Browsing by Author "Exner, Derek"

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    Canadian national electrophysiology ablation registry report 2011–2016
    (2021-05-06) Kaoutskaia, Anna; Shurrab, Mohammed; Amit, Guy; Parkash, Ratika; Exner, Derek; Toal, Satish; Sterns, Laurence; Sarrazin, Jean-Francois; Chauhan, Vijay; Sultan, Omar; Nair, Girish; Deyell, Marc; Macle, Laurent; Klassen, Steve; Glover, Benedict; Crystal, Eugene
    Abstract Background/purpose : Interventional cardiac electrophysiology (EP) is a rapidly evolving field in Canada; a nationwide registry was established in 2011 to conduct a periodic review of resource allocation. Methods The registry collects annual data on EP lab infrastructure, imaging, tools, human resources, procedural volumes, success rates, and wait times. Leading physicians from each EP lab were contacted electronically; participation was voluntary. Results All Canadian EP centres were identified (n = 30); 50 and 45 % of active centres participated in the last 2 instalments of the registry. A mean of 508 ± 270 standard and complex catheter ablation procedures were reported annually for 2015–2016 by all responding centres. The most frequently performed ablation targets atrial fibrillation (PVI) arrhythmia accounting for 36 % of all procedures (mean = 164 ± 85). The number of full time physicians ranges between 1 and 7 per centre, (mean = 4). The mean wait time to see an electrophysiologist for an initial non-urgent consult is 23 weeks. The wait time between an EP consult and ablation date is 17.8 weeks for simple ablation, and 30.1 weeks for AF ablation. On average centres have 2 (range: 1–4) rooms equipped for ablations; each centre uses the EP lab an average of 7 shifts per week. While diagnostic studies and radiofrequency ablations are performed in all centres, point-by-point cryoablation is available in 85 % centres; 38 % of the respondents use circular ablation techniques. Conclusions This initiative provides contemporary data on invasive electrophysiology lab practices. The EP registry provides activity benchmarks on national trends and practices.
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    Initial Orthostatic Hypotension: Underlying Physiology, Symptom Management, and the Patient Experience
    (2021-04-30) Sheikh, Nasia A.; Raj, Satish; Sheldon, Robert; Exner, Derek; Phillips, Aaron; Runte, Mary
    Background: Initial orthostatic hypotension (IOH) is a common form of orthostatic intolerance defined by a large reduction in blood pressure (BP) within 15s of active standing resulting in symptoms of presyncope or a faint. Symptoms may occur numerous times within a single day, which significantly affects patients’ quality of life. However, there is limited data exploring IOH physiology and symptom management. Aims: We aimed to: (1) determine if the reflex underlying IOH had a refractory period; (2) better understand the physiology underlying IOH; (3) provide effective symptom management options; and (4) explore the patient experience living with IOH. Methods: A total of 26 participants enrolled in aims 1-3 and 16 in the fourth and final aim. Aims 1-3 involved series of randomized sit-to-stand maneuvers with and without interventions. Data are presented as mean±SD. Aim 4 consisted of one-on-one semi-structured interviews between the IOH participant and researcher probing into the patient experience living with IOH. Results: In aim 1, the drop in systolic BP (SBP) after standing was blunted following a short sit (-12±6 mmHg) compared to a long sit (-34±16 mmHg; p<0.001). In aim 2, the drops in SBP following the Serial 7 test (-26±12 mmHg; p=0.004), Cold Pressor test (-20±15 mmHg; p<0.001), and functional electrical stimulation (-28±12 mmHg; p=0.01) were significantly reduced compared to no intervention (-34±11 mmHg). In aim 3, the drop in SBP after standing was blunted following muscle pre-activation (-23±13 mmHg; p<0.001) and muscle post-tensing (-22±12 mmHg; p<0.001) compared to no intervention (-35±12 mmHg). In aim 4, an overriding theme was identified: Life, when simply standing up becomes a burden. Conclusions: Aim 1 illustrated that the reflex underlying IOH has a refractory period (<2 minutes) and a short sit blunts the IOH response. Aim 2 illustrated that both sympathetic activation and muscle activation plays an important role in mitigating the IOH BP response. Aim 3 concluded that both muscle pre-activation and post-tensing reduces the IOH BP drop and symptoms. Aim 4 illustrated that IOH negatively affects many aspects of patient life, including social, employment, and emotional aspects.
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    Validating administrative data to identify complex surgical site infections following cardiac implantable electronic device implantation: a comparison of traditional methods and machine learning
    (2022-11-10) Rennert-May, Elissa; Leal, Jenine; MacDonald, Matthew K.; Cannon, Kristine; Smith, Stephanie; Exner, Derek; Larios, Oscar E.; Bush, Kathryn; Chew, Derek
    Abstract 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.