Browsing by Author "Chew, Derek"

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    Healthcare utilization and adverse outcomes stratified by sex, age and long-term care residency using the Alberta COVID-19 Analytics and Research Database (ACARD): a population-based descriptive study
    (2023-05-19) Rennert-May, Elissa; Crocker, Alysha; D’Souza, Adam G.; Zhang, Zuying; Chew, Derek; Beall, Reed; Vickers, David M.; Leal, Jenine
    Abstract Background Understanding the epidemiology of Coronavirus Disease of 2019 (COVID-19) in a local context is valuable for both future pandemic preparedness and potential increases in COVID-19 case volume, particularly due to variant strains. Methods Our work allowed us to complete a population-based study on patients who tested positive for COVID-19 in Alberta from March 1, 2020 to December 15, 2021. We completed a multi-centre, retrospective population-based descriptive study using secondary data sources in Alberta, Canada. We identified all adult patients (≥ 18 years of age) tested and subsequently positive for COVID-19 (including only the first incident case of COVID-19) on a laboratory test. We determined positive COVID-19 tests, gender, age, comorbidities, residency in a long-term care (LTC) facility, time to hospitalization, length of stay (LOS) in hospital, and mortality. Patients were followed for 60 days from a COVID-19 positive test. Results Between March 1, 2020 and December 15, 2021, 255,037 adults were identified with COVID-19 in Alberta. Most confirmed cases occurred among those less than 60 years of age (84.3%); however, most deaths (89.3%) occurred among those older than 60 years. Overall hospitalization rate among those who tested positive was 5.9%. Being a resident of LTC was associated with substantial mortality of 24.6% within 60 days of a positive COVID-19 test. The most common comorbidity among those with COVID-19 was depression. Across all patients 17.3% of males and 18.6% of females had an unplanned ambulatory visit subsequent to their positive COVID-19 test. Conclusions COVID-19 is associated with extensive healthcare utilization. Residents of LTC were substantially impacted during the COVID-19 pandemic with high associated mortality. Further work should be done to better understand the economic burden associated with related healthcare utilization following a COVID-19 infection to inform healthcare system resource allocation, planning, and forecasting.
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    The economic burden of cardiac implantable electronic device infections in Alberta, Canada: a population-based study using validated administrative data
    (2023-12-05) Rennert-May, Elissa; Chew, Derek; Cannon, Kristine; Zhang, Zuying; Smith, Stephanie; King, Teagan; Exner, Derek V.; Larios, Oscar E.; Leal, Jenine
    Abstract Background Cardiac implantable electronic devices (CIED) are being inserted with increasing frequency. Severe surgical site infections (SSI) that occur after device implantation substantially impact patient morbidity and mortality and can result in multiple hospital admissions and repeat surgeries. It is important to understand the costs associated with these infections as well as healthcare utilization. Therefore, we conducted a population-based study in the province of Alberta, Canada to understand the economic burden of these infections. Methods A cohort of adult patients in Alberta who had CIEDs inserted or generators replaced between January 1, 2011 and December 31, 2019 was used. A validated algorithm of International Classification of Diseases (ICD) codes to identify complex (deep/organ space) SSIs that occurred within the subsequent year was applied to the cohort. The overall mean 12-month inpatient and outpatient costs for the infection and non-infection groups were assessed. In order to control for variables that may influence costs, propensity score matching was completed and incremental costs between those with and without infection were calculated. As secondary outcomes, number of outpatient visits, hospitalizations and length of stay were assessed. Results There were 26,049 procedures performed during our study period, of which 320 (1.23%) resulted in SSIs. In both unadjusted costs and propensity score matched costs the infection group was associated with increased costs. Overall mean cost was $145,312 in the infection group versus $34,264 in the non-infection group. The incremental difference in those with infection versus those without in the propensity score match was $90,620 (Standard deviation $190,185). Approximately 70% of costs were driven by inpatient hospitalizations. Inpatients hospitalizations, length of stay and outpatient visits were all increased in the infection group. Conclusions CIED infections are associated with increased costs and are a burden to the healthcare system. This highlights a need to recognize increasing SSI rates and implement measures to minimize infection risk. Further studies should endeavor to apply this work to full economic evaluations to better understand and identify cost-effective infection mitigation strategies.
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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.