TRAjectories and CLinical ExpeRiences of ICD Therapy Study
Study Objectives and Design
The specific aims, applicable methods, and main hypotheses of the TRACER-ICD study are as follows:
Aim 1: To characterize the clinical course of patients aged >65 after ICD implantation by establishing a multicenter prospective cohort (N=500). Baseline (verbal and in-person) and quarterly (phone, electronic health records [EHR], remote monitoring) data collected up to 18 months or death will include: a. geriatric assessments (e.g. frailty, cognition,1 functional status; b. comorbidities; c. quality of life and symptom burden; d. health services use; e. SDM history; and f. ICD-recorded physical activity (minutes/day).
Factors associated with death and poor quality of life will be identified. Group-based trajectory modeling will identify patient clusters with distinct trajectories of functional status and quality of life following ICD implantation.
H1: A model integrating frailty and clinical factors will predict (a) death, (b) poor quality of life, and (c) unfavorable functional trajectories after ICD implantation.
Aim 2: To validate a personalized prediction model of treatment outcomes of ICDs. We will apply our established semi-competing risks approach5 to the prospective cohort, enriched by the baseline geriatric assessments, to predict individual patients' probabilities of the following outcome profiles at 6, 12, and 18 months post-implant: a. death without prior shock, b. death with prior shock, c. survival without prior shock, and d. survival with prior shock. The independent contribution of frailty, cognitive dysfunction, baseline quality of life, and functional status (e.g. total deficits in ADL and IADL) to model performance will be evaluated.
H2: Addition of frailty, cognitive dysfunction, quality of life, and functional status to a model with cardiovascular variables will significantly improve model performance as assessed by area under the curve (AUC).
Aim 3: To identify optimal strategies for incorporating personalized outcome profiles into SDM. The main cohort's baseline interview and EHR review will provide quantitative data on past SDM tools used by physicians. We will develop a prototype individualized version of the Colorado SDM tool that incorporates 18-month outcome profiles using our semi-competing risk model. We will perform semi-structured interviews of cardiologists (N=20) as well as patients (N=20) recruited from the main cohort regarding process mapping of SDM from clinic to implant procedure, including timing, content, format preferences, and impressions of the prototype individualized tool.