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Predictors of excess mortality after fracture: a population-based cohort study.

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.

SUBMITTER: Melton LJ

PROVIDER: S-EPMC4133107 | biostudies-literature | 2014 Jul

REPOSITORIES: biostudies-literature

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Predictors of excess mortality after fracture: a population-based cohort study.

Melton L Joseph LJ Atkinson Elizabeth J EJ St Sauver Jennifer L JL Achenbach Sara J SJ Therneau Terry M TM Rocca Walter A WA Amin Shreyasee S

Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research 20140701 7

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 ...[more]

PMID: 24677169

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Project description:BackgroundIt is unclear whether an upper gastrointestinal bleed is an isolated gastrointestinal event or an indicator of a deterioration in a patient's overall health status. Therefore, we investigated the excess causes of death in individuals after a non-variceal bleed compared with deaths in a matched sample of the general population.Methods and findingsLinked longitudinal data from the English Hospital Episodes Statistics (HES) data, General Practice Research Database (GPRD), and Office of National Statistics death register were used to define a cohort of non-variceal bleeds between 1997 and 2010. Controls were matched at the start of the study by age, sex, practice, and year. The excess risk of each cause of death in the 5 years subsequent to a bleed was then calculated whilst adjusting for competing risks using cumulative incidence functions. 16,355 patients with a non-variceal upper gastrointestinal bleed were matched to 81,523 controls. The total 5-year risk of death due to gastrointestinal causes (malignant or non-malignant) ranged from 3.6% (≤ 50 years, 95% CI 3.0%-4.3%) to 15.2% (≥ 80 years, 14.2%-16.3%), representing an excess over controls of between 3.6% (3.0%-4.2%) and 13.4% (12.4%-14.5%), respectively. In contrast the total 5-year risk of death due to non-gastrointestinal causes ranged from 4.1% (≤ 50 years, 3.4%-4.8%) to 46.6% (≥ 80 years, 45.2%-48.1%), representing an excess over controls of between 3.8% (3.1%-4.5%) and 19.0% (17.5%-20.6%), respectively. The main limitation of this study was potential misclassification of the exposure and outcome; however, we sought to minimise this by using information derived across multiple linked datasets.ConclusionsDeaths from all causes were increased following an upper gastrointestinal bleed compared to matched controls, and over half the excess risk of death was due to seemingly unrelated co-morbidity. A non-variceal bleed may therefore warrant a careful assessment of co-morbid illness seemingly unrelated to the bleed.

Project description:BackgroundThe estimation of influenza-associated excess mortality in countries can help to improve estimates of the global mortality burden attributable to influenza virus infections. We did a study to estimate the influenza-associated excess respiratory mortality in mainland China for the 2010-11 through 2014-15 seasons.MethodsWe obtained provincial weekly influenza surveillance data and population mortality data for 161 disease surveillance points in 31 provinces in mainland China from the Chinese Center for Disease Control and Prevention for the years 2005-15. Disease surveillance points with an annual average mortality rate of less than 0·4% between 2005 and 2015 or an annual mortality rate of less than 0·3% in any given years were excluded. We extracted data for respiratory deaths based on codes J00-J99 under the tenth edition of the International Classification of Diseases. Data on respiratory mortality and population were stratified by age group (age <60 years and ≥60 years) and aggregated by province. The overall annual population data of each province and national annual respiratory mortality data were compiled from the China Statistical Yearbook. Influenza surveillance data on weekly proportion of samples testing positive for influenza virus by type or subtype for 31 provinces were extracted from the National Sentinel Hospital-based Influenza Surveillance Network. We estimated influenza-associated excess respiratory mortality rates between the 2010-11 and 2014-15 seasons for 22 provinces with valid data in the country using linear regression models. Extrapolation of excess respiratory mortality rates was done using random-effect meta-regression models for nine provinces without valid data for a direct estimation of the rates.FindingsWe fitted the linear regression model with the data from 22 of 31 provinces in mainland China, representing 83·0% of the total population. We estimated that an annual mean of 88 100 (95% CI 84 200-92 000) influenza-associated excess respiratory deaths occurred in China in the 5 years studied, corresponding to 8·2% (95% CI 7·9-8·6) of respiratory deaths. The mean excess respiratory mortality rates per 100 000 person-seasons for influenza A(H1N1)pdm09, A(H3N2), and B viruses were 1·6 (95% CI 1·5-1·7), 2·6 (2·4-2·8), and 2·3 (2·1-2·5), respectively. Estimated excess respiratory mortality rates per 100 000 person-seasons were 1·5 (95% CI 1·1-1·9) for individuals younger than 60 years and 38·5 (36·8-40·2) for individuals aged 60 years or older. Approximately 71 000 (95% CI 67 800-74 100) influenza-associated excess respiratory deaths occurred in individuals aged 60 years or older, corresponding to 80% of such deaths.InterpretationInfluenza was associated with substantial excess respiratory mortality in China between 2010-11 and 2014-15 seasons, especially in older adults aged at least 60 years. Continuous and high-quality surveillance data across China are needed to improve the estimation of the disease burden attributable to influenza and the best public health interventions are needed to curb this burden.FundingNational Science Fund for Distinguished Young Scholars, National Science and Technology Major Project of China, National Institute of Health Research, the Harvard Center for Communicable Disease Dynamics from the National Institute of General Medical Sciences, and the China-US Collaborative Program on Emerging and Re-emerging Infectious Disease.

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Dataset Information

Predictors of excess mortality after fracture: a population-based cohort study.

Publications

Predictors of excess mortality after fracture: a population-based cohort study.

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