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Self-perceived quality of life predicts mortality risk better than a multi-biomarker panel, but the combination of both does best

Research output: Journal contributions › Journal articles › Research › peer-review

Standard

Self-perceived quality of life predicts mortality risk better than a multi-biomarker panel, but the combination of both does best. / Haring, Robin; Feng, You-Shan; Moock, Jörn et al.
In: BMC Medical Research Methodology, Vol. 11, No. 103, 103, 12.07.2011.

Research output: Journal contributions › Journal articles › Research › peer-review

Harvard

Haring, R, Feng, Y-S, Moock, J, Völzke, H, Dörr, M, Nauck, M, Wallaschofski, H & Kohlmann, T 2011, 'Self-perceived quality of life predicts mortality risk better than a multi-biomarker panel, but the combination of both does best', BMC Medical Research Methodology, vol. 11, no. 103, 103. https://doi.org/10.1186/1471-2288-11-103

APA

Haring, R., Feng, Y.-S., Moock, J., Völzke, H., Dörr, M., Nauck, M., Wallaschofski, H., & Kohlmann, T. (2011). Self-perceived quality of life predicts mortality risk better than a multi-biomarker panel, but the combination of both does best. BMC Medical Research Methodology, 11(103), Article 103. https://doi.org/10.1186/1471-2288-11-103

Vancouver

Haring R, Feng YS, Moock J, Völzke H, Dörr M, Nauck M et al. Self-perceived quality of life predicts mortality risk better than a multi-biomarker panel, but the combination of both does best. BMC Medical Research Methodology. 2011 Jul 12;11(103):103. doi: 10.1186/1471-2288-11-103

Bibtex

@article{57e89c81d783489caf79b364b92caad9,
title = "Self-perceived quality of life predicts mortality risk better than a multi-biomarker panel, but the combination of both does best",
abstract = "Background: Associations between measures of subjective health and mortality risk have previously been shown. We assessed the impact and comparative predictive performance of a multi-biomarker panel on this association. Methods. Data from 4,261 individuals aged 20-79 years recruited for the population-based Study of Health in Pomerania was used. During an average 9.7 year follow-up, 456 deaths (10.7%) occurred. Subjective health was assessed by SF-12 derived physical (PCS-12) and mental component summaries (MCS-12), and a single-item self-rated health (SRH) question. We implemented Cox proportional-hazards regression models to investigate the association of subjective health with mortality and to assess the impact of a combination of 10 biomarkers on this association. Variable selection procedures were used to identify a parsimonious set of subjective health measures and biomarkers, whose predictive ability was compared using receiver operating characteristic (ROC) curves, C-statistics, and reclassification methods. Results: In age- and gender-adjusted Cox models, poor SRH (hazard ratio (HR), 2.07; 95% CI, 1.34-3.20) and low PCS-12 scores (lowest vs. highest quartile: HR, 1.75; 95% CI, 1.31-2.33) were significantly associated with increased risk of all-cause mortality; an association independent of various covariates and biomarkers. Furthermore, selected subjective health measures yielded a significantly higher C-statistic (0.883) compared to the selected biomarker panel (0.872), whereas a combined assessment showed the highest C-statistic (0.887) with a highly significant integrated discrimination improvement of 1.5% (p < 0.01). Conclusion: Adding biomarker information did not affect the association of subjective health measures with mortality, but significantly improved risk stratification. Thus, a combined assessment of self-reported subjective health and measured biomarkers may be useful to identify high-risk individuals for intensified monitoring.",
keywords = "Health sciences, Adult, Aged, Biological Markers, Cluster Analysis, Diagnostic Self Evaluation, Female, Germany, Humans, Kaplan-Meier Estimate, Male, Middle Aged, Mortality, Proportional Hazards Models, Quality of Life, ROC Curve, Risk Factors, Self Report, Young Adult",
author = "Robin Haring and You-Shan Feng and J{\"o}rn Moock and Henry V{\"o}lzke and Marcus D{\"o}rr and Matthias Nauck and Henri Wallaschofski and Thomas Kohlmann",
note = "Funding Information: Statistical analysis were supported by the Community Medicine Research net (CMR) of the University of Greifswald, Germany, which is funded by the Federal Ministry of Education and Research, the Ministry of Cultural Affairs as well as the Social Ministry of the Federal State of Mecklenburg-West Pomerania. The CMR encompasses several research projects which are sharing data of the population-based Study of Health in Pomerania (SHIP; http://www.community-medicine.de). This work is part of the research project Greifswald Approach to Individualized Medicine (GANI_MED). The GANI_MED consortium is funded by the Federal Ministry of Education and Research and the Ministry of Cultural Affairs of the Federal State of Mecklenburg -West Pomerania (03IS2061A).",
year = "2011",
month = jul,
day = "12",
doi = "10.1186/1471-2288-11-103",
language = "English",
volume = "11",
journal = "BMC Medical Research Methodology",
issn = "1471-2288",
publisher = "BioMed Central Ltd.",
number = "103",

}

RIS

TY - JOUR

T1 - Self-perceived quality of life predicts mortality risk better than a multi-biomarker panel, but the combination of both does best

AU - Haring, Robin

AU - Feng, You-Shan

AU - Moock, Jörn

AU - Völzke, Henry

AU - Dörr, Marcus

AU - Nauck, Matthias

AU - Wallaschofski, Henri

AU - Kohlmann, Thomas

N1 - Funding Information: Statistical analysis were supported by the Community Medicine Research net (CMR) of the University of Greifswald, Germany, which is funded by the Federal Ministry of Education and Research, the Ministry of Cultural Affairs as well as the Social Ministry of the Federal State of Mecklenburg-West Pomerania. The CMR encompasses several research projects which are sharing data of the population-based Study of Health in Pomerania (SHIP; http://www.community-medicine.de). This work is part of the research project Greifswald Approach to Individualized Medicine (GANI_MED). The GANI_MED consortium is funded by the Federal Ministry of Education and Research and the Ministry of Cultural Affairs of the Federal State of Mecklenburg -West Pomerania (03IS2061A).

PY - 2011/7/12

Y1 - 2011/7/12

N2 - Background: Associations between measures of subjective health and mortality risk have previously been shown. We assessed the impact and comparative predictive performance of a multi-biomarker panel on this association. Methods. Data from 4,261 individuals aged 20-79 years recruited for the population-based Study of Health in Pomerania was used. During an average 9.7 year follow-up, 456 deaths (10.7%) occurred. Subjective health was assessed by SF-12 derived physical (PCS-12) and mental component summaries (MCS-12), and a single-item self-rated health (SRH) question. We implemented Cox proportional-hazards regression models to investigate the association of subjective health with mortality and to assess the impact of a combination of 10 biomarkers on this association. Variable selection procedures were used to identify a parsimonious set of subjective health measures and biomarkers, whose predictive ability was compared using receiver operating characteristic (ROC) curves, C-statistics, and reclassification methods. Results: In age- and gender-adjusted Cox models, poor SRH (hazard ratio (HR), 2.07; 95% CI, 1.34-3.20) and low PCS-12 scores (lowest vs. highest quartile: HR, 1.75; 95% CI, 1.31-2.33) were significantly associated with increased risk of all-cause mortality; an association independent of various covariates and biomarkers. Furthermore, selected subjective health measures yielded a significantly higher C-statistic (0.883) compared to the selected biomarker panel (0.872), whereas a combined assessment showed the highest C-statistic (0.887) with a highly significant integrated discrimination improvement of 1.5% (p < 0.01). Conclusion: Adding biomarker information did not affect the association of subjective health measures with mortality, but significantly improved risk stratification. Thus, a combined assessment of self-reported subjective health and measured biomarkers may be useful to identify high-risk individuals for intensified monitoring.

AB - Background: Associations between measures of subjective health and mortality risk have previously been shown. We assessed the impact and comparative predictive performance of a multi-biomarker panel on this association. Methods. Data from 4,261 individuals aged 20-79 years recruited for the population-based Study of Health in Pomerania was used. During an average 9.7 year follow-up, 456 deaths (10.7%) occurred. Subjective health was assessed by SF-12 derived physical (PCS-12) and mental component summaries (MCS-12), and a single-item self-rated health (SRH) question. We implemented Cox proportional-hazards regression models to investigate the association of subjective health with mortality and to assess the impact of a combination of 10 biomarkers on this association. Variable selection procedures were used to identify a parsimonious set of subjective health measures and biomarkers, whose predictive ability was compared using receiver operating characteristic (ROC) curves, C-statistics, and reclassification methods. Results: In age- and gender-adjusted Cox models, poor SRH (hazard ratio (HR), 2.07; 95% CI, 1.34-3.20) and low PCS-12 scores (lowest vs. highest quartile: HR, 1.75; 95% CI, 1.31-2.33) were significantly associated with increased risk of all-cause mortality; an association independent of various covariates and biomarkers. Furthermore, selected subjective health measures yielded a significantly higher C-statistic (0.883) compared to the selected biomarker panel (0.872), whereas a combined assessment showed the highest C-statistic (0.887) with a highly significant integrated discrimination improvement of 1.5% (p < 0.01). Conclusion: Adding biomarker information did not affect the association of subjective health measures with mortality, but significantly improved risk stratification. Thus, a combined assessment of self-reported subjective health and measured biomarkers may be useful to identify high-risk individuals for intensified monitoring.

KW - Health sciences

KW - Adult

KW - Aged

KW - Biological Markers

KW - Cluster Analysis

KW - Diagnostic Self Evaluation

KW - Female

KW - Germany

KW - Humans

KW - Kaplan-Meier Estimate

KW - Male

KW - Middle Aged

KW - Mortality

KW - Proportional Hazards Models

KW - Quality of Life

KW - ROC Curve

KW - Risk Factors

KW - Self Report

KW - Young Adult

UR - http://www.scopus.com/inward/record.url?scp=79960177244&partnerID=8YFLogxK

UR - https://www.mendeley.com/catalogue/9b84447a-07af-3227-bba4-b2c30bd56d35/

U2 - 10.1186/1471-2288-11-103

DO - 10.1186/1471-2288-11-103

M3 - Journal articles

C2 - 21749697

VL - 11

JO - BMC Medical Research Methodology

JF - BMC Medical Research Methodology

SN - 1471-2288

IS - 103

M1 - 103

ER -

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