Literature DB >> 19486718

Exceptional mortality prediction by risk scores from common laboratory tests.

Benjamin D Horne1, Heidi T May, Joseph B Muhlestein, Brianna S Ronnow, Donald L Lappé, Dale G Renlund, Abdallah G Kfoury, John F Carlquist, Patrick W Fisher, Robert R Pearson, Tami L Bair, Jeffrey L Anderson.   

Abstract

BACKGROUND: Some components of the complete blood count and basic metabolic profile are commonly used risk predictors. Many of their components are not commonly used, but they might contain independent risk information. This study tested the ability of a risk score combining all components to predict all-cause mortality.
METHODS: Patients with baseline complete blood count and basic metabolic profile measurements were randomly assigned (60%/40%) to independent training (N = 71,921) and test (N = 47,458) populations. A third population (N = 16,372) from the Third National Health and Nutrition Examination Survey and a fourth population of patients who underwent coronary angiography (N = 2558) were used as additional validation groups. Risk scores were computed in the training population for 30-day, 1-year, and 5-year mortality using age- and sex-adjusted weights from multivariable modeling of all complete blood count and basic metabolic profile components.
RESULTS: Area under the curve c-statistics were exceptional in the training population for death at 30 days (c = 0.90 for women, 0.87 for men), 1 year (c = 0.87, 0.83), and 5-years (c = 0.90, 0.85) and in the test population for death at 30 days (c = 0.88 for women, 0.85 for men), 1 year (c = 0.86, 0.82), and 5 years (c = 0.89, 0.83). In the test, the Third National Health and Nutrition Examination Survey, and the angiography populations, risk scores were highly associated with death (P <.001), and thresholds of risk significantly stratified all 3 populations.
CONCLUSION: In large patient and general populations, risk scores combining complete blood count and basic metabolic profile components were highly predictive of death. Easily computed in a clinical laboratory at negligible incremental cost, these risk scores aggregate baseline risk information from both the popular and the underused components of ubiquitous laboratory tests.

Entities:  

Mesh:

Substances:

Year:  2009        PMID: 19486718     DOI: 10.1016/j.amjmed.2008.10.043

Source DB:  PubMed          Journal:  Am J Med        ISSN: 0002-9343            Impact factor:   4.965


  31 in total

1.  Hematologic variables and venous thrombosis: red cell distribution width and blood monocyte count are associated with an increased risk.

Authors:  Suely Meireles Rezende; Willem M Lijfering; Frits R Rosendaal; Suzanne C Cannegieter
Journal:  Haematologica       Date:  2013-07-26       Impact factor: 9.941

2.  Extreme erythrocyte macrocytic and microcytic percentages are highly predictive of morbidity and mortality.

Authors:  Benjamin D Horne; Joseph B Muhlestein; Sterling T Bennett; Joseph Boone Muhlestein; Kurt R Jensen; Diane Marshall; Tami L Bair; Heidi T May; John F Carlquist; Matthew Hegewald; Stacey Knight; Viet T Le; T Jared Bunch; Donald L Lappé; Jeffrey L Anderson; Kirk U Knowlton
Journal:  JCI Insight       Date:  2018-07-26

3.  Adverse cardiovascular events in acute coronary syndrome with indications for anticoagulation.

Authors:  Stacey Knight; Raymond O McCubrey; Zhong Yuan; Scott C Woller; Benjamin D Horne; T Jared Bunch; Viet T Le; Roger M Mills; Joseph B Muhlestein
Journal:  Ther Adv Cardiovasc Dis       Date:  2016-02-25

4.  Linking empirical estimates of body burden of environmental chemicals and wellness using NHANES data.

Authors:  Chris Gennings; Rhonda Ellis; Joseph K Ritter
Journal:  Environ Int       Date:  2011-11-01       Impact factor: 9.621

5.  Elevated levels of RDW is associated with non-valvular atrial fibrillation.

Authors:  Barış Güngör; Kazım Serhan Özcan; İzzet Erdinler; Ahmet Ekmekçi; Ahmet Taha Alper; Damirbek Osmonov; Nazmi Çalık; Sukru Akyuz; Ercan Toprak; Hale Yılmaz; Aydın Yıldırım; Osman Bolca
Journal:  J Thromb Thrombolysis       Date:  2014-05       Impact factor: 2.300

6.  Do clinicians recommend aspirin to patients for primary prevention of cardiovascular disease?

Authors:  Kevin Fiscella; Paul C Winters; Michael Mendoza; Gary J Noronha; Carlos M Swanger; John D Bisognano; Robert J Fortuna
Journal:  J Gen Intern Med       Date:  2015-02       Impact factor: 5.128

7.  Red cell distribution width improves the simplified acute physiology score for risk prediction in unselected critically ill patients.

Authors:  Sabina Hunziker; Leo A Celi; Joon Lee; Michael D Howell
Journal:  Crit Care       Date:  2012-05-18       Impact factor: 9.097

8.  Cardiovascular Risk Assessment Using Artificial Intelligence-Enabled Event Adjudication and Hematologic Predictors.

Authors:  James G Truslow; Shinichi Goto; Max Homilius; Christopher Mow; John M Higgins; Calum A MacRae; Rahul C Deo
Journal:  Circ Cardiovasc Qual Outcomes       Date:  2022-04-28

9.  Predictive value of a profile of routine blood measurements on mortality in older persons in the general population: the Leiden 85-plus Study.

Authors:  Anne H van Houwelingen; Wendy P J den Elzen; Simon P Mooijaart; Margot Heijmans; Jeanet W Blom; Anton J M de Craen; Jacobijn Gussekloo
Journal:  PLoS One       Date:  2013-03-04       Impact factor: 3.240

10.  Post-discharge thrombosis and bleeding in medical patients: A novel risk score derived from ubiquitous biomarkers.

Authors:  Scott C Woller; Scott M Stevens; Masarret Fazili; James F Lloyd; Emily L Wilson; Gregory L Snow; Joseph R Bledsoe; Benjamin D Horne
Journal:  Res Pract Thromb Haemost       Date:  2021-07-07
View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.