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Article: Association of NPAC score with survival after acute myocardial infarction

TitleAssociation of NPAC score with survival after acute myocardial infarction
Authors
KeywordsCardiovascular
Heart disease
Mortality
Myocardial infarction
Neutrophil-to-lymphocyte ratio
Issue Date2020
PublisherElsevier Ireland Ltd. The Journal's web site is located at http://www.elsevier.com/locate/atherosclerosis
Citation
Atherosclerosis, 2020, v. 301, p. 30-36 How to Cite?
AbstractBackground 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.
Persistent Identifierhttp://hdl.handle.net/10722/285038
ISSN
2019 Impact Factor: 3.919
2015 SCImago Journal Rankings: 1.819

 

DC FieldValueLanguage
dc.contributor.authorLi, CKH-
dc.contributor.authorXu, Z-
dc.contributor.authorHo, J-
dc.contributor.authorLakhani, I-
dc.contributor.authorLiu, YZ-
dc.contributor.authorBazoukis, G-
dc.contributor.authorLiu, T-
dc.contributor.authorWong, WT-
dc.contributor.authorCheng, SH-
dc.contributor.authorChan, MTV-
dc.contributor.authorZhang, L-
dc.contributor.authorGin, T-
dc.contributor.authorWong, MCS-
dc.contributor.authorWong, ICK-
dc.contributor.authorWu, WKK-
dc.contributor.authorZhang, QP-
dc.contributor.authorTse, G-
dc.date.accessioned2020-08-07T09:05:55Z-
dc.date.available2020-08-07T09:05:55Z-
dc.date.issued2020-
dc.identifier.citationAtherosclerosis, 2020, v. 301, p. 30-36-
dc.identifier.issn0021-9150-
dc.identifier.urihttp://hdl.handle.net/10722/285038-
dc.description.abstractBackground 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.-
dc.languageeng-
dc.publisherElsevier Ireland Ltd. The Journal's web site is located at http://www.elsevier.com/locate/atherosclerosis-
dc.relation.ispartofAtherosclerosis-
dc.subjectCardiovascular-
dc.subjectHeart disease-
dc.subjectMortality-
dc.subjectMyocardial infarction-
dc.subjectNeutrophil-to-lymphocyte ratio-
dc.titleAssociation of NPAC score with survival after acute myocardial infarction-
dc.typeArticle-
dc.identifier.emailWong, ICK: wongick@hku.hk-
dc.identifier.authorityWong, ICK=rp01480-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.atherosclerosis.2020.03.004-
dc.identifier.pmid32304975-
dc.identifier.scopuseid_2-s2.0-85083095434-
dc.identifier.hkuros311745-
dc.identifier.volume301-
dc.identifier.spage30-
dc.identifier.epage36-
dc.publisher.placeIreland-

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