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How to model temporal changes in comorbidity for cancer patients using prospective cohort data

Overview of attention for article published in BMC Medical Informatics and Decision Making, November 2015
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Title
How to model temporal changes in comorbidity for cancer patients using prospective cohort data
Published in
BMC Medical Informatics and Decision Making, November 2015
DOI 10.1186/s12911-015-0217-8
Pubmed ID
Authors

Lars Lindhagen, Mieke Van Hemelrijck, David Robinson, Pär Stattin, Hans Garmo

Abstract

The presence of comorbid conditions is strongly related to survival and also affects treatment choices in cancer patients. This comorbidity is often quantified by the Charlson Comorbidity Index (CCI) using specific weights (1, 2, 3, or 6) for different comorbidities. It has been shown that the CCI increases at different times and with different sizes, so that traditional time to event analysis is not adequate to assess these temporal changes. Here, we present a method to model temporal changes in CCI in cancer patients using data from PCBaSe Sweden, a nation-wide population-based prospective cohort of men diagnosed with prostate cancer. Our proposed model is based on the assumption that a change in comorbidity, as quantified by the CCI, is an irreversible one-way process, i.e., CCI accumulates over time and cannot decrease. CCI was calculated based on 17 disease categories, which were defined using ICD-codes for discharge diagnoses in the National Patient Register. A state transition model in discrete time steps (i.e., four weeks) was applied to capture all changes in CCI. The transition probabilities were estimated from three modelling steps: 1) Logistic regression model for vital status, 2) Logistic regression model to define any changes in CCI, and 3) Poisson regression model to determine the size of CCI change, with an additional logistic regression model for CCI changes ≥ 6. The four models combined yielded parameter estimates to calculate changes in CCI with their confidence intervals. These methods were applied to men with low-risk prostate cancer who received active surveillance (AS), radical prostatectomy (RP), or curative radiotherapy (RT) as primary treatment. There were large differences in CCI changes according to treatment. Our method to model temporal changes in CCI efficiently captures changes in comorbidity over time with a small number of regression analyses to perform - which would be impossible with tradition time to event analyses. However, our approach involves a simulation step that is not yet included in standard statistical software packages. In our prostate cancer example we showed that there are large differences in development of comorbidities among men receiving different treatments for prostate cancer.

Twitter Demographics

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Mendeley readers

The data shown below were compiled from readership statistics for 43 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 2%
Denmark 1 2%
Unknown 41 95%

Demographic breakdown

Readers by professional status Count As %
Student > Master 9 21%
Researcher 6 14%
Student > Postgraduate 6 14%
Lecturer 5 12%
Student > Bachelor 4 9%
Other 10 23%
Unknown 3 7%
Readers by discipline Count As %
Medicine and Dentistry 23 53%
Nursing and Health Professions 3 7%
Computer Science 2 5%
Unspecified 1 2%
Economics, Econometrics and Finance 1 2%
Other 6 14%
Unknown 7 16%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 23 November 2015.
All research outputs
#14,241,439
of 22,833,393 outputs
Outputs from BMC Medical Informatics and Decision Making
#1,102
of 1,989 outputs
Outputs of similar age
#201,656
of 386,431 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#27
of 41 outputs
Altmetric has tracked 22,833,393 research outputs across all sources so far. This one is in the 35th percentile – i.e., 35% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,989 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 38th percentile – i.e., 38% of its peers scored the same or lower than it.
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We're also able to compare this research output to 41 others from the same source and published within six weeks on either side of this one. This one is in the 24th percentile – i.e., 24% of its contemporaries scored the same or lower than it.