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Predicting mortality from change-over-time in the Charlson Comorbidity Index: A retrospective cohort study in a
Paolo Fraccaro1, Evangelos Kontopantelis, Matthew Sperrin
1Health 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.
Insights
Tracking changes in multimorbidity over time, not just the number of conditions, significantly improves mortality prediction in older adults. This longitudinal approach offers better insights than static measures for healthcare management.
Area of Science:
- Gerontology
- Public Health
- Health Services Research
Background:
- Multimorbidity, the co-occurrence of multiple chronic conditions, is prevalent in older populations and poses challenges to healthcare systems.
- Current multimorbidity metrics often fail to capture the dynamic, evolving nature of chronic diseases over time.
- A longitudinal perspective is needed to better understand disease progression and its impact on health outcomes.
Purpose of the Study:
- To investigate the prognostic value of longitudinal changes in multimorbidity for predicting mortality.
- To compare the predictive power of dynamic comorbidity metrics against static measures.
- To explore the impact of different time windows for assessing comorbidity changes.
Main Methods:
- Retrospective cohort study using linked primary and secondary care data (2005-2014) in Salford, UK (n=287,459).
- Multimorbidity measured using the Charlson Comorbidity Index (CCI), with analysis of CCI changes over various time windows.
- Survival models employed to assess the relationship between CCI changes and mortality, controlling for covariates.
Main Results:
- 15.9% of patients experienced a change in their CCI over 10 years, with an overall mortality rate of 19.8%.
- Models incorporating time-dependent CCI and CCI change demonstrated superior data fit compared to static models.
- Changes in CCI, especially over shorter intervals (e.g., 3 months), showed a greater impact on mortality prediction (HR 1.63) than absolute CCI scores.
Conclusions:
- Longitudinal changes in comorbidity are a significant, yet often overlooked, predictor of mortality in older adults.
- Incorporating dynamic comorbidity assessment into clinical practice and research can enhance prognostic accuracy.
- This approach supports improved care quality management and personalized health strategies for aging populations.
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.
