Evidence map›Paper›PMID 27787358›Full record

ArticleMedicine2016

Predicting mortality from change-over-time in the Charlson Comorbidity Index: A retrospective cohort study in a data-intensive UK health system.

Paolo Fraccaro, Evangelos Kontopantelis, Matthew Sperrin, Niels Peek, Christian Mallen, Philip Urban, Iain E Buchan, Mamas A Mamas

Abstract readMulticenter Study
In one paragraph

Article in Medicine, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
49citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

49 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Observational
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Higher risk of traumatic intracranial hemorrhage with antiplatelet therapy compared to oral anticoagulation-a single-center experience.European journal of trauma and emergency surgery : official publication of the European Trauma Society · 2024
    Article
  9. Article
  10. Article
  11. Article
  12. Observational
  13. Article
  14. Article
  15. Article
  16. Article
  17. Observational
  18. Article
  19. Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Paolo FraccaroHealth eResearch Centre, Farr Institute for Health Informatics Research NIHR Greater Manchester Primary Care Patient Safety Translational Research Centre, Institute of Population Health NIHR School for Primary Care Research, University of Manchester, Manchester Research Institute for Primary Care & Health Sciences, Arthritis Research UK Primary Care Centre, Keele University, Keele, Staffordshire, United Kingdom Cardiovascular Department, Hôpital de La Tour, Geneva, Switzerland Keele Cardiovascular Research Group, Keele University Stoke-on-Trent and Royal Stoke Hospital, University Hospital North Midlands, Stoke-on-Trent, United Kingdom.
Evangelos Kontopantelis
Matthew Sperrin
Niels Peek
Christian Mallen
Philip Urban
Iain E Buchan
Mamas A Mamas

Funding

Department of Health WMCLAHRC-2014-1Medical Research Council MC_PC_13042
6 · The paper itself

Abstract

Multimorbidity is common among older people and presents a major challenge to health systems worldwide. Metrics of multimorbidity are, however, crude: focusing on measuring comorbid conditions at single time-points rather than reflecting the longitudinal and additive nature of chronic conditions. In this paper, we explore longitudinal comorbidity metrics and their value in predicting mortality.Using linked primary and secondary care data, we conducted a retrospective cohort study on adults in Salford, UK from 2005 to 2014 (n = 287,459). We measured multimorbidity with the Charlson Comorbidity Index (CCI) and quantified its changes in various time windows. We used survival models to assess the relationship between CCI changes and mortality, controlling for gender, age, baseline CCI, and time-dependent CCI. Goodness-of-fit was assessed with the Akaike Information Criterion and discrimination with the c-statistic.Overall, 15.9% patients experienced a change in CCI after 10 years, with a mortality rate of 19.8%. The model that included gender and time-dependent age, CCI, and CCI change across consecutive time windows had the best fit to the data but equivalent discrimination to the other time-dependent models. The absolute CCI score gave a constant hazard ratio (HR) of around 1.3 per unit increase, while CCI change afforded greater prognostic impact, particularly when it occurred in shorter time windows (maximum HR value for the 3-month time window, with 1.63 and 95% confidence interval 1.59-1.66).Change over time in comorbidity is an important but overlooked predictor of mortality, which should be considered in research and care quality management.

Indexed as

AdultComorbidityDelivery of Health CareFemaleFollow-Up StudiesHumansMaleMortalityPrognosisRetrospective StudiesRisk AssessmentTime FactorsUnited Kingdom

Identifiers

PMID27787358
PMCPMC5089087

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.