Predicting physical fitness outcomes of exercise rehabilitation: An retrospective examination of program admission data from patient records in a hospital-based early outpatient cardiac rehabilitation program

• Main Author: Fabiato, Francois Stephane
• Other Authors: Health and Physical Education
• Format: Others
• Published: Virginia Tech 2014
• Subjects: Cardiac Rehabilitation; outcome-based research; predictor variables
• Online Access: http://hdl.handle.net/10919/36880; http://scholar.lib.vt.edu/theses/available/etd-63198-9314/

Economic justification for rehabilitative services has resulted in the need for outcome based research which could quantify success or failure in individual patients and formulate baseline variables which could predict outcomes. The purpose of this study is to investigate the utilization of baseline clinical, exercise test, and psychosocial variables to predict clinically relevant changes in exercise tolerance of cardiac patients who participated in early outpatient cardiac rehabilitation. Clinical records were analyzed retrospectively to obtain clinical, psychosocial and exercise test data for 94 patients referred to an early outpatient cardiac rehabilitation program at a large urban hospital in the Southeast US. All patients participated in supervised exercise training 3d/wk for 2-3 months. A standardized training outcome score STO) was devised to evaluate training effect by tabulating changes in patients predicted VO2, body weight and exercising heart rates after 8-12 weeks of exercise based cardiac rehabilitation. STO = Predicted VO2 change + BW change- HR change. The Multi-Factorial Analysis was applied to derive coefficients in the STO formula so that the STO scores reflected the independent effects of BW, HR and Predicted V02 changes on training outcome. Patients were classified into one of three possible outcome categories based on STO scores, i.e. improvement, no change, or decline. Thresholds for classifying patients were the following; STO scores greater than or equal to 3 SEM above the mean = improved, (N= 40: 41%), STO scores less than or equal to 3 SEM below the mean = decline, (N=34: 35%), STO scores within 3 SEM= no change, (N=23: 24%). Multiple logistic regression was used to identify patient attributes predictive of improvement, decline, or no change from measures routinely collected at the point of admission to rehabilitation. The model for prediction of improvement correctly classified 70% of patients as those who improved vs. those who did not (sensitivity 70%, specificity 71%). This model generated the following variables as having predictive capabilities; recent CABG, emotional status, social status, calcium channel blocker, recent angioplasty, maximum diastolic BP, maximum systolic BP and resting systolic BP. The model for predicting those who declined vs. those who did not decline demonstrated higher correct classification rate of 74% and specificity (84%). This model generated the following variables as having predictive capabilities; social status, calcium channel blocker, orthopedic limitation, role function, QOL score and Digitalis. However, these models may include certain bias because the same observations to fit the model were also used to estimate the classification errors. Therefore, cross validation was performed utilizing the single point deletion method; this method yielded somewhat lower fraction correct classification rates (66%,69%) and sensitivity rates (56%,44%) for improvement vs. no improvement and decline vs. no decline groups respectively. Conclusion A combined set of baseline clinical, psychosocial and exercise measures can demonstrate moderate success in predicting training outcome based on STO scores in hospital outpatient cardiac rehabilitation. In contrast psychosocial data seem to account for more of the variance in prediction of decline than other types of baseline variables examined in this study. Baseline blood pressure responses both at rest and during exercise were the greatest predictors of improvement. However, cross validation of these models indicates that these results could be biased eliciting overly optimistic predictive capabilities, due to the analysis of fitted data. These models need to be validated in independent sample with patients in similar settings. === Master of Science