Clinically Distinct Subtypes of Acute Kidney Injury on Hospital Admission Identified by Machine Learning Consensus Clustering

Vaitla, Pradeep; Nissaisorakarn, Voravech; Mao, Michael A; Genovez, Jose L. Zabala; Kattah, Andrea; Pattharanitima, Pattharawin; Vallabhajosyula, Saraschandra; Keddis, Mira T.; Qureshi, Fawad; Dillon, John; Garovic, Vesna D.; Kashani, Kianoush; Cheungpasitporn, Wisit

doi:10.3390/medsci9040060

Cited by 5 publications

(8 citation statements)

References 32 publications

(40 reference statements)

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“…The unsupervised ML consensus clustering approach offers the ability to more efficiently analyze, identify, and classify groups of patients based on phenotypic features in large volumes of data. [21][22][23][24] In this study, the unsupervised ML consensus clustering algorithm was applied to classify patients with phosphate disorders into unique clusters. Age, comorbidities, and kidney function were the important features used to differentiate the phenotypes of phosphate disorders upon hospital admission, both hypophosphatemia and hyperphosphatemia.…”

Section: Discussionmentioning

confidence: 99%

“…The use of ML to process large and complex data from electronic health records (EHRs) has led to advances in precision medicine [20]. Unsupervised ML techniques have identified novel data patterns and distinct subtypes in different diseases [21][22][23][24][25]. It can identify similarities and differences among multiple data variables and divide them into meaningful clusters [21,22].…”

Section: Introductionmentioning

confidence: 99%

“…Unsupervised ML techniques have identified novel data patterns and distinct subtypes in different diseases [21][22][23][24][25]. It can identify similarities and differences among multiple data variables and divide them into meaningful clusters [21,22]. Furthermore, previous investigations have indicated that distinct subtypes identified by ML consensus clustering algorithms are associated with different clinical outcomes [26,27].…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning Consensus Clustering Approach for Hospitalized Patients with Phosphate Derangements

Dumancas

Nissaisorakarn

Keddis

et al. 2021

JCM

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Background: The goal of this study was to categorize patients with abnormal serum phosphate upon hospital admission into distinct clusters utilizing an unsupervised machine learning approach, and to assess the mortality risk associated with these clusters. Methods: We utilized the consensus clustering approach on demographic information, comorbidities, principal diagnoses, and laboratory data of hypophosphatemia (serum phosphate ≤ 2.4 mg/dL) and hyperphosphatemia cohorts (serum phosphate ≥ 4.6 mg/dL). The standardized mean difference was applied to determine each cluster’s key features. We assessed the association of the clusters with mortality. Results: In the hypophosphatemia cohort (n = 3113), the consensus cluster analysis identified two clusters. The key features of patients in Cluster 2, compared with Cluster 1, included: older age; a higher comorbidity burden, particularly hypertension; diabetes mellitus; coronary artery disease; lower eGFR; and more acute kidney injury (AKI) at admission. Cluster 2 had a comparable hospital mortality (3.7% vs. 2.9%; p = 0.17), but a higher one-year mortality (26.8% vs. 14.0%; p < 0.001), and five-year mortality (20.2% vs. 44.3%; p < 0.001), compared to Cluster 1. In the hyperphosphatemia cohort (n = 7252), the analysis identified two clusters. The key features of patients in Cluster 2, compared with Cluster 1, included: older age; more primary admission for kidney disease; more history of hypertension; more end-stage kidney disease; more AKI at admission; and higher admission potassium, magnesium, and phosphate. Cluster 2 had a higher hospital (8.9% vs. 2.4%; p < 0.001) one-year mortality (32.9% vs. 14.8%; p < 0.001), and five-year mortality (24.5% vs. 51.1%; p < 0.001), compared with Cluster 1. Conclusion: Our cluster analysis classified clinically distinct phenotypes with different mortality risks among hospitalized patients with serum phosphate derangements. Age, comorbidities, and kidney function were the key features that differentiated the phenotypes.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Machine Learning Consensus Clustering Approach for Hospitalized Patients with Phosphate Derangements

Dumancas

Nissaisorakarn

Keddis

et al. 2021

JCM

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“…One such application is in the identification of HF patients carrying higher AKI risk. Given the intricate relationship between cardiac function and kidney health, the ability to proactively identify these high-risk patients can lead to more personalized care strategies, timely interventions, and improved patient outcomes [33].…”

Section: Unsupervised Machine Learning To Assess Aki Risk Among Patie...mentioning

confidence: 99%

Artificial Intelligence in Heart Failure and Acute Kidney Injury: Emerging Concepts and Controversial Dimensions

Cheungpasitporn,

Thongprayoon,

Kashani

2024

Cardiorenal Med

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Background: The growing complexity of patient data and the intricate relationship between heart failure (HF) and acute kidney injury (AKI) underscore the potential benefits of integrating artificial intelligence (AI) and machine learning into healthcare. These advanced analytical tools aim to improve the understanding of the pathophysiological relationship between kidney and heart, provide optimized, individualized, and timely care, and improve outcomes of HF with AKI patients. Summary: This comprehensive review article examines the transformative potential of AI and machine learning solutions in addressing the challenges within this domain. The article explores a range of methodologies, including supervised and unsupervised learning, reinforcement learning, and AI-driven tools like chatbots and large language models. We highlight how these technologies can be tailored to tackle the complex issues prevalent among HF patients with AKI. The potential applications identified span predictive modeling, personalized interventions, real-time monitoring, and collaborative treatment planning. Additionally, we emphasize the necessity of thorough validation, the importance of collaborative efforts between cardiologists and nephrologists, and the consideration of ethical aspects. These factors are critical for the effective application of AI in this area. Key Messages: As the healthcare field evolves, the synergy of advanced analytical tools and clinical expertise holds significant promise to enhance the care and outcomes of individuals who deal with the combined challenges of HF and AKI.

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“…Consensus matrix heat map (k = 10) depicting consensus values on a white to blue color scale of each cluster. References [52][53][54][55][56][57][58][59][60][61][62][63][64][65][66][67] are cited in the supplementary materials. Data Availability Statement: Data will be made available by the authors upon reasonable request.…”

Section: Supplementary Materialsmentioning

confidence: 99%

Clinical Phenotypes of Dual Kidney Transplant Recipients in the United States as Identified through Machine Learning Consensus Clustering

et al. 2022

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Background and Objectives: Our study aimed to cluster dual kidney transplant recipients using an unsupervised machine learning approach to characterize donors and recipients better and to compare the survival outcomes across these various clusters. Materials and Methods: We performed consensus cluster analysis based on recipient-, donor-, and transplant-related characteristics in 2821 dual kidney transplant recipients from 2010 to 2019 in the OPTN/UNOS database. We determined the important characteristics of each assigned cluster and compared the post-transplant outcomes between clusters. Results: Two clinically distinct clusters were identified by consensus cluster analysis. Cluster 1 patients was characterized by younger patients (mean recipient age 49 ± 13 years) who received dual kidney transplant from pediatric (mean donor age 3 ± 8 years) non-expanded criteria deceased donor (100% non-ECD). In contrast, Cluster 2 patients were characterized by older patients (mean recipient age 63 ± 9 years) who received dual kidney transplant from adult (mean donor age 59 ± 11 years) donor with high kidney donor profile index (KDPI) score (59% had KDPI ≥ 85). Cluster 1 had higher patient survival (98.0% vs. 94.6% at 1 year, and 92.1% vs. 76.3% at 5 years), and lower acute rejection (4.2% vs. 6.1% within 1 year), when compared to cluster 2. Death-censored graft survival was comparable between two groups (93.5% vs. 94.9% at 1 year, and 89.2% vs. 84.8% at 5 years). Conclusions: In summary, DKT in the United States remains uncommon. Two clusters, based on specific recipient and donor characteristics, were identified through an unsupervised machine learning approach. Despite varying differences in donor and recipient age between the two clusters, death-censored graft survival was excellent and comparable. Broader utilization of DKT from high KDPI kidneys and pediatric en bloc kidneys should be encouraged to better address the ongoing organ shortage.

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Clinically Distinct Subtypes of Acute Kidney Injury on Hospital Admission Identified by Machine Learning Consensus Clustering

Cited by 5 publications

References 32 publications

Machine Learning Consensus Clustering Approach for Hospitalized Patients with Phosphate Derangements

Machine Learning Consensus Clustering Approach for Hospitalized Patients with Phosphate Derangements

Artificial Intelligence in Heart Failure and Acute Kidney Injury: Emerging Concepts and Controversial Dimensions

Clinical Phenotypes of Dual Kidney Transplant Recipients in the United States as Identified through Machine Learning Consensus Clustering

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