Development and Validation of a Dementia Life Expectancy Tool

Overview

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.

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. Notably, predictor values from only a single randomly selected assessment after dementia diagnosis (index assessment) will be included in the model. The investigators did not include values from subsequent assessments since the tool would be applied cross-sectionally, not longitudinally. Indeed, the team wants to avoid using values from subsequent assessments, which would not have been known at the time of the randomly selected assessment. The variables in our model will be organized in the following categories: sociodemographic, clinical (comorbidities, treatment), caregiver-specific, functional, nutritional, cognitive, psychological/behavioural, home care, healthcare utilization, and assessment-specific information. The investigators will include interactions between age and variables that represent comorbidities since the association of these and life expectancy may vary with age. A linear term of age, not a restricted cubic spline thereof, will be used in interactions. Outcome variable The outcome variable will be survival time from the index assessment up to the maximum follow-up date (December 31st, 2022). Mortality will be discerned from the Registered Persons Database, which houses a historical listing of all individuals eligible for the Ontario Health Insurance Program, including sociodemographic (e.g., age, sex, postal code) and vital information (e.g., date of death). The investigators have pre-specified survival times of interest, which are compatible with current eligibility guidelines for specialist palliative care services (i.e., 3, 6, and 12 months). Model specification Predictor variables will be explored before assessing predictor-outcome associations or model fitting. Continuous variables will be explored using descriptive statistics and boxplots, and categorical variables using descriptive statistics and frequency distributions. Any identified invalid values will be corrected, if possible, or set to missing otherwise. Continuous variables will be specified using restricted cubic splines with knots at fixed quantiles (e.g., in a 5-knot spline, quantiles are placed at the 5th, 27.5th, 50th, 72.5th, and 95th percentiles). Categorization of continuous variables will be avoided since this is associated with inefficiency and bias and does not respect the non-linear nature of continuous variables. Combination of levels of a categorical variable will be avoided unless a category has a very low proportion of total observations. Variables with a high degree of missing values or insufficient variation will be excluded. Multi-collinearity will be evaluated using variable clustering (VARCLUS function in SAS). The minimum proportion of variance explained by a cluster (eigenvalue) will be set to 0.7. Missing values will be imputed using multiple imputation so long as missingness was judged to have been completely at random or at random. Despite its simplicity, complete case analysis will be avoided to prevent the inefficiency and bias associated with this method. The imputation model will include the outcome variable, predictor variables, and auxiliary variables (i.e., variables that are not included in the full model but that could inform the missing value of a variable). The number of imputed datasets will be based on the proportion of missing values in the dataset. The final model will be estimated in each of the imputed datasets. The parameter estimates based on each dataset will be combined using Rubin's rules, which integrate the uncertainty associated with imputation in the final parameter estimates. Model estimation The model will be estimated in the derivation cohort using a Cox proportional hazards regression. The assumption of proportional hazards will be checked visually by examining plots of Schoenfeld residuals versus time, and statistically by adding time-interacted predictors to the model. If the assumption is violated, then the investigators will consider the addition of time-interacted predictors to the model. Considering the high ratio of expected events to degrees of freedom and the avoidance of data-driven variable selection, the risk of overfitting is judged to be low. However, this will be assessed statistically using the heuristic shrinkage estimator \[(likelihood ratio Chi-square of the model - degree of freedom of the model)/likelihood ratio Chi-square of the model\]. If this is \<0.90, then the model will require adjustment for overfitting (e.g., by pursuing a variable reduction method or by applying shrinkage coefficients to the parameter estimates). Overfitting will also be assessed visually using the calibration curve. Since the intention is to apply our model as a manual web-based calculator that could be used by healthcare providers, caregivers, and patients, the investigators will estimate a reduced model that seeks to optimize parsimony without a significant decrease in model performance. Indeed, the initial model may be too complex, labour-intensive, and time-consuming to be implemented. The reduced model will be estimated using the stepdown method, whereby sequentially, the variable with the lowest Wald Chi-square will be removed from the model until a minimally acceptable model performance is achieved. The reduced model will be compared to the initial model using Akaike's Information Criterion and measures of discrimination and calibration. The investigators will consider least absolute shrinkage and selection operator (LASSO), since it could result in the shrinkage of some regression coefficients to 0, thereby reducing the model. In addition to statistical means of model reduction, the investigators will consider the clinical relevance of the variable based existing literature and content expertise, in addition to the ability of patients and their caregivers to assess and input the variable. The model will be developed and validated using temporally split samples; however, the final regression coefficients will be based on the full sample. The final model will have the same specifications as the derivation model. Model performance The model's performance will be assessed in the validation cohort in multiple domains. Specifically, it will be assessed in terms of overall performance, as measured by Nagelkerke's R2, which is a measure of the proportion of variability in the outcome that is explained by the model. Historically, clinical prediction tools have had R2 that ranged from 0.2 to 0.3. The model will also be assessed in terms of discrimination, as measured by the concordance (c) statistic and visualized by the receiver operating characteristic curve. The c statistic ranges from 0.5, which represents no discriminative ability, to 1.0, which represents perfect discriminative ability. Finally, the model will be assessed in terms of calibration. This will be evaluated visually using the calibration curve of predicted versus observed mortality based on Kaplan Meier estimates at the abovementioned pre-specified survival times (3, 6, and 12 months). A perfectly calibrated model is represented by a 45-degree line with an intercept of 0 and a slope of 1. The calibration curve informs whether the model systematically over- or underestimates mortality risk (mean calibration or calibration-in-the-large) and whether it provides extreme predictions of mortality risk (i.e., underestimates risk in low-risk individuals and overestimates risk in high-risk individuals), which suggests overfitting. The mean relative difference between observed and predicted mortality risk will be calculated. An acceptable difference is \<20% when the event rate is \<=5%. Finally, to enable comparison to other prognostic models in community-dwelling individuals with dementia, the investigators will calculate the Integrated Calibration Index, the mean absolute difference between observed and predicted mortality risk; E50, the median absolute difference; and E90, the 90th percentile of absolute difference. Goodness-of-fit will not be measured by the Hosmer-Lemeshow statistic or its equivalent in a Cox proportional hazards model; these tests cannot provide a magnitude of miscalibration or determine whether miscalibration is present in only specific ranges of predicted mortality risk. Calibration will also be assessed in decile groups based on predicted mortality risk (moderate calibration). Finally, subgroups of meaning to clinicians and policymakers will be pre-specified (e.g., defined by age, sex, comorbidities), in which calibration will be assessed. A calibration graph will be visualized and a mean relative difference will be calculated in each subgroup. Considering that individuals who underwent their randomly selected assessment in the hospital may be systematically different than those who underwent their assessment in the community, the investigators will specifically assess the model's performance in individuals who underwent an in-hospital assessment. Model presentation The final regression model, based on the total sample, will be presented using hazard ratios and associated 95% confidence intervals. The regression formula will be published online and be the basis for web-based implementation. Specifically, the model will be converted into a publicly accessible web-based manual calculator on www.projectbiglife.com, which houses multiple clinical prediction tools developed by our team. The tool could be used not only by healthcare providers, but also by patients and caregivers, to calculate life expectancy. Considering this, a team of web developers, web designers, implementation scientists, patients and caregivers, and clinicians will inform implementation to make the tool user-friendly and to make its output interpretable. The model interface and output may differ depending on whether a clinician or a patient/caregiver is using the tool. The investigators will respect the uncertainty associated with the output of the tool, by including interquartile ranges that transparently reflect prognostic uncertainty.