Association of NPAC score with survival after acute myocardial infarction
Copyright © 2020. Published by Elsevier B.V..
BACKGROUND AND AIMS: Risk stratification in acute myocardial infarction (AMI) is important for guiding clinical management. Current risk scores are mostly derived from clinical trials with stringent patient selection. We aimed to establish and evaluate a composite scoring system to improve short-term mortality classification after index episodes of AMI, independent of electrocardiography (ECG) pattern, in a large real-world cohort.
METHODS: Using electronic health records, patients admitted to our regional teaching hospital (derivation cohort, n = 1747) and an independent tertiary care center (validation cohort, n = 1276), with index acute myocardial infarction between January 2013 and December 2017, as confirmed by principal diagnosis and laboratory findings, were identified retrospectively.
RESULTS: Univariate logistic regression was used as the primary model to identify potential contributors to mortality. Stepwise forward likelihood ratio logistic regression revealed that neutrophil-to-lymphocyte ratio, peripheral vascular disease, age, and serum creatinine (NPAC) were significant for 90-day mortality (Hosmer- Lemeshow test, p = 0.21). Each component of the NPAC score was weighted by beta-coefficients in multivariate analysis. The C-statistic of the NPAC score was 0.75, which was higher than the conventional Charlson's score (C-statistic = 0.63). Judicious application of a deep learning model to our dataset improved the accuracy of classification with a C-statistic of 0.81.
CONCLUSIONS: The NPAC score comprises four items from routine laboratory parameters to basic clinical information and can facilitate early identification of cases at risk of short-term mortality following index myocardial infarction. Deep learning model can serve as a gatekeeper to facilitate clinical decision-making.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2020 |
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| Erschienen: |
2020 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:301 |
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| Enthalten in: |
Atherosclerosis - 301(2020) vom: 10. Mai, Seite 30-36 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Li, Christien Kh [VerfasserIn] |
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| Links: |
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| Themen: |
Cardiovascular |
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| Anmerkungen: |
Date Completed 23.06.2021 Date Revised 23.06.2021 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1016/j.atherosclerosis.2020.03.004 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM308897633 |
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| 100 | 1 | |a Li, Christien Kh |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Association of NPAC score with survival after acute myocardial infarction |
| 264 | 1 | |c 2020 | |
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| 500 | |a Date Revised 23.06.2021 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Copyright © 2020. Published by Elsevier B.V. | ||
| 520 | |a BACKGROUND AND AIMS: Risk stratification in acute myocardial infarction (AMI) is important for guiding clinical management. Current risk scores are mostly derived from clinical trials with stringent patient selection. We aimed to establish and evaluate a composite scoring system to improve short-term mortality classification after index episodes of AMI, independent of electrocardiography (ECG) pattern, in a large real-world cohort | ||
| 520 | |a METHODS: Using electronic health records, patients admitted to our regional teaching hospital (derivation cohort, n = 1747) and an independent tertiary care center (validation cohort, n = 1276), with index acute myocardial infarction between January 2013 and December 2017, as confirmed by principal diagnosis and laboratory findings, were identified retrospectively | ||
| 520 | |a RESULTS: Univariate logistic regression was used as the primary model to identify potential contributors to mortality. Stepwise forward likelihood ratio logistic regression revealed that neutrophil-to-lymphocyte ratio, peripheral vascular disease, age, and serum creatinine (NPAC) were significant for 90-day mortality (Hosmer- Lemeshow test, p = 0.21). Each component of the NPAC score was weighted by beta-coefficients in multivariate analysis. The C-statistic of the NPAC score was 0.75, which was higher than the conventional Charlson's score (C-statistic = 0.63). Judicious application of a deep learning model to our dataset improved the accuracy of classification with a C-statistic of 0.81 | ||
| 520 | |a CONCLUSIONS: The NPAC score comprises four items from routine laboratory parameters to basic clinical information and can facilitate early identification of cases at risk of short-term mortality following index myocardial infarction. Deep learning model can serve as a gatekeeper to facilitate clinical decision-making | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 650 | 4 | |a Cardiovascular | |
| 650 | 4 | |a Heart disease | |
| 650 | 4 | |a Mortality | |
| 650 | 4 | |a Myocardial infarction | |
| 650 | 4 | |a Neutrophil-to-lymphocyte ratio | |
| 700 | 1 | |a Xu, Zhongzhi |e verfasserin |4 aut | |
| 700 | 1 | |a Ho, Jeffery |e verfasserin |4 aut | |
| 700 | 1 | |a Lakhani, Ishan |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Ying Zhi |e verfasserin |4 aut | |
| 700 | 1 | |a Bazoukis, George |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Tong |e verfasserin |4 aut | |
| 700 | 1 | |a Wong, Wing Tak |e verfasserin |4 aut | |
| 700 | 1 | |a Cheng, Shuk Han |e verfasserin |4 aut | |
| 700 | 1 | |a Chan, Matthew Tv |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Lin |e verfasserin |4 aut | |
| 700 | 1 | |a Gin, Tony |e verfasserin |4 aut | |
| 700 | 1 | |a Wong, Martin Cs |e verfasserin |4 aut | |
| 700 | 1 | |a Wong, Ian Chi Kei |e verfasserin |4 aut | |
| 700 | 1 | |a Wu, William Ka Kei |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Qingpeng |e verfasserin |4 aut | |
| 700 | 1 | |a Tse, Gary |e verfasserin |4 aut | |
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