Predictors of Excess Mortality After Fracture: A Population-Based Cohort Study

Authors

  • L Joseph Melton III,

    Corresponding author
    1. Division of Epidemiology, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
    • Address correspondence to: L Joseph Melton III, MD, Division of Epidemiology, Department of Health Sciences Research, Mayo Clinic, 200 First Street Southwest, Rochester, MN 55905, USA. E-mail: melton.j@mayo.edu

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  • Elizabeth J Atkinson,

    1. Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
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  • Jennifer L St. Sauver,

    1. Division of Epidemiology, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
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  • Sara J Achenbach,

    1. Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
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  • Terry M Therneau,

    1. Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
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  • Walter A Rocca,

    1. Division of Epidemiology, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
    2. Department of Neurology, Mayo Clinic, Rochester, MN, USA
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  • Shreyasee Amin

    1. Division of Epidemiology, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
    2. Division of Rheumatology, Department of Internal Medicine, Mayo Clinic, Rochester, MN, USA
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ABSTRACT

To determine the extent to which excess mortality after fractures attributable to particular causes at specific skeletal sites can be predicted using data about all medical diagnoses, we conducted a historical cohort study among 1991 Olmsted County, Minnesota, residents aged ≥50 years who experienced any fracture in 1989 to 1991 and who were followed passively for up to 22 years for death from any cause. We used a machine learning approach, gradient boosting machine (GBM) modeling, to determine whether the comorbid conditions present at the time of fracture and those that arose subsequently could, in aggregate, identify patients at the greatest increased risk of death. During 21,867 person-years of follow-up, 1245 deaths were observed when 1061 were expected (standardized mortality ratio, 1.2; 95% confidence interval [CI] 1.1–1.2). Patients presented with a median history of 26 comorbid conditions each as assessed by the Clinical Classification Software system and 57 each over the total duration of follow-up. Using all available information, the excess deaths could be predicted with good accuracy (c-index ≥0.80) in 89% of the GBM models built for patients with different types of fracture; in one-third of the models, the c-index was ≥0.90. The conditions most prominent in the GBM prediction models were also reflected in the specific causes of death that were elevated, suggesting the influence of confounding on the relationship. However, the predominant comorbid conditions were mainly those responsible for mortality in the general population, rather than the specific diseases most closely associated with secondary osteoporosis. To reduce long-term deaths in the fracture population as a whole, a more general approach to the fracture patient is indicated. © 2014 American Society for Bone and Mineral Research.

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