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Health Security Perceptions: Initial Psychometric Analysis of the Construct of Health Security in Chronic Illness within Cardiac Device Patients

Open AccessPublished:May 17, 2023DOI:https://doi.org/10.1016/j.cjco.2023.05.005
Health Security Perceptions: Initial Psychometric Analysis of the Construct of Health Security in Chronic Illness within Cardiac Device Patients
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      Abstract

      Scale measuring the construct of Health Security in Chronic Illness (HSCI) was piloted in Canadian cardiac device patients (N=176) enrolled in remote monitoring study at two timepoints. Analysis revealed two-factor solution labeled: Support and Certainty. Patients reported receiving less support over time but consistent health certainty. ICD patients felt less secure over time and reported lower HSCI than pacemaker patients.

      Introduction

      Cardiac implantable electronic devices (CIED), such as pacemakers (PM) and implantable cardioverter defibrillators (ICD), have reduced mortality among cardiac arrhythmic patients.
      • Al-Khatib S.M.
      • Fonarow G.C.
      • Joglar J.A.
      • Inoue L.Y.
      • Mark D.B.
      • Lee K.L.
      • Kadish A.
      • Bardy G.
      • Sanders G.D.
      Primary prevention implantable cardioverter defibrillators in patients with nonischemic cardiomyopathy: a meta-analysis.
      JAMA Cardiol. 2017 Jun; 2: 685-688https://doi.org/10.1001/jamacardio.2017.0630
      Patient reported outcomes (PROs) have emerged as a critical consideration in cardiac research, but optimal metrics remain to be identified. While PM report improved quality of life (QOL),

      Van Eck JM, van Hemel NM, van den Bos A, Taks W, Grobbee DE, Moons KG. Predictors of improved quality of life 1 year after pacemaker implantation.Am Heart J. 2008 Sep;156(3): 491-497. https://doi.org/10.1016/j.ahj.2008.04.029

      it is mixed for ICD patients.

      Sears SF, Kirian, K. Shock and patient‐centered outcomes research: is an ICD shock still a critical event?.Pacing Clin Electrophysiol. 2010 Aug;33(12): 1437-1441. doi:10.9778/cmajo.20200041

      Of course, these devices differ based on their functions: the ICD administers painful shocks while PM’s timed pulses are painless. Nonetheless, these devices share the benefit of sustaining normal cardiac rate/rhythm. More nuanced measures are needed to understand why CIED patients score differently on QOL.
      Novel metrics have provided distinctions from QOL,

      Sears SF, Force Z, Khan S, Nekkanti R. Patient acceptance: Metrics, meaning, and the “missing piece” in evaluating novel devices.J of Cardiovasc Electrophysiol. 2021 Nov;33(1): 90-92. https://doi.org/10.1111/jce.15292

      such as patient acceptance of device technology, described as understanding, recommending, and deriving benefit from their device.

      Burns JL, Serber ER, Keim S, Sears SF. Measuring patient acceptance of implantable cardiac device therapy: initial psychometric investigation of the Florida Patient Acceptance Survey. J of Cardiovasc Electrophysiol. 2005 Apr;16(4):384-390. https://doi.org/10.1046/j.1540-8167.2005.40134.x

      The Florida Patient Acceptance Survey (FPAS) was able to distinguish between pacemaker and ICD patients and was sensitive to changes within interventions.

      Burns JL, Sears SF, Sotile R, Schwartzman DS, Hoyt RH, Alvarez LG, Ujhelyi MR. Do patients accept implantable atrial defibrillation therapy? Results from the Patient Atrial Shock Survey of Acceptance and Tolerance (PASSAT) Study. J of Cardiovasc Electrophysiol. 2004 Mar;15(3):286-291. https://doi.org/10.1111/j.1540-8167.2004.03406.x

      FPAS scores are moderately correlated with measures of QOL and negatively correlated with psychosocial distress. Quantitative effort to disentangle the impact of disease vs. device on CIED patients provides more selective information for potential intervention.
      We postulated a construct termed Health Security in Chronic Illness (HSCI),defined as the expectation of reliable and desirable health in the near term. A scale was developed to assess HSCI; items were generated from expert input from cardiac psychologists, cardiologists, and electrophysiologists. The purpose of this paper was to develop the novel measure of HSCI in a Canadian sample of CIED patients over time.

      Methods

      Participants

      Data was collected from three Canadian Centres with cardiac electrophysiologists. Eligibility for enrollment included age ≥ 18, providing informed consent, and having CIEDs with remote patient monitoring (RPM).

      Procedures

      Standard of care for CIED patients after implantation involves a device check within 48 hours and after 6-8 weeks. Participants were from a larger multi-center study examining RPM.

      Sapp JA, Gillis AM, AbdelWahab A, Nault I, Nery PB, Healey JS, Raj SR, Lockwood E, Sterns LD, Sears SF, Wells GA. Remote-only monitoring for patients with cardiac implantable electronic devices: a before-and-after pilot study.Can Med Assoc J. 2021 Jan;9(1): E53-E61. doi: 10.9778/cmajo.20200041

      Enrollment started after the 8-week post-operative check.

      Outcome Measures

      The Health Security in Chronic Illness Scale (HSCI) was developed to 24-item measure the novel construct. Prior subscales were entitled: Availability of Help, Preventive Action, Controlling Outcomes, and Future Expectations using a 5-point Likert scale (Strongly Disagree to Strongly Agree).
      The Florida Patient Acceptance Survey (FPAS) is an 18-item measure on Device Acceptance for CIED patients with four subscales: Return to Life, Device-Related Distress, Positive Appraisal, and Body Image Concerns.

      Burns JL, Serber ER, Keim S, Sears SF. Measuring patient acceptance of implantable cardiac device therapy: initial psychometric investigation of the Florida Patient Acceptance Survey. J of Cardiovasc Electrophysiol. 2005 Apr;16(4):384-390. https://doi.org/10.1046/j.1540-8167.2005.40134.x

      The FPAS has been validated in several countries.

      Burns JL, Serber ER, Keim S, Sears SF. Measuring patient acceptance of implantable cardiac device therapy: initial psychometric investigation of the Florida Patient Acceptance Survey. J of Cardiovasc Electrophysiol. 2005 Apr;16(4):384-390. https://doi.org/10.1046/j.1540-8167.2005.40134.x

      ,

      Burns JL, Sears SF, Sotile R, Schwartzman DS, Hoyt RH, Alvarez LG, Ujhelyi MR. Do patients accept implantable atrial defibrillation therapy? Results from the Patient Atrial Shock Survey of Acceptance and Tolerance (PASSAT) Study. J of Cardiovasc Electrophysiol. 2004 Mar;15(3):286-291. https://doi.org/10.1111/j.1540-8167.2004.03406.x

      with satisfactory internal consistency (α = .74 to .89) for the total and subscales.
      The Florida Shock Anxiety Scale (FSAS) is a 10-item scale was developed to evaluate anxiety surrounding ICD shocks.

      Kuhl EA, Dixit NK, Walker RL, Conti JB, Sears SF. Measurement of patient fears about implantable cardioverter defibrillator shock: an initial evaluation of the Florida Shock Anxiety Scale.Pacing Clin Electrophysiol. 2006 Jun;29(6):614-618. https://doi.org/10.1111/j.1540-8159.2006.00408.x

      The scale has high internal consistency (α = .91), split-half reliability (α = .92) and test–retest reliability (α =.79).

      Statistical Analyses

      Descriptive statistics were first analyzed. Variables are reported with standard deviation (SD) for continuous data and frequencies for categorical. Factor structuring used Velicer’s Minimum Average Partial (MAP) Test and Principal Axis Factoring (PAF) using Olbique Oblimin rotation. Confirmatory factor analysis (CFA) was used for refinement. Correlations to other measures were used to examine construct validity. Mixed Model Analysis of Variances (ANOVAs) were conducted to examine differences on health-related QOL measures from baseline to 12-month follow-up. Analyses were performed using Statistical Package for the Social Sciences 28 (IBM Corp.) and Just Another Statistics Program (JASP; Version 0.16.3.0) was used for the CFA.

      Results

      Descriptive Statistics

      ICD patients (N = 100) and 76 pacemaker (N = 76) patients were included in the study (N = 176). Most of the ICD sample were men (71%), middle aged (62.5±11.8 years), had been living with their device for 2.0±2.3 years, had an indication for primary prevention (55%), had ischemic heart disease (55%), and were prescribed medication including β-Blocker (88%), Statin (68%), Antiplatelet (68%), and/or ACE inhibitor/ARB (51%). For all ICD participants, there were six device visits due to ICD shocks. For pacemaker patients, most of the sample were men (63.2%), middle aged (63.4±16.5 years), had been living with their device for (2.7±3.2 years), had an indication for AV nodal disease (32%), sinus node dysfunction (38%) or syncope (25%), had various cardiovascular history including atrial fibrillation or flutter (37%), ischemic heart disease (17%), or valvular disease (17%) and were prescribed medication including β-Blocker (32%), Antiplatelet (30%) and/or Statin (29%).

      Health Security in Chronic Illness: Scale Construction

      A parallel analysis and the original and revised Velicer’s MAP Test suggested four factors.

      Zwick WR, Velicer WF. Comparison of five rules for determining the number of components to retain.Psychol Bull. 1986 May; 99(3), 432-442. https://doi.org/10.1037/0033-2909.99.3.432

      This structure was examined with PAF and the Olbique Oblimin rotation. Items that did not load/loaded weakly onto factors were removed.
      A CFA did not support the model and had poor fit. A two-factor model was proposed and indicated good fit χ2 (64) = 77.579, CFI = .99, TLI = .99, SRMR = .08, RMSEA = .04 (.00 - .072). A cutoff of 0.375 was administered for factor loadings (see Table I). Thirteen items were retained (see Figure I) and used to calculate validity and reliability, which was acceptable (α = .79).
      Table IFactor Loadings for Health Security in Chronic Illness
      ItemSingle factor StructureFactor 1 (Support)Factor 2 (Certainty)
      1 I have the information that I need to be as healthy as possible..51
      2 I have the support that I need to be as healthy as possible..58.75
      3 I have access to the right health care providers to take care of my health..48.74
      4 I know what action to take if my health condition worsens..34
      5 If my health condition changes, I can get it back on track..53
      6 If my health condition changes, my health care providers can help me get it back on track..66.86
      7 If my health condition changes, my family can help me get it back on track..6.71
      8 If my health condition changes, my faith can help me get it back on track..38.5
      9 I can deal with my condition..56
      10 My health condition is stable and will not change much..46
      11 What I do now to take care of my health will help my health condition in the future..45
      12 I believe that I will die of old age..54
      13 I look forward to better health in the future..39
      14 I am not sure what to do to be healthy in the future..48.63
      15 I cannot pay for my health care as needed..46.64
      16 I don’t know what symptoms of my condition that I should watch for..25.44
      17 I get mixed messages from different providers about what I need to do to take care of my health.4.61
      18 I don’t know what to do if my condition worsens..41.78
      19 No one knows how hard it is to live with my condition..46.62
      20 My condition is dangerous on a day-to-day basis..51
      21 My condition is dangerous to me in the long run.56.58
      22 I do not believe that I will return to full physical functioning..42
      23 My health problems are not predictable..37
      24 It’s hard to know when I will have “good days” or “bad days” as far as my health problems go..37.39
      Figure thumbnail gr1
      Figure IFinal Version of Health Security in Chronic Illness
      One factor appeared to reflect “Support,” (α = .73), which measures how much an individual feels they are supported by various external sources in terms of their health (i.e., family, their faith). The second factor, “Certainty,” (α = .76), appears to reflect how much an individual feels internally secure about their health or future of their condition. The two subscales were moderately, positively correlated (r = .34). Scores at both timepoints were positively, moderately correlated for Certainty (r = .43), and Support (r = .67) suggesting test-retest reliability.

      Health Security in Chronic Illness: Analysis

      A 2 X 2 Mixed Model Analysis of Variance (ANOVA) was run to determine the effect of device type, time, and interaction effects on the newly constructed subscales.
      There was no significant main effect on device type for Support (F(1, 93) = .04, p = .84, η2 = .00) with ICD patients (M = 3.94) reporting receiving a similar level of support as compared to PM patients (M = 3.96). There was a significant main effect of time on Support (F(1, 93) = 11.6 , p < .001, η2 = .11) with baseline scores (M = 4.08) higher than follow-up scores (M = 3.82), suggesting that participants reported receiving less support over time regardless of device type. Interaction between device type and time in terms of Support didn’t reach significance (F(1, 93) = 3.8, p = .05, η2 = .04). There was a significant main effect of device type for Certainty (F(1, 89) = 6.36, p = .01, η2 = .07) with ICD patients (M = 3.41) reporting less certainty regarding their health than PM patients (M = 3.76). There was no significant main effect of time (F(1, 89) = 1.42, p = .24, η2 = .02). Also, there was not a significant interaction between device type and time in terms of Certainty (F(1, 89) = .01, p = .91, η2 = .00).

      Device Acceptance: Analysis

      A 2 X 2 mixed model ANOVA was used on device acceptance. There was no significant main effect for device type (F(1, 80) = 3.12, p = .08, η2 = .04), time (F(1, 80) = 3.6 , p = .06, η2 = .04), or their interaction (F(1, 80) = 1.19, p = .28, η2 = .02).

      Correlations Among Outcome Measures

      Correlations examined relationships between HSCI and existing heart patient measures. Certainty was positively, strongly correlated with device acceptance but not related to shock anxiety (for ICD). Support was not correlated with anything (see Table II).
      Table IICorrelation Matrix for Outcome Measures
      Column Label1234
      1. FPAS-
      2. FSAS−.37**-
      3. Certainty (HSCI).59**−.14-
      4. Support (HSCI).18.21.34**-
      Note. **p <.01; Florida Patient Acceptance Survey (FPAS), Florida Shock Anxiety Scale (FSAS), and Health Security in Chronic Illness (HSCI).

      Health Security in Chronic Illness: Final Scale

      The HSCI retained thirteen items and two subscales: Support and Certainty (see Figure I). All items and each subscale can be averaged for a total score. Higher scores indicate higher HSCI, Support, and Certainty.

      Discussion

      This study developed the novel construct HSCI with CIED patients. Patients reported less Support over time. As a standard, RPM patients are only contacted when there is an adverse event. This can lead patients to view the technology intended to reassure them as a stimulus of fear. Because this data suggests that support remains valuable to patients over time, providers could remind patients of their “safety net” (CIED) and health status at more frequent intervals.
      Certainty remained stable over time but ICD patients reported lower levels than PM patients. This difference may be due to disease severity or active device therapy (e.g., ICD shocks). Certainty was positively, strongly correlated with device acceptance but unrelated to shock anxiety for ICD patients. Individuals who are more trusting of their device may be more certain about their health. This is clinically relevant as uncertainty about future health and perceived support are associated with psychosocial distress and health behavior.

      Karataş, T, Bostanoğlu, H. Perceived social support and psychosocial adjustment in patients with coronary heart disease. Int J of Nurs Prac. 2017 Jun;23(4): e12558. https://doi.org/10.1111/ijn.12558

      The subscales Support and Certainty have intuitive value, and these constructs are often what shared decision-making attempts to accomplish in the pre-device period. An ongoing process for CIED patient may be needed.

      Barisone M, Hayter M, Ghirotto L, Catania G, Zanini M, Dal Molin A, Sasso L, Bagnasco A. The experience of patients with an implantable cardioverter-defibrillator: a systematic review and meta-synthesis of qualitative studies.Eur J Cardiovasc Nurs. 2022 Jan. https://doi.org/10.1093/eurjcn/zvab135

      Though beneficial, the increased reliance on RPM for CIED patients has reduced the opportunities for patient-provider discussions. Device function may inadvertently be prioritized over the validation of the patient experience. The availability of measures that assess constructs like HSCI may be a way for providers to monitor other important aspects of patient QOL.
      This study had several limitations: 1) it is unknown how HSCI relates to other constructs (aside FPAS and FSAS) or whether it could be more specific to device/disease type (i.e., number of shocks received, symptom burden), 2) a PAF and a CFA were used on the sample, limiting the confidence in the existing factor structure,

      Worthington R, Whittaker TA. Scale development research: A content analysis and recommendations for best practices. The Counseling Psychologist. 2006 May;34(6), 806-838. https://doi.org/10.1177/0011000006288127

      3) there was no control group, making it difficult to ascertain the extent that RPM or other chronic medical conditions interacted, 4) the sample was experienced with their device and may not experience as fluctuations in QOL, 5) patients were not involved in scale construction limiting patient-centered approach, 6) HSCI should be examined beyond rural Canada and 7) RPM patients may have unique considerations. However, other research suggests that RPM does not affect PROs such as device acceptance (which is strongly correlated with Certainty in the current study).

      Barisone M, Hayter M, Ghirotto L, Catania G, Zanini M, Dal Molin A, Sasso L, Bagnasco A. The experience of patients with an implantable cardioverter-defibrillator: a systematic review and meta-synthesis of qualitative studies.Eur J Cardiovasc Nurs. 2022 Jan. https://doi.org/10.1093/eurjcn/zvab135

      ,

      Versteeg H, Timmermans I, Widdershoven J, Kimman, G-J, Prevot S, Rauwolf T, Scholten MF, Zitron E, Mabo P, Denollet, J, Pedersen SS, Meine M. Effect of remote monitoring on patient-reported outcomes in European heart failure patients with an implantable cardioverter-defibrillator: primary results of the REMOTE-CIED randomized trial. EP Europace. 2019 Sept; 21(9), 1360–1368. https://doi.org/10.1093/europace/euz140

      Even when RPM is added to usual care, PROs such as anxiety, depression, and device acceptance did not change.

      Leppert F, Siebermair J, Wesemann U, Martens E, Sattler SM, Scholz S, Veith S, Greiner W, Rassaf T, Kääb S, Wakili R. The INFluence of Remote monitoring on Anxiety/depRession, quality of lifE, and Device acceptance in ICD patients: a prospective, randomized, controlled, single-center trial. Clin Res Cardiol. 2021 Jun;110(6):789-800. https://doi.org/10.1007/s00392-020-01667-0

      It would be beneficial to examine HSCI with a longitudinal approach, use of a control group or non-RPM sample, a larger, diverse sample, or differing device implementation times (pre-, post-implant, and follow-up), examination of shock history for ICD patients, and applicability to other chronic diseases.
      Overall, this pilot study provided further specificity for QOL among CIED patients.

      Uncited reference

      Versteeg H, Starrenburg A, Denollet J, Palen JVD, Sears SF, Pedersen SS. Monitoring device acceptance in implantable cardioverter defibrillator patients using the Florida Patient Acceptance Survey.Pacing Clin Electrophysiol. 2012 Jan;35(3): 283-293. https://doi.org/10.1111/j.1540-8159.2011.03299.x

      .

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        • Mark D.B.
        • Lee K.L.
        • Kadish A.
        • Bardy G.
        • Sanders G.D.
        Primary prevention implantable cardioverter defibrillators in patients with nonischemic cardiomyopathy: a meta-analysis.
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        View in Article

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        View in Article

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        View in Article

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        View in Article

      7. Sapp JA, Gillis AM, AbdelWahab A, Nault I, Nery PB, Healey JS, Raj SR, Lockwood E, Sterns LD, Sears SF, Wells GA. Remote-only monitoring for patients with cardiac implantable electronic devices: a before-and-after pilot study.Can Med Assoc J. 2021 Jan;9(1): E53-E61. doi: 10.9778/cmajo.20200041

        View in Article

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        View in Article

      9. Kuhl EA, Dixit NK, Walker RL, Conti JB, Sears SF. Measurement of patient fears about implantable cardioverter defibrillator shock: an initial evaluation of the Florida Shock Anxiety Scale.Pacing Clin Electrophysiol. 2006 Jun;29(6):614-618. https://doi.org/10.1111/j.1540-8159.2006.00408.x

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        View in Article

      11. Karataş, T, Bostanoğlu, H. Perceived social support and psychosocial adjustment in patients with coronary heart disease. Int J of Nurs Prac. 2017 Jun;23(4): e12558. https://doi.org/10.1111/ijn.12558

        View in Article

      12. Barisone M, Hayter M, Ghirotto L, Catania G, Zanini M, Dal Molin A, Sasso L, Bagnasco A. The experience of patients with an implantable cardioverter-defibrillator: a systematic review and meta-synthesis of qualitative studies.Eur J Cardiovasc Nurs. 2022 Jan. https://doi.org/10.1093/eurjcn/zvab135

        View in Article

      13. Worthington R, Whittaker TA. Scale development research: A content analysis and recommendations for best practices. The Counseling Psychologist. 2006 May;34(6), 806-838. https://doi.org/10.1177/0011000006288127

        View in Article

      14. Versteeg H, Timmermans I, Widdershoven J, Kimman, G-J, Prevot S, Rauwolf T, Scholten MF, Zitron E, Mabo P, Denollet, J, Pedersen SS, Meine M. Effect of remote monitoring on patient-reported outcomes in European heart failure patients with an implantable cardioverter-defibrillator: primary results of the REMOTE-CIED randomized trial. EP Europace. 2019 Sept; 21(9), 1360–1368. https://doi.org/10.1093/europace/euz140

        View in Article

      15. Leppert F, Siebermair J, Wesemann U, Martens E, Sattler SM, Scholz S, Veith S, Greiner W, Rassaf T, Kääb S, Wakili R. The INFluence of Remote monitoring on Anxiety/depRession, quality of lifE, and Device acceptance in ICD patients: a prospective, randomized, controlled, single-center trial. Clin Res Cardiol. 2021 Jun;110(6):789-800. https://doi.org/10.1007/s00392-020-01667-0

        View in Article

      Article info

      Publication history

      Accepted: May 10, 2023
      Received in revised form: May 8, 2023
      Received: November 7, 2022

      Publication stage

      In Press Journal Pre-Proof

      Footnotes

      Disclosures and Acknowledgements: Samuel F. Sears serves as a consultant to Medtronic, Abbott, Milestone Pharmaceutical, and Zoll Medical. He has received honorarium from Medtronic and Zoll Medical in the past 12 months. All other authors have no conflicts of interest or disclosures regarding financial activities, relationships, and affiliations to report.

      Funding was provided by Cardiac Arrhythmia Network of Canada (CANet)

      Identification

      DOI: https://doi.org/10.1016/j.cjco.2023.05.005

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      © 2023 Published by Elsevier Inc. on behalf of the Canadian Cardiovascular Society.

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