Mortality Benefits of Cardiac Rehabilitation in Coronary Artery Disease Are Mediated by Comprehensive Risk Factor Modification: A Retrospective Cohort Study.

BACKGROUND: Cardiac rehabilitation (CR) is a multicomponent intervention to reduce adverse outcomes from coronary artery disease, but its mechanisms are not fully understood. The aims of this study were to examine the impact of CR on survival and cardiovascular risk factors, and to determine potential mediators between CR attendance and reduced mortality. METHODS AND RESULTS: A retrospective mediation analysis was conducted among 11 196 patients referred to a 12-week CR program following an acute coronary syndrome event between 2009 and 2019. A panel of cardiovascular risk factors was assessed at a CR intake visit and repeated on CR completion. All-cause and cardiovascular mortality were ascertained via health care administrative data sets at mean 4.2-year follow-up (SD, 2.81 years). CR completion was associated with reduced all-cause (adjusted hazard ratio [HR], 0.67 [95% CI, 0.54-0.83]) and cardiovascular (adjusted HR, 0.57 [95% CI, 0.40-0.81]) mortality, as well as improved cardiorespiratory fitness, lipid profile, body composition, psychological distress, and smoking rates (P<0.001). CR attendance had an indirect effect on all-cause mortality via improved cardiorespiratory fitness (ab=-0.006 [95% CI, -0.008 to -0.003]) and via low-density lipoprotein cholesterol (ab=-0.002 [95% CI, -0.003 to -0.0003]) and had an indirect effect on cardiovascular mortality via cardiorespiratory fitness (ab=-0.007 [95% CI, -0.012 to -0.003]). CONCLUSIONS: Cardiorespiratory fitness and lipid control partly explain the mortality benefits of CR and represent important secondary prevention targets.

Validating administrative data to identify complex surgical site infections following cardiac implantable electronic device implantation: a comparison of traditional methods and machine learning.

BACKGROUND: Cardiac implantable electronic device (CIED) surgical site infections (SSIs) have been outpacing the increases in implantation of these devices. While traditional surveillance of these SSIs by infection prevention and control would likely be the most accurate, this is not practical in many centers where resources are constrained. Therefore, we explored the validity of administrative data at identifying these SSIs. METHODS: We used a cohort of all patients with CIED implantation in Calgary, Alberta where traditional surveillance was done for infections from Jan 1, 2013 to December 31, 2019. We used this infection subgroup as our "gold standard" and then utilized various combinations of administrative data to determine which best optimized the sensitivity and specificity at identifying infection. We evaluated six approaches to identifying CIED infection using administrative data, which included four algorithms using International Classification of Diseases codes and/or Canadian Classification of Health Intervention codes, and two machine learning models. A secondary objective of our study was to assess if machine learning techniques with training of logistic regression models would outperform our pre-selected codes. RESULTS: We determined that all of the pre-selected algorithms performed well at identifying CIED infections but the machine learning model was able to produce the optimal method of identification with an area under the receiver operating characteristic curve (AUC) of 96.8%. The best performing pre-selected algorithm yielded an AUC of 94.6%. CONCLUSIONS: Our findings suggest that administrative data can be used to effectively identify CIED infections. While machine learning performed the most optimally, in centers with limited analytic capabilities a simpler algorithm of pre-selected codes also has excellent yield. This can be valuable for centers without traditional surveillance to follow trends in SSIs over time and identify when rates of infection are increasing. This can lead to enhanced interventions for prevention of SSIs.

Cardiorespiratory fitness estimations and their ability to predict all-cause mortality in patients with cardiovascular disease.

Background: In cardiac rehabilitation programs, cardiorespiratory fitness is commonly estimated (eCRF) from the maximum workload achieved on a graded exercise test. This study compared four well-established eCRF equations in their ability to predict mortality in patients with cardiovascular disease (CVD). Methods: A total of 7269 individuals with CVD were studied (81% male; age 59.4 ± 10.3yr). eCRF was calculated using equations from the American College of Sports Medicine, Bruce et al., the Fitness Registry and the Importance of Exercise International Database, and McConnell and Clark. The eCRF from each equation was compared with a RMANOVA. Cox proportional hazard models assessed the relationship between the eCRF equations and mortality risk. The predictive ability of the models was compared using the concordance index. Results: There were 284 deaths (85% male) over a follow-up period of 5.8 ± 2.8yr. Although differences in eCRF were observed between each equation (P < 0.05), the eCRF from each of the four equations was predictive of mortality (P < 0.05). The concordance index values for each of the models were the same (0.77) indicating similar predictive performance. Conclusions: The four well-established eCRF equations did not differ in their ability to predict mortality in patients with CVD, indicating any could be used for this purpose. However, the differences in eCRF from each of the equations suggest potential differences in their ability to guide clinical care and should be the focus of future research.