Use of CIED Generated Heart Failure Risk Score (HFRS) Alerts in an Integrated, Multi-Disciplinary Approach to HF Management—A Prospective Cohort Study
Abstract
:1. Background (Introduction)
2. Objectives
3. Methods
3.1. Setting
3.2. CIED Diagnostics
3.3. Study Design
3.4. Protocol
3.5. Setting up of Co-Management Clinic
3.6. Outcomes
3.7. Data Collection
3.8. Statistical Analysis
3.9. Ethics
4. Results
4.1. Patient Contact
4.2. Response to “High Risk” Alerts
4.3. Patient Outcomes
4.4. Factors Predicting Adverse Outcome
4.5. End User Experience
5. Discussion
5.1. Workload
5.2. Limitations
5.3. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A
- Have you experienced any new or worsening breathlessness?
- Have you experienced any new or worsening leg swelling?
- Have you gained any weight?
- Have you experienced any new or worsening fatigue?
- Have you been experiencing any recent ill health or visited a doctor or nurse about anything else?
Appendix B
- How do you rate the overall quality of the telemedicine consultation?
- Excellent
- Good
- Fair
- Poor
- How would you rate the technical quality of the telemedicine consultation?
- Excellent
- Good
- Fair
- Poor
- How do you rate the quality of care delivered by the telemedicine service when compared to the quality of traditional care?
- Better
- About the same
- Not as good
- Not sure
- Were you comfortable during the telemedicine consultation?
- Yes, very comfortable
- Yes, somewhat comfortable
- No, somewhat uncomfortable
- No, very uncomfortable
- Do you feel that the telemedicine consultation service may influence the health status of your patients?
- Improved health
- No change
- Negative effects on health
- Did you experience technical difficulties that might affect the quality of care delivered by the telemedicine service?
- Not at all
- Sometimes
- Often
- Did you experience organisational or other difficulties that might affect the quality of care delivered by the telemedicine service?
- Not at all
- Sometimes
- Often
- Would you continue to use the telemedicine service?
- Yes, in the same way as the service has been deployed
- Yes, but with improvements
- No
References
- Cleland, J.G.; Abraham, W.T.; Linde, C.; Gold, M.R.; Young, J.B.; Claude Daubert, J.; Sherfesee, L.; Wells, G.A.; Tang, A.S. An individual patient meta-analysis of five randomized trials assessing the effects of cardiac resynchronization therapy on morbidity and mortality in patients with symptomatic heart failure. Eur. Heart J. 2013, 34, 3547–3556. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Klersy, C.; Boriani, G.; De Silvestri, A.; Mairesse, G.H.; Braunschweig, F.; Scotti, V.; Balduini, A.; Cowie, M.R.; Leyva, F.; Health Economics Committee of the European Heart Rhythm Association. Effect of telemonitoring of cardiac implantable electronic devices on healthcare utilization: A meta-analysis of randomized controlled trials in patients with heart failure. Eur. J. Heart Fail. 2016, 18, 195–204. [Google Scholar] [CrossRef] [PubMed]
- Nieminen, M.S.; Dickstein, K.; Fonseca, C.; Serrano, J.M.; Parissis, J.; Fedele, F.; Wikstrom, G.; Agostoni, P.; Atar, S.; Baholli, L.; et al. The patient perspective: Quality of life in advanced heart failure with frequent hospitalisations. Int. J. Cardiol. 2015, 191, 256–264. [Google Scholar] [CrossRef] [PubMed]
- National Heart Failure Audit: 2019 Summary Report (2017/18 Data); National Institute for Cardiovascular Outcomes Research (NICOR): London, UK, 2019.
- Sankaranarayanan, R.; Hartshorne-Evans, N.; Redmond-Lyon, S.; Wilson, J.; Essa, H.; Gray, A.; Clayton, L.; Barton, C.; Ahmed, F.Z.; Cunnington, C.; et al. The impact of COVID-19 on the management of heart failure: A United Kingdom patient questionnaire study. ESC Heart Fail. 2021, 8, 1324–1332. [Google Scholar] [CrossRef]
- Bohm, M.; Drexler, H.; Oswald, H.; Rybak, K.; Bosch, R.; Butter, C.; Klein, G.; Gerritse, B.; Monteiro, J.; Israel, C.; et al. Fluid status telemedicine alerts for heart failure: A randomized controlled trial. Eur. Heart J. 2016, 37, 3154–3163. [Google Scholar] [CrossRef] [Green Version]
- Cowie, M.R.; Sarkar, S.; Koehler, J.; Whellan, D.J.; Crossley, G.H.; Tang, W.H.; Abraham, W.T.; Sharma, V.; Santini, M. Development and validation of an integrated diagnostic algorithm derived from parameters monitored in implantable devices for identifying patients at risk for heart failure hospitalization in an ambulatory setting. Eur. Heart J. 2013, 34, 2472–2480. [Google Scholar] [CrossRef]
- Morgan, J.M.; Kitt, S.; Gill, J.; McComb, J.M.; Ng, G.A.; Raftery, J.; Roderick, P.; Seed, A.; Williams, S.G.; Witte, K.K.; et al. Remote management of heart failure using implantable electronic devices. Eur. Heart J. 2017, 38, 2352–2360. [Google Scholar] [CrossRef] [Green Version]
- Whellan, D.J.; Ousdigian, K.T.; Al-Khatib, S.M.; Pu, W.; Sarkar, S.; Porter, C.B.; Pavri, B.B.; O’Connor, C.M.; Investigators, P.S. Combined heart failure device diagnostics identify patients at higher risk of subsequent heart failure hospitalizations: Results from PARTNERS HF (Program to Access and Review Trending Information and Evaluate Correlation to Symptoms in Patients With Heart Failure) study. J. Am. Coll. Cardiol. 2010, 55, 1803–1810. [Google Scholar] [CrossRef] [Green Version]
- Virani, S.A.; Sharma, V.; McCann, M.; Koehler, J.; Tsang, B.; Zieroth, S. Prospective evaluation of integrated device diagnostics for heart failure management: Results of the TRIAGE-HF study. ESC Heart Fail. 2018, 5, 809–817. [Google Scholar] [CrossRef]
- Brahmbhatt, D.H.; Cowie, M.R. Remote Management of Heart Failure: An Overview of Telemonitoring Technologies. Card. Fail. Rev. 2019, 5, 86–92. [Google Scholar] [CrossRef]
- Ponikowski, P.; Voors, A.A.; Anker, S.D.; Bueno, H.; Cleland, J.G.; Coats, A.J.; Falk, V.; Gonzalez-Juanatey, J.R.; Harjola, V.P.; Jankowska, E.A.; et al. 2016 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure. Kardiol. Pol. 2016, 74, 1037–1147. [Google Scholar] [CrossRef] [Green Version]
- Zanotto, G.; Melissano, D.; Baccillieri, S.; Campana, A.; Caravati, F.; Maines, M.; Platania, F.; Zuccaro, L.; Landolina, M.; Berisso, M.Z.; et al. Intrahospital organizational model of remote monitoring data sharing, for a global management of patients with cardiac implantable electronic devices: A document of the Italian Association of Arrhythmology and Cardiac Pacing. J. Cardiovasc. Med. 2020, 21, 171–181. [Google Scholar] [CrossRef]
- Wilkoff, B.L.; Auricchio, A.; Brugada, J.; Cowie, M.; Ellenbogen, K.A.; Gillis, A.M.; Hayes, D.L.; Howlett, J.G.; Kautzner, J.; Love, C.J.; et al. HRS/EHRA expert consensus on the monitoring of cardiovascular implantable electronic devices (CIEDs): Description of techniques, indications, personnel, frequency and ethical considerations. Heart Rhythm. 2008, 5, 907–925. [Google Scholar] [CrossRef]
- Bossuyt, P.M.; Cohen, J.F.; Gatsonis, C.A.; Korevaar, D.A.; The STARD Group. STARD 2015: Updated reporting guidelines for all diagnostic accuracy studies. Ann. Transl. Med. 2016, 4, 85. [Google Scholar] [CrossRef]
- Rockwood, K.; Song, X.; MacKnight, C.; Bergman, H.; Hogan, D.B.; McDowell, I.; Mitnitski, A. A global clinical measure of fitness and frailty in elderly people. CMAJ 2005, 173, 489–495. [Google Scholar] [CrossRef] [Green Version]
- Charlson, M.; Szatrowski, T.P.; Peterson, J.; Gold, J. Validation of a combined comorbidity index. J. Clin. Epidemiol. 1994, 47, 1245–1251. [Google Scholar] [CrossRef]
- Vitale, C.; Jankowska, E.; Hill, L.; Piepoli, M.; Doehner, W.; Anker, S.D.; Lainscak, M.; Jaarsma, T.; Ponikowski, P.; Rosano, G.M.C.; et al. Heart Failure Association/European Society of Cardiology position paper on frailty in patients with heart failure. Eur. J. Heart Fail. 2019, 21, 1299–1305. [Google Scholar] [CrossRef]
- Formiga, F.; Moreno-Gonzalez, R.; Chivite, D.; Franco, J.; Montero, A.; Corbella, X. High comorbidity, measured by the Charlson Comorbidity Index, associates with higher 1-year mortality risks in elderly patients experiencing a first acute heart failure hospitalization. Aging Clin. Exp. Res. 2018, 30, 927–933. [Google Scholar] [CrossRef]
- Vidal-Alaball, J.; Mateo, G.F.; Domingo, J.L.G.; Gomez, X.M.; Valmana, G.S.; Ruiz-Comellas, A.; Segui, F.L.; Cuyas, F.G. Validation of a Short Questionnaire to Assess Healthcare Professionals’ Perceptions of Asynchronous Telemedicine Services: The Catalan Version of the Health Optimum Telemedicine Acceptance Questionnaire. Int. J. Environ. Res. Public Health 2020, 17, 2202. [Google Scholar] [CrossRef] [Green Version]
- Boehmer, J.P.; Hariharan, R.; Devecchi, F.G.; Smith, A.L.; Molon, G.; Capucci, A.; An, Q.; Averina, V.; Stolen, C.M.; Thakur, P.H.; et al. A Multisensor Algorithm Predicts Heart Failure Events in Patients With Implanted Devices: Results From the MultiSENSE Study. JACC Heart Fail. 2017, 5, 216–225. [Google Scholar] [CrossRef]
- Armstrong, P.W.; Pieske, B.; Anstrom, K.J.; Ezekowitz, J.; Hernandez, A.F.; Butler, J.; Lam, C.S.P.; Ponikowski, P.; Voors, A.A.; Jia, G.; et al. Vericiguat in Patients with Heart Failure and Reduced Ejection Fraction. N. Engl. J. Med. 2020, 382, 1883–1893. [Google Scholar] [CrossRef]
- Manlucu, J.; Sharma, V.; Koehler, J.; Warman, E.N.; Wells, G.A.; Gula, L.J.; Yee, R.; Tang, A.S. Incremental Value of Implantable Cardiac Device Diagnostic Variables over Clinical Parameters to Predict Mortality in Patients with Mild to Moderate Heart Failure. J. Am. Heart Assoc. 2019, 8, e010998. [Google Scholar] [CrossRef] [Green Version]
- Ahmed, F.Z.; Taylor, J.K.; Green, C.; Moore, L.; Goode, A.; Black, P.; Howard, L.; Fullwood, C.; Zaidi, A.; Seed, A.; et al. Triage-HF Plus: A novel device-based remote monitoring pathway to identify worsening heart failure. ESC Heart Fail. 2020, 7, 107–116. [Google Scholar] [CrossRef] [Green Version]
- Boriani, G.; Da Costa, A.; Ricci, R.P.; Quesada, A.; Favale, S.; Iacopino, S.; Romeo, F.; Risi, A.; Mangoni Di, S.S.L.; Navarro, X.; et al. The MOnitoring Resynchronization dEvices and CARdiac patiEnts (MORE-CARE) randomized controlled trial: Phase 1 results on dynamics of early intervention with remote monitoring. J. Med. Internet Res. 2013, 15, e167. [Google Scholar] [CrossRef] [Green Version]
No of Patients (n = 188) | ||
---|---|---|
Male | 147 | 78% |
Female | 41 | 22% |
Mean age (±SD) | 70.3 years | ±11.5 |
Aetiology | ||
Ischaemic cardiomyopathy | 105 | 56% |
Non-Ischaemic | 74 | 49% |
Congenital/valvular | 9 | 5% |
Device | ||
CRTD | 176 | 94% |
CRTP | 9 | 5% |
ICD | 3 | 1% |
Number of alerts | 365 | |
Mean alerts per patient | 1.9 | |
Patients with single alert | 101 | 54% |
Patients with 2 alerts | 43 | 23% |
>3 | 44 | |
Medical therapy | ||
ACE/ARB | 126 | 67% |
ARNI | 34 | 18% |
Beta blocker | 175 | 93% |
MRA | 116 | 62% |
Diuretic | 135 | 72% |
Diabetes | 71 | 38% |
Mean BMI (±S.D) | 29.6 | ±6.2 |
Clinical frailty score | ||
Mean score (±S.D) | 4.1 | ±1.5 |
>6 | 25 | 14% |
Charleson Comorbidity score | ||
Mean score (±S.D) | 5.5 | ±2.3 |
>6 | 92 | 49% |
Responses to High Risk Alerts | n = 367 | |
---|---|---|
Telephone contact made | 303 | 83% |
No intervention required | 128 | 35% |
Asymptomatic | 68 | 19% |
Cardiac compass improving | 49 | 13% |
Alert previously actioned | 11 | 3% |
Reviewed by Heart failure nurses | 85 | 23% |
Referral to cardiology for review | 47 | 13% |
Referral to GP to further action | 21 | 6% |
Referral to palliative care | 4 | 1% |
Inpatient during alert | 18 | 5% |
Patient outcomes | n = 188 | |
Unplanned hospital admission | 53 | 28% |
Heart failure admission | 24 | 13% |
Death | 33 | 18% |
Elective admission | 23 | 12% |
AV node ablation | 5 | 3% |
Upgrade of device | 2 | 1% |
Box change | 7 | 4% |
IV iron therapy | 4 | 2% |
Other (DCCV, downgrade, LAAO, EBUS) | 5 | 3% |
Device therapy | 15 | 8% |
Device deactivation | 18 | 10% |
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Garner, D.; Lunt, L.; Leung, W.; Llewellyn, J.; Kahn, M.; Wright, D.J.; Rao, A. Use of CIED Generated Heart Failure Risk Score (HFRS) Alerts in an Integrated, Multi-Disciplinary Approach to HF Management—A Prospective Cohort Study. Sensors 2022, 22, 1825. https://doi.org/10.3390/s22051825
Garner D, Lunt L, Leung W, Llewellyn J, Kahn M, Wright DJ, Rao A. Use of CIED Generated Heart Failure Risk Score (HFRS) Alerts in an Integrated, Multi-Disciplinary Approach to HF Management—A Prospective Cohort Study. Sensors. 2022; 22(5):1825. https://doi.org/10.3390/s22051825
Chicago/Turabian StyleGarner, Daniel, Lindsay Lunt, Wing Leung, Jennifer Llewellyn, Matthew Kahn, David Jay Wright, and Archana Rao. 2022. "Use of CIED Generated Heart Failure Risk Score (HFRS) Alerts in an Integrated, Multi-Disciplinary Approach to HF Management—A Prospective Cohort Study" Sensors 22, no. 5: 1825. https://doi.org/10.3390/s22051825