Key result
Novel clinical model predicts cardiac rehabilitation dropout with modest discrimination in external validation.
Why the study?
Nonadherence to cardiac rehabilitation is common despite its benefits, and identifying patients at risk of early dropout could help target interventions to improve adherence.
Can a simple clinical risk model using routine baseline characteristics predict early dropout (<12 sessions) in patients enrolling in cardiac rehabilitation?
Cohort (n=1,199)
No
Can a simple clinical risk model using routine baseline characteristics predict early dropout (<12 sessions) in patients enrolling in cardiac rehabilitation?
A simple four-factor clinical risk score calculated at cardiac rehabilitation enrollment can stratify patients into low (38%), intermediate (49%), and high (62%) risk of early dropout (<12 sessions) to help target adherence interventions.
May support risk stratification for cardiac rehabilitation adherence; leaves open prospective validation before clinical adoption.
BACKGROUND: Nonadherence to cardiac rehabilitation (CR) is common despite the benefits of completing a full program. Adherence might be improved if patients at risk of early dropout were identified and received an intervention. METHODS: Using records from patients who completed ≥1 CR session in 2016 (derivation cohort), we employed multivariable logistic regression to identify independent patient-level characteristics associated with attending <12 sessions of CR in a predictive model. We then evaluated model discrimination and validity among patients who enrolled in 2017 (validation cohort). RESULTS: Of the 657 patients in our derivation cohort, 318 (48%) completed <12 sessions. Independent risk factors for not attending ≥12 sessions were age <55 yr (OR = 0.23, P < .001), age 55 to 64 yr (OR = 0.35, P < .001), age ≥75 yr (OR = 0.64, P = .06), smoker within 30 d of CR enrollment (OR = 0.40, P = .001), low risk for exercise adverse events (OR = 0.54, P = .03), and nonsurgical referral diagnosis (OR = 0.66, P = .02). Our model predicted nonadherence risk from 23-90%, had acceptable discrimination and calibration (C-statistics = 0.70, Harrell's E50 and E90 2.0 and 3.6, respectively) but had fair validity among 542 patients in the validation cohort (C-statistic = 0.62, Harrell's E50 and E90 2.1 and 11.3, respectively). CONCLUSION: We developed and evaluated a single-center simple risk model to predict nonadherence to CR. Although the model has limitations, this tool may help clinicians identify patients at risk of early dropout and guide intervention efforts to improve adherence so that the full benefits of CR can be realized for all patients.
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Pack et al. (2020) conducted a cohort in Cardiovascular disease requiring cardiac rehabilitation (recent MI, PCI, CABG, heart valve surgery or repair, TAVR, stable angina, heart failure, LVAD, or heart transplant) (n=1,199). Predictive risk model for cardiac rehabilitation early dropout (incorporating age, smoking within 30 days, AACVPR low exercise risk, and nonsurgical referral diagnosis) vs. Observed attendance and base case risk (aged 65-74 yr, nonsmoker, surgical qualifying diagnosis, and moderate/high AACVPR exercise risk) was evaluated on Early dropout from cardiac rehabilitation (defined as attending <12 sessions). A clinical risk model using age, smoking, exercise risk, and referral diagnosis predicted cardiac rehabilitation dropout (<12 sessions) with C-statistics of 0.70 in derivation and 0.62 in validation.
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