INTERVENE-HF: feasibility study of individualized, risk stratification-based, medication intervention in patients with heart failure with reduced ejection fraction

Michael R Zile, Maria Rosa R Costanzo, Ekaterina M Ippolito, Yan Zhang, Russell Stapleton, Ashish Sadhu, Javier Jimenez, Joe Hobbs, Vinod Sharma, Eduardo N Warman, Lindsay Streeter, Javed Butler, Michael R Zile, Maria Rosa R Costanzo, Ekaterina M Ippolito, Yan Zhang, Russell Stapleton, Ashish Sadhu, Javier Jimenez, Joe Hobbs, Vinod Sharma, Eduardo N Warman, Lindsay Streeter, Javed Butler

Abstract

Aims: Determine the feasibility of implementing a heart failure (HF) management strategy that (i) uses a device-based, remote, dynamic, multimetric risk stratification model to predict the risk of HF events and (ii) uses a standardized, centrally administered, ambulatory medication intervention protocol to reproducibly and safely decrease elevated risk scores.

Methods and results: Prospective, non-randomized, single-arm, multicenter feasibility study (Intervene-HF) was conducted in HF patients implanted with a cardiac resynchronization therapy with implantable cardio defibrillator (CRT-D) with TriageHF risk score feature. Certified HF nurses (CHFN) in the Medtronic Care Management Services Program implemented an ambulatory medication intervention strategy by following a standardized guided action pathway triggered by risk-based alert. When CHFN received notification of increased risk score (HF care alert), they implemented a 3 day course of diuretic up-titration (PRN) previously prescribed by a physician. Safety was monitored daily. Recovery after PRN was defined as ≥70% recovery of impedance toward baseline levels. Sixty-six patients followed for 8.2 ± 3.9 months had 49 HF care alerts. Twenty-three of 49 alerts did not receive PRN due to protocol-mandated criteria. Twenty-six of 49 alerts received PRN, 22 were completed, and 19 led to impedance recovery. Four interventions were stopped for safety without leading to an adverse event (AE). One of 26 PRNs was followed by a HF event. Eighty-five per cent (22/26) of PRNs were completed without an AE; 69% (18/26) met the recovery criteria.

Conclusions: The Intervene-HF study supports the feasibility of testing, in a large randomized clinical trial, an ambulatory medication intervention strategy that is physician-directed, CHFN-implemented, and based on individualized device risk stratification.

Trial registration: ClinicalTrials.gov NCT02698241.

Keywords: Congestive; Heart failure; Remote metric.

Conflict of interest statement

Drs Zile, Costanzo, Butler, Stapleton, Sadhu, Jimenez served as consultants to Medtronic. Zhang, Ippolito, Sharma, Warman, Hobbs, and Streeter are Medtronic Inc. employees.

© 2021 The Authors. ESC Heart Failure published by John Wiley & Sons Ltd on behalf of European Society of Cardiology.

Figures

Figure 1
Figure 1
Intervene‐HF study information flow chart. This strategy entails a physician‐directed, nurse‐implemented, ambulatory, medication intervention described in detail in the manuscript text.
Figure 2
Figure 2
Guided PRN medication intervention pathway. Patients were assessed by certified heart failure nurses to determine whether the increased risk could/should be intervened upon using the ‘patient assessment’ algorithm shown. If a PRN medication intervention was appropriate to be instituted, the ‘PRN medication intervention’ algorithm was followed by certified heart failure nurses.
Figure 3
Figure 3
Schematic representation of guided action pathway. A heart failure care alert based on integrated TriageHF risk score and peripheral biometric data were used by CHFNs to implement PRNs and monitor safety and attainment of recovery criterion (recovery criterion presented in Figure4). CHFNs interaction with and notification of clinical provider team is also denoted. BP, blood pressure; CHFNs, certified heart failure nurses.
Figure 4
Figure 4
Recovery criterion resulting from PRNs. Recovery criterion after PRN medication intervention was based on the presence of a ≥70% recovery of impedance (daily impedance) toward baseline values (reference impedance). HF, heart failure.
Figure 5
Figure 5
Consort diagram: enrolment, screening, and follow‐up. The number of patients enrolled, screen/eligibility failed, length of follow‐up, completed follow‐up, and exited patients are listed. HF, heart failure.
Figure 6
Figure 6
Consort diagram: HF care alerts and PRN interventions. The number of HF care alerts occurring in the number of patients, their PRN medication interventions, impedance recoveries, and outcomes are listed. HF, heart failure.

References

    1. Whellan DJ, Ousdigian KT, Al‐Khatib SM, Pu W, Sarkar S, Porter CB, Pavri BB, O'Connor CM, PARTNERS Study Investigators . 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.
    1. Sarkar S, Koehler J. A dynamic risk score to identify increased risk for heart failure decompensation. IEEE Trans Biomed Eng 2012; 60: 147–150.
    1. Whellan DJ, Sarkar S, Koehler J, Small RS, Boyle A, Warman EN, Abraham WT. Development of a method to risk stratify patients with heart failure for 30‐day readmission using implantable device diagnostics. Am J Cardiol 2013; 111: 79–84.
    1. Cowie MR, Sarkar S, Koehler J, Whellan DJ, Crossley GH, Tang WHW, Abraham WT, 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.
    1. Gula LJ, Wells GA, Yee R, Koehler J, Sarkar S, Sharma V, Skanes AC, Sapp JL, Redfearn DP, Manlucu J, Tang ASL. A novel algorithm to assess risk of heart failure exacerbation using ICD diagnostics: validation from RAFT. Heart Rhythm 2014; 11: 1626–1631.
    1. Boehmer JP, Hariharan R, Devecchi FG, Smith AL, Molon G, Capucci A, An Q, Averina V, Stolen CM, Thakur PH, Thompson JA. A multisensor algorithm predicts heart failure events in patients with implanted devices: results from the MultiSENSE study. J Am Coll Cardiol 2017; 27: 216–225.
    1. Burri H, da Costa A, Quesada A, Ricci RP, Favale S, Clementy N, Boscolo G, Villalobos FS, di Mangoni S, Stefano L, Sharma V, Boriani G. Risk stratification of cardiovascular and heart failure hospitalizations using integrated device diagnostics in patients with a cardiac resynchronization therapy defibrillator. EP Europace 2018; 20: e69–e77.
    1. Virani SA, 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.
    1. Gardner RS, Singh JP, Stancak B, Nair DG, Cao M, Schulze C, Thakur PH, An Q, Wehrenberg S, Hammill EF, Zhang Y. HeartLogic multisensor algorithm identifies patients during periods of significantly increased risk of heart failure events: results from the MultiSENSE study. Circ Heart Fail 2018; 11: e004669.
    1. Ahmed FZ, Taylor JK, Green C, Moore L, Goode A, Black P, Howard L, Fullwood C, Zaidi A, Seed A, Cunnington C, Motwani M. Triage‐HF Plus: a novel device‐based remote monitoring pathway to identify worsening heart failure. ESC Heart Fail 2020; 7: 108–117.
    1. Zile MR, Sharma V, Johnson JW, Warman EN, Baicu CF, Bennett TD. Prediction of all‐cause mortality based on the direct measurement of intrathoracic impedance. Circ Heart Fail 2016; 9: e002543.
    1. Zile MR, Koehle J, Sarkar S, Butler J. Prediction of worsening heart failure events and all‐cause mortality using an individualized risk stratification strategy. ESC Heart Failure in press; 2020.
    1. Böhm M, Drexler H, Oswald H, Rybak K, Bosch R, Butter C, Klein G, Gerritse B, Monteiro J, Israel C, Bimmel D, Käab S, Huegl B, Brachmann J, OptiLink HF Study Investigators . Fluid status telemedicine alerts for heart failure: a randomized controlled trial. Eur Heart J 2016; 37: 3154–3163.
    1. Zile MR, Sharma V, Baicu CF, Koehler J, Tang AS. Prediction of heart failure hospitalizations based on the direct measurement of intrathoracic impedance. ESC Heart Fail 2020; 7: 3040–3048.
    1. Bourge RC, Abraham WT, Adamson PB, Aaron MF, Aranda JM Jr, Magalski A, Zile MR, Smith AL, Smart FW, O'Shaughnessy MA, Jessup ML, Sparks B, Naftel DL, Stevenson LW, COMPASS‐HF Study Group . Randomized controlled trial of an implantable continuous hemodynamic monitor in patients with advanced heart failure: the COMPASS‐HF study. J Am Coll Cardiol 2008; 51: 1073–1079.
    1. van Veldhuisen DJ, Braunschweig F, Conraads V, Ford I, Cowie MR, Jondeau G, Kautzner J, Muñoz Aguilera R, Lunati M, Yu CM, Gerritse B, Borggrefe M, for the DOT‐HF Investigators . Intrathoracic impedance monitoring, audible patient alerts, and outcome in patients with heart failure. Circulation 2011; 124: 1719–1726.
    1. Adamson PB, Gold MR, Bennett T, Bourge RC, Stevenson LW, Trupp R, Stromberg K, Wilkoff BL, Costanzo MR, Luby A, Aranda JM, Heywood JT, Baldwin HA, Aaron M, Smith A, Zile M. Continuous hemodynamic monitoring in patients with mild to moderate heart failure: results of the Reducing Decompensation Events Utilizing Intracardiac Pressures in Patients with Chronic Heart Failure (REDUCEhf) trial. Congest Heart 2011; 17: 248–254.
    1. Abraham WT, Adamson PB, Bourge RC, Aaron MF, Costanzo MR, Stevenson LW, Strickland W, Neelagaru S, Raval N, Krueger S, Weiner S, Shavelle D, Jeffries B, Yadav JS. Wireless pulmonary artery haemodynamic monitoring in chronic heart failure: a randomized controlled trial. Lancet 2011; 377: 658–666.

Source: PubMed

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