Impact of serum phosphate changes on in-hospital mortality

Cheungpasitporn, Wisit; Hansrivijit, Panupong; Thirunavukkarasu, Sorkko; Chewcharat, Api; Medaura, Juan; Mao, Michael A; Kashani, Kianoush

doi:10.1186/s12882-020-02090-3

Cited by 20 publications

(22 citation statements)

References 39 publications

(54 reference statements)

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“…An oversampling strategy (Synthetic Minority Oversampling Technique -SMOTE) was used to overcome the unbalancing of the two classes for outcomes (discharged patients -class 0-and deceased patients -class 1) 17 .…”

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confidence: 99%

An Individualized Algorithm to Predict Mortality in COVID-19 Pneumonia: a Machine Learning Based Study

et al. 2022

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IntroductionIdentifying SARS-CoV-2 patients at higher risk of mortality is crucial in the management of a pandemic. Artificial intelligence techniques allow to analyze big amount of data to find hidden patterns. We aimed to develop and validate a mortality score at admission for COVID-19 based on high-level machine learning.Material and methodsWe conducted a retrospective cohort study on hospitalized adults COVID-19 patients between March and December 2020. The primary outcome was in-hospital mortality. A machine learning approach on vital parameters, laboratory values, and demographic features was applied to develop different models. Then, a feature importance analysis was performed to reduce the number of variables included in the model, to develop a risk score with good overall performance, that was finally evaluated in terms of discrimination and calibration capabilities. All results underwent cross-validation.Results1,135 consecutive patients (median age 70 years, 64% males) were enrolled, 48 patients were excluded, the cohort was randomly divided in training (760) and test (327). During hospitalization, 251 (22%) patients died. After feature selection, the best performing classifier was random forest (AUC 0.88±0.03). Based on the relative importance of each variable, a pragmatic score was developed, showing good performances (AUC 0.85, ±0.025), and three levels were defined that correlated well with in-hospital mortality.ConclusionsMachine learning techniques were applied in order to develop an accurate in-hospital mortality risk score for COVID-19 based on ten variables. The application of the proposed score has utility in clinical settings to guide the management and prognostication of COVID-19 patients.

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confidence: 99%

An Individualized Algorithm to Predict Mortality in COVID-19 Pneumonia: a Machine Learning Based Study

et al. 2022

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“…The kidney is an important regulator of phosphorus homeostasis, and hypophosphatemia can be caused by poor phosphorus intake/intestinal absorption, redistribution from extracellular to intracellular compartments (refeeding syndrome and respiratory alkalosis), and/or excessive urinary phosphate excretion [ 7 , 16 , 31 ]. Upon hospital admission, the findings of our ML consensus clustering suggest that kidney functions (surrogates included baseline eGFR and AKI on admission) played an important role in differentiating the phenotypes of patients with hypophosphatemia.…”

Section: Discussionmentioning

confidence: 99%

“…Compared with Cluster 1, Cluster 2 had more principal diagnoses of cardiovascular, genitourinary, and hematoma oncology. Hypophosphatemia in Cluster 2 could be a surrogate marker of illness, or related to comorbidities and their treatment [ 7 , 33 ]. For instance, diabetes mellitus being treated with insulin therapy is another common comorbidity that is associated with hypophosphatemia.…”

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confidence: 99%

“…Phosphate is an essential element in the body and the most abundant intracellular anion, with the majority of it stored in bones, and < 1% found in the serum [1][2][3][4][5]. Normal serum phosphate levels are maintained within a relatively narrow range (2.5-4.5 mg/dL) because they are vital in cellular metabolism, signal transduction, and bone homeostasis [6][7][8][9][10]. Abnormal serum phosphate levels are common, with a prevalence up to 45% in hospitalized patients [11,12], and are associated with poor clinical outcomes, including cardiovascular disease [2,3], respiratory failure [13], and increased mortality [7,11,[14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

“…Normal serum phosphate levels are maintained within a relatively narrow range (2.5-4.5 mg/dL) because they are vital in cellular metabolism, signal transduction, and bone homeostasis [6][7][8][9][10]. Abnormal serum phosphate levels are common, with a prevalence up to 45% in hospitalized patients [11,12], and are associated with poor clinical outcomes, including cardiovascular disease [2,3], respiratory failure [13], and increased mortality [7,11,[14][15][16][17].…”

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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Association between serum phosphate level and mortality of patients with aneurysmal subarachnoid hemorrhage

Wang,

Zhang,

et al. 2024

Neurosurg Rev

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Impact of serum phosphate changes on in-hospital mortality

Cited by 20 publications

References 39 publications

An Individualized Algorithm to Predict Mortality in COVID-19 Pneumonia: a Machine Learning Based Study

An Individualized Algorithm to Predict Mortality in COVID-19 Pneumonia: a Machine Learning Based Study

Machine Learning Consensus Clustering Approach for Hospitalized Patients with Phosphate Derangements

Association between serum phosphate level and mortality of patients with aneurysmal subarachnoid hemorrhage

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