Molecular Biomarkers of Health

Nehlin, Jan O.; Andersen, Ove

doi:10.1007/978-3-030-52663-4_15

Cited by 3 publications

(1 citation statement)

References 157 publications

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“…On admission, blood samples were taken, and a standard panel of markers was measured at the Department of Clinical Biochemistry, including C-reactive protein (CRP), Soluble urokinase plasminogen activator receptor (suPAR), Alanine Aminotransferase (ALAT), Albumin (ALB), International Normalized Ratio (INR), coagulation factors 2,7,10 (KF2710), total Bilirubin (BILI), Alkaline Phosphatase, Creatinine, Lactate dehydrogenase (LDH), Blood urea nitrogen (BUN), Potassium (K), Sodium (NA), Estimated Glomerular Filtration Rate (eGFR), Hemoglobin (HB), mean corpuscular volume (MCV) and mean corpuscular hemoglobin concentration (MCHC), number of leukocytes, lymphocytes, neutrocytes, monocytes, thrombocytes, eosinophils, basophils, and Metamyelo-, Myelo.—Promyelocytes (PROMM) 42 . Age and sex were also included as variables in the algorithms (Table 1 ).…”

Section: Methodsmentioning

confidence: 99%

Development and validation of prognostic machine learning models for short- and long-term mortality among acutely admitted patients based on blood tests

Jawad,

Shaker,

Altintas

et al. 2024

Sci Rep

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Several scores predicting mortality at the emergency department have been developed. However, all with shortcomings either simple and applicable in a clinical setting, with poor performance, or advanced, with high performance, but clinically difficult to implement. This study aimed to explore if machine learning algorithms could predict all-cause short- and long-term mortality based on the routine blood test collected at admission. Methods: We analyzed data from a retrospective cohort study, including patients > 18 years admitted to the Emergency Department (ED) of Copenhagen University Hospital Hvidovre, Denmark between November 2013 and March 2017. The primary outcomes were 3-, 10-, 30-, and 365-day mortality after admission. PyCaret, an automated machine learning library, was used to evaluate the predictive performance of fifteen machine learning algorithms using the area under the receiver operating characteristic curve (AUC). Results: Data from 48,841 admissions were analyzed, of these 34,190 (70%) were randomly divided into training data, and 14,651 (30%) were in test data. Eight machine learning algorithms achieved very good to excellent results of AUC on test data in a of range 0.85–0.93. In prediction of short-term mortality, lactate dehydrogenase (LDH), leukocyte counts and differentials, Blood urea nitrogen (BUN) and mean corpuscular hemoglobin concentration (MCHC) were the best predictors, whereas prediction of long-term mortality was favored by age, LDH, soluble urokinase plasminogen activator receptor (suPAR), albumin, and blood urea nitrogen (BUN). Conclusion: The findings suggest that measures of biomarkers taken from one blood sample during admission to the ED can identify patients at high risk of short-and long-term mortality following emergency admissions.

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Section: Methodsmentioning

confidence: 99%

Development and validation of prognostic machine learning models for short- and long-term mortality among acutely admitted patients based on blood tests

Jawad,

Shaker,

Altintas

et al. 2024

Sci Rep

Self Cite

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Development and validation of prognostic machine learning models for short- and long-term mortality among acutely hospitalized patients.

Jawad

Shaker

Altintas

et al. 2023

Preprint

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Background: Several scores predicting mortality at the emergency department have been developed. However, all with shortcomings either simple and applicable in a clinical setting, with poor performance, or advanced, with high performance, but clinically difficult to implement. This study aimed to explore if machine learning algorithms could predict all-cause short- and long-term mortality based on the routine blood test collected at admission. Methods: We analyzed data from a retrospective cohort study, including patients > 18 years admitted to the Emergency Department (ED) of Copenhagen University Hospital Hvidovre, Denmark between November 2013 and March 2017. The primary outcomes were 3-,10-,30-, and 365-day mortality after admission. PyCaret, an automated machine learning library, was used to evaluate the predictive performance of fifteen machine learning algorithms using the area under the receiver operating characteristic curve (AUC). Results: Data from 48841 admissions were analyzed, of these 34190 (70%) were randomly divided into training data, and 14651 (30%) were in test data. Eight machine learning algorithms achieved very good to excellent results of AUC on test data in a of range 0.85-0.90. In prediction of short-term mortality, lactate dehydrogenase (LDH), leukocyte counts and differentials, Blood urea nitrogen (BUN) and mean corpuscular hemoglobin concentration (MCHC) were the best predictors, whereas prediction of long-term mortality was favored by age, LDH, soluble urokinase plasminogen activator receptor (suPAR), albumin, and blood urea nitrogen (BUN). Conclusion: The findings suggest that measures of biomarkers taken from one blood sample during admission to the ED can identify patients at high risk of short-and long-term mortality following emergency admissions.

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Molecular Biomarkers: Current Advances in Liquid Biopsy in the Diagnosis of Cancer

Ghagane,

Nerli

2024

Journal of the Scientific Society

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Molecular Biomarkers of Health

Cited by 3 publications

References 157 publications

Development and validation of prognostic machine learning models for short- and long-term mortality among acutely admitted patients based on blood tests

Development and validation of prognostic machine learning models for short- and long-term mortality among acutely admitted patients based on blood tests

Development and validation of prognostic machine learning models for short- and long-term mortality among acutely hospitalized patients.

Molecular Biomarkers: Current Advances in Liquid Biopsy in the Diagnosis of Cancer

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