Which risk predictors are more likely to indicate severe AKI in hospitalized patients?

An effective multi-step feature selection framework for clinical outcome prediction using electronic medical records

BACKGROUND

Identifying key variables is essential for developing clinical outcome prediction models based on high-dimensional electronic medical records (EMR). However, despite the abundance of feature selection (FS) methods available, challenges remain in choosing the most appropriate method, deciding how many top-ranked variables to include, and ensuring these selections are meaningful from a medical perspective.

METHODS

We developed a practical multi-step feature selection (FS) framework that integrates data-driven statistical inference with a knowledge verification strategy. This framework was validated using two distinct EMR datasets targeting different clinical outcomes. The first cohort, sourced from the Medical Information Mart for Intensive Care III (MIMIC-III), focused on predicting acute kidney injury (AKI) in ICU patients. The second cohort, drawn from the MIMIC-IV Emergency Department (MIMIC-IV-ED), aimed to estimate in-hospital mortality (IHM) for patients transferred from the ED to the ICU. We employed various machine learning (ML) methods and conducted a comparative analysis considering accuracy, stability, similarity, and interpretability. The effectiveness of our FS framework was evaluated using discrimination and calibration metrics, with SHAP applied to enhance the interpretability of model decisions.

RESULTS

Cohort 1 comprised 48,780 ICU encounters, of which 8,883 (18.21%) developed AKI. Cohort 2 included 29,197 transfers from the ED to the ICU, with 3,219 (11.03%) resulting in IHM. Among the ten ML methods evaluated, the tree-based ensemble method achieved the highest accuracy. As the number of top-ranking features increased, the models' accuracy began to stabilize, while feature subset stability (considering sample variations) and inter-method feature similarity reached optimal levels, confirming the validity of the FS framework. The integration of interpretative methods and expert knowledge in the final step further improved feature interpretability. The FS framework effectively reduced the number of features (e.g., from 380 to 35 for Cohort 1, and from 273 to 54 for Cohort 2) without significantly affecting prediction performance (Delong test, p > 0.05).

CONCLUSION

The multi-step FS method developed in this study successfully reduces the dimensionality of features in EMR while preserving the accuracy of clinical outcome prediction. Furthermore, it improves the interpretability of risk factors by incorporating expert knowledge validation.

Development and validation of a Multi-Causal investigation and discovery framework for knowledge harmonization (MINDMerge): A case study with acute kidney injury risk factor discovery using electronic medical records

OBJECTIVE

Accurate diagnoses and personalized treatments in medicine rely on identifying causality. However, existing causal discovery algorithms often yield inconsistent results due to distinct learning mechanisms. To address this challenge, we introduce MINDMerge, a multi-causal investigation and discovery framework designed to synthesize causal graphs from various algorithms.

METHODS

MINDMerge integrates five causal models to reconcile inconsistencies arising from different algorithms. Employing credibility weighting and a novel cycle-breaking mechanism in causal networks, we initially developed and tested MINDMerge using three synthetic networks. Subsequently, we validated its effectiveness in discovering risk factors and predicting acute kidney injury (AKI) using two electronic medical records (EMR) datasets, eICU Collaborative Research Database and MIMIC-III Database. Causal reasoning was employed to analyze the relationships between risk factors and AKI. The identified causal risk factors of AKI were used in building a prediction model, and the prediction model was evaluated using the area under the receiver operating characteristics curve (AUC) and recall.

RESULTS

Synthetic data experiments demonstrated that our model outperformed significantly in capturing ground-truth network structure compared to other causal models. Application of MINDMerge on real-world data revealed direct connections of pulmonary disease, hypertension, diabetes, x-ray assessment, and BUN with AKI. With the identified variables, AKI risk can be inferred at the individual level based on established BNs and prior information. Compared against existing benchmark models, MINDMerge maintained a higher AUC for AKI prediction in both internal (AUC: 0.832) and external network validations (AUC: 0.861).

CONCLUSION

MINDMerge can identify causal risk factors of AKI, serving as a valuable diagnostic tool for clinical decision-making and facilitating effective intervention.

Epithelial Transport in Disease: An Overview of Pathophysiology and Treatment

Epithelial transport is a multifaceted process crucial for maintaining normal physiological functions in the human body. This comprehensive review delves into the pathophysiological mechanisms underlying epithelial transport and its significance in disease pathogenesis. Beginning with an introduction to epithelial transport, it covers various forms, including ion, water, and nutrient transfer, followed by an exploration of the processes governing ion transport and hormonal regulation. The review then addresses genetic disorders, like cystic fibrosis and Bartter syndrome, that affect epithelial transport. Furthermore, it investigates the involvement of epithelial transport in the pathophysiology of conditions such as diarrhea, hypertension, and edema. Finally, the review analyzes the impact of renal disease on epithelial transport and highlights the potential for future research to uncover novel therapeutic interventions for conditions like cystic fibrosis, hypertension, and renal failure.

Platelet-to-Lymphocyte Ratio and In-Hospital Mortality in Patients With AKI Receiving Continuous Kidney Replacement Therapy: A Retrospective Observational Cohort Study

Rationale & Objective

The platelet-to-lymphocyte ratio (PLR) is a marker of inflammation and a predictor of mortality in a variety of diseases. However, the effectiveness of PLR as a predictor of mortality in patients with severe acute kidney injury (AKI) is uncertain. We evaluated the association between the PLR and mortality in critically ill patients with severe AKI who underwent continuous kidney replacement therapy (CKRT).

Study Design

Retrospective cohort study.

Setting & Participants

A total of 1,044 patients who underwent CKRT in a single center, from February 2017 to March 2021.

Exposures

PLR.

Outcomes

In-hospital mortality.

Analytical Approach

The study patients were classified into quintiles according to the PLR values. A Cox proportional hazards model was used to investigate the association between PLR and mortality.

Results

The PLR value was associated with in-hospital mortality in a nonlinear manner, showing a higher mortality at both ends of the PLR. The Kaplan-Meier curve revealed the highest mortality with the first and fifth quintiles, whereas the lowest mortality occurred with the third quintile. Compared with the third quintile, the first (adjusted HR, 1.94; 95% CI, 1.44-2.62; P < 0.001) and fifth (adjusted HR, 1.60; 95% CI, 1.18-2.18; P = 0.002) quintiles of the PLR group had a significantly higher in-hospital mortality rate. The first and fifth quintiles showed a consistently increased risk of 30- and 90-day mortality rates compared with those of the third quintile. In the subgroup analysis, the lower and higher PLR values were predictors of in-hospital mortality in patients with older age, of female sex, and with hypertension, diabetes, and higher Sequential Organ Failure Assessment score.

Limitations

There may be bias owing to the single-center retrospective nature of this study. We only had PLR values at the time of initiation of CKRT.

Conclusions

Both the lower and higher PLR values were independent predictors of in-hospital mortality in critically ill patients with severe AKI who underwent CKRT.

Bibliometric and visual analysis of machine learning-based research in acute kidney injury worldwide

Background

Acute kidney injury (AKI) is a serious clinical complication associated with adverse short-term and long-term outcomes. In recent years, with the rapid popularization of electronic health records and artificial intelligence machine learning technology, the detection rate and treatment of AKI have been greatly improved. At present, there are many studies in this field, and a large number of articles have been published, but we do not know much about the quality of research production in this field, as well as the focus and trend of current research.

Methods

Based on the Web of Science Core Collection, studies reporting machine learning-based AKI research that were published from 2013 to 2022 were retrieved and collected after manual review. VOSviewer and other software were used for bibliometric visualization analysis, including publication trends, geographical distribution characteristics, journal distribution characteristics, author contributions, citations, funding source characteristics, and keyword clustering.

Results

A total of 336 documents were analyzed. Since 2018, publications and citations have increased dramatically, with the United States (143) and China (101) as the main contributors. Regarding authors, Bihorac, A and Ozrazgat-Baslanti, T from the Kansas City Medical Center have published 10 articles. Regarding institutions, the University of California (18) had the most publications. Approximately 1/3 of the publications were published in Q1 and Q2 journals, of which Scientific Reports (19) was the most prolific journal. Tomašev et al.'s study that was published in 2019 has been widely cited by researchers. The results of cluster analysis of co-occurrence keywords suggest that the construction of AKI prediction model related to critical patients and sepsis patients is the research frontier, and XGBoost algorithm is also popular.

Conclusion

This study first provides an updated perspective on machine learning-based AKI research, which may be beneficial for subsequent researchers to choose suitable journals and collaborators and may provide a more convenient and in-depth understanding of the research basis, hotspots and frontiers.

Mortality Prediction in Patients with Severe Acute Kidney Injury Requiring Renal Replacement Therapy

Background and Objective: Acute kidney injury (AKI) remains a serious health condition around the world, and is related to high morbidity, mortality, longer hospitalization duration and worse long-term outcomes. The aim of our study was to estimate the significant related factors for poor outcomes of patients with severe AKI requiring renal replacement therapy (RRT). Materials and Methods: We retrospectively analyzed data from patients (n = 573) with severe AKI requiring RRT within a 5-year period and analyzed the outcomes on discharge from the hospital. We also compared the clinical data of the surviving and non-surviving patients and examined possible related factors for poor patient outcomes. Logistic regression was used to analyze the odds ratio for patient mortality and its related factors. Results: In 32.5% (n = 186) of the patients, the renal function improved and RRT was stopped, 51.7% (n = 296) of the patients died, and 15.9% (n = 91) of the patients remained dialysis-dependent on the day of discharge from the hospital. During the period of 5 years, the outcomes of the investigated patients did not change statistically significantly. Administration of vasopressors, aminoglycosides, sepsis, pulmonary edema, oliguria, artificial pulmonary ventilation (APV), patient age ≥ 65 y, renal cause of AKI, AKI after cardiac surgery, a combination of two or more RRT methods, dysfunction of three or more organs, systolic blood pressure (BP) ≤ 120 mmHg, diastolic BP ≤ 65 mmHg, and Sequential Organ Failure Assessment (SOFA) score on the day of the first RRT procedure ≥ 7.5 were related factors for lethal patient outcome. Conclusions: The mortality rate among patients with severe AKI requiring RRT is very high—52%. Patient death was significantly predicted by the causes of AKI (sepsis, cardiac surgery), clinical course (oliguria, pulmonary edema, hypotension, acidosis, lesion of other organs) and the need for a continuous renal replacement therapy.

Development and Validation of a Model to Predict Severe Hospital-Acquired Acute Kidney Injury in Non-Critically Ill Patients

BACKGROUND

The current models developed to predict hospital-acquired AKI (HA-AKI) in non-critically ill fail to identify the patients at risk of severe HA-AKI stage 3.

OBJECTIVE

To develop and externally validate a model to predict the individual probability of developing HA-AKI stage 3 through the integration of electronic health databases.

METHODS

Study set: 165,893 non-critically ill hospitalized patients. Using stepwise logistic regression analyses, including demography, chronic comorbidities, and exposure to risk factors prior to AKI detection, we developed a multivariate model to predict HA-AKI stage 3. This model was then externally validated in 43,569 non-critical patients admitted to the validation center.

RESULTS

The incidence of HA-AKI stage 3 in the study set was 0.6%. Among chronic comorbidities, the highest odds ratios were conferred by ischemic heart disease, ischemic cerebrovascular disease, chronic congestive heart failure, chronic obstructive pulmonary disease, chronic kidney disease and liver disease. Among acute complications, the highest odd ratios were associated with acute respiratory failure, major surgery and exposure to nephrotoxic drugs. The model showed an AUC of 0.906 (95% CI 0.904 to 0.908), a sensitivity of 89.1 (95% CI 87.0-91.0) and a specificity of 80.5 (95% CI 80.2-80.7) to predict HA-AKI stage 3, but tended to overestimate the risk at low-risk categories with an adequate goodness-of-fit for all risk categories (Chi2: 16.4, p: 0.034). In the validation set, incidence of HA-AKI stage 3 was 0.62%. The model showed an AUC of 0.861 (95% CI 0.859-0.863), a sensitivity of 83.0 (95% CI 80.5-85.3) and a specificity of 76.5 (95% CI 76.2-76.8) to predict HA-AKI stage 3 with an adequate goodness of fit for all risk categories (Chi2: 15.42, p: 0.052).

CONCLUSIONS

Our study provides a model that can be used in clinical practice to obtain an accurate dynamic assessment of the individual risk of HA-AKI stage 3 along the hospital stay period in non-critically ill patients.