Machine learning for predicting 5-year mortality risks: data from the ESSE-RF study in Primorsky Krai
Aim. To develop and perform comparative assessment of the accuracy of models for predicting 5-year mortality risks according to the Epidemiology of Cardiovascular Diseases and their Risk Factors in Regions of Russian Federation (ESSE-RF) study in Primorsky Krai.Material and methods. The study includ...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | Russian |
| Published: |
«SILICEA-POLIGRAF» LLC
2022-01-01
|
| Series: | Кардиоваскулярная терапия и профилактика |
| Subjects: | |
| Online Access: | https://cardiovascular.elpub.ru/jour/article/view/2908 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849698896028106752 |
|---|---|
| author | V. A. Nevzorova T. A. Brodskaya K. I. Shakhgeldyan B. I. Geltser V. V. Kosterin L. G. Priseko |
| author_facet | V. A. Nevzorova T. A. Brodskaya K. I. Shakhgeldyan B. I. Geltser V. V. Kosterin L. G. Priseko |
| author_sort | V. A. Nevzorova |
| collection | DOAJ |
| description | Aim. To develop and perform comparative assessment of the accuracy of models for predicting 5-year mortality risks according to the Epidemiology of Cardiovascular Diseases and their Risk Factors in Regions of Russian Federation (ESSE-RF) study in Primorsky Krai.Material and methods. The study included 2131 people (1257 women and 874 men) aged 23-67 years with a median of 47 years (95% confidence interval [46; 48]). The study protocol included measurement of blood pressure (BP), heart rate (HR), waist circumference, hip circumference, and waist-to-hip ratio (WHR). The following blood biochemical parameters: total cholesterol (TC), low and high density lipoprotein cholesterol, triglycerides, apolipoproteins AI and B, lipoprotein(a), N-terminal pro-brain natriuretic peptide (NT-proNBP), D-dimer, fibrinogen, C-reactive protein (CRP), glucose, creatinine, uric acid. The study endpoint was 5-year all-cause death (2013-2018). The group of deceased patients during this period consisted of 42 (2%) people, while those continued the study — 2089 (98%). The χ2, Fisher and MannWhitney tests, univariate logistic regression (LR) were used for data processing and analysis. To build predictive models, we used following machine learning (ML) methods: multivariate LR, Weibull regression, and stochastic gradient boosting.Results. The prognostic models developed on the ML basis, using parameters of age, sex, smoking, systolic blood pressure (SBP) and TC level in their structure, had higher quality metrics than Systematic COronary Risk Evaluation (SCORE) system. The inclusion of CRP, glucose, NT-proNBP, and heart rate into the predictors increased the accuracy of all models with the maximum rise in quality metrics in the multivariate LR model. Predictive potential of other factors (WHR, lipid profile, fibrinogen, D-dimer, etc.) was low and did not improve the prediction quality. An analysis of the influence degree of individual predictors on the mortality rate indicated the prevailing contribution of five factors as follows: age, levels of TC, NT-proNBP, CRP, and glucose. A less noticeable effect was associated with the level of HR, SBP and smoking, while the contribution of sex was minimal.Conclusion. The use of modern ML methods increases the accuracy of predictive models and provides a higher efficiency of risk stratification, especially among individuals with a low and moderate death risk from cardiovascular diseases. |
| format | Article |
| id | doaj-art-2c54eeb8abb14dabafa9b664413bfa02 |
| institution | DOAJ |
| issn | 1728-8800 2619-0125 |
| language | Russian |
| publishDate | 2022-01-01 |
| publisher | «SILICEA-POLIGRAF» LLC |
| record_format | Article |
| series | Кардиоваскулярная терапия и профилактика |
| spelling | doaj-art-2c54eeb8abb14dabafa9b664413bfa022025-08-20T03:18:46Zrus«SILICEA-POLIGRAF» LLCКардиоваскулярная терапия и профилактика1728-88002619-01252022-01-0121110.15829/1728-8800-2022-29082358Machine learning for predicting 5-year mortality risks: data from the ESSE-RF study in Primorsky KraiV. A. Nevzorova0T. A. Brodskaya1K. I. Shakhgeldyan2B. I. Geltser3V. V. Kosterin4L. G. Priseko5Pacific State Medical University, Institute of Therapy and Instrumental DiagnosticsPacific State Medical University, Institute of Therapy and Instrumental DiagnosticsPacific State Medical University, Institute of Therapy and Instrumental Diagnostics; Far Eastern Federal University, School of Biomedicine; Vladivostok State University of Economics and Service, Institute of Information TechnologiesPacific State Medical University, Institute of Therapy and Instrumental Diagnostics; Far Eastern Federal University, School of BiomedicineVladivostok State University of Economics and Service, Institute of Information TechnologiesPacific State Medical University, Institute of Therapy and Instrumental DiagnosticsAim. To develop and perform comparative assessment of the accuracy of models for predicting 5-year mortality risks according to the Epidemiology of Cardiovascular Diseases and their Risk Factors in Regions of Russian Federation (ESSE-RF) study in Primorsky Krai.Material and methods. The study included 2131 people (1257 women and 874 men) aged 23-67 years with a median of 47 years (95% confidence interval [46; 48]). The study protocol included measurement of blood pressure (BP), heart rate (HR), waist circumference, hip circumference, and waist-to-hip ratio (WHR). The following blood biochemical parameters: total cholesterol (TC), low and high density lipoprotein cholesterol, triglycerides, apolipoproteins AI and B, lipoprotein(a), N-terminal pro-brain natriuretic peptide (NT-proNBP), D-dimer, fibrinogen, C-reactive protein (CRP), glucose, creatinine, uric acid. The study endpoint was 5-year all-cause death (2013-2018). The group of deceased patients during this period consisted of 42 (2%) people, while those continued the study — 2089 (98%). The χ2, Fisher and MannWhitney tests, univariate logistic regression (LR) were used for data processing and analysis. To build predictive models, we used following machine learning (ML) methods: multivariate LR, Weibull regression, and stochastic gradient boosting.Results. The prognostic models developed on the ML basis, using parameters of age, sex, smoking, systolic blood pressure (SBP) and TC level in their structure, had higher quality metrics than Systematic COronary Risk Evaluation (SCORE) system. The inclusion of CRP, glucose, NT-proNBP, and heart rate into the predictors increased the accuracy of all models with the maximum rise in quality metrics in the multivariate LR model. Predictive potential of other factors (WHR, lipid profile, fibrinogen, D-dimer, etc.) was low and did not improve the prediction quality. An analysis of the influence degree of individual predictors on the mortality rate indicated the prevailing contribution of five factors as follows: age, levels of TC, NT-proNBP, CRP, and glucose. A less noticeable effect was associated with the level of HR, SBP and smoking, while the contribution of sex was minimal.Conclusion. The use of modern ML methods increases the accuracy of predictive models and provides a higher efficiency of risk stratification, especially among individuals with a low and moderate death risk from cardiovascular diseases.https://cardiovascular.elpub.ru/jour/article/view/2908machine learning methodspredictionrisk factorsmortalityesse-rf |
| spellingShingle | V. A. Nevzorova T. A. Brodskaya K. I. Shakhgeldyan B. I. Geltser V. V. Kosterin L. G. Priseko Machine learning for predicting 5-year mortality risks: data from the ESSE-RF study in Primorsky Krai Кардиоваскулярная терапия и профилактика machine learning methods prediction risk factors mortality esse-rf |
| title | Machine learning for predicting 5-year mortality risks: data from the ESSE-RF study in Primorsky Krai |
| title_full | Machine learning for predicting 5-year mortality risks: data from the ESSE-RF study in Primorsky Krai |
| title_fullStr | Machine learning for predicting 5-year mortality risks: data from the ESSE-RF study in Primorsky Krai |
| title_full_unstemmed | Machine learning for predicting 5-year mortality risks: data from the ESSE-RF study in Primorsky Krai |
| title_short | Machine learning for predicting 5-year mortality risks: data from the ESSE-RF study in Primorsky Krai |
| title_sort | machine learning for predicting 5 year mortality risks data from the esse rf study in primorsky krai |
| topic | machine learning methods prediction risk factors mortality esse-rf |
| url | https://cardiovascular.elpub.ru/jour/article/view/2908 |
| work_keys_str_mv | AT vanevzorova machinelearningforpredicting5yearmortalityrisksdatafromtheesserfstudyinprimorskykrai AT tabrodskaya machinelearningforpredicting5yearmortalityrisksdatafromtheesserfstudyinprimorskykrai AT kishakhgeldyan machinelearningforpredicting5yearmortalityrisksdatafromtheesserfstudyinprimorskykrai AT bigeltser machinelearningforpredicting5yearmortalityrisksdatafromtheesserfstudyinprimorskykrai AT vvkosterin machinelearningforpredicting5yearmortalityrisksdatafromtheesserfstudyinprimorskykrai AT lgpriseko machinelearningforpredicting5yearmortalityrisksdatafromtheesserfstudyinprimorskykrai |