Identifying subtypes of chronic kidney disease with machine learning: development, internal validation and prognostic validation using linked electronic health records in 350,067 individuals

Research on the development of an intelligent prediction model for blood pressure variability during hemodialysis

OBJECTIVE

Blood pressure fluctuations during dialysis, including intradialytic hypotension (IDH) and intradialytic hypertension (IDHTN), are common complications among patients undergoing maintenance hemodialysis. Early prediction of IDH and IDHTN can help reduce the occurrence of these fluctuations. With the development of artificial intelligence, machine learning and deep learning models have become increasingly sophisticated in the field of hemodialysis. Utilizing machine learning to predict blood pressure fluctuations during dialysis has become a viable predictive method.

METHODS

Our study included data from 67,524 hemodialysis sessions conducted at Ningbo No.2 Hospital and Xiangshan First People's Hospital from August 1, 2019, to September 30, 2023. 47,053 sessions were used for model training and testing, while 20,471 sessions were used for external validation. We collected 45 features, including general information, vital signs, blood routine, blood biochemistry, and other relevant data. Data not meeting the inclusion criteria were excluded, and feature engineering was performed. The definitions of IDH and IDHTN were clarified, and 10 machine learning algorithms were used to build the models. For model development, the dialysis data were randomly split into a training set (80%) and a testing set (20%). To evaluate model performance, six metrics were used: accuracy, precision, recall, F1 score, ROC-AUC, and PR-AUC. Shapley Additive Explanation (SHAP) method was employed to identify eight key features, which were used to develop a clinical application utilizing the Streamlit framework.

RESULTS

Statistical analysis showed that IDH occurred in 56.63% of hemodialysis sessions, while the incidence of IDHTN was 23.53%. Multiple machine learning models (e.g., CatBoost, RF) were developed to predict IDH and IDHTN events. XGBoost performed the best, achieving ROC-AUC scores of 0.89 for both IDH and IDHTN in internal validation, with PR-AUC scores of 0.95 and 0.78, and high accuracy, precision, recall, and F1 scores. The SHAP method identified pre-dialysis systolic blood pressure, BMI, and pre-dialysis mean arterial pressure as the top three important features. It has been translated into a convenient application for use in clinical settings.

CONCLUSION

Using machine learning models to predict IDH and IDHTN during hemodialysis is feasible and provides clinically reliable predictive performance. This can help timely implement interventions during hemodialysis to prevent problems, reduce blood pressure fluctuations during dialysis, and improve patient outcomes.

Unlocking precision medicine: clinical applications of integrating health records, genetics, and immunology through artificial intelligence

Artificial intelligence (AI) has emerged as a transformative force in precision medicine, revolutionizing the integration and analysis of health records, genetics, and immunology data. This comprehensive review explores the clinical applications of AI-driven analytics in unlocking personalized insights for patients with autoimmune rheumatic diseases. Through the synergistic approach of integrating AI across diverse data sets, clinicians gain a holistic view of patient health and potential risks. Machine learning models excel at identifying high-risk patients, predicting disease activity, and optimizing therapeutic strategies based on clinical, genomic, and immunological profiles. Deep learning techniques have significantly advanced variant calling, pathogenicity prediction, splicing analysis, and MHC-peptide binding predictions in genetics. AI-enabled immunology data analysis, including dimensionality reduction, cell population identification, and sample classification, provides unprecedented insights into complex immune responses. The review highlights real-world examples of AI-driven precision medicine platforms and clinical decision support tools in rheumatology. Evaluation of outcomes demonstrates the clinical benefits and impact of these approaches in revolutionizing patient care. However, challenges such as data quality, privacy, and clinician trust must be navigated for successful implementation. The future of precision medicine lies in the continued research, development, and clinical integration of AI-driven strategies to unlock personalized patient care and drive innovation in rheumatology.

Machine-learning based subgroups of AL amyloidosis and cumulative incidence of mortality and end stage kidney disease

Immunoglobulin light chain (AL) amyloidosis is a multisystem disease with varied treatment options and disease-related outcomes. Current staging systems rely on a limited number of cardiac, renal, and plasma cell dyscrasia biomarkers. To improve prognostication for all-cause mortality and end-stage kidney disease (ESKD), we applied unsupervised machine learning using a comprehensive set of clinical and laboratory parameters. Our study cohort comprised 2067 patients with newly diagnosed, biopsy-proven AL amyloidosis from the Boston University Amyloidosis Center. Variables included 31 clinical symptoms and 28 baseline laboratory values. Our clustering algorithm identified three subgroups of AL amyloidosis (low-risk, intermediate-risk, and high-risk) with distinct clinical phenotypes and median overall survival (OS) estimates of 6.1, 3.7, and 1.2 years, respectively. The 10-year adjusted cumulative incidences of all-cause mortality were 66.8% (95% CI 63.4-70.1), 75.4% (95% CI 72.1-78.6), and 90.6% (95% CI 87.4-93.3) for low, intermediate, and high-risk subgroups. The 10-year adjusted cumulative incidences of end-stage kidney disease (ESKD) were 20.4% (95% CI 6.1-24.5), 37.6% (95% CI 31.8-43.8), and 6.7% (95% CI 2.8-11.3) for low-risk, intermediate-risk, and high-risk subgroups. Finally, we trained a classifier for external validation with high cross-validation accuracy (85% [95% CI 83-86]) using a subset of easily obtainable clinical parameters. This marks an initial stride toward integrating precision medicine into risk stratification of AL amyloidosis for both all-cause mortality and ESKD.

Identifying subtypes of type 2 diabetes mellitus with machine learning: development, internal validation, prognostic validation and medication burden in linked electronic health records in 420 448 individuals

INTRODUCTION

None of the studies of type 2 diabetes (T2D) subtyping to date have used linked population-level data for incident and prevalent T2D, incorporating a diverse set of variables, explainable methods for cluster characterization, or adhered to an established framework. We aimed to develop and validate machine learning (ML)-informed subtypes for type 2 diabetes mellitus (T2D) using nationally representative data.

RESEARCH DESIGN AND METHODS

In population-based electronic health records (2006-2020; Clinical Practice Research Datalink) in individuals ≥18 years with incident T2D (n=420 448), we included factors (n=3787), including demography, history, examination, biomarkers and medications. Using a published framework, we identified subtypes through nine unsupervised ML methods (K-means, K-means++, K-mode, K-prototype, mini-batch, agglomerative hierarchical clustering, Birch, Gaussian mixture models, and consensus clustering). We characterized clusters using intracluster distributions and explainable artificial intelligence (AI) techniques. We evaluated subtypes for (1) internal validity (within dataset; across methods); (2) prognostic validity (prediction for 5-year all-cause mortality, hospitalization and new chronic diseases); and (3) medication burden.

RESULTS

Development: We identified four T2D subtypes: metabolic, early onset, late onset and cardiometabolic. Internal validity: Subtypes were predicted with high accuracy (F1 score >0.98). Prognostic validity: 5-year all-cause mortality, hospitalization, new chronic disease incidence and medication burden differed across T2D subtypes. Compared with the metabolic subtype, 5-year risks of mortality and hospitalization in incident T2D were highest in late-onset subtype (HR 1.95, 1.85-2.05 and 1.66, 1.58-1.75) and lowest in early-onset subtype (1.18, 1.11-1.27 and 0.85, 0.80-0.90). Incidence of chronic diseases was highest in late-onset subtype and lowest in early-onset subtype. Medications: Compared with the metabolic subtype, after adjusting for age, sex, and pre-T2D medications, late-onset subtype (1.31, 1.28-1.35) and early-onset subtype (0.83, 0.81-0.85) were most and least likely, respectively, to be prescribed medications within 5 years following T2D onset.

CONCLUSIONS

In the largest study using ML to date in incident T2D, we identified four distinct subtypes, with potential future implications for etiology, therapeutics, and risk prediction.

Circulating miR-423-5p levels are associated with carotid atherosclerosis in patients with chronic kidney disease

BACKGROUND AND AIMS

Carotid atherosclerosis is associated with an elevated risk of stroke in patients with chronic kidney disease. However, the molecular basis for the incidence of carotid atherosclerosis in patients with CKD is poorly understood. Here, we investigated whether circulating miR-423-5p is a crucial link between CKD and carotid atherosclerosis.

METHODS AND RESULTS

We recruited 375 participants for a cross-sectional study to examine the occurrence of carotid plaque and plaque thicknesses. Levels of miR-423-5p were determined by qPCR analysis. We found that non-dialysis CKD patients had higher circulating exosomal and plasma miR-423-5p levels, and dialysis-dependent patients had lower miR-423-5p levels than non-dialysis CKD patients. After excluding for the influence of dialysis patients, linear regression analysis indicated that levels of circulating miR-423-5p are negatively correlated with eGFR (P < 0.001). Higher plasma miR-423-5p levels were associated with the incidence and severity of carotid plaques. In parallel, we constructed a murine model of CKD with a 5/6 nephrectomy protocol and performed RNA sequencing studies of aortic tissues. Consistent with these findings in CKD patients, circulating exosomal miR-423-5p levels in CKD mice were elevated. Furthermore, our RNA-seq studies indicated that the putative target genes of miR-423-5p were related to oxidative stress functions for aorta of CKD mice.

CONCLUSION

Levels of miR-423-5p are associated with the presence and severity of carotid plaque in CKD. Data from our mouse model suggests that miR-423-5p likely influences gene expression programs related to oxidative stress in aorta of CKD mice.