presented, with a comprehensive evaluation from the perspectives of discrimination (AUROC), calibration (calibration curve) and clinical practicability (DCA curves).

RESULTS

Finally, 1395 eligible patients were enrolled, including 1068 patients in the training cohort and 327 patients in the external validation cohort. In the training cohort, LASSO regression with cross-validation was employed to select features and nine models were individually constructed. Compared to the other eight models, the Lasso regularized logistic regression (Lasso-LR) model exhibited the highest AUROC (0.756) and the optimal calibration curve. The DCA curve suggested a certain clinical utility in predicting in-hospital mortality among critically ill patients with AKI undergoing CRRT. Consequently, the Lasso-LR model was the optimal model and it was visualized as a common nomogram (static nomogram) and a web-based dynamic nomogram (https://chsyh2006.shinyapps.io/dynnomapp/). Discrimination, calibration and DCA curves were employed to assess the performance of the nomogram. The AUROC for the training and external validation cohorts in the nomogram model was 0.771 (95%CI: 0.743, 0.799) and 0.756 (95%CI: 0.702, 0.809), respectively. The calibration slope and Brier score for the training cohort were 1.000 and 0.195, while for the external validation cohort, they were 0.849 and 0.197, respectively. The DCA indicated that the model had a certain clinical application value.

CONCLUSIONS

Our study selected the optimal model and visualized it as a static and dynamic nomogram integrating clinical predictors, so that clinicians can personalized predict the in-hospital outcome of critically ill patients with AKI undergoing CRRT upon ICU admission.

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.

Attributable mortality of acute kidney injury among critically ill patients with sepsis: a multicenter, retrospective cohort study

BACKGROUND

Sepsis and acute kidney injury (AKI) are common severe diseases in the intensive care unit (ICU). This study aimed to estimate the attributable mortality of AKI among critically ill patients with sepsis and to assess whether AKI was an independent risk factor for 30-day mortality.

METHODS

The information we used was derived from a multicenter prospective cohort study conducted in 18 Chinese ICUs, focusing on septic patients post ICU admission. The patients were categorized into two groups: those who developed AKI (AKI group) within seven days following a sepsis diagnosis and those who did not develop AKI (non-AKI group). Using propensity score matching (PSM), patients were matched 1:1 as AKI and non-AKI groups. We then calculated the mortality rate attributable to AKI in septic patients. Furthermore, a survival analysis was conducted comparing the matched AKI and non-AKI septic patients. The primary outcome of interest was the 30-day mortality rate following the diagnosis of sepsis.

RESULTS

Out of the 2175 eligible septic patients, 61.7% developed AKI. After the application of PSM, a total of 784 septic patients who developed AKI were matched in a 1:1 ratio with 784 septic patients who did not develop AKI. The overall 30-day attributable mortality of AKI was 6.6% (95% CI 2.3 ∼ 10.9%, p = 0.002). A subgroup analysis revealed that the 30-day attributable mortality rates for stage 1, stage 2, and stage 3 AKI were 0.6% (95% CI -5.9 ∼ 7.2%, p = 0.846), 4.7% (95% CI -3.1 ∼ 12.4%, p = 0.221) and 16.8% (95% CI 8.1 ∼ 25.2%, p < 0.001), respectively. Particularly noteworthy was that stage 3 AKI emerged as an independent risk factor for 30-day mortality, possessing an adjusted hazard ratio of 1.80 (95% CI 1.31 ∼ 2.47, p < 0.001).

CONCLUSIONS

The overall 30-day attributable mortality of AKI among critically ill patients with sepsis was 6.6%. Stage 3 AKI had the most significant contribution to 30-day mortality, while stage 1 and stage 2 AKI did not increase excess mortality.

Nephrotoxicity caused by colistin use in ICU: a single centre experience

BACKGROUND

We aimed to determine the risk factors that may be associated with colistin-induced acute kidney injury (AKI) to promote the safer use of colistin in the treatment of nosocomial infections caused by multidrug-resistant Gram-negative bacteria in intensive care units.

MATERIALS AND METHODS

This retrospective observational study was conducted among adult patients who received a minimum of 48 h of intravenous colistin from January 2020 to December 2020 at the intensive care unit of a tertiary care hospital. AKI diagnosis and staging were made based on the Kidney Disease Improving Global Outcome Criteria.

RESULTS

Of 148 patients who received intravenous colistin at a daily dose of 9 million IU, 54 (36%) developed AKI. In the univariate analysis, age, Charlson comorbidity index, APACHE II score, duration of colistin treatment, basal creatinine level, use of vasopressors, and vancomycin were significantly associated with AKI (p < 0.05). The multivariate analysis revealed that the independent predictor of AKI was the use of vasopressors (OR: 3.14; 95% confidence interval: 1.39-97.07; p = 0.06).

CONCLUSION

The use of vasopressors in critically ill patients was independently associated with AKI developing during colistin treatment.

Amikacin-induced acute kidney injury in mechanically ventilated critically ill patients with sepsis

In this retrospective cohort study, we aimed to evaluate the incidence, risk factors and outcomes of amikacin-induced acute kidney injury (AKI) in critically ill patients with sepsis. A total of 311 patients were included in the study. Of them, 83 (26.7%) had amikacin-induced AKI. In model 1, the multivariable analysis demonstrated concurrent use of colistin (OR 25.51, 95%CI 6.99-93.05, p< 0.001), presence of septic shock during amikacin treatment (OR 4.22, 95%CI 1.76-10.11, p=0.001), and Charlson Comorbidity Index (OR 1.14, 95%CI 1.02-1.28, p=0.025) as factors independently associated with an increased risk of amikacin-induced AKI. In model 2, the multivariable analysis demonstrated concurrent use of at least one nephrotoxic agent (OR 1.95, 95%CI 1.10-3.45; p=0.022), presence of septic shock during amikacin treatment (OR 3.48, 95%CI 1.61-7.53; p=0.002), and Charlson Comorbidity Index (OR 1.12, 95%CI 1.01-1.26; p=0.037) as factors independently associated with an increased risk of amikacin-induced AKI. In conclusion, before amikacin administration, the risk of AKI should be considered, especially in patients with multiple complicated comorbid diseases, septic shock, and those receiving colistin therapy.

Explainable ensemble machine learning model for prediction of 28-day mortality risk in patients with sepsis-associated acute kidney injury

Background

Sepsis-associated acute kidney injury (S-AKI) is a major contributor to mortality in intensive care units (ICU). Early prediction of mortality risk is crucial to enhance prognosis and optimize clinical decisions. This study aims to develop a 28-day mortality risk prediction model for S-AKI utilizing an explainable ensemble machine learning (ML) algorithm.

Methods

This study utilized data from the Medical Information Mart for Intensive Care IV (MIMIC-IV 2.0) database to gather information on patients with S-AKI. Univariate regression, correlation analysis and Boruta were combined for feature selection. To construct the four ML models, hyperparameters were tuned via random search and five-fold cross-validation. To evaluate the performance of all models, ROC, K-S, and LIFT curves were used. The discrimination of ML models and traditional scoring systems was compared using area under the receiver operating characteristic curve (AUC). Additionally, the SHapley Additive exPlanation (SHAP) was utilized to interpret the ML model and identify essential variables. To investigate the relationship between the top nine continuous variables and the risk of 28-day mortality. COX regression-restricted cubic splines were utilized while controlling for age and comorbidities.

Results

The study analyzed data from 9,158 patients with S-AKI, dividing them into a 28-day mortality group of 1,940 and a survival group of 7,578. The results showed that XGBoost was the best performing model of the four ML models with AUC of 0.873. All models outperformed APS-III 0.713 and SAPS-II 0.681. The K-S and LIFT curves indicated XGBoost as the most effective predictor for 28-day mortality risk. The model's performance was evaluated using ROCpr curves, calibration curves, accuracy, precision, and F1 scores. SHAP force plots were utilized to interpret and visualize the personalized predictive power of the 28-day mortality risk model. Additionally, COX regression restricted cubic splines revealed an interesting non-linear relationship between the top nine variables and 28-day mortality.

Conclusion

The use of ensemble ML models has shown to be more effective than the LR model and conventional scoring systems in predicting 28-day mortality risk in S-AKI patients. By visualizing the XGBoost model with the best predictive performance, clinicians are able to identify high-risk patients early on and improve prognosis.

A proof-of-concept assay for quantitative and optical assessment of drug-induced toxicity in renal organoids

Kidneys are complex organs, and reproducing their function and physiology in a laboratory setting remains difficult. During drug development, potential compounds may exhibit unexpected nephrotoxic effects, which imposes a significant financial burden on pharmaceutical companies. As a result, there is an ongoing need for more accurate model systems. The use of renal organoids to simulate responses to nephrotoxic insults has the potential to bridge the gap between preclinical drug efficacy studies in cell cultures and animal models, and the stages of clinical trials in humans. Here we established an accessible fluorescent whole-mount approach for nuclear and membrane staining to first provide an overview of the organoid histology. Furthermore, we investigated the potential of renal organoids to model responses to drug toxicity. For this purpose, organoids were treated with the chemotherapeutic agent doxorubicin for 48 h. When cell viability was assessed biochemically, the organoids demonstrated a significant, dose-dependent decline in response to the treatment. Confocal microscopy revealed visible tubular disintegration and a loss of cellular boundaries at high drug concentrations. This observation was further reinforced by a dose-dependent decrease of the nuclear area in the analyzed images. In contrast to other approaches, in this study, we provide a straightforward experimental framework for drug toxicity assessment in renal organoids that may be used in early research stages to assist screen for potential adverse effects of compounds.