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Gupta A, Tisdale RL, Calma J, Stafford RS, Maron DJ, Hernandez-Boussard T, Ambrosy AP, Heidenreich PA, Sandhu AT. Equity in the Setting of Heart Failure Diagnosis: An Analysis of Differences Between and Within Clinician Practices. Circ Heart Fail 2024; 17:e010718. [PMID: 38847082 DOI: 10.1161/circheartfailure.123.010718] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Accepted: 03/28/2024] [Indexed: 06/20/2024]
Abstract
BACKGROUND Timely heart failure (HF) diagnosis can lead to earlier intervention and reduced morbidity. Among historically marginalized patients, new-onset HF diagnosis is more likely to occur in acute care settings (emergency department or inpatient hospitalization) than outpatient settings. Whether inequity within outpatient clinician practices affects diagnosis settings is unknown. METHODS We determined the setting of incident HF diagnosis among Medicare fee-for-service beneficiaries between 2013 and 2017. We identified sociodemographic and medical characteristics associated with HF diagnosis in the acute care setting. Within each outpatient clinician practice, we compared acute care diagnosis rates across sociodemographic characteristics: female versus male sex, non-Hispanic White versus other racial and ethnic groups, and dual Medicare-Medicaid eligible (a surrogate for low income) versus nondual-eligible patients. Based on within-practice differences in acute diagnosis rates, we stratified clinician practices by equity (high, intermediate, and low) and compared clinician practice characteristics. RESULTS Among 315 439 Medicare patients with incident HF, 173 121 (54.9%) were first diagnosed in acute care settings. Higher adjusted acute care diagnosis rates were associated with female sex (6.4% [95% CI, 6.1%-6.8%]), American Indian (3.6% [95% CI, 1.1%-6.1%]) race, and dual eligibility (4.1% [95% CI, 3.7%-4.5%]). These differences persisted within clinician practices. With clinician practice adjustment, dual-eligible patients had a 4.9% (95% CI, 4.5%-5.4%) greater acute care diagnosis rate than nondual-eligible patients. Clinician practices with greater equity across dual eligibility also had greater equity across sex and race and ethnicity and were more likely to be composed of predominantly primary care clinicians. CONCLUSIONS Differences in HF diagnosis rates in the acute care setting between and within clinician practices highlight an opportunity to improve equity in diagnosing historically marginalized patients.
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Affiliation(s)
- Anshal Gupta
- Stanford University School of Medicine, Palo Alto, CA (A.G.)
| | - Rebecca L Tisdale
- Veterans Affairs Palo Alto Health Care System, Palo Alto, CA (R.L.T., P.A.H., A.T.S.)
- Division of Primary Care and Population Health, Department of Medicine, Stanford University School of Medicine, Palo Alto, CA (R.L.T.)
| | - Jamie Calma
- Division of Cardiovascular Medicine and the Cardiovascular Institute, Department of Medicine, Stanford University School of Medicine, Palo Alto, CA (J.C., D.J.M., P.A.H., A.T.S.)
| | - Randall S Stafford
- Stanford Prevention Research Center, Department of Medicine, Stanford University School of Medicine, Palo Alto, CA (R.S.S., D.J.M., A.T.S.)
| | - David J Maron
- Division of Cardiovascular Medicine and the Cardiovascular Institute, Department of Medicine, Stanford University School of Medicine, Palo Alto, CA (J.C., D.J.M., P.A.H., A.T.S.)
- Stanford Prevention Research Center, Department of Medicine, Stanford University School of Medicine, Palo Alto, CA (R.S.S., D.J.M., A.T.S.)
| | | | - Andrew P Ambrosy
- Division of Research, Kaiser Permanente Northern California, Oakland (A.P.A.)
| | - Paul A Heidenreich
- Veterans Affairs Palo Alto Health Care System, Palo Alto, CA (R.L.T., P.A.H., A.T.S.)
- Division of Cardiovascular Medicine and the Cardiovascular Institute, Department of Medicine, Stanford University School of Medicine, Palo Alto, CA (J.C., D.J.M., P.A.H., A.T.S.)
| | - Alexander T Sandhu
- Veterans Affairs Palo Alto Health Care System, Palo Alto, CA (R.L.T., P.A.H., A.T.S.)
- Division of Cardiovascular Medicine and the Cardiovascular Institute, Department of Medicine, Stanford University School of Medicine, Palo Alto, CA (J.C., D.J.M., P.A.H., A.T.S.)
- Stanford Prevention Research Center, Department of Medicine, Stanford University School of Medicine, Palo Alto, CA (R.S.S., D.J.M., A.T.S.)
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2
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Benipal H, Demers C, Cerasuolo JO, Perez R, You JJ, Amin F, Keshavjee K, Lee DS. Association of a Heart Failure Management Incentive in Primary Care With Clinical Outcomes: A Retrospective Cohort Study. J Am Heart Assoc 2024; 13:e031498. [PMID: 38156519 PMCID: PMC10863798 DOI: 10.1161/jaha.123.031498] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Accepted: 08/23/2023] [Indexed: 12/30/2023]
Abstract
BACKGROUND We aim to examine the association between primary care physicians' billing of Q050A, a pay-for-performance heart failure (HF) management incentive fee code, and the composite outcome of mortality, hospitalization, and emergency department visits. METHODS AND RESULTS This population-based cohort study linked administrative health databases in Ontario, Canada, for patients with HF aged >66 years between January 1, 2008, and March 31, 2020. Cases were patients with HF who had a Q050A fee code billed. Cases and controls were matched 1:1 on age, sex, patient status on being rostered to a primary care physician, cardiologist, or internist visit in the 6 months before study enrollment, Johns Hopkins Adjusted Clinical Group resource use bands, days between HF diagnosis and study enrollment (±2 years), and the logit of the propensity score. A Cox proportional hazards model assessed the association of Q050A with the outcome. A total of 59 664 cases had a Q050A billed, whereas 244 883 patients did not. Before matching, patients who had a Q050A billed were more likely to be men (52% versus 49%), were rostered to a primary care physician (100% versus 96%), had a higher Charlson Comorbidity Index, and had higher health care costs. The mean follow-up was 481 days for cases and 530 days for controls. The composite outcome (hazard ratio, 1.11 [95% CI, 1.09-1.12]) was significantly higher for cases than controls. CONCLUSIONS The Q050A incentive improved financial compensation for primary care physicians managing patients with HF but was not associated with improvements in the outcome. Research on promoting evidence-based HF management is warranted.
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Affiliation(s)
- Harsukh Benipal
- Temerty Faculty of MedicineUniversity of TorontoToronto, OntarioCanada
| | - Catherine Demers
- Department of MedicineMcMaster UniversityHamiltonOntarioCanada
- Department of Health Research Methods, Evidence and ImpactMcMaster UniversityHamiltonOntarioCanada
| | - Joshua O. Cerasuolo
- Department of Health Research Methods, Evidence and ImpactMcMaster UniversityHamiltonOntarioCanada
- Institute of Clinical Evaluative SciencesTorontoOntarioCanada
| | - Richard Perez
- Institute of Clinical Evaluative SciencesTorontoOntarioCanada
| | - John J. You
- Division of General Internal and Hospitalist MedicineCredit Valley Hospital, Trillium Health PartnersMississaugaOntarioCanada
| | - Faizan Amin
- Department of MedicineMcMaster UniversityHamiltonOntarioCanada
| | - Karim Keshavjee
- Institute of Health Policy, Management and EvaluationUniversity of TorontoToronto, OntarioCanada
- InfoClin IncTorontoOntarioCanada
| | - Douglas S. Lee
- Temerty Faculty of MedicineUniversity of TorontoToronto, OntarioCanada
- Institute of Clinical Evaluative SciencesTorontoOntarioCanada
- Institute of Health Policy, Management and EvaluationUniversity of TorontoToronto, OntarioCanada
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3
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Bobrowski D, Dorovenis A, Abdel-Qadir H, McNaughton CD, Alonzo R, Fang J, Austin PC, Udell JA, Jackevicius CA, Alter DA, Atzema CL, Bhatia RS, Booth GL, Ha ACT, Johnston S, Dhalla I, Kapral MK, Krumholz HM, Roifman I, Wijeysundera HC, Ko DT, Tu K, Ross HJ, Schull MJ, Lee DS. Association of neighbourhood-level material deprivation with adverse outcomes and processes of care among patients with heart failure in a single-payer healthcare system: A population-based cohort study. Eur J Heart Fail 2023; 25:2274-2286. [PMID: 37953731 DOI: 10.1002/ejhf.3090] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Revised: 10/10/2023] [Accepted: 11/07/2023] [Indexed: 11/14/2023] Open
Abstract
AIM We studied the association between neighbourhood material deprivation, a metric estimating inability to attain basic material needs, with outcomes and processes of care among incident heart failure patients in a universal healthcare system. METHODS AND RESULTS In a population-based retrospective study (2007-2019), we examined the association of material deprivation with 1-year all-cause mortality, cause-specific hospitalization, and 90-day processes of care. Using cause-specific hazards regression, we quantified the relative rate of events after multiple covariate adjustment, stratifying by age ≤65 or ≥66 years. Among 395 763 patients (median age 76 [interquartile range 66-84] years, 47% women), there was significant interaction between age and deprivation quintile for mortality/hospitalization outcomes (p ≤ 0.001). Younger residents (age ≤65 years) of the most versus least deprived neighbourhoods had higher hazards of all-cause death (hazard ratio [HR] 1.19, 95% confidence interval [CI] 1.10-1.29]) and cardiovascular hospitalization (HR 1.29 [95% CI 1.19-1.39]). Older individuals (≥66 years) in the most deprived neighbourhoods had significantly higher hazard of death (HR 1.11 [95% CI 1.08-1.14]) and cardiovascular hospitalization (HR 1.13 [95% CI 1.09-1.18]) compared to the least deprived. The magnitude of the association between deprivation and outcomes was amplified in the younger compared to the older age group. More deprived individuals in both age groups had a lower hazard of cardiology visits and advanced cardiac imaging (all p < 0.001), while the most deprived of younger ages were less likely to undergo implantable cardioverter-defibrillator/cardiac resynchronization therapy-pacemaker implantation (p = 0.023), compared to the least deprived. CONCLUSION Patients with newly-diagnosed heart failure residing in the most deprived neighbourhoods had worse outcomes and reduced access to care than those less deprived.
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Affiliation(s)
- David Bobrowski
- Department of Medicine, University of Toronto, Toronto, ON, Canada
| | | | - Husam Abdel-Qadir
- Department of Medicine, University of Toronto, Toronto, ON, Canada
- Women's College Hospital, Toronto, ON, Canada
- University Health Network, Toronto, ON, Canada
- ICES (formerly known as the Institute for Clinical Evaluative Sciences), Toronto, ON, Canada
- Institute of Health Policy, Management, and Evaluation, University of Toronto, Toronto, ON, Canada
| | - Candace D McNaughton
- Department of Medicine, University of Toronto, Toronto, ON, Canada
- ICES (formerly known as the Institute for Clinical Evaluative Sciences), Toronto, ON, Canada
- Sunnybrook Health Sciences Centre, Toronto, ON, Canada
| | - Rea Alonzo
- ICES (formerly known as the Institute for Clinical Evaluative Sciences), Toronto, ON, Canada
| | - Jiming Fang
- ICES (formerly known as the Institute for Clinical Evaluative Sciences), Toronto, ON, Canada
| | - Peter C Austin
- ICES (formerly known as the Institute for Clinical Evaluative Sciences), Toronto, ON, Canada
- Institute of Health Policy, Management, and Evaluation, University of Toronto, Toronto, ON, Canada
| | - Jacob A Udell
- Department of Medicine, University of Toronto, Toronto, ON, Canada
- Women's College Hospital, Toronto, ON, Canada
- University Health Network, Toronto, ON, Canada
- ICES (formerly known as the Institute for Clinical Evaluative Sciences), Toronto, ON, Canada
- Institute of Health Policy, Management, and Evaluation, University of Toronto, Toronto, ON, Canada
| | - Cynthia A Jackevicius
- University Health Network, Toronto, ON, Canada
- ICES (formerly known as the Institute for Clinical Evaluative Sciences), Toronto, ON, Canada
- Institute of Health Policy, Management, and Evaluation, University of Toronto, Toronto, ON, Canada
- Western University of Health Sciences, Pomona, CA, USA
| | - David A Alter
- Department of Medicine, University of Toronto, Toronto, ON, Canada
- University Health Network, Toronto, ON, Canada
- ICES (formerly known as the Institute for Clinical Evaluative Sciences), Toronto, ON, Canada
- Institute of Health Policy, Management, and Evaluation, University of Toronto, Toronto, ON, Canada
| | - Clare L Atzema
- Department of Medicine, University of Toronto, Toronto, ON, Canada
- ICES (formerly known as the Institute for Clinical Evaluative Sciences), Toronto, ON, Canada
- Institute of Health Policy, Management, and Evaluation, University of Toronto, Toronto, ON, Canada
- Sunnybrook Health Sciences Centre, Toronto, ON, Canada
| | - R Sacha Bhatia
- Department of Medicine, University of Toronto, Toronto, ON, Canada
- University Health Network, Toronto, ON, Canada
| | - Gillian L Booth
- Department of Medicine, University of Toronto, Toronto, ON, Canada
- ICES (formerly known as the Institute for Clinical Evaluative Sciences), Toronto, ON, Canada
- Institute of Health Policy, Management, and Evaluation, University of Toronto, Toronto, ON, Canada
- Li Ka Shing Knowledge Institute of St Michael's Hospital, Toronto, ON, Canada
| | - Andrew C T Ha
- Department of Medicine, University of Toronto, Toronto, ON, Canada
- University Health Network, Toronto, ON, Canada
| | - Sharon Johnston
- Departments of Family Medicine, University of Ottawa, Ottawa, ON, Canada
- Institut du Savoir, Hôpital Montfort, Ottawa, ON, Canada
| | - Irfan Dhalla
- Department of Medicine, University of Toronto, Toronto, ON, Canada
- Institute of Health Policy, Management, and Evaluation, University of Toronto, Toronto, ON, Canada
- Li Ka Shing Knowledge Institute of St Michael's Hospital, Toronto, ON, Canada
| | - Moira K Kapral
- Department of Medicine, University of Toronto, Toronto, ON, Canada
- University Health Network, Toronto, ON, Canada
- ICES (formerly known as the Institute for Clinical Evaluative Sciences), Toronto, ON, Canada
- Institute of Health Policy, Management, and Evaluation, University of Toronto, Toronto, ON, Canada
| | - Harlan M Krumholz
- Center for Outcomes Research and Evaluation, Yale New Haven Hospital, New Haven, CT, USA
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
- Department of Health Policy and Management, Yale School of Public Health, New Haven, CT, USA
| | - Idan Roifman
- Department of Medicine, University of Toronto, Toronto, ON, Canada
- ICES (formerly known as the Institute for Clinical Evaluative Sciences), Toronto, ON, Canada
- Institute of Health Policy, Management, and Evaluation, University of Toronto, Toronto, ON, Canada
- Sunnybrook Health Sciences Centre, Toronto, ON, Canada
| | - Harindra C Wijeysundera
- Department of Medicine, University of Toronto, Toronto, ON, Canada
- ICES (formerly known as the Institute for Clinical Evaluative Sciences), Toronto, ON, Canada
- Institute of Health Policy, Management, and Evaluation, University of Toronto, Toronto, ON, Canada
- Sunnybrook Health Sciences Centre, Toronto, ON, Canada
| | - Dennis T Ko
- Department of Medicine, University of Toronto, Toronto, ON, Canada
- ICES (formerly known as the Institute for Clinical Evaluative Sciences), Toronto, ON, Canada
- Institute of Health Policy, Management, and Evaluation, University of Toronto, Toronto, ON, Canada
- Sunnybrook Health Sciences Centre, Toronto, ON, Canada
| | - Karen Tu
- University Health Network, Toronto, ON, Canada
- Institute of Health Policy, Management, and Evaluation, University of Toronto, Toronto, ON, Canada
- Department of Family and Community Medicine, University of Toronto, Toronto, ON, Canada
- North York General Hospital, Toronto, ON, Canada
| | - Heather J Ross
- Department of Medicine, University of Toronto, Toronto, ON, Canada
- University Health Network, Toronto, ON, Canada
| | - Michael J Schull
- Department of Medicine, University of Toronto, Toronto, ON, Canada
- ICES (formerly known as the Institute for Clinical Evaluative Sciences), Toronto, ON, Canada
- Institute of Health Policy, Management, and Evaluation, University of Toronto, Toronto, ON, Canada
- Sunnybrook Health Sciences Centre, Toronto, ON, Canada
| | - Douglas S Lee
- Department of Medicine, University of Toronto, Toronto, ON, Canada
- University Health Network, Toronto, ON, Canada
- ICES (formerly known as the Institute for Clinical Evaluative Sciences), Toronto, ON, Canada
- Institute of Health Policy, Management, and Evaluation, University of Toronto, Toronto, ON, Canada
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4
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Ducharme A, Zieroth S, Ahooja V, Anderson K, Andrade J, Boivin-Proulx LA, Ezekowitz J, Howlett J, Lepage S, Leong D, McDonald MA, O'Meara E, Poon S, Swiggum E, Virani S. Canadian Cardiovascular Society-Canadian Heart Failure Society Focused Clinical Practice Update of Patients With Differing Heart Failure Phenotypes. Can J Cardiol 2023; 39:1030-1040. [PMID: 37169222 DOI: 10.1016/j.cjca.2023.04.022] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Revised: 04/20/2023] [Accepted: 04/21/2023] [Indexed: 05/13/2023] Open
Abstract
A number of societies produce heart failure (HF) management guidelines, comprising official recommendations on the basis of recent research discoveries, but their applicability to specific situations encountered in daily practice might be difficult. In this clinical practice update we aim to provide responses to fundamental questions that face health care providers, like appropriate timing for the introduction and optimization of different classes of medication according to specific patient phenotypes, when second-line therapies and valvular interventions should be considered, and management of difficult clinical scenarios such as cardiorenal syndrome and frailty. A consensus-based methodology was used. Approaches to 5 different phenotypes are presented: (1) The wet HF phenotype is the easiest to manage, decongestion being performed alongside introduction of guideline-directed medical therapy (GDMT); (2) The de novo HF phenotype requires the introduction of the 4 pillars of GDMT, personalizing the order on the basis of the individuals' biological and physiological characteristics; (3) The worsening HF phenotype is a marker of poor prognosis, and therefore should motivate optimization of GDMT, start second-line therapies, and/or reevaluate goals of care/advanced HF therapies; (4) The cardiorenal phenotypes require correct volume assessment, because renal function usually improves with decongestion; and (5) The frail HF phenotype require special attention, careful drug titration, and consideration of cardiac rehabilitation programs. In conclusion, specific common HF phenotypes call for a personalized approach to improve adoption of the HF guidelines into clinical practice.
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Affiliation(s)
- Anique Ducharme
- Department of Medicine, Institut de Cardiologie de Montréal, Université de Montréal, Montréal, Québec, Canada.
| | - Shelley Zieroth
- Department of Medicine, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Vineeta Ahooja
- Department of Medicine, The Heart Health Institute, Scarborough, Ontario, Canada
| | - Kim Anderson
- Department of Medicine, Dalhousie University QEII Health Sciences Centre, Halifax, Nova Scotia, Canada
| | - Jason Andrade
- Department of Medicine, University of British Columbia, Vancouver, British Columbia, Canada
| | | | - Justin Ezekowitz
- Department of Medicine, University of Alberta, Edmonton, Alberta, Canada
| | - Jonathan Howlett
- Department of Medicine, Cumming School of Medicine, University of Calgary, Libin Cardiovascular Institute, Calgary, Alberta, Canada
| | - Serge Lepage
- Department of Medicine, Université de Sherbrooke, Sherbrooke, Québec, Canada
| | - Derek Leong
- Department of Medicine, Toronto General Hospital, University Health Network, Toronto, Ontario, Canada
| | - Michael A McDonald
- Department of Medicine, Peter Munk Cardiac Centre, University Health Network, Toronto, Ontario, Canada
| | - Eileen O'Meara
- Department of Medicine, Institut de Cardiologie de Montréal, Université de Montréal, Montréal, Québec, Canada
| | - Stephanie Poon
- Department of Medicine, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario, Canada
| | - Elizabeth Swiggum
- Department of Medicine, Royal Jubilee Hospital, University of British Columbia, Victoria, British Columbia, Canada
| | - Sean Virani
- Department of Medicine, University of British Columbia, Vancouver, British Columbia, Canada
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5
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Buchan TA, Kugathasan L, Kobulnik J, Poon S, Runeckles K, Fan S, Ross HJ, Alba AC. Increased Mortality in Patients With Acutely Decompensated Heart Failure During the COVID-19 Pandemic in Toronto, Canada. CJC Open 2022; 4:772-781. [PMID: 35765461 PMCID: PMC9221741 DOI: 10.1016/j.cjco.2022.06.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Accepted: 06/20/2022] [Indexed: 11/03/2022] Open
Abstract
Background Methods Results Conclusions
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6
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Austin DE, Lee DS, Wang CX, Ma S, Wang X, Porter J, Wang B. Comparison of machine learning and the regression-based EHMRG model for predicting early mortality in acute heart failure. Int J Cardiol 2022; 365:78-84. [PMID: 35868354 DOI: 10.1016/j.ijcard.2022.07.035] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Revised: 06/26/2022] [Accepted: 07/17/2022] [Indexed: 12/23/2022]
Abstract
BACKGROUND Although risk stratification of patients with acute decompensated heart failure (HF) is important, it is unknown whether machine learning (ML) or conventional statistical models are optimal. We developed ML algorithms to predict 7-day and 30-day mortality in patients with acute HF and compared these with an existing logistic regression model at the same timepoints. METHODS Patients presenting to one of 86 hospitals, who were either admitted to hospital or discharged home directly from the emergency department, were randomly selected using stratified random sampling. ML approaches, including neural networks, random forest, XGBoost, and the Lasso, were compared with a validated logistic regression model for discrimination and calibration. RESULTS Among 12,608 patients in our analysis, lasso regression (c-statistic 0.774; 95% CI, 0.743, 0.806) performed better than other ML models for 7-day mortality but did not outperform the baseline logistic regression model (0.794; 95% CI, 0.789, 0.800). For 30-day mortality, XGBoost performed better than other ML models (c-statistic 0.759; 95% CI; 0.740, 0.779), but was not significantly better than logistic regression (c-statistic 0.755; 95% CI, 0.750, 0.762). Logistic regression demonstrated better calibration at 7 days (calibration-in-the-large 0.017; 95% CI, -0.657, 0.692, and calibration slope 0.954; 95% CI,0.769, 1.139) and at 30 days (-0.026; 95% CI, -0.374, 0.322 and 0.964; 95% CI, 0.831, 1.098), and best Brier scores, compared to ML approaches. CONCLUSIONS Logistic regression was comparable to ML in discrimination, but was superior to ML algorithms in calibration overall. ML algorithms for prognosis should routinely report calibration metrics in addition to discrimination.
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Affiliation(s)
| | - Douglas S Lee
- ICES, Institute for Clinical Evaluative Sciences; Peter Munk Cardiac Centre of University Health Network; Ted Roger Centre for Heart Research.
| | - Chloe X Wang
- ICES, Institute for Clinical Evaluative Sciences; Division of Vascular Surgery, University Health Network
| | - Shihao Ma
- ICES, Institute for Clinical Evaluative Sciences; Department of Computer Science, University of Toronto; Vector Institute of Artificial Intelligence
| | - Xuesong Wang
- ICES, Institute for Clinical Evaluative Sciences
| | - Joan Porter
- ICES, Institute for Clinical Evaluative Sciences
| | - Bo Wang
- ICES, Institute for Clinical Evaluative Sciences; Peter Munk Cardiac Centre of University Health Network; Department of Computer Science, University of Toronto; Vector Institute of Artificial Intelligence; Division of Vascular Surgery, University Health Network; Department of Laboratory Medicine and Pathobiology, University of Toronto, Canada
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7
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Chu A, Shah BR, Rashid M, Booth GL, Fazli GS, Tu K, Sun LY, Abdel-Qadir H, Yu CH, Shin S, Connelly KA, Tobe S, Liu PP, Lee DS. Trends in glucose testing among individuals without diabetes in Ontario between 2010 and 2017: a population-based cohort study. CMAJ Open 2022; 10:E772-E780. [PMID: 35998927 PMCID: PMC9402266 DOI: 10.9778/cmajo.20210195] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
Abstract
BACKGROUND Early identification of people with diabetes or prediabetes enables greater opportunities for glycemic control and management strategies to prevent related complications. To identify gaps in screening for these conditions, we examined population trends in receipt of timely glucose testing overall and in specific clinical subgroups. METHODS Using linked administrative databases, we conducted a retrospective cohort study of people aged 40 years and older without diabetes at baseline. Our primary outcome was up-to-date glucose testing, defined as having received testing at least once in the 3 years before each index year from 2010 to 2017, using linked administrative databases of people residing in Ontario, Canada. We calculated rates of up-to-date testing by age group, sex, ethnicity (South Asian, Chinese, general population) and comorbidities (hypertension, hyperlipidemia, cardiovascular disease). RESULTS Over the 8-year study period, up-to-date glucose testing rates were stable at 67% for men and 77% for women (both relative risk 1.00 per year; 95% confidence interval 1.00-1.00). Testing rates were significantly lower in men than in women (all age groups p < 0.001) and lower in younger than older age groups (except those aged ≥ 80 yr). South Asian people had the highest testing rates, although among people aged 70 years or older, testing was highest in the general population (p < 0.001). Among people with hypertension, hyperlipidemia and cardiovascular disease, annual testing rates were also stable, but only 58% overall among people with hypertension. INTERPRETATION We found lower glucose testing rates in younger men and people with hypertension. Our findings reinforce the need for initiatives to increase awareness of glycemic testing.
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Affiliation(s)
- Anna Chu
- ICES Central (Chu, Shah, Rashid, Booth, Sun, Abdel-Qadir, Tobe, Lee); University of Toronto (Chu, Shah, Booth, Fazli, Tu, Abdel-Qadir, Yu, Connelly, Tobe, Lee); Sunnybrook Health Sciences Centre (Shah, Tobe); Unity Health Toronto (Booth, Fazli, Yu, Connelly); University Health Network (Tu, Abdel-Qadir, Lee); North York General Hospital (Tu), Toronto, Ont.; University of Ottawa Heart Institute (Sun, Liu); University of Ottawa (Sun, Shin), Ottawa, Ont.; Woman's College Hospital (Abdel-Qadir); Keenan Research Centre (Connelly), Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, Ont.; Northern Ontario School of Medicine (Tobe), Sudbury, Ont
| | - Baiju R Shah
- ICES Central (Chu, Shah, Rashid, Booth, Sun, Abdel-Qadir, Tobe, Lee); University of Toronto (Chu, Shah, Booth, Fazli, Tu, Abdel-Qadir, Yu, Connelly, Tobe, Lee); Sunnybrook Health Sciences Centre (Shah, Tobe); Unity Health Toronto (Booth, Fazli, Yu, Connelly); University Health Network (Tu, Abdel-Qadir, Lee); North York General Hospital (Tu), Toronto, Ont.; University of Ottawa Heart Institute (Sun, Liu); University of Ottawa (Sun, Shin), Ottawa, Ont.; Woman's College Hospital (Abdel-Qadir); Keenan Research Centre (Connelly), Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, Ont.; Northern Ontario School of Medicine (Tobe), Sudbury, Ont
| | - Mohammed Rashid
- ICES Central (Chu, Shah, Rashid, Booth, Sun, Abdel-Qadir, Tobe, Lee); University of Toronto (Chu, Shah, Booth, Fazli, Tu, Abdel-Qadir, Yu, Connelly, Tobe, Lee); Sunnybrook Health Sciences Centre (Shah, Tobe); Unity Health Toronto (Booth, Fazli, Yu, Connelly); University Health Network (Tu, Abdel-Qadir, Lee); North York General Hospital (Tu), Toronto, Ont.; University of Ottawa Heart Institute (Sun, Liu); University of Ottawa (Sun, Shin), Ottawa, Ont.; Woman's College Hospital (Abdel-Qadir); Keenan Research Centre (Connelly), Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, Ont.; Northern Ontario School of Medicine (Tobe), Sudbury, Ont
| | - Gillian L Booth
- ICES Central (Chu, Shah, Rashid, Booth, Sun, Abdel-Qadir, Tobe, Lee); University of Toronto (Chu, Shah, Booth, Fazli, Tu, Abdel-Qadir, Yu, Connelly, Tobe, Lee); Sunnybrook Health Sciences Centre (Shah, Tobe); Unity Health Toronto (Booth, Fazli, Yu, Connelly); University Health Network (Tu, Abdel-Qadir, Lee); North York General Hospital (Tu), Toronto, Ont.; University of Ottawa Heart Institute (Sun, Liu); University of Ottawa (Sun, Shin), Ottawa, Ont.; Woman's College Hospital (Abdel-Qadir); Keenan Research Centre (Connelly), Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, Ont.; Northern Ontario School of Medicine (Tobe), Sudbury, Ont
| | - Ghazal S Fazli
- ICES Central (Chu, Shah, Rashid, Booth, Sun, Abdel-Qadir, Tobe, Lee); University of Toronto (Chu, Shah, Booth, Fazli, Tu, Abdel-Qadir, Yu, Connelly, Tobe, Lee); Sunnybrook Health Sciences Centre (Shah, Tobe); Unity Health Toronto (Booth, Fazli, Yu, Connelly); University Health Network (Tu, Abdel-Qadir, Lee); North York General Hospital (Tu), Toronto, Ont.; University of Ottawa Heart Institute (Sun, Liu); University of Ottawa (Sun, Shin), Ottawa, Ont.; Woman's College Hospital (Abdel-Qadir); Keenan Research Centre (Connelly), Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, Ont.; Northern Ontario School of Medicine (Tobe), Sudbury, Ont
| | - Karen Tu
- ICES Central (Chu, Shah, Rashid, Booth, Sun, Abdel-Qadir, Tobe, Lee); University of Toronto (Chu, Shah, Booth, Fazli, Tu, Abdel-Qadir, Yu, Connelly, Tobe, Lee); Sunnybrook Health Sciences Centre (Shah, Tobe); Unity Health Toronto (Booth, Fazli, Yu, Connelly); University Health Network (Tu, Abdel-Qadir, Lee); North York General Hospital (Tu), Toronto, Ont.; University of Ottawa Heart Institute (Sun, Liu); University of Ottawa (Sun, Shin), Ottawa, Ont.; Woman's College Hospital (Abdel-Qadir); Keenan Research Centre (Connelly), Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, Ont.; Northern Ontario School of Medicine (Tobe), Sudbury, Ont
| | - Louise Y Sun
- ICES Central (Chu, Shah, Rashid, Booth, Sun, Abdel-Qadir, Tobe, Lee); University of Toronto (Chu, Shah, Booth, Fazli, Tu, Abdel-Qadir, Yu, Connelly, Tobe, Lee); Sunnybrook Health Sciences Centre (Shah, Tobe); Unity Health Toronto (Booth, Fazli, Yu, Connelly); University Health Network (Tu, Abdel-Qadir, Lee); North York General Hospital (Tu), Toronto, Ont.; University of Ottawa Heart Institute (Sun, Liu); University of Ottawa (Sun, Shin), Ottawa, Ont.; Woman's College Hospital (Abdel-Qadir); Keenan Research Centre (Connelly), Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, Ont.; Northern Ontario School of Medicine (Tobe), Sudbury, Ont
| | - Husam Abdel-Qadir
- ICES Central (Chu, Shah, Rashid, Booth, Sun, Abdel-Qadir, Tobe, Lee); University of Toronto (Chu, Shah, Booth, Fazli, Tu, Abdel-Qadir, Yu, Connelly, Tobe, Lee); Sunnybrook Health Sciences Centre (Shah, Tobe); Unity Health Toronto (Booth, Fazli, Yu, Connelly); University Health Network (Tu, Abdel-Qadir, Lee); North York General Hospital (Tu), Toronto, Ont.; University of Ottawa Heart Institute (Sun, Liu); University of Ottawa (Sun, Shin), Ottawa, Ont.; Woman's College Hospital (Abdel-Qadir); Keenan Research Centre (Connelly), Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, Ont.; Northern Ontario School of Medicine (Tobe), Sudbury, Ont
| | - Catherine H Yu
- ICES Central (Chu, Shah, Rashid, Booth, Sun, Abdel-Qadir, Tobe, Lee); University of Toronto (Chu, Shah, Booth, Fazli, Tu, Abdel-Qadir, Yu, Connelly, Tobe, Lee); Sunnybrook Health Sciences Centre (Shah, Tobe); Unity Health Toronto (Booth, Fazli, Yu, Connelly); University Health Network (Tu, Abdel-Qadir, Lee); North York General Hospital (Tu), Toronto, Ont.; University of Ottawa Heart Institute (Sun, Liu); University of Ottawa (Sun, Shin), Ottawa, Ont.; Woman's College Hospital (Abdel-Qadir); Keenan Research Centre (Connelly), Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, Ont.; Northern Ontario School of Medicine (Tobe), Sudbury, Ont
| | - Sheojung Shin
- ICES Central (Chu, Shah, Rashid, Booth, Sun, Abdel-Qadir, Tobe, Lee); University of Toronto (Chu, Shah, Booth, Fazli, Tu, Abdel-Qadir, Yu, Connelly, Tobe, Lee); Sunnybrook Health Sciences Centre (Shah, Tobe); Unity Health Toronto (Booth, Fazli, Yu, Connelly); University Health Network (Tu, Abdel-Qadir, Lee); North York General Hospital (Tu), Toronto, Ont.; University of Ottawa Heart Institute (Sun, Liu); University of Ottawa (Sun, Shin), Ottawa, Ont.; Woman's College Hospital (Abdel-Qadir); Keenan Research Centre (Connelly), Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, Ont.; Northern Ontario School of Medicine (Tobe), Sudbury, Ont
| | - Kim A Connelly
- ICES Central (Chu, Shah, Rashid, Booth, Sun, Abdel-Qadir, Tobe, Lee); University of Toronto (Chu, Shah, Booth, Fazli, Tu, Abdel-Qadir, Yu, Connelly, Tobe, Lee); Sunnybrook Health Sciences Centre (Shah, Tobe); Unity Health Toronto (Booth, Fazli, Yu, Connelly); University Health Network (Tu, Abdel-Qadir, Lee); North York General Hospital (Tu), Toronto, Ont.; University of Ottawa Heart Institute (Sun, Liu); University of Ottawa (Sun, Shin), Ottawa, Ont.; Woman's College Hospital (Abdel-Qadir); Keenan Research Centre (Connelly), Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, Ont.; Northern Ontario School of Medicine (Tobe), Sudbury, Ont
| | - Sheldon Tobe
- ICES Central (Chu, Shah, Rashid, Booth, Sun, Abdel-Qadir, Tobe, Lee); University of Toronto (Chu, Shah, Booth, Fazli, Tu, Abdel-Qadir, Yu, Connelly, Tobe, Lee); Sunnybrook Health Sciences Centre (Shah, Tobe); Unity Health Toronto (Booth, Fazli, Yu, Connelly); University Health Network (Tu, Abdel-Qadir, Lee); North York General Hospital (Tu), Toronto, Ont.; University of Ottawa Heart Institute (Sun, Liu); University of Ottawa (Sun, Shin), Ottawa, Ont.; Woman's College Hospital (Abdel-Qadir); Keenan Research Centre (Connelly), Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, Ont.; Northern Ontario School of Medicine (Tobe), Sudbury, Ont
| | - Peter P Liu
- ICES Central (Chu, Shah, Rashid, Booth, Sun, Abdel-Qadir, Tobe, Lee); University of Toronto (Chu, Shah, Booth, Fazli, Tu, Abdel-Qadir, Yu, Connelly, Tobe, Lee); Sunnybrook Health Sciences Centre (Shah, Tobe); Unity Health Toronto (Booth, Fazli, Yu, Connelly); University Health Network (Tu, Abdel-Qadir, Lee); North York General Hospital (Tu), Toronto, Ont.; University of Ottawa Heart Institute (Sun, Liu); University of Ottawa (Sun, Shin), Ottawa, Ont.; Woman's College Hospital (Abdel-Qadir); Keenan Research Centre (Connelly), Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, Ont.; Northern Ontario School of Medicine (Tobe), Sudbury, Ont
| | - Douglas S Lee
- ICES Central (Chu, Shah, Rashid, Booth, Sun, Abdel-Qadir, Tobe, Lee); University of Toronto (Chu, Shah, Booth, Fazli, Tu, Abdel-Qadir, Yu, Connelly, Tobe, Lee); Sunnybrook Health Sciences Centre (Shah, Tobe); Unity Health Toronto (Booth, Fazli, Yu, Connelly); University Health Network (Tu, Abdel-Qadir, Lee); North York General Hospital (Tu), Toronto, Ont.; University of Ottawa Heart Institute (Sun, Liu); University of Ottawa (Sun, Shin), Ottawa, Ont.; Woman's College Hospital (Abdel-Qadir); Keenan Research Centre (Connelly), Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, Ont.; Northern Ontario School of Medicine (Tobe), Sudbury, Ont.
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Lee DS, Straus SE, Austin PC, Mohamed S, Taljaard M, Chong A, Fang J, Prasad T, Farkouh ME, Schull MJ, Mak S, Ross HJ. Rationale and design of the comparison of outcomes and access to care for heart failure (COACH) trial: A stepped wedge cluster randomized trial. Am Heart J 2021; 240:1-10. [PMID: 33984316 DOI: 10.1016/j.ahj.2021.05.003] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/19/2020] [Accepted: 05/04/2021] [Indexed: 12/14/2022]
Abstract
BACKGROUND Heart failure (HF) is an ambulatory care sensitive condition and a leading reason for emergency department (ED) visits and hospitalizations. Improved decision-making and care may enhance safety and efficiency for patients presenting to the ED with acute HF. OBJECTIVES We will evaluate an intervention comprised of 2 complementary components: (1) the Emergency Heart Failure Mortality Risk Grade simultaneous 7- and 30-day (EHMRG30-ST) risk scores, which will inform admission-discharge decisions, and (2) a rapid outpatient follow-up (RAPID-HF) clinic for low-to-intermediate risk patients on cardiovascular readmissions or death. STUDY DESIGN Stepped wedge cluster randomized trial with cross-sectional measurement at 10 acute care hospitals in Ontario, Canada. Patients presenting during control and intervention periods are eligible if they have a primary ED diagnosis of HF. In the intervention periods, access to the EHMRG30-ST web calculator will become available to hospitals' internet protocol (IP) addresses, and referral to the RAPID-HF clinic will be facilitated by a study nurse navigator. Patients with a high risk EHMRG30-ST score will be admitted to hospital. The RAPID-HF clinic will accept referrals for patients: (1) with low risk 7- and 30-day EHMRG30-ST scores who are discharged directly from the ED, or (2) intermediate risk patients with hospital length of stay < 72 hours. The RAPID-HF clinic, staffed by a nurse-clinician and cardiologist, will provide care during the 30-day transition after hospital separation. CONCLUSION This trial will determine whether novel risk stratification coupled with rapid ambulatory care achieves better outcomes than conventional decision-making and care for patients with HF.
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Affiliation(s)
- Douglas S Lee
- ICES (formerly the Institute for Clinical Evaluative Sciences), Toronto, Canada; Peter Munk Cardiac Centre of the University Health Network, Toronto, Canada; Ted Rogers Centre for Heart Research, Toronto, Canada; University of Toronto, Toronto, Canada.
| | - Sharon E Straus
- University of Toronto, Toronto, Canada; Li Ka Shing Knowledge Institute and Unity Health, Toronto, Canada
| | - Peter C Austin
- ICES (formerly the Institute for Clinical Evaluative Sciences), Toronto, Canada; University of Toronto, Toronto, Canada
| | - Shanas Mohamed
- Peter Munk Cardiac Centre of the University Health Network, Toronto, Canada
| | - Monica Taljaard
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada; School of Epidemiology and Public Health, University of Ottawa, Ottawa, Canada
| | - Alice Chong
- ICES (formerly the Institute for Clinical Evaluative Sciences), Toronto, Canada
| | - Jiming Fang
- ICES (formerly the Institute for Clinical Evaluative Sciences), Toronto, Canada
| | - Treesa Prasad
- ICES (formerly the Institute for Clinical Evaluative Sciences), Toronto, Canada
| | - Michael E Farkouh
- Peter Munk Cardiac Centre of the University Health Network, Toronto, Canada; University of Toronto, Toronto, Canada
| | - Michael J Schull
- ICES (formerly the Institute for Clinical Evaluative Sciences), Toronto, Canada; University of Toronto, Toronto, Canada; Sunnybrook Health Sciences Centre, Toronto, Canada
| | - Susanna Mak
- University of Toronto, Toronto, Canada; Sinai Health, Toronto, Canada
| | - Heather J Ross
- Peter Munk Cardiac Centre of the University Health Network, Toronto, Canada; Ted Rogers Centre for Heart Research, Toronto, Canada; University of Toronto, Toronto, Canada
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9
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Tsang W, Silversides CK, Rashid M, Roche SL, Alonso-Gonzalez R, Austin PC, Lee DS. Outcomes and healthcare resource utilization in adult congenital heart disease patients with heart failure. ESC Heart Fail 2021; 8:4139-4151. [PMID: 34402222 PMCID: PMC8497229 DOI: 10.1002/ehf2.13529] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Revised: 06/11/2021] [Accepted: 07/05/2021] [Indexed: 12/14/2022] Open
Abstract
AIMS While heart failure (HF) is a leading cause of death in adults with congenital heart disease (ACHD), few studies report contemporary outcomes after the first HF hospitalization. We examined outcomes of ACHD patients newly admitted for HF compared with ACHD patients without HF and the general HF population without ACHD. METHODS AND RESULTS Using population databases from a single-payer health system from 1994 to 2018, ACHD patients newly admitted for HF were matched 1:1 to ACHD patients without HF (n = 4030 matched pairs). Similarly, ACHD patients newly admitted for HF were matched 1:1 to HF patients without ACHD (n = 4336 matched pairs). Patients with ACHD and HF (median age 68 years, 45% women) experienced higher mortality in short-term [30 day adjusted hazard ratio (HR) 4.68, 95% confidence interval (CI) 4.06, 5.43, P < 0.001], near-term (1 year HR 3.87, 95% CI 3.77, 4.92, P < 0.001), and long-term (24 year HR 1.59, 95% CI 1.13, 2.36, P = 0.008) follow-up. Patients with ACHD and HF had fewer baseline cardiovascular comorbidities than non-ACHD HF but demonstrated higher 30 day (HR 1.56, 95% CI 1.41, 1.73, P < 0.001), 1 year (HR 1.30, 95% CI 1.20, 1.40, P < 0.001), and 24 year (HR 2.40, 95% CI 1.73, 3.38, P < 0.001) mortality. Those with ACHD and HF also exhibited higher cardiovascular readmission rates at 30 days with HRs 9.15 (95% CI; 8.00, 10.48, P < 0.001) vs. ACHD without HF, and 1.71 (95% CI; 1.54, 1.85, P < 0.001) vs. HF without ACHD, and the higher readmission risk extended to 10 year follow-up. CONCLUSIONS Adults with congenital heart disease patients with new HF have high risks of death and cardiovascular hospitalization, and preventative strategies to improve outcomes are urgently needed.
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Affiliation(s)
- Wendy Tsang
- Division of Cardiology, Toronto General Hospital, University Health Network, Peter Munk Cardiovascular Center, University of Toronto, Toronto, Ontario, Canada
| | - Candice K Silversides
- Division of Cardiology, Toronto General Hospital, University Health Network, Peter Munk Cardiovascular Center, University of Toronto, Toronto, Ontario, Canada.,Toronto Adult Congenital Heart Disease Program, Toronto, Ontario, Canada
| | - Mohammed Rashid
- ICES (formerly the Institute for Clinical Evaluative Sciences), 2075 Bayview Avenue, Toronto, Ontario, M4N 3M5, Canada
| | - S Lucy Roche
- Division of Cardiology, Toronto General Hospital, University Health Network, Peter Munk Cardiovascular Center, University of Toronto, Toronto, Ontario, Canada.,Toronto Adult Congenital Heart Disease Program, Toronto, Ontario, Canada
| | - Rafael Alonso-Gonzalez
- Division of Cardiology, Toronto General Hospital, University Health Network, Peter Munk Cardiovascular Center, University of Toronto, Toronto, Ontario, Canada.,Toronto Adult Congenital Heart Disease Program, Toronto, Ontario, Canada
| | - Peter C Austin
- ICES (formerly the Institute for Clinical Evaluative Sciences), 2075 Bayview Avenue, Toronto, Ontario, M4N 3M5, Canada
| | - Douglas S Lee
- Division of Cardiology, Toronto General Hospital, University Health Network, Peter Munk Cardiovascular Center, University of Toronto, Toronto, Ontario, Canada.,ICES (formerly the Institute for Clinical Evaluative Sciences), 2075 Bayview Avenue, Toronto, Ontario, M4N 3M5, Canada.,Ted Rogers Centre for Heart Research, Toronto, Ontario, Canada
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10
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Sandhu AT, Tisdale RL, Rodriguez F, Stafford RS, Maron DJ, Hernandez-Boussard T, Lewis E, Heidenreich PA. Disparity in the Setting of Incident Heart Failure Diagnosis. Circ Heart Fail 2021; 14:e008538. [PMID: 34311559 PMCID: PMC9070116 DOI: 10.1161/circheartfailure.121.008538] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
BACKGROUND Early heart failure (HF) recognition can reduce morbidity, yet HF is often initially diagnosed only after a patient clinically worsens. We sought to identify characteristics that predict diagnosis in the acute care setting versus the outpatient setting. METHODS We estimated the proportion of incident HF diagnosed in the acute care setting (inpatient hospital or emergency department) versus outpatient setting based on diagnostic codes from a claims database covering commercial insurance and Medicare Advantage between 2003 and 2019. After excluding new-onset HF potentially caused by a concurrent acute cause (eg, acute myocardial infarction), we identified demographic, clinical, and socioeconomic predictors of diagnosis setting. Patients were linked to their primary care clinicians to evaluate diagnosis setting variation across clinicians. RESULTS Of 959 438 patients with new HF, 38% were diagnosed in acute care. Of these, 46% had potential HF symptoms in the prior 6 months. Over time, the relative odds of acute care diagnosis increased by 3.2% annually after adjustment for patient characteristics (95% CI, 3.1%-3.3%). Acute care diagnosis setting was more likely for women compared with men (adjusted odds ratio, 1.11 [95% CI, 1.10-1.12]) and for Black patients compared with White patients (adjusted odds ratio, 1.18 [95% CI, 1.16-1.19]). The proportion of acute care diagnosis varied substantially (interquartile range: 24%-39%) among clinicians after adjusting for patient-level risk factors. CONCLUSIONS A large proportion of first HF diagnoses occur in the acute care setting, particularly among women and Black patients, yet many had potential HF symptoms in the months before acute care visits. These results raise concerns that many HF diagnoses are missed in the outpatient setting. Earlier diagnosis could allow for timelier high-value interventions, addressing disparities and reducing the progression of HF.
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Affiliation(s)
- Alexander T Sandhu
- Division of Cardiovascular Medicine and the Cardiovascular Institute, Department of Medicine (A.T.S., F.R., D.J.M., E.L., P.A.H.), Stanford University, CA
| | - Rebecca L Tisdale
- Veteran's Affairs Palo Alto Healthcare System, CA (R.L.T., P.A.H.).,Stanford Health Policy, Centers for Health Policy/Primary Care and Outcomes Research, Department of Medicine, Stanford University School of Medicine, CA (R.L.T.)
| | - Fatima Rodriguez
- Division of Cardiovascular Medicine and the Cardiovascular Institute, Department of Medicine (A.T.S., F.R., D.J.M., E.L., P.A.H.), Stanford University, CA.,Stanford Prevention Research Center, Stanford University School of Medicine, CA (F.R., R.S.S., D.J.M.)
| | - Randall S Stafford
- Stanford Prevention Research Center, Stanford University School of Medicine, CA (F.R., R.S.S., D.J.M.)
| | - David J Maron
- Division of Cardiovascular Medicine and the Cardiovascular Institute, Department of Medicine (A.T.S., F.R., D.J.M., E.L., P.A.H.), Stanford University, CA.,Stanford Prevention Research Center, Stanford University School of Medicine, CA (F.R., R.S.S., D.J.M.)
| | - Tina Hernandez-Boussard
- Department of Medicine (R.S.S., T.H.-B.), and Department of Biomedical Data Sciences (T.H.-B.), Stanford University, CA
| | - Eldrin Lewis
- Division of Cardiovascular Medicine and the Cardiovascular Institute, Department of Medicine (A.T.S., F.R., D.J.M., E.L., P.A.H.), Stanford University, CA
| | - Paul A Heidenreich
- Division of Cardiovascular Medicine and the Cardiovascular Institute, Department of Medicine (A.T.S., F.R., D.J.M., E.L., P.A.H.), Stanford University, CA.,Veteran's Affairs Palo Alto Healthcare System, CA (R.L.T., P.A.H.)
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