Kofink, D;
Muller, SA;
Patel, RS;
Dorresteijn, JAN;
Berkelmans, GFN;
de Groot, MCH;
van Solinge, WW;
... SMART Study Group; + view all
(2018)
Routinely measured hematological parameters and prediction of recurrent vascular events in patients with clinically manifest vascular disease.
PLoS One
, 13
(9)
, Article e0202682. 10.1371/journal.pone.0202682.
Preview |
Text
Routinely measured hematological parameters and prediction of recurrent vascular events in patients with clinically manifest vascular disease.pdf - Published Version Download (2MB) | Preview |
Abstract
BACKGROUND AND AIMS: The predictive value of traditional risk factors for vascular events in patients with manifest vascular disease is limited, underscoring the need for novel biomarkers to improve risk stratification. Since hematological parameters are routinely assessed in clinical practice, they are readily available candidates. METHODS: We used data from 3,922 vascular patients, who participated in the Second Manifestations of ARTerial Disease (SMART) study. We first investigated associations between recurrent vascular events and 22 hematological parameters, obtained from the Utrecht Patient Oriented Database (UPOD), and then assessed whether parameters associated with outcome improved risk prediction. RESULTS: After adjustment for all SMART risk score (SRS) variables, lymphocyte %, neutrophil count, neutrophil % and red cell distribution width (RDW) were significantly associated with vascular events. When individually added to the SRS, lymphocyte % improved prediction of recurrent vascular events with a continuous net reclassification improvement (cNRI) of 17.4% [95% CI: 2.1, 32.1%] and an increase in c-statistic of 0.011 [0.000, 0.022]. The combination of lymphocyte % and neutrophil count resulted in a cNRI of 22.2% [3.2, 33.4%] and improved c-statistic by 0.011 [95% CI: 0.000, 0.022]. Lymphocyte % and RDW yielded a cNRI of 18.7% [3.3, 31.9%] and improved c-statistic by 0.016 [0.004, 0.028]. However, the addition of hematological parameters only modestly increased risk estimates for patients with an event during follow-up. CONCLUSIONS: Several hematological parameters were independently associated with recurrent vascular events. Lymphocyte % alone and in combination with other parameters enhanced discrimination and reclassification. However, the incremental value for patients with a recurrent event was limited.
| Type: | Article |
|---|---|
| Title: | Routinely measured hematological parameters and prediction of recurrent vascular events in patients with clinically manifest vascular disease |
| Location: | United States |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1371/journal.pone.0202682 |
| Publisher version: | https://doi.org/10.1371/journal.pone.0202682 |
| Language: | English |
| Additional information: | This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ |
| Keywords: | Hematology, Lymphocytes, Neutrophils, Vascular diseases, Vascular medicine, Biomarkers, Monocytes, Coronary heart disease |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Cardiovascular Science UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Cardiovascular Science > Population Science and Experimental Medicine UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Health Informatics |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10057108 |
Archive Staff Only
 |
View Item |
