Hypernatremia subgroups among hospitalized patients by machine learning consensus clustering with different patient survival

Mao, Michael A; Keddis, Mira T.; Kattah, Andrea; Chong, Grace Y.; Pattharanitima, Pattharawin; Nissaisorakarn, Voravech; Garg, Arvind; Erickson, Stephen B.; Dillon, John; Garovic, Vesna D.; Cheungpasitporn, Wisit

doi:10.1007/s40620-021-01163-2

Cited by 8 publications

(9 citation statements)

References 33 publications

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“…ML consensus clustering algorithms offer the ability to efficiently analyze and identify unique clusters of patients with different characteristics in a large amount of data [24,25,38,39]. In this study, we identified three clinically distinct clusters of patients with lactic acidosis at time of ICU admission utilizing a ML unsupervised consensus clustering approach.…”

Section: Discussionmentioning

confidence: 99%

“…With the advancement of electronic medical records (EMR) and artificial intelligence, machine learning (ML) algorithms have become more widely utilized in individualized medicine to assist clinical decision-making [22,23]. Consensus clustering is an unsupervised ML technique that is utilized to identify similarities and differences among various data variables, and then assign them into meaningful clusters [24,25]. Recent studies have demonstrated that ML consensus clustering approach can distinguish meaningful disease subtypes that forecast unique clinical outcomes [26,27].…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning Consensus Clustering Approach for Patients with Lactic Acidosis in Intensive Care Units

Pattharanitima

Petnak

Srivali

et al. 2021

JPM

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Background: Lactic acidosis is a heterogeneous condition with multiple underlying causes and associated outcomes. The use of multi-dimensional patient data to subtype lactic acidosis can personalize patient care. Machine learning consensus clustering may identify lactic acidosis subgroups with unique clinical profiles and outcomes. Methods: We used the Medical Information Mart for Intensive Care III database to abstract electronic medical record data from patients admitted to intensive care units (ICU) in a tertiary care hospital in the United States. We included patients who developed lactic acidosis (defined as serum lactate ≥ 4 mmol/L) within 48 h of ICU admission. We performed consensus clustering analysis based on patient characteristics, comorbidities, vital signs, organ supports, and laboratory data to identify clinically distinct lactic acidosis subgroups. We calculated standardized mean differences to show key subgroup features. We compared outcomes among subgroups. Results: We identified 1919 patients with lactic acidosis. The algorithm revealed three best unique lactic acidosis subgroups based on patient variables. Cluster 1 (n = 554) was characterized by old age, elective admission to cardiac surgery ICU, vasopressor use, mechanical ventilation use, and higher pH and serum bicarbonate. Cluster 2 (n = 815) was characterized by young age, admission to trauma/surgical ICU with higher blood pressure, lower comorbidity burden, lower severity index, and less vasopressor use. Cluster 3 (n = 550) was characterized by admission to medical ICU, history of liver disease and coagulopathy, acute kidney injury, lower blood pressure, higher comorbidity burden, higher severity index, higher serum lactate, and lower pH and serum bicarbonate. Cluster 3 had the worst outcomes, while cluster 1 had the most favorable outcomes in terms of persistent lactic acidosis and mortality. Conclusions: Consensus clustering analysis synthesized the pattern of clinical and laboratory data to reveal clinically distinct lactic acidosis subgroups with different outcomes.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Machine Learning Consensus Clustering Approach for Patients with Lactic Acidosis in Intensive Care Units

Pattharanitima

Petnak

Srivali

et al. 2021

JPM

Self Cite

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“…ML consensus clustering algorithms offer the ability to efficiently analyze and identify clusters of patients with different characteristics in a large amount of data [22,35,42,43]. In this study, the unsupervised ML consensus clustering approach was utilized to distinguish patients with dysmagnesemia into distinct clusters.…”

Section: Discussionmentioning

confidence: 99%

“…Consensus clustering is an unsupervised ML technique used to identify patterns of data, and provides a visualization tool to inspect cluster numbers, membership, and boundaries [34]. It can be utilized to search for similarities and heterogeneities among data and isolate them into clinically meaningful clusters [22,35]. Recent investigations have demonstrated that ML clustering methods can distinguish meaningful disease subtypes associated with different clinical outcomes [36,37].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Consensus Clustering Approach for Hospitalized Patients with Dysmagnesemia

et al. 2021

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Background: The objectives of this study were to classify patients with serum magnesium derangement on hospital admission into clusters using unsupervised machine learning approach and to evaluate the mortality risks among these distinct clusters. Methods: Consensus cluster analysis was performed based on demographic information, principal diagnoses, comorbidities, and laboratory data in hypomagnesemia (serum magnesium ≤ 1.6 mg/dL) and hypermagnesemia cohorts (serum magnesium ≥ 2.4 mg/dL). Each cluster’s key features were determined using the standardized mean difference. The associations of the clusters with hospital mortality and one-year mortality were assessed. Results: In hypomagnesemia cohort (n = 13,320), consensus cluster analysis identified three clusters. Cluster 1 patients had the highest comorbidity burden and lowest serum magnesium. Cluster 2 patients had the youngest age, lowest comorbidity burden, and highest kidney function. Cluster 3 patients had the oldest age and lowest kidney function. Cluster 1 and cluster 3 were associated with higher hospital and one-year mortality compared to cluster 2. In hypermagnesemia cohort (n = 4671), the analysis identified two clusters. Compared to cluster 1, the key features of cluster 2 included older age, higher comorbidity burden, more hospital admissions primarily due to kidney disease, more acute kidney injury, and lower kidney function. Compared to cluster 1, cluster 2 was associated with higher hospital mortality and one-year mortality. Conclusion: Our cluster analysis identified clinically distinct phenotypes with differing mortality risks in hospitalized patients with dysmagnesemia. Future studies are required to assess the application of this ML consensus clustering approach to care for hospitalized patients with dysmagnesemia.

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“…Consensus clustering is an unsupervised ML technique used to identify novel data patterns [14]. It can search for similarities and heterogeneities among large categories of data variables and isolate them into clinically meaningful clusters [8,[15][16][17]. Recent studies have shown that disease subtypes determined by ML clustering methods can forecast different clinical outcomes [18,19].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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Background: We aimed to cluster patients with acute kidney injury at hospital admission into clinically distinct subtypes using an unsupervised machine learning approach and assess the mortality risk among the distinct clusters. Methods: We performed consensus clustering analysis based on demographic information, principal diagnoses, comorbidities, and laboratory data among 4289 hospitalized adult patients with acute kidney injury at admission. The standardized difference of each variable was calculated to identify each cluster’s key features. We assessed the association of each acute kidney injury cluster with hospital and one-year mortality. Results: Consensus clustering analysis identified four distinct clusters. There were 1201 (28%) patients in cluster 1, 1396 (33%) patients in cluster 2, 1191 (28%) patients in cluster 3, and 501 (12%) patients in cluster 4. Cluster 1 patients were the youngest and had the least comorbidities. Cluster 2 and cluster 3 patients were older and had lower baseline kidney function. Cluster 2 patients had lower serum bicarbonate, strong ion difference, and hemoglobin, but higher serum chloride, whereas cluster 3 patients had lower serum chloride but higher serum bicarbonate and strong ion difference. Cluster 4 patients were younger and more likely to be admitted for genitourinary disease and infectious disease but less likely to be admitted for cardiovascular disease. Cluster 4 patients also had more severe acute kidney injury, lower serum sodium, serum chloride, and serum bicarbonate, but higher serum potassium and anion gap. Cluster 2, 3, and 4 patients had significantly higher hospital and one-year mortality than cluster 1 patients (p < 0.001). Conclusion: Our study demonstrated using machine learning consensus clustering analysis to characterize a heterogeneous cohort of patients with acute kidney injury on hospital admission into four clinically distinct clusters with different associated mortality risks.

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Hypernatremia subgroups among hospitalized patients by machine learning consensus clustering with different patient survival

Cited by 8 publications

References 33 publications

Machine Learning Consensus Clustering Approach for Patients with Lactic Acidosis in Intensive Care Units

Machine Learning Consensus Clustering Approach for Patients with Lactic Acidosis in Intensive Care Units

Machine Learning Consensus Clustering Approach for Hospitalized Patients with Dysmagnesemia

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

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