Development and Validation of a Clinical Prediction Tool to Estimate Life Expectancy in Community-dwelling Individuals With Dementia
Trial Snapshot
- Phase
- Not Applicable
- Status
- Completed
- Sponsor
- University of Toronto
- Enrollment
- 202,217
- Primary Endpoint
- Mortality
Study Overview
Brief Summary
Individuals with dementia and their caregivers are faced with challenging decisions throughout the course of the disease. These decisions may be about medical care (e.g., continuation of routine cancer screening, pursuit of cardiopulmonary resuscitation, initiation of palliative care services), institutionalization (i.e., transition to a long-term care facility), or financial planning. These inherently difficult decisions are made more difficult by prognostic uncertainty. Indeed, life expectancy is challenging to predict in dementia. Consequently, prognosis is infrequently discussed by healthcare providers with individuals with dementia and their families, which compromises their ability to plan for the future. A lack of prognostic awareness makes it difficult for patients, their caregivers, and their healthcare providers to make medical decisions that strike the appropriate balance between prolonging life and promoting the quality of it. A clinical prediction tool has the promise to provide personalized and accurate estimations of life expectancy in individuals with dementia. Therefore, similar to the existing clinical prediction tools on our Project Big Life platform (www.projectbiglife.ca), we seek to create and to test a statistical model to predict survival, and to implement the model as a user-friendly, web-based calculator. The calculator will use self-reported sociodemographic, clinical, cognitive, functional, and nutritional information that is entered by patients, their caregivers, and/or their healthcare providers to output an estimated life expectancy. This estimate could inform the shared decision-making process, thereby empowering decisions that are compatible with a patient's clinical reality and concordant with their life goals.
Detailed Description
Analysis plan
The analysis plan was informed by guidelines for clinical prediction modelling. The plan was developed after accessing the derivation dataset but before assessing predictor-outcome associations and model fitting. Key considerations are full pre-specification of the model, including selection of predictors, such that data-driven variable selection will be avoided. This will decrease the risk of bias and overfitting in the model. Second, continuous variables will be specified as restricted cubic splines with knots at fixed quantiles, such that categorization of continuous variables will be avoided. This will respect the non-linear nature of continuous variables, and will avoid the inefficiency and bias associated with categorization. Third, emphasis will be placed on the assessment of the model's calibration, not only in the validation cohort but also in subgroups of meaning to clinicians and policymakers. Statistical analysis will be performed using SAS Enterprise Guide V.9.4.
Validation will be performed using temporal validation, whereby the model's performance will be evaluated in a temporally distinct (more recent) cohort of individuals with dementia. This is a more rigorous form of validation compared to internal validation, which includes random splitting or resampling (bootstrapping, cross-validation). Whereas temporal validation evaluates transportability, internal validation evaluates only reproducibility. The size of the derivation cohort and the expected number of events therein enables temporal validation without significantly increasing the risk of overfitting.
Predictor variables
The candidate predictor variables were fully pre-specified, such that data-driven variable selection was avoided. The investigators reviewed variables in the home care databases to identify predictors. In addition, existing reviews of prognostic models in dementia were explored. Variables were reviewed by the research team in an itemized way to determine which to include in the initial model.
Study Design
- Study Type
- Observational
- Observational Model
- Cohort
- Time Perspective
- Retrospective
Eligibility Criteria
- Ages
- 65 Years to — (Older Adult)
- Sex
- All
- Accepts Healthy Volunteers
- No
Inclusion Criteria
- •Diagnosed with dementia determined by a combination of a validated case definition and by indicators of dementia in the home care assessment
- •Recipients of home care, who, after diagnosis of dementia, underwent any home care assessment from April 1st, 2010 to March 31st, 2020
Exclusion Criteria
- •Age <65 on the date of dementia diagnosis
- •Invalid age or sex
- •Invalid birthdate (e.g., after date of dementia diagnosis) or death date (e.g., before date of dementia diagnosis)
- •Ineligibility for Ontario Health Insurance Program at the time of the home care assessment
Outcomes
Primary Outcomes
Mortality
Time Frame: Maximum follow-up date is December 31, 2022
Operationalized as time-to-event outcome
Secondary Outcomes
No secondary outcomes reported
Investigators
Michael Bonares
Assistant Professor
University of Toronto