Objectives: The predictive value of frailty and comorbidity, in addition to more readily available information, is not widely studied. We determined the incremental predictive value of frailty and comorbidity for mortality and institutionalization across both short and long prediction periods in persons with dementia.

Design: Longitudinal clinical cohort study with a follow-up of institutionalization and mortality occurrence across 7 years after baseline.

Setting and Participants: 331 newly diagnosed dementia patients, originating from 3 Alzheimer centers (Amsterdam, Maastricht, and Nijmegen) in the Netherlands, contributed to the Clinical Course of Cognition and Comorbidity (4C) Study.

Measures: We measured comorbidity burden using the Cumulative Illness Rating Scale for Geriatrics (CIRS-G) and constructed a Frailty Index (FI) based on 35 items. Time-to-death and time-to-institutionalization from dementia diagnosis onward were verified through linkage to the Dutch population registry.

Results: After 7 years, 131 patients were institutionalized and 160 patients had died. Compared with a previously developed prediction model for survival in dementia, our Cox regression model showed a significant improvement in model concordance (U) after the addition of baseline CIRS-G or FI when examining mortality across 3 years (FI: U = 0.178, P = .005, CIRS-G: U = 0.180, P = .012), but not for mortality across 6 years (FI: U = 0.068, P = .176, CIRS-G: U = 0.084, P = .119). In a competing risk regression model for time-to-institutionalization, baseline CIRS-G and FI did not improve the prediction across any of the periods.

Conclusions: Characteristics such as frailty and comorbidity change over time and therefore their predictive value is likely maximized in the short term. These results call for a shift in our approach to prognostic modeling for chronic diseases, focusing on yearly predictions rather than a single prediction across multiple years. Our findings underline the importance of considering possible fluctuations in predictors over time by performing regular longitudinal assessments in future studies as well as in clinical practice.

Background

Although the definition of multimorbidity as the simultaneous presence of two or more chronic diseases is well established, its operationalization is not yet agreed. This study aims to provide a clinically driven comprehensive list of chronic conditions to be included when measuring multimorbidity.

Methods

Based on a consensus definition of chronic disease, all four-digit level codes from the International Classification of Diseases, 10th revision (ICD-10) were classified as chronic or not by an international and multidisciplinary team. Chronic ICD-10 codes were subsequently grouped into broader categories according to clinical criteria. Last, we showed proof of concept by applying the classification to older adults from the Swedish National study of Aging and Care in Kungsholmen (SNAC-K) using also inpatient data from the Swedish National Patient Register.

Results

A disease or condition was considered to be chronic if it had a prolonged duration and either (a) left residual disability or worsening quality of life or (b) required a long period of care, treatment, or rehabilitation. After applying this definition in relation to populations of older adults, 918 chronic ICD-10 codes were identified and grouped into 60 chronic disease categories. In SNAC-K, 88.6% had >= 2 of these 60 disease categories, 73.2% had >= 3, and 55.8% had >= 4.

Conclusions

This operational measure of multimorbidity, which can be implemented using either or both clinical and administrative data, may facilitate its monitoring and international comparison. Once validated, it may enable the advancement and evolution of conceptual and theoretical aspects of multimorbidity that will eventually lead to better care.