Journal of cardiopulmonary rehabilitation and prevention · 2026
Predicting Outpatient Cardiac Rehabilitation Enrollment: CAN STAFF MEMBERS ACCURATELY ASSESS PATIENTS FUTURE BEHAVIOR?
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Busenkell Emma, Jebb Kamryn, Broccoli Madeline, Schilling Patrick, McAnally Kyle, Lagu Tara C, Lindenauer Peter K, Pack Quinn R
Аннотация
PURPOSE: Hospital-based interventions can increase cardiac rehabilitation (CR) enrollment but require staff time and resources. Enrollment could potentially be optimized if staff members focused on undecided patients who might respond to intensive interventions. The purpose of this study was to evaluate whether staff members can accurately predict whether patients will attend CR.
METHODS: After a standard phase I CR visit, staff members at the Baystate Medical Center were asked to estimate patient likelihood to attend outpatient phase II CR. This score ranged from 0 ("Not likely at all") to 10 ("Extremely likely"). We used logistic regression to predict attendance at ≥1 CR session as the outcome. Model strength was evaluated using the C-statistic from the receiver operating curve.
RESULTS: Eleven staff members scored 876 patients for likelihood to attend CR. A total of 524 (60%) patients attended CR at 1 of 11 regional programs. A higher likelihood score was associated with higher odds of attendance (OR = 1.16 per 1-point increase: 95% CI, 1.08-1.25; P < .0001), but this model had poor discrimination (C-statistic 0.58). The addition of facility, staff member, medical/surgical treatment, language, and insurance type improved model discrimination (C-statistic 0.67, P < .001).
CONCLUSION: CR staff members can somewhat predict CR attendance, but these subjective evaluations are not reliable enough for routine use, even when adding other key variables. Yet staff member performance significantly influences CR attendance. Thus, until better models are developed, all patients need an equally intensive intervention to promote CR, and staff performance should be monitored and optimized.
METHODS: After a standard phase I CR visit, staff members at the Baystate Medical Center were asked to estimate patient likelihood to attend outpatient phase II CR. This score ranged from 0 ("Not likely at all") to 10 ("Extremely likely"). We used logistic regression to predict attendance at ≥1 CR session as the outcome. Model strength was evaluated using the C-statistic from the receiver operating curve.
RESULTS: Eleven staff members scored 876 patients for likelihood to attend CR. A total of 524 (60%) patients attended CR at 1 of 11 regional programs. A higher likelihood score was associated with higher odds of attendance (OR = 1.16 per 1-point increase: 95% CI, 1.08-1.25; P < .0001), but this model had poor discrimination (C-statistic 0.58). The addition of facility, staff member, medical/surgical treatment, language, and insurance type improved model discrimination (C-statistic 0.67, P < .001).
CONCLUSION: CR staff members can somewhat predict CR attendance, but these subjective evaluations are not reliable enough for routine use, even when adding other key variables. Yet staff member performance significantly influences CR attendance. Thus, until better models are developed, all patients need an equally intensive intervention to promote CR, and staff performance should be monitored and optimized.