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Predicting Long-Term Mortality after Acute Coronary Syndrome Using Machine Learning Techniques and Hematological Markers
Joint Authors
Pieszko, Konrad
Hiczkiewicz, Jarosław
Budzianowski, Paweł
Budzianowski, Jan
Rzeźniczak, Janusz
Pieszko, Karolina
Burchardt, Paweł
Source
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-01-30
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
Introduction.
Hematological indices including red cell distribution width and neutrophil to lymphocyte ratio are proven to be associated with outcomes of acute coronary syndrome.
The usefulness of machine learning techniques in predicting mortality after acute coronary syndrome based on such features has not been studied before.
Objective.
We aim to create an alternative risk assessment tool, which is based on easily obtainable features, including hematological indices and inflammation markers.
Patients and Methods.
We obtained the study data from the electronic medical records of 5053 patients hospitalized with acute coronary syndrome during a 5-year period.
The time of follow-up ranged from 12 to 72 months.
A machine learning classifier was trained to predict death during hospitalization and within 180 and 365 days from admission.
Our method was compared with the Global Registry of Acute Coronary Events (GRACE) Score 2.0 on a test dataset.
Results.
For in-hospital mortality, our model achieved a c-statistic of 0.89 while the GRACE score 2.0 achieved 0.90.
For six-month mortality, the results of our model and the GRACE score on the test set were 0.77 and 0.73, respectively.
Red cell distribution width (HR 1.23; 95% CL 1.16-1.30; P<0.001) and neutrophil to lymphocyte ratio (HR 1.08; 95% CL 1.05-1.10; P<0.001) showed independent association with all-cause mortality in multivariable Cox regression.
Conclusions.
Hematological markers, such as neutrophil count and red cell distribution width have a strong association with all-cause mortality after acute coronary syndrome.
A machine-learned model which uses the abovementioned parameters can provide long-term predictions of accuracy comparable or superior to well-validated risk scores.
American Psychological Association (APA)
Pieszko, Konrad& Hiczkiewicz, Jarosław& Budzianowski, Paweł& Budzianowski, Jan& Rzeźniczak, Janusz& Pieszko, Karolina…[et al.]. 2019. Predicting Long-Term Mortality after Acute Coronary Syndrome Using Machine Learning Techniques and Hematological Markers. Disease Markers،Vol. 2019, no. 2019, pp.1-9.
https://search.emarefa.net/detail/BIM-1147948
Modern Language Association (MLA)
Pieszko, Konrad…[et al.]. Predicting Long-Term Mortality after Acute Coronary Syndrome Using Machine Learning Techniques and Hematological Markers. Disease Markers No. 2019 (2019), pp.1-9.
https://search.emarefa.net/detail/BIM-1147948
American Medical Association (AMA)
Pieszko, Konrad& Hiczkiewicz, Jarosław& Budzianowski, Paweł& Budzianowski, Jan& Rzeźniczak, Janusz& Pieszko, Karolina…[et al.]. Predicting Long-Term Mortality after Acute Coronary Syndrome Using Machine Learning Techniques and Hematological Markers. Disease Markers. 2019. Vol. 2019, no. 2019, pp.1-9.
https://search.emarefa.net/detail/BIM-1147948
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1147948