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1.
Aging Clin Exp Res ; 2022 Jul 01.
Article in English | MEDLINE | ID: covidwho-1920348

ABSTRACT

BACKGROUND: There is a paucity of the literature on the relationship between frailty and excess mortality due to the COVID-19 pandemic. METHODS: The entire community-dwelling adult population of Ontario, Canada, as of January 1st, 2018, was identified using the Cardiovascular Health in Ambulatory Care Research Team (CANHEART) cohort. Residents of long-term care facilities were excluded. Frailty was categorized through the Johns Hopkins Adjusted Clinical Groups (ACG® System) frailty indicator. Follow-up was until December 31st, 2020, with March 11th, 2020, indicating the beginning of the COVID-19 pandemic. Using multivariable Cox models with patient age as the timescale, we determined the relationship between frailty status and pandemic period on all-cause mortality. We evaluated the modifier effect of frailty using both stratified models as well as incorporating an interaction between frailty and the pandemic period. RESULTS: We identified 11,481,391 persons in our cohort, of whom 3.2% were frail based on the ACG indicator. Crude mortality increased from 0.75 to 0.87% per 100 person years from the pre- to post-pandemic period, translating to ~ 13,800 excess deaths among the community-dwelling adult population of Ontario (HR 1.11 95% CI 1.09-1.11). Frailty was associated with a statistically significant increase in all-cause mortality (HR 3.02, 95% CI 2.99-3.06). However, all-cause mortality increased similarly during the pandemic in frail (aHR 1.13, 95% CI 1.09-1.16) and non-frail (aHR 1.15, 95% CI 1.13-1.17) persons. CONCLUSION: Although frailty was associated with greater mortality, frailty did not modify the excess mortality associated with the pandemic.

2.
BMJ Open ; 12(6): e059309, 2022 06 16.
Article in English | MEDLINE | ID: covidwho-1902009

ABSTRACT

OBJECTIVES: To provide estimates for how different treatment pathways for the management of severe aortic stenosis (AS) may affect National Health Service (NHS) England waiting list duration and associated mortality. DESIGN: We constructed a mathematical model of the excess waiting list and found the closed-form analytic solution to that model. From published data, we calculated estimates for how the strategies listed under Interventions may affect the time to clear the backlog of patients waiting for treatment and the associated waiting list mortality. SETTING: The NHS in England. PARTICIPANTS: Estimated patients with AS in England. INTERVENTIONS: (1) Increasing the capacity for the treatment of severe AS, (2) converting proportions of cases from surgery to transcatheter aortic valve implantation and (3) a combination of these two. RESULTS: In a capacitated system, clearing the backlog by returning to pre-COVID-19 capacity is not possible. A conversion rate of 50% would clear the backlog within 666 (533-848) days with 1419 (597-2189) deaths while waiting during this time. A 20% capacity increase would require 535 (434-666) days, with an associated mortality of 1172 (466-1859). A combination of converting 40% cases and increasing capacity by 20% would clear the backlog within a year (343 (281-410) days) with 784 (292-1324) deaths while awaiting treatment. CONCLUSION: A strategy change to the management of severe AS is required to reduce the NHS backlog and waiting list deaths during the post-COVID-19 'recovery' period. However, plausible adaptations will still incur a substantial wait to treatment and many hundreds dying while waiting.


Subject(s)
Aortic Valve Stenosis , COVID-19 , Aortic Valve Stenosis/surgery , Humans , Models, Theoretical , State Medicine , Waiting Lists
3.
CMAJ Open ; 10(1): E173-E182, 2022.
Article in English | MEDLINE | ID: covidwho-1737355

ABSTRACT

BACKGROUND: Surgical delay may result in unintended harm to patients needing cardiac surgery, who are at risk for death if their condition is left untreated. Our objective was to derive and internally validate a clinical risk score to predict death among patients awaiting major cardiac surgery. METHODS: We used the CorHealth Ontario Registry and linked ICES health administrative databases with information on all Ontario residents to identify patients aged 18 years or more who were referred for isolated coronary artery bypass grafting (CABG), valvular procedures, combined CABG-valvular procedures or thoracic aorta procedures between Oct. 1, 2008, and Sept. 30, 2019. We used a hybrid modelling approach with the random forest method for initial variable selection, followed by backward stepwise logistic regression modelling for clinical interpretability and parsimony. We internally validated the logistic regression model, termed the CardiOttawa Waitlist Mortality Score, using 200 bootstraps. RESULTS: Of the 112 266 patients referred for cardiac surgery, 269 (0.2%) died while awaiting surgery (118/72 366 [0.2%] isolated CABG, 81/24 461 [0.3%] valvular procedures, 63/12 046 [0.5%] combined CABG-valvular procedures and 7/3393 [0.2%] thoracic aorta procedures). Age, sex, surgery type, left main stenosis, Canadian Cardiovascular Society classification, left ventricular ejection fraction, heart failure, atrial fibrillation, dialysis, psychosis and operative priority were predictors of waitlist mortality. The model discriminated (C-statistic 0.76 [optimism-corrected 0.73]). It calibrated well in the overall cohort (Hosmer-Lemeshow p = 0.2) and across surgery types. INTERPRETATION: The CardiOttawa Waitlist Mortality Score is a simple clinical risk model that predicts the likelihood of death while awaiting cardiac surgery. It has the potential to provide data-driven decision support for managing access to cardiac care and preserve system capacity during the COVID-19 pandemic, the recovery period and beyond.


Subject(s)
COVID-19 , Cardiac Surgical Procedures , Adolescent , Cardiac Surgical Procedures/adverse effects , Humans , Ontario/epidemiology , Pandemics , Risk Factors , SARS-CoV-2 , Stroke Volume , Ventricular Function, Left
4.
Am Heart J Plus ; 132022 Jan.
Article in English | MEDLINE | ID: covidwho-1663367

ABSTRACT

Study objective: A multi-institutional interdisciplinary team was created to develop a research group focused on leveraging artificial intelligence and informatics for cardio-oncology patients. Cardio-oncology is an emerging medical field dedicated to prevention, screening, and management of adverse cardiovascular effects of cancer/ cancer therapies. Cardiovascular disease is a leading cause of death in cancer survivors. Cardiovascular risk in these patients is higher than in the general population. However, prediction and prevention of adverse cardiovascular events in individuals with a history of cancer/cancer treatment is challenging. Thus, establishing an interdisciplinary team to create cardiovascular risk stratification clinical decision aids for integration into electronic health records for oncology patients was considered crucial. Design/setting/participants: Core team members from the Medical College of Wisconsin (MCW), University of Wisconsin-Milwaukee (UWM), and Milwaukee School of Engineering (MSOE), and additional members from Cleveland Clinic, Mayo Clinic, and other institutions have joined forces to apply high-performance computing in cardio-oncology. Results: The team is comprised of clinicians and researchers from relevant complementary and synergistic fields relevant to this work. The team has built an epidemiological cohort of ~5000 cancer survivors that will serve as a database for interdisciplinary multi-institutional artificial intelligence projects. Conclusion: Lessons learned from establishing this team, as well as initial findings from the epidemiology cohort, are presented. Barriers have been broken down to form a multi-institutional interdisciplinary team for health informatics research in cardio-oncology. A database of cancer survivors has been created collaboratively by the team and provides initial insight into cardiovascular outcomes and comorbidities in this population.

5.
Can J Cardiol ; 37(10): 1547-1554, 2021 10.
Article in English | MEDLINE | ID: covidwho-1439940

ABSTRACT

BACKGROUND: The novel SARS-CoV-2 (COVID-19) pandemic has dramatically altered the delivery of healthcare services, resulting in significant referral pattern changes, delayed presentations, and procedural delays. Our objective was to determine the effect of the COVID-19 pandemic on all-cause mortality in patients awaiting commonly performed cardiac procedures. METHODS: Clinical and administrative data sets were linked to identify all adults referred for: (1) percutaneous coronary intervention; (2) coronary artery bypass grafting; (3) valve surgery; and (4) transcatheter aortic valve implantation, from January 2014 to September 2020 in Ontario, Canada. Piece-wise regression models were used to determine the effect of the COVID-19 pandemic on referrals and procedural volume. Multivariable Cox proportional hazards models were used to determine the effect of the pandemic on waitlist mortality for the 4 procedures. RESULTS: We included 584,341 patients who were first-time referrals for 1 of the 4 procedures, of whom 37,718 (6.4%) were referred during the pandemic. The pandemic period was associated with a significant decline in the number of referrals and procedures completed compared with the prepandemic period. Referral during the pandemic period was a significant predictor for increased all-cause mortality for the percutaneous coronary intervention (hazard ratio, 1.83; 95% confidence interval, 1.47-2.27) and coronary artery bypass grafting (hazard ratio, 1.96; 95% confidence interval, 1.28-3.01), but not for surgical valve or transcatheter aortic valve implantation referrals. Procedural wait times were shorter during the pandemic period compared with the prepandemic period. CONCLUSIONS: There was a significant decrease in referrals and procedures completed for cardiac procedures during the pandemic period. Referral during the pandemic was associated with increased all-cause mortality while awaiting coronary revascularization.


Subject(s)
COVID-19 , Cardiovascular Diseases , Coronary Artery Bypass/statistics & numerical data , Delayed Diagnosis , Percutaneous Coronary Intervention/statistics & numerical data , Transcatheter Aortic Valve Replacement/statistics & numerical data , Waiting Lists/mortality , COVID-19/epidemiology , COVID-19/prevention & control , Cardiovascular Diseases/diagnosis , Cardiovascular Diseases/mortality , Cardiovascular Diseases/psychology , Cardiovascular Diseases/surgery , Delayed Diagnosis/psychology , Delayed Diagnosis/statistics & numerical data , Delivery of Health Care/statistics & numerical data , Female , Humans , Infection Control/methods , Male , Middle Aged , Mortality , Ontario/epidemiology , SARS-CoV-2 , Time-to-Treatment/organization & administration
6.
J Am Heart Assoc ; 9(21): e017847, 2020 11 03.
Article in English | MEDLINE | ID: covidwho-1255740

ABSTRACT

Background Across the globe, elective surgeries have been postponed to limit infectious exposure and preserve hospital capacity for coronavirus disease 2019 (COVID-19). However, the ramp down in cardiac surgery volumes may result in unintended harm to patients who are at high risk of mortality if their conditions are left untreated. To help optimize triage decisions, we derived and ambispectively validated a clinical score to predict intensive care unit length of stay after cardiac surgery. Methods and Results Following ethics approval, we derived and performed multicenter valida tion of clinical models to predict the likelihood of short (≤2 days) and prolonged intensive care unit length of stay (≥7 days) in patients aged ≥18 years, who underwent coronary artery bypass grafting and/or aortic, mitral, and tricuspid value surgery in Ontario, Canada. Multivariable logistic regression with backward variable selection was used, along with clinical judgment, in the modeling process. For the model that predicted short intensive care unit stay, the c-statistic was 0.78 in the derivation cohort and 0.71 in the validation cohort. For the model that predicted prolonged stay, c-statistic was 0.85 in the derivation and 0.78 in the validation cohort. The models, together termed the CardiOttawa LOS Score, demonstrated a high degree of accuracy during prospective testing. Conclusions Clinical judgment alone has been shown to be inaccurate in predicting postoperative intensive care unit length of stay. The CardiOttawa LOS Score performed well in prospective validation and will complement the clinician's gestalt in making more efficient resource allocation during the COVID-19 period and beyond.


Subject(s)
Cardiac Surgical Procedures , Clinical Decision Rules , Intensive Care Units , Length of Stay , Adult , Aged , Aged, 80 and over , Cardiac Surgical Procedures/adverse effects , Clinical Decision-Making , Female , Humans , Male , Middle Aged , Ontario , Predictive Value of Tests , Risk Assessment , Risk Factors , Time Factors , Treatment Outcome , Triage
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