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Return to Work after Percutaneous Coronary Intervention: The Predictive Value of Self-Reported Health Compared to Clinical Measures

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  • Karin Biering
  • Torsten Toftegaard Nielsen
  • Kurt Rasmussen
  • Troels Niemann
  • Niels Henrik Hjollund

Abstract

Aims: Coronary heart disease is prevalent in the working-age population. Traditional outcome measures like mortality and readmission are of importance to evaluate the prognosis but are hardly sufficient. Ability to work is an additional outcome of clinical and societal significance. We describe trends and predictors of Return To Work (RTW) after PCI and describe a possible benefit using patient-reported measures in risk stratification of RTW. Methods: A total of 1585 patients aged less than 67 years treated with PCI in 2006–2008 at the Aarhus University Hospital were enrolled. Clinical information was provided through the West Denmark Heart Registry, and 4 weeks after PCI we mailed a questionnaire regarding self-rated health (response rate 83.5%). RTW was defined at weekly basis using extensive register data on transfer payments. Predictors of RTW were analysed as time to event. ROC curves constructed by logistic regression of predicting variables were evaluated by the c-statistic. Results: Four weeks before PCI 50% of the patients were working; the corresponding figures were 25% after 4 weeks, 36% after 12 weeks, and 43% after one year. The patients’ self-rated health one month after the procedure was a significant better predictor of RTW compared to other variables including LVEF, both at short (12 weeks) and long (one year) term. Conclusions: The patient's self-rated health four weeks after the procedure was a stronger predictor than left ventricular ejection fraction (LVEF), and consequently useful when patients seek medical advice with respect to RWT.

Suggested Citation

  • Karin Biering & Torsten Toftegaard Nielsen & Kurt Rasmussen & Troels Niemann & Niels Henrik Hjollund, 2012. "Return to Work after Percutaneous Coronary Intervention: The Predictive Value of Self-Reported Health Compared to Clinical Measures," PLOS ONE, Public Library of Science, vol. 7(11), pages 1-7, November.
    • Handle: RePEc:plo:pone00:0049268
    DOI: 10.1371/journal.pone.0049268
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    References listed on IDEAS

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    1. John P. Klein & Per Kragh Andersen, 2005. "Regression Modeling of Competing Risks Data Based on Pseudovalues of the Cumulative Incidence Function," Biometrics, The International Biometric Society, vol. 61(1), pages 223-229, March.
    2. Söderman, Eva & Lisspers, Jan & Sundin, Örjan, 2003. "Depression as a predictor of return to work in patients with coronary artery disease," Social Science & Medicine, Elsevier, vol. 56(1), pages 193-202, January.
    3. Fukuoka, Yoshimi & Dracup, Kathleen & Takeshima, Masako & Ishii, Noriko & Makaya, Miyuki & Groah, Linda & Kyriakidis, Erick, 2009. "Effect of job strain and depressive symptoms upon returning to work after acute coronary syndrome," Social Science & Medicine, Elsevier, vol. 68(10), pages 1875-1881, May.
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    1. Mariarita Stendardo & Melissa Bonci & Valeria Casillo & Rossella Miglio & Giulia Giovannini & Marco Nardini & Gianluca Campo & Alessandro Fucili & Piera Boschetto, 2018. "Predicting return to work after acute myocardial infarction: Socio-occupational factors overcome clinical conditions," PLOS ONE, Public Library of Science, vol. 13(12), pages 1-11, December.
    2. Adriano Dias & João Marcos Bernardes & Sandro Augusto Servilha Coquemala & Juan Gómez-Salgado & Carlos Ruiz-Frutos, 2019. "Predictors of return to work with and without restrictions in public workers," PLOS ONE, Public Library of Science, vol. 14(1), pages 1-16, January.
    3. Marco Franceschini & Maria Pia Massimiani & Stefano Paravati & Maurizio Agosti, 2016. "Return to Work: A Cut-Off of FIM Gain with Montebello Rehabilitation Factor Score in Order to Identify Predictive Factors in Subjects with Acquired Brain Injury," PLOS ONE, Public Library of Science, vol. 11(10), pages 1-11, October.

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