Machine Learning Consensus Clustering of Hospitalized Patients with Admission Hyponatremia
Background: The objective of this study was to characterize patients with hyponatremia at hospital admission into clusters using an unsupervised machine learning approach, and to evaluate the short- and long-term mortality risk among these distinct clusters. Methods: We performed consensus cluster a...
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| Vydané v: | Diseases Ročník 9; číslo 3; s. 54 |
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01.08.2021
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| Abstract | Background: The objective of this study was to characterize patients with hyponatremia at hospital admission into clusters using an unsupervised machine learning approach, and to evaluate the short- and long-term mortality risk among these distinct clusters. Methods: We performed consensus cluster analysis based on demographic information, principal diagnoses, comorbidities, and laboratory data among 11,099 hospitalized adult hyponatremia patients with an admission serum sodium below 135 mEq/L. The standardized mean difference was utilized to identify each cluster’s key features. We assessed the association of each hyponatremia cluster with hospital and one-year mortality using logistic and Cox proportional hazard analysis, respectively. Results: There were three distinct clusters of hyponatremia patients: 2033 (18%) in cluster 1, 3064 (28%) in cluster 2, and 6002 (54%) in cluster 3. Among these three distinct clusters, clusters 3 patients were the youngest, had lowest comorbidity burden, and highest kidney function. Cluster 1 patients were more likely to be admitted for genitourinary disease, and have diabetes and end-stage kidney disease. Cluster 1 patients had the lowest kidney function, serum bicarbonate, and hemoglobin, but highest serum potassium and prevalence of acute kidney injury. In contrast, cluster 2 patients were the oldest and were more likely to be admitted for respiratory disease, have coronary artery disease, congestive heart failure, stroke, and chronic obstructive pulmonary disease. Cluster 2 patients had lowest serum sodium and serum chloride, but highest serum bicarbonate. Cluster 1 patients had the highest hospital mortality and one-year mortality, followed by cluster 2 and cluster 3, respectively. Conclusion: We identified three clinically distinct phenotypes with differing mortality risks in a heterogeneous cohort of hospitalized hyponatremic patients using an unsupervised machine learning approach. |
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| AbstractList | Background: The objective of this study was to characterize patients with hyponatremia at hospital admission into clusters using an unsupervised machine learning approach, and to evaluate the short- and long-term mortality risk among these distinct clusters. Methods: We performed consensus cluster analysis based on demographic information, principal diagnoses, comorbidities, and laboratory data among 11,099 hospitalized adult hyponatremia patients with an admission serum sodium below 135 mEq/L. The standardized mean difference was utilized to identify each cluster’s key features. We assessed the association of each hyponatremia cluster with hospital and one-year mortality using logistic and Cox proportional hazard analysis, respectively. Results: There were three distinct clusters of hyponatremia patients: 2033 (18%) in cluster 1, 3064 (28%) in cluster 2, and 6002 (54%) in cluster 3. Among these three distinct clusters, clusters 3 patients were the youngest, had lowest comorbidity burden, and highest kidney function. Cluster 1 patients were more likely to be admitted for genitourinary disease, and have diabetes and end-stage kidney disease. Cluster 1 patients had the lowest kidney function, serum bicarbonate, and hemoglobin, but highest serum potassium and prevalence of acute kidney injury. In contrast, cluster 2 patients were the oldest and were more likely to be admitted for respiratory disease, have coronary artery disease, congestive heart failure, stroke, and chronic obstructive pulmonary disease. Cluster 2 patients had lowest serum sodium and serum chloride, but highest serum bicarbonate. Cluster 1 patients had the highest hospital mortality and one-year mortality, followed by cluster 2 and cluster 3, respectively. Conclusion: We identified three clinically distinct phenotypes with differing mortality risks in a heterogeneous cohort of hospitalized hyponatremic patients using an unsupervised machine learning approach. The objective of this study was to characterize patients with hyponatremia at hospital admission into clusters using an unsupervised machine learning approach, and to evaluate the short- and long-term mortality risk among these distinct clusters.BACKGROUNDThe objective of this study was to characterize patients with hyponatremia at hospital admission into clusters using an unsupervised machine learning approach, and to evaluate the short- and long-term mortality risk among these distinct clusters.We performed consensus cluster analysis based on demographic information, principal diagnoses, comorbidities, and laboratory data among 11,099 hospitalized adult hyponatremia patients with an admission serum sodium below 135 mEq/L. The standardized mean difference was utilized to identify each cluster's key features. We assessed the association of each hyponatremia cluster with hospital and one-year mortality using logistic and Cox proportional hazard analysis, respectively.METHODSWe performed consensus cluster analysis based on demographic information, principal diagnoses, comorbidities, and laboratory data among 11,099 hospitalized adult hyponatremia patients with an admission serum sodium below 135 mEq/L. The standardized mean difference was utilized to identify each cluster's key features. We assessed the association of each hyponatremia cluster with hospital and one-year mortality using logistic and Cox proportional hazard analysis, respectively.There were three distinct clusters of hyponatremia patients: 2033 (18%) in cluster 1, 3064 (28%) in cluster 2, and 6002 (54%) in cluster 3. Among these three distinct clusters, clusters 3 patients were the youngest, had lowest comorbidity burden, and highest kidney function. Cluster 1 patients were more likely to be admitted for genitourinary disease, and have diabetes and end-stage kidney disease. Cluster 1 patients had the lowest kidney function, serum bicarbonate, and hemoglobin, but highest serum potassium and prevalence of acute kidney injury. In contrast, cluster 2 patients were the oldest and were more likely to be admitted for respiratory disease, have coronary artery disease, congestive heart failure, stroke, and chronic obstructive pulmonary disease. Cluster 2 patients had lowest serum sodium and serum chloride, but highest serum bicarbonate. Cluster 1 patients had the highest hospital mortality and one-year mortality, followed by cluster 2 and cluster 3, respectively.RESULTSThere were three distinct clusters of hyponatremia patients: 2033 (18%) in cluster 1, 3064 (28%) in cluster 2, and 6002 (54%) in cluster 3. Among these three distinct clusters, clusters 3 patients were the youngest, had lowest comorbidity burden, and highest kidney function. Cluster 1 patients were more likely to be admitted for genitourinary disease, and have diabetes and end-stage kidney disease. Cluster 1 patients had the lowest kidney function, serum bicarbonate, and hemoglobin, but highest serum potassium and prevalence of acute kidney injury. In contrast, cluster 2 patients were the oldest and were more likely to be admitted for respiratory disease, have coronary artery disease, congestive heart failure, stroke, and chronic obstructive pulmonary disease. Cluster 2 patients had lowest serum sodium and serum chloride, but highest serum bicarbonate. Cluster 1 patients had the highest hospital mortality and one-year mortality, followed by cluster 2 and cluster 3, respectively.We identified three clinically distinct phenotypes with differing mortality risks in a heterogeneous cohort of hospitalized hyponatremic patients using an unsupervised machine learning approach.CONCLUSIONWe identified three clinically distinct phenotypes with differing mortality risks in a heterogeneous cohort of hospitalized hyponatremic patients using an unsupervised machine learning approach. |
| Author | Cheungpasitporn, Wisit Kattah, Andrea G. Vaitla, Pradeep K. Vallabhajosyula, Saraschandra Garovic, Vesna D. Pattharanitima, Pattharawin Erickson, Stephen B. Mao, Michael A. Keddis, Mira T. Hansrivijit, Panupong Nissaisorakarn, Voravech Thongprayoon, Charat Petnak, Tananchai Dillon, John J. |
| AuthorAffiliation | 9 Department of Medicine, Division of Nephrology and Hypertension, Mayo Clinic, Phoenix, AZ 85054, USA; keddis.mira@mayo.edu 1 Department of Medicine, Division of Nephrology and Hypertension, Mayo Clinic, Rochester, MN 55905, USA; kattah.andrea@mayo.edu (A.G.K.); Erickson.Stephen@mayo.edu (S.B.E.); Dillon.john@mayo.edu (J.J.D.); garovic.vesna@mayo.edu (V.D.G.) 8 Division of Pulmonary and Pulmonary Critical Care Medicine, Faculty of Medicine, Ramathibodi Hospital, Mahidol University, Bangkok 10400, Thailand; petnak@yahoo.com 3 Department of Medicine, Division of Nephrology and Hypertension, Mayo Clinic, Jacksonville, FL 32224, USA; mao.michael@mayo.edu 5 Department of Internal Medicine, Faculty of Medicine, Thammasat University, Pathum Thani 10120, Thailand 6 Department of Medicine, Section of Cardiovascular Medicine, Wake Forest University School of Medicine, Winston-Salem, NC 27101, USA; saraschandra21@gmail.com 7 MetroWest Medical Center, Department of Internal Medicine, Tufts University S |
| AuthorAffiliation_xml | – name: 4 Department of Internal Medicine, Division of Nephrology, University of Mississippi Medical Center, Jackson, MS 39216, USA; pvaitla@umc.edu – name: 5 Department of Internal Medicine, Faculty of Medicine, Thammasat University, Pathum Thani 10120, Thailand – name: 1 Department of Medicine, Division of Nephrology and Hypertension, Mayo Clinic, Rochester, MN 55905, USA; kattah.andrea@mayo.edu (A.G.K.); Erickson.Stephen@mayo.edu (S.B.E.); Dillon.john@mayo.edu (J.J.D.); garovic.vesna@mayo.edu (V.D.G.) – name: 2 Department of Internal Medicine, UPMC Pinnacle, Harrisburg, PA 17105, USA; hansrivijitp@upmc.edu – name: 9 Department of Medicine, Division of Nephrology and Hypertension, Mayo Clinic, Phoenix, AZ 85054, USA; keddis.mira@mayo.edu – name: 6 Department of Medicine, Section of Cardiovascular Medicine, Wake Forest University School of Medicine, Winston-Salem, NC 27101, USA; saraschandra21@gmail.com – name: 7 MetroWest Medical Center, Department of Internal Medicine, Tufts University School of Medicine, Boston, MA 01760, USA; voravech.niss@gmail.com – name: 3 Department of Medicine, Division of Nephrology and Hypertension, Mayo Clinic, Jacksonville, FL 32224, USA; mao.michael@mayo.edu – name: 8 Division of Pulmonary and Pulmonary Critical Care Medicine, Faculty of Medicine, Ramathibodi Hospital, Mahidol University, Bangkok 10400, Thailand; petnak@yahoo.com |
| Author_xml | – sequence: 1 givenname: Charat surname: Thongprayoon fullname: Thongprayoon, Charat – sequence: 2 givenname: Panupong orcidid: 0000-0002-5041-4290 surname: Hansrivijit fullname: Hansrivijit, Panupong – sequence: 3 givenname: Michael A. orcidid: 0000-0003-1814-7003 surname: Mao fullname: Mao, Michael A. – sequence: 4 givenname: Pradeep K. orcidid: 0000-0001-5234-6722 surname: Vaitla fullname: Vaitla, Pradeep K. – sequence: 5 givenname: Andrea G. surname: Kattah fullname: Kattah, Andrea G. – sequence: 6 givenname: Pattharawin orcidid: 0000-0002-6010-0033 surname: Pattharanitima fullname: Pattharanitima, Pattharawin – sequence: 7 givenname: Saraschandra orcidid: 0000-0002-1631-8238 surname: Vallabhajosyula fullname: Vallabhajosyula, Saraschandra – sequence: 8 givenname: Voravech orcidid: 0000-0002-9389-073X surname: Nissaisorakarn fullname: Nissaisorakarn, Voravech – sequence: 9 givenname: Tananchai orcidid: 0000-0002-7633-4029 surname: Petnak fullname: Petnak, Tananchai – sequence: 10 givenname: Mira T. orcidid: 0000-0001-8249-0848 surname: Keddis fullname: Keddis, Mira T. – sequence: 11 givenname: Stephen B. surname: Erickson fullname: Erickson, Stephen B. – sequence: 12 givenname: John J. surname: Dillon fullname: Dillon, John J. – sequence: 13 givenname: Vesna D. surname: Garovic fullname: Garovic, Vesna D. – sequence: 14 givenname: Wisit orcidid: 0000-0001-9954-9711 surname: Cheungpasitporn fullname: Cheungpasitporn, Wisit |
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| SubjectTerms | Algorithms Artificial intelligence Bicarbonates Cardiovascular disease Chronic obstructive pulmonary disease Cluster analysis Clustering Congestive heart failure Coronary artery Coronary artery disease Diabetes Diabetes mellitus End-stage renal disease Hemoglobin Hospitalization Hyponatremia Kidney diseases Laboratories Learning algorithms Lung diseases Machine learning Mortality Obstructive lung disease Patients Phenotypes Potassium Respiratory diseases Sodium Urine Variance analysis |
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| Title | Machine Learning Consensus Clustering of Hospitalized Patients with Admission Hyponatremia |
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