Artificial intelligence enabled applications in kidney disease

Advancing Cardiovascular, Kidney, and Metabolic Medicine: A Narrative Review of Insights and Innovations for the Future

Cardiovascular, kidney and metabolic (CKM) conditions are interrelated, significantly contributing to morbidity, mortality and healthcare burden. Despite therapeutic advances, traditional disease-specific approaches often fail to address their complex interplay. Key therapeutic agents-including glucagon-like peptide-1 receptor agonists (GLP-1 RAs), dual GLP-1/glucose-dependent insulinotropic polypeptide RAs, sodium glucose co-transporter inhibitors and the nonsteroidal mineralocorticoid receptor antagonist (MRA) finerenone-offer multi-organ benefits. Emerging therapies, such as triple receptor agonists and second-generation MRAs, target new pathways further expanding treatment options for CKM conditions. A holistic CKM management approach must address and recognise that conditions such as metabolic dysfunction-associated steatotic liver disease, metabolic dysfunction-associated steatohepatitis, obstructive sleep apnoea and obesity are part of the CKM spectrum. Frailty assessment is also important alongside CKM conditions, warranting comprehensive geriatric assessment and deprescribing when appropriate. Multidisciplinary care-including lifestyle interventions, pathway redesign, pharmacological advances and novel technologies-is essential for improving outcomes. As the CKM landscape evolves, future strategies should prioritise early intervention, personalised treatment and addressing unmet needs in high-risk populations. This review advocates for an integrated CKM framework, exploring treatment strategies, emerging therapies and technological innovations. It also examines the role of artificial intelligence and digital health tools in risk stratification, early diagnosis and long-term condition management, alongside ethical and regulatory considerations.

A population based optimization of convolutional neural networks for chronic kidney disease prediction

Chronic kidney disease (CKD) is a global public health concern, and the timely detection of the disease is priceless. Most of the classical machine learning models have the major drawbacks of being unsophisticated, non-robust, and non-accurate. This research work is therefore seeking to introduce OptiNet-CKD, a paradigm based on a DNN that has been integrated with a developed population optimization algorithm (POA) for CKD prediction optimization. POA is unlike gradient-based optimization methods in that it uses an initialized population of networks and perturbs their weight values to provide a broader exploration of the solution space. The model is more robust and less likely to overfit, and the predictions are likely to be more accurate since this approach helps to avoid the local minima problem suffered by gradient-based optimizers. To preprocess it for DNN learning, a CKD dataset with 400 records containing numerical and categorical features was imputed for missing data and scaled for its features. The model was evaluated using performance metrics such as accuracy, precision, recall, F1-score, and ROC AUC. OptiNet-CKD achieved 100% accuracy, 1.0 precision, 1.0 recall, 1.0 F1-score, and 1.0 ROC-AUC from traditional models (logistic regression, decision trees) and even fundamental deep neural networks. Results show that OptiNet-CKD is a reliable and robust prediction method for CKD, with more substantial generalization and performance than the existing methods. A combination of DNN and POA constitutes a promising approach for medical data analysis, especially for the diagnosis of CKD. POA expands the solution space, helping to expunge the model from falling into local minima and giving the model increased power in generalizing complicated medical data. Based on the simplicity of the algorithm, together with the structured formula and the extractions made in the preprocessing step, this framework can be extended to other medical conditions with similar data complexities, providing a potent tool for improving diagnostic accuracy in healthcare.

Synthetic data as an investigative tool in hypertension and renal diseases research

There is a growing body of clinical research on the utility of synthetic data derivatives, an emerging research tool in medicine. In nephrology, clinicians can use machine learning and artificial intelligence as powerful aids in their clinical decision-making while also preserving patient privacy. This is especially important given the epidemiology of chronic kidney disease, renal oncology, and hypertension worldwide. However, there remains a need to create a framework for guidance regarding how to better utilize synthetic data as a practical application in this research.

Leveraging Artificial Intelligence to Predict and Manage Complications in Patients With Multimorbidity: A Literature Review

Artificial intelligence (AI) is revolutionizing healthcare by improving diagnostic accuracy, streamlining treatment protocols, and augmenting patient care, especially in the management of multimorbidity. This review assesses the applications of AI in forecasting and controlling problems in multimorbid patients, emphasizing predictive analytics, real-time data integration, and enhancements in diagnostics. Utilizing extensive datasets from electronic health records and medical imaging, AI models facilitate early complication prediction and prompt therapies in diseases such as cancer, cardiovascular disorders, and diabetes. Notable developments encompass AI systems for the diagnosis of lung and breast cancer, markedly decreasing false positives and minimizing superfluous follow-ups. A comprehensive literature search was performed via PubMed and Google Scholar, applying Boolean logic with keywords such as "artificial intelligence", "multimorbidity", "predictive analytics", "machine learning", and "diagnosis". Articles published in English from January 2010 to December 2024, encompassing original research, systematic reviews, and meta-analyses regarding the use of AI in managing multimorbidity and healthcare decision-making, were included. Studies not pertinent to therapeutic applications, devoid of outcome measurements, or restricted to editorials were discarded. This review emphasizes AI's capacity to augment diagnostic precision and boost clinical results while also identifying substantial hurdles, including data bias, ethical issues, and the necessity for rigorous validation and longitudinal research to guarantee sustainable integration in clinical environments. This review's limitations encompass the possible exclusion of pertinent studies due to language and publication year constraints, as well as the disregard for grey literature, potentially constraining the comprehensiveness of the findings.

Artificial intelligence and machine learning in peritoneal dialysis: a systematic review of clinical outcomes and predictive modeling

BACKGROUND

The integration of artificial intelligence (AI) and machine learning (ML) in peritoneal dialysis (PD) presents transformative opportunities for optimizing treatment outcomes and informing clinical decision-making. This study aims to provide a comprehensive overview of the applications of AI/ML techniques in PD, focusing on their potential to predict clinical outcomes and enhance patient care.

MATERIALS AND METHODS

This systematic review was conducted according to PRISMA guidelines (2020), searching key databases for articles on AI and ML applications in PD. The inclusion criteria were stringent, ensuring the selection of high-quality studies. The search strategy comprised MeSH terms and keywords related to PD, AI, and ML. 793 articles were identified, with nine ultimately meeting the inclusion criteria. The review utilized a narrative synthesis approach to summarize findings due to anticipated study heterogeneity.

RESULTS

Nine studies met the inclusion criteria. The studies varied in sample size and employed diverse AI and ML techniques, reflecting the breadth of data considered. Mortality prediction emerged as a recurrent theme, demonstrating the significance of AI and ML in prognostic accuracy. Predictive modeling extended to technique failure, hospital stay prediction, and pathogen-specific immune responses, showcasing the versatility of AI and ML applications in PD.

CONCLUSIONS

This systematic review highlights the diverse applications of AI/ML in peritoneal dialysis, demonstrating their potential to enhance predictive accuracy, risk stratification, and decision support. However, limitations such as small sample sizes, single-center studies, and potential biases warrant further research and external validation. Future perspectives include integrating these AI/ML models into routine clinical practice and exploring additional use cases to improve patient outcomes and healthcare decision-making in PD.

Artificial intelligence in predicting chronic kidney disease prognosis. A systematic review and meta-analysis

BACKGROUND

Chronic kidney disease (CKD) is a common condition that can lead to serious health complications. Artificial Intelligence (AI) has shown the potential to improve the prediction of CKD progression, offering increased accuracy over traditional methods. Therefore, this systematic review and meta-analysis examine the diagnostic performance of various AI models in predicting CKD.

METHOD

Search was performed in different databases for studies reporting the diagnostic accuracy of AI-based prediction models for the progression of CKD. Meanwhile, pre-defined eligibility criteria were used for the selection of studies. Pooled sensitivity, specificity, and area under curve (AUC) were calculated utilizing Meta-disc 1.4. Quality assessment was performed using the prediction model risk of bias assessment tool (PROBAST).

RESULTS

A total of 33 studies were included. The pooled sensitivity of prediction tools was 0.43 (95% CI, 0.41-0.44, I2 = 99.3%, p < 0.01). A significant difference (p < 0.01) was also observed in the pooled specificity 0.92 (95% CI, 0.91-0.92, I2 = 99.5%). Positive likelihood ratio (PLP) and negative likelihood ratio (NLR) were 5.12 (95% CI: 3.60-7.27, I2 = 91.3%, p < 0.01) and 0.28 (95% CI: 0.21-0.37, I2 = 99.3%, p < 0.01), respectively and AUC was 0.89, suggesting a diagnostic accuracy of AI-based prediction models for the progression of CKD.

CONCLUSIONS

This study demonstrates the promising potential of AI models in predicting CKD progression. However, further efforts are needed to optimize model performance, particularly in balancing sensitivity and specificity to ensure generalizability across diverse populations. Limitations of this study include the potential for overfitting in certain AI models due to imbalanced datasets. The high heterogeneity and the lack of standardized predictors limit the generalizability of findings across different populations.

Predicting early mortality in hemodialysis patients: a deep learning approach using a nationwide prospective cohort in South Korea

Early mortality after hemodialysis (HD) initiation significantly impacts the longevity of HD patients. This study aimed to quantify the effect sizes of risk factors on mortality using various machine learning approaches. A cohort of 3284 HD patients from the CRC-ESRD (2008-2014) was analyzed. Mortality risk models were validated using logistic regression, ridge regression, lasso regression, and decision trees, as well as ensemble methods like bagging and random forest. To better handle missing data and time-series variables, a recurrent neural network (RNN) with an autoencoder was also developed. Additionally, survival models predicting hazard ratios were employed using survival analysis techniques. The analysis included 1750 prevalent and 1534 incident HD patients (mean age 58.4 ± 13.6 years, 59.3% male). Over a median follow-up of 66.2 months, the overall mortality rate was 19.3%. Random forest models achieved an AUC of 0.8321 for first-year mortality prediction, which was further improved by the RNN with autoencoder (AUC 0.8357). The survival bagging model had the highest hazard ratio predictability (C-index 0.7756). A shorter dialysis duration (< 14.9 months) and high modified Charlson comorbidity index scores (7-9) were associated with hazard ratios up to 7.76 (C-index 0.7693). Comorbidities were more influential than age in predicting early mortality. Monitoring dialysis adequacy (KT/V), RAAS inhibitor use, and urine output is crucial for assessing early prognosis.

Prediction of gastrointestinal bleeding hospitalization risk in hemodialysis using machine learning

BACKGROUND

Gastrointestinal bleeding (GIB) is a clinical challenge in kidney failure. INSPIRE group assessed if machine learning could determine a hemodialysis (HD) patient's 180-day GIB hospitalization risk.

METHODS

An eXtreme Gradient Boosting (XGBoost) and logistic regression model were developed using an HD dataset in United States (2017-2020). Patient data was randomly split (50% training, 30% validation, and 20% testing). HD treatments ≤ 180 days before GIB hospitalization were classified as positive observations; others were negative. Models considered 1,303 exposures/covariates. Performance was measured using unseen testing data.

RESULTS

Incidence of 180-day GIB hospitalization was 1.18% in HD population (n = 451,579), and 1.12% in testing dataset (n = 38,853). XGBoost showed area under the receiver operating curve (AUROC) = 0.74 (95% confidence interval (CI) 0.72, 0.76) versus logistic regression showed AUROC = 0.68 (95% CI 0.66, 0.71). Sensitivity and specificity were 65.3% (60.9, 69.7) and 68.0% (67.6, 68.5) for XGBoost versus 68.9% (64.7, 73.0) and 57.0% (56.5, 57.5) for logistic regression, respectively. Associations in exposures were consistent for many factors. Both models showed GIB hospitalization risk was associated with older age, disturbances in anemia/iron indices, recent all-cause hospitalizations, and bone mineral metabolism markers. XGBoost showed high importance on outcome prediction for serum 25 hydroxy (25OH) vitamin D levels, while logistic regression showed high importance for parathyroid hormone (PTH) levels.

CONCLUSIONS

Machine learning can be considered for early detection of GIB event risk in HD. XGBoost outperforms logistic regression, yet both appear suitable. External and prospective validation of these models is needed. Association between bone mineral metabolism markers and GIB events was unexpected and warrants investigation.

TRIAL REGISTRATION

This retrospective analysis of real-world data was not a prospective clinical trial and registration is not applicable.

A Comprehensive Review of Biomarkers for Chronic Kidney Disease in Older Individuals: Current Perspectives and Future Directions

Chronic kidney disease (CKD) is a progressive condition characterized by a gradual loss of kidney function, leading to significant health complications and an increased risk of cardiovascular events. Early detection and effective management are crucial for slowing disease progression and improving patient outcomes. Biomarkers are valuable tools in CKD diagnosis, prognosis, and treatment. Traditional biomarkers, such as serum creatinine and urine protein, are widely used, but emerging biomarkers like cystatin C, kidney injury molecule-1 (KIM-1), and neutrophil gelatinase-associated lipocalin (NGAL) offer enhanced diagnostic precision and insights into disease severity. These advanced biomarkers are particularly important in older adults, who may present with age-related physiological changes and comorbid conditions that complicate CKD management. This review explores the current state of biomarker research in CKD, focusing on their application in older populations. It highlights the role of traditional and emerging biomarkers, discusses their relevance for early detection and prognosis, and examines future directions in biomarker research, including technological innovations and personalized medicine approaches. By integrating biomarkers into clinical practice, healthcare providers can achieve more accurate diagnoses, tailor treatments to individual patient needs, and potentially improve the overall management of CKD. Continued research and development in this field are essential for addressing the complexities of CKD and advancing patient care.

Data-driven, two-stage machine learning algorithm-based prediction scheme for assessing 1-year and 3-year mortality risk in chronic hemodialysis patients

Life expectancy is likely to be substantially reduced in patients undergoing chronic hemodialysis (CHD). However, machine learning (ML) may predict the risk factors of mortality in patients with CHD by analyzing the serum laboratory data from regular dialysis routine. This study aimed to establish the mortality prediction model of CHD patients by adopting two-stage ML algorithm-based prediction scheme, combined with importance of risk factors identified by different ML methods. This is a retrospective, observational cohort study. We included 800 patients undergoing CHD between December 2006 and December 2012 in Shin-Kong Wu Ho-Su Memorial Hospital. This study analyzed laboratory data including 44 indicators. We used five ML methods, namely, logistic regression (LGR), decision tree (DT), random forest (RF), gradient boosting (GB), and eXtreme gradient boosting (XGB), to develop a two-stage ML algorithm-based prediction scheme and evaluate the important factors that predict CHD mortality. LGR served as a bench method. Regarding the validation and testing datasets from 1- and 3-year mortality prediction model, the RF had better accuracy and area-under-curve results among the five different ML methods. The stepwise RF model, which incorporates the most important factors of CHD mortality risk based on the average rank from DT, RF, GB, and XGB, exhibited superior predictive performance compared to LGR in predicting mortality among CHD patients over both 1-year and 3-year periods. We had developed a two-stage ML algorithm-based prediction scheme by implementing the stepwise RF that demonstrated satisfactory performance in predicting mortality in patients with CHD over 1- and 3-year periods. The findings of this study can offer valuable information to nephrologists, enhancing patient-centered decision-making and increasing awareness about risky laboratory data, particularly for patients with a high short-term mortality risk.

Quantifying the advantages and acceptability of linking dialysis machines to an electronic medical record

AIM

To establish and quantify the time saved by redirecting nursing workload from recording and entering haemodynamic data during chronic dialysis sessions by linking dialysis machines directly to the electronic medical record.

METHODS

We developed a bespoke interface from the HL7 feed from the dialysis machines (largely Fresenius 5008) to our EMR system (Cerner). We quantified the time nurses spent with the patient, computer, dialysis machine and sorting our patient related issues by observation using independent observers in a time and motion study. We performed these observations before and after implementation of the computer interface. We established patient and nursing acceptance by survey. We established adequacy of observations by counting the number of patients who received the minimum number of observations recorded in the system before and after implementation.

RESULTS

Implementation of a dialysis machine direct EMR interface reduced the time the nurses spent with the computer significantly by ∼9 % (around 28 min, p < 0.05) per dialysis shift, and this was accompanied by a similar increase in time spent sorting out patient-related issues. The interface was well accepted by staff and patients. An immediate benefit was a ∼60 % improvement in the adequacy of recording vital signs in our dialysis patients. Then simply by showing these results to the nursing staff there was further improvement.

CONCLUSIONS

In these days of machine interconnectivity there is really no good reason why dialysis nurses should be used to transfer data between machines. It is far better to utilise their skills in helping patients with their medical issues. We have shown that such a link improves efficiency, patient and staff satisfaction and dialysis governance.

AI-Powered Renal Diet Support: Performance of ChatGPT, Bard AI, and Bing Chat

Patients with chronic kidney disease (CKD) necessitate specialized renal diets to prevent complications such as hyperkalemia and hyperphosphatemia. A comprehensive assessment of food components is pivotal, yet burdensome for healthcare providers. With evolving artificial intelligence (AI) technology, models such as ChatGPT, Bard AI, and Bing Chat can be instrumental in educating patients and assisting professionals. To gauge the efficacy of different AI models in discerning potassium and phosphorus content in foods, four AI models-ChatGPT 3.5, ChatGPT 4, Bard AI, and Bing Chat-were evaluated. A total of 240 food items, curated from the Mayo Clinic Renal Diet Handbook for CKD patients, were input into each model. These items were characterized by their potassium (149 items) and phosphorus (91 items) content. Each model was tasked to categorize the items into high or low potassium and high phosphorus content. The results were juxtaposed with the Mayo Clinic Renal Diet Handbook's recommendations. The concordance between repeated sessions was also evaluated to assess model consistency. Among the models tested, ChatGPT 4 displayed superior performance in identifying potassium content, correctly classifying 81% of the foods. It accurately discerned 60% of low potassium and 99% of high potassium foods. In comparison, ChatGPT 3.5 exhibited a 66% accuracy rate. Bard AI and Bing Chat models had an accuracy rate of 79% and 81%, respectively. Regarding phosphorus content, Bard AI stood out with a flawless 100% accuracy rate. ChatGPT 3.5 and Bing Chat recognized 85% and 89% of the high phosphorus foods correctly, while ChatGPT 4 registered a 77% accuracy rate. Emerging AI models manifest a diverse range of accuracy in discerning potassium and phosphorus content in foods suitable for CKD patients. ChatGPT 4, in particular, showed a marked improvement over its predecessor, especially in detecting potassium content. The Bard AI model exhibited exceptional precision for phosphorus identification. This study underscores the potential of AI models as efficient tools in renal dietary planning, though refinements are warranted for optimal utility.

Symptoms for early diagnosis of chronic kidney disease in children - a machine learning-based score

The objective of this study was to reveal the signs and symptoms for the classification of pediatric patients at risk of CKD using decision trees and extreme gradient boost models for predicting outcomes. A case-control study was carried out involving children with 376 chronic kidney disease (cases) and a control group of healthy children (n = 376). A family member responsible for the children answered a questionnaire with variables potentially associated with the disease. Decision tree and extreme gradient boost models were developed to test signs and symptoms for the classification of children. As a result, the decision tree model revealed 6 variables associated with CKD, whereas twelve variables that distinguish CKD from healthy children were found in the "XGBoost". The accuracy of the "XGBoost" model (ROC AUC = 0.939, 95%CI: 0.911 to 0.977) was the highest, while the decision tree model was a little lower (ROC AUC = 0.896, 95%CI: 0.850 to 0.942). The cross-validation of results showed that the accuracy of the evaluation database model was like that of the training.

CONCLUSION

In conclusion, a dozen symptoms that are easy to be clinically verified emerged as risk indicators for chronic kidney disease. This information can contribute to increasing awareness of the diagnosis, mainly in primary care settings. Therefore, healthcare professionals can select patients for more detailed investigation, which will reduce the chance of wasting time and improve early disease detection.

WHAT IS KNOWN

• Late diagnosis of chronic kidney disease in children is common, increasing morbidity. • Mass screening of the whole population is not cost-effective.

WHAT IS NEW

• With two machine-learning methods, this study revealed 12 symptoms to aid early CKD diagnosis. • These symptoms are easily obtainable and can be useful mainly in primary care settings.

Predicting mortality risk in dialysis: Assessment of risk factors using traditional and advanced modeling techniques within the Monitoring Dialysis Outcomes initiative

INTRODUCTION

Several factors affect the survival of End Stage Kidney Disease (ESKD) patients on dialysis. Machine learning (ML) models may help tackle multivariable and complex, often non-linear predictors of adverse clinical events in ESKD patients. In this study, we used advanced ML method as well as a traditional statistical method to develop and compare the risk factors for mortality prediction model in hemodialysis (HD) patients.

MATERIALS AND METHODS

We included data HD patients who had data across a baseline period of at least 1 year and 1 day in the internationally representative Monitoring Dialysis Outcomes (MONDO) Initiative dataset. Twenty-three input parameters considered in the model were chosen in an a priori manner. The prediction model used 1 year baseline data to predict death in the following 3 years. The dataset was randomly split into 80% training data and 20% testing data for model development. Two different modeling techniques were used to build the mortality prediction model.

FINDINGS

A total of 95,142 patients were included in the analysis sample. The area under the receiver operating curve (AUROC) of the model on the test data with XGBoost ML model was 0.84 on the training data and 0.80 on the test data. AUROC of the logistic regression model was 0.73 on training data and 0.75 on test data. Four out of the top five predictors were common to both modeling strategies.

DISCUSSION

In the internationally representative MONDO data for HD patients, we describe the development of a ML model and a traditional statistical model that was suitable for classification of a prevalent HD patient's 3-year risk of death. While both models had a reasonably high AUROC, the ML model was able to identify levels of hematocrit (HCT) as an important risk factor in mortality. If implemented in clinical practice, such proof-of-concept models could be used to provide pre-emptive care for HD patients.

Predictors of shorter- and longer-term mortality after COVID-19 presentation among dialysis patients: parallel use of machine learning models in Latin and North American countries

Background

We developed machine learning models to understand the predictors of shorter-, intermediate-, and longer-term mortality among hemodialysis (HD) patients affected by COVID-19 in four countries in the Americas.

Methods

We used data from adult HD patients treated at regional institutions of a global provider in Latin America (LatAm) and North America who contracted COVID-19 in 2020 before SARS-CoV-2 vaccines were available. Using 93 commonly captured variables, we developed machine learning models that predicted the likelihood of death overall, as well as during 0–14, 15–30, > 30 days after COVID-19 presentation and identified the importance of predictors. XGBoost models were built in parallel using the same programming with a 60%:20%:20% random split for training, validation, & testing data for the datasets from LatAm (Argentina, Columbia, Ecuador) and North America (United States) countries.

Results

Among HD patients with COVID-19, 28.8% (1,001/3,473) died in LatAm and 20.5% (4,426/21,624) died in North America. Mortality occurred earlier in LatAm versus North America; 15.0% and 7.3% of patients died within 0–14 days, 7.9% and 4.6% of patients died within 15–30 days, and 5.9% and 8.6% of patients died > 30 days after COVID-19 presentation, respectively. Area under curve ranged from 0.73 to 0.83 across prediction models in both regions. Top predictors of death after COVID-19 consistently included older age, longer vintage, markers of poor nutrition and more inflammation in both regions at all timepoints. Unique patient attributes (higher BMI, male sex) were top predictors of mortality during 0–14 and 15–30 days after COVID-19, yet not mortality > 30 days after presentation.

Conclusions

Findings showed distinct profiles of mortality in COVID-19 in LatAm and North America throughout 2020. Mortality rate was higher within 0–14 and 15–30 days after COVID-19 in LatAm, while mortality rate was higher in North America > 30 days after presentation. Nonetheless, a remarkable proportion of HD patients died > 30 days after COVID-19 presentation in both regions. We were able to develop a series of suitable prognostic prediction models and establish the top predictors of death in COVID-19 during shorter-, intermediate-, and longer-term follow up periods.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12882-022-02961-x.

A Simpler Machine Learning Model for Acute Kidney Injury Risk Stratification in Hospitalized Patients

Background: Hospitalization-associated acute kidney injury (AKI), affecting one-in-five inpatients, is associated with increased mortality and major adverse cardiac/kidney endpoints. Early AKI risk stratification may enable closer monitoring and prevention. Given the complexity and resource utilization of existing machine learning models, we aimed to develop a simpler prediction model. Methods: Models were trained and validated to predict risk of AKI using electronic health record (EHR) data available at 24 h of inpatient admission. Input variables included demographics, laboratory values, medications, and comorbidities. Missing values were imputed using multiple imputation by chained equations. Results: 26,410 of 209,300 (12.6%) inpatients developed AKI during admission between 13 July 2012 and 11 July 2018. The area under the receiver operating characteristic curve (AUROC) was 0.86 for Random Forest and 0.85 for LASSO. Based on Youden’s Index, a probability cutoff of >0.15 provided sensitivity and specificity of 0.80 and 0.79, respectively. AKI risk could be successfully predicted in 91% patients who required dialysis. The model predicted AKI an average of 2.3 days before it developed. Conclusions: The proposed simpler machine learning model utilizing data available at 24 h of admission is promising for early AKI risk stratification. It requires external validation and evaluation of effects of risk prediction on clinician behavior and patient outcomes.

Prediction models used in the progression of chronic kidney disease: A scoping review

OBJECTIVE

To provide a review of prediction models that have been used to measure clinical or pathological progression of chronic kidney disease (CKD).

DESIGN

Scoping review.

DATA SOURCES

Medline, EMBASE, CINAHL and Scopus from the year 2011 to 17th February 2022.

STUDY SELECTION

All English written studies that are published in peer-reviewed journals in any country, that developed at least a statistical or computational model that predicted the risk of CKD progression.

DATA EXTRACTION

Eligible studies for full text review were assessed on the methods that were used to predict the progression of CKD. The type of information extracted included: the author(s), title of article, year of publication, study dates, study location, number of participants, study design, predicted outcomes, type of prediction model, prediction variables used, validation assessment, limitations and implications.

RESULTS

From 516 studies, 33 were included for full-text review. A qualitative analysis of the articles was compared following the extracted information. The study populations across the studies were heterogenous and data acquired by the studies were sourced from different levels and locations of healthcare systems. 31 studies implemented supervised models, and 2 studies included unsupervised models. Regardless of the model used, the predicted outcome included measurement of risk of progression towards end-stage kidney disease (ESKD) of related definitions, over given time intervals. However, there is a lack of reporting consistency on details of the development of their prediction models.

CONCLUSIONS

Researchers are working towards producing an effective model to provide key insights into the progression of CKD. This review found that cox regression modelling was predominantly used among the small number of studies in the review. This made it difficult to perform a comparison between ML algorithms, more so when different validation methods were used in different cohort types. There needs to be increased investment in a more consistent and reproducible approach for future studies looking to develop risk prediction models for CKD progression.

Aristolochic acid I as an emerging biogenic contaminant involved in chronic kidney diseases: A comprehensive review on exposure pathways, environmental health issues and future challenges

Described in the 1950s, Balkan Endemic Nephropathy (BEN) has been recognized as a chronic kidney disease (CKD) with clinical peculiarities and multiple etiological factors. Environmental contaminants - aromatic compounds, mycotoxins and phytotoxins like aristolochic acids (AAs) - polluting food and drinking water sources, were incriminated in BEN, due to their nephrotoxic and carcinogenic properties. The implication of AAs in BEN etiology is currently a highly debated topic due to the fact that they are found within the Aristolochiaceae plants family, used around the globe as traditional medicine and they were also incriminated in Aristolochic Acid Nephropathy (AAN). Exposure pathways have been investigated, but it is unclear to what extent AAs are acting alone or in synergy with other cofactors (environmental, genetics) in triggering kidney damage. Experimental studies strengthen the hypothesis that AAI, the most studied compound in the AAs class, is a significant environmental contaminant and a most important causative factor of BEN. The aim of this review is to compile information about the natural exposure pathways to AAI, via traditional medicinal plants, soil, crop plants, water, food, air. Data that either supports or contradicts the AAI theory concerning BEN etiology was consolidated and available solutions to reduce human exposure were discussed. Because AAI is a phytotoxin with physicochemical properties that allow its transportation in environmental matrices from different types of areas (endemic, nonendemic), and induce CKDs (BEN, AAN) and urinary cancers through bioaccumulation, this review aims to shed a new light on this compound as a biogenic emerging pollutant.

Artificial intelligence enabled applications in kidney disease

Artificial intelligence (AI) is considered as the next natural progression of traditional statistical techniques. Advances in analytical methods and infrastructure enable AI to be applied in health care. While AI applications are relatively common in fields like ophthalmology and cardiology, its use is scarcely reported in nephrology. We present the current status of AI in research toward kidney disease and discuss future pathways for AI. The clinical applications of AI in progression to end-stage kidney disease and dialysis can be broadly subdivided into three main topics: (a) predicting events in the future such as mortality and hospitalization; (b) providing treatment and decision aids such as automating drug prescription; and (c) identifying patterns such as phenotypical clusters and arteriovenous fistula aneurysm. At present, the use of prediction models in treating patients with kidney disease is still in its infancy and further evidence is needed to identify its relative value. Policies and regulations need to be addressed before implementing AI solutions at the point of care in clinics. AI is not anticipated to replace the nephrologists' medical decision-making, but instead assist them in providing optimal personalized care for their patients.