A multiparametric ICD algorithm for heart failure risk stratification and management: an analysis in clinical practice

EP Europace ◽  
2021 ◽  
Vol 23 (Supplement_3) ◽  
Author(s):  
L Calo" ◽  
V Bianchi ◽  
D Ferraioli ◽  
L Santini ◽  
A Dello Russo ◽  
...  
Keyword(s):  
Heart Failure ◽  
At Risk ◽  
Clinical Practice ◽  
Risk Stratification ◽  
Management Strategies ◽  
Cardiac Resynchronization ◽  
Office Visits ◽  
Increased Risk ◽  
Patients At Risk ◽  
Observation Period

Abstract Funding Acknowledgements Type of funding sources: None. Introduction The HeartLogic algorithm combines multiple implantable cardioverter defibrillator (ICD) sensors to identify patients at risk of heart failure (HF) events. Purpose We sought to evaluate the risk stratification ability of this algorithm in clinical practice. We also analyzed the alert management strategies adopted in the study group and their association with the occurrence of HF events. Methods The HeartLogic feature was activated in 366 ICD and cardiac resynchronization therapy ICD patients at 22 centers. The HeartLogic algorithm automatically calculates a daily HF index and identifies periods IN or OUT of an alert state on the basis of a configurable threshold (in this analysis set to 16). Results The HeartLogic index crossed the threshold value 273 times (0.76 alerts/patient-year) in 150 patients over a median follow-up of 11 months [25-75 percentile: 6-16]. Overall, the time IN the alert state was 11% of the total observation period. Patients experienced 36 HF hospitalizations and 8 patients died of HF (rate: 0.12 events/patient-year) during the observation period. Thirty-five events were associated with the IN alert state (0.92 events/patient-year versus 0.03 events/patient-year in the OUT of alert state). The hazard ratio in the IN/OUT of alert state comparison was (HR: 24.53, 95% CI: 8.55-70.38, p < 0.001), after adjustment for baseline clinical confounders. Alerts followed by clinical actions were associated with a lower rate of HF events (HR: 0.37, 95% CI: 0.14-0.99, p = 0.047). No differences in event rates were observed between in-office and remote alert management. By contrast, verification of HF symptoms during post-alert examination was associated with a higher risk of HF events (HR: 5.23, 95% CI: 1.98-13.83, p < 0.001). Conclusions This multiparametric ICD algorithm identifies patients during periods of significantly increased risk of HF events. The rate of HF events seemed lower when clinical actions were undertaken in response to alerts. Extra in-office visits did not seem to be required in order to effectively manage HeartLogic alerts, while post-alert verification of symptoms seemed useful in order to better stratify patients at risk of HF events.

scholarly journals Multiparametric Implantable Cardioverter-Defibrillator Algorithm for Heart Failure Risk Stratification and Management: An Analysis in Clinical Practice

2021 ◽  
Author(s):  
Leonardo Calò ◽  
Valter Bianchi ◽  
Donatella Ferraioli ◽  
Luca Santini ◽  
Antonio Dello Russo ◽  
...  
Keyword(s):  
Heart Failure ◽  
Clinical Practice ◽  
Risk Stratification ◽  
Implantable Cardioverter Defibrillator ◽  
Hazard Ratio ◽  
Cardiac Resynchronization ◽  
Office Visits ◽  
Cardioverter Defibrillator ◽  
Increased Risk ◽  
Observation Period

Background: The HeartLogic algorithm combines multiple implantable cardioverter-defibrillator sensors to identify patients at risk of heart failure (HF) events. We sought to evaluate the risk stratification ability of this algorithm in clinical practice. We also analyzed the alert management strategies adopted in the study group and their association with the occurrence of HF events. Methods: The HeartLogic feature was activated in 366 implantable cardioverter-defibrillator and cardiac resynchronization therapy implantable cardioverter-defibrillator patients at 22 centers. The median follow-up was 11 months [25th–75th percentile: 6–16]. The HeartLogic algorithm calculates a daily HF index and identifies periods IN alert state on the basis of a configurable threshold. Results: The HeartLogic index crossed the threshold value 273 times (0.76 alerts/patient-year) in 150 patients. The time IN alert state was 11% of the total observation period. Patients experienced 36 HF hospitalizations, and 8 patients died of HF during the observation period. Thirty-five events were associated with the IN alert state (0.92 events/patient-year versus 0.03 events/patient-year in the OUT of alert state). The hazard ratio in the IN/OUT of alert state comparison was (hazard ratio, 24.53 [95% CI, 8.55–70.38], P <0.001), after adjustment for baseline clinical confounders. Alerts followed by clinical actions were associated with less HF events (hazard ratio, 0.37 [95% CI, 0.14–0.99], P =0.047). No differences in event rates were observed between in-office and remote alert management. Conclusions: This multiparametric algorithm identifies patients during periods of significantly increased risk of HF events. The rate of HF events seemed lower when clinical actions were undertaken in response to alerts. Extra in-office visits did not seem to be required to effectively manage HeartLogic alerts. REGISTRATION: URL: https://www.clinicaltrials.gov ; Unique identifier: NCT02275637.

A multiparametric ICD algorithm for heart failure risk stratification: an analysis in clinical practice

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
L Calo ◽  
M Manzo ◽  
L Santini ◽  
A Dello Russo ◽  
V.E Santobuono ◽  
...  
Keyword(s):  
Heart Failure ◽  
Clinical Practice ◽  
Risk Stratification ◽  
Left Ventricular ◽  
Cardiac Resynchronization ◽  
Left Ventricular Ejection ◽  
Ventricular Ejection Fraction ◽  
Increased Risk ◽  
Observation Period

Abstract Purpose A novel multiparametric algorithm based on implantable cardioverter defibrillator (ICD) sensors has been recently developed. The HeartLogic index combines multiple parameters, i.e. heart sounds, intrathoracic impedance, respiration pattern, night heart rate, and patient activity, in a single index. In the validation study, the HeartLogic alert condition was shown to identify patients during periods of significantly increased risk of heart failure (HF) events. We sought to evaluate the risk stratification ability of the algorithm in a group of patients who received the system in clinical practice. Methods The HeartLogic feature was activated in 257 ICD and cardiac resynchronization therapy ICD patients (186 male, 70±11 years, left ventricular ejection fraction 30±8%) at 11 centers. The median follow-up duration was 14 months [25–75 percentile: 7–19]. The HeartLogic algorithm automatically calculates a daily HF index and identifies periods in or out of an alert state relative to a configurable threshold (in this analysis set to 16). Results Patients experienced 40 HF hospitalizations requiring at least 1 overnight stay (0.14/patient-year) during the observation period (285 patient-years). The HeartLogic index crossed the threshold value 191 times in 105 patients. The time in the alert state was 27 patient-years, i.e. 9.5% of the total observation period. HF hospitalization rate while in alert was 0.96/patient-year and 0.05/patient-year while out of alert. The occurrence of ≥1 index crossing during follow-up was associated with the risk of HF hospitalization (odds ratio: 4.70, CI 95%: 1.79–12.4, p=0.002), independently from other baseline clinical variables. Conclusions Our analysis of data collected in clinical practice confirms that the multiparametric ICD algorithm is an independent predictor of higher risk of HF. In particular, it allows dynamic identification of time-intervals when patients are at significantly increased risk of worsening HF. This potentially helps better triage resources to a more vulnerable patient population. Funding Acknowledgement Type of funding source: None

scholarly journals Protecting the most vulnerable during COVID-19 and beyond: a case report on the remote management of heart failure patients with cardiac implantable electronic devices

2020 ◽  
Vol 4 (FI1) ◽  
pp. 1-6
Author(s):  
Fozia Zahir Ahmed ◽  
Carol Crosbie ◽  
Matthew Kahn ◽  
Manish Motwani
Keyword(s):  
Heart Failure ◽  
High Risk ◽  
Electronic Devices ◽  
Management Strategies ◽  
Phone Call ◽  
Cardiac Resynchronization ◽  
Physiological Data ◽  
Related Data ◽  
Cardiac Implantable Electronic Devices ◽  
Increased Risk

Abstract Background Heart failure (HF) patients with cardiac implantable electronic devices (CIEDs) represent an important cohort. They are at increased risk of hospitalization and mortality. We outline how remote-only management strategies, which leverage transmitted health-related data, can be used to optimize care for HF patients with a CIED during the COVID-19 pandemic. Case summary An 82-year-old man with HF, stable on medical therapy, underwent cardiac resynchronization therapy implantation in 2016. Modern CIEDs facilitate remote monitoring by providing real-time physiological data (thoracic impedance, heart rate and rhythm, etc.). The ‘Triage Heart Failure Risk Score’ (Triage-HFRS), available on Medtronic CIEDs, integrates several monitored physiological parameters into a risk prediction model classifying patients as low, medium, or high risk of HF events within 30 days. In November 2019, the patient was enrolled in an innovative clinical pathway (Triage-HF Plus) whereby any ‘high’ Triage-HF risk status transmission prompts a phone call-based virtual consultation. A high-risk alert was received via remote transmission on 11 March, triggering a phone call assessment. Upon reporting increasing breathlessness, diuretics were initiated. The prescription was remotely issued and delivered to the patient’s home. This approach circumvented the need for all face-to-face reviews, delivering care in an entirely remote manner. Discussion The challenges posed by COVID-19 have prompted us to think differently about how we deliver care for patients, both now and following the pandemic. Contemporary CIEDs facilitate the ability to remotely monitor HF patients by providing rich physiological data that can help identify individuals at elevated risk of decompensation using automated device-generated alerts.

scholarly journals Proteomic mechanistic profile of patients with diabetes at risk of developing heart failure: insights from the HOMAGE trial

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Job A. J. Verdonschot ◽  
João Pedro Ferreira ◽  
Pierpaolo Pellicori ◽  
Hans-Peter Brunner-La Rocca ◽  
Andrew L. Clark ◽  
...  
Keyword(s):  
Heart Failure ◽  
At Risk ◽  
Plasma Concentrations ◽  
Diabetic Patients ◽  
Proteomic Profile ◽  
Developing Heart ◽  
Diabetes Status ◽  
Increased Risk ◽  
Patients With Diabetes ◽  
Patients At Risk

Abstract Background Patients with diabetes mellitus (DM) are at increased risk of developing heart failure (HF). The “Heart OMics in AGEing” (HOMAGE) trial suggested that spironolactone had beneficial effect on fibrosis and cardiac remodelling in an at risk population, potentially slowing the progression towards HF. We compared the proteomic profile of patients with and without diabetes among patients at risk for HF in the HOMAGE trial. Methods Protein biomarkers (n = 276) from the Olink®Proseek-Multiplex cardiovascular and inflammation panels were measured in plasma collected at baseline and 9 months (or last visit) from HOMAGE trial participants including 217 patients with, and 310 without, diabetes. Results Twenty-one biomarkers were increased and five decreased in patients with diabetes compared to non-diabetics at baseline. The markers clustered mainly within inflammatory and proteolytic pathways, with granulin as the key-hub, as revealed by knowledge-induced network and subsequent gene enrichment analysis. Treatment with spironolactone in diabetic patients did not lead to large changes in biomarkers. The effects of spironolactone on NTproBNP, fibrosis biomarkers and echocardiographic measures of diastolic function were similar in patients with and without diabetes (all interaction analyses p > 0.05). Conclusions Amongst patients at risk for HF, those with diabetes have higher plasma concentrations of proteins involved in inflammation and proteolysis. Diabetes does not influence the effects of spironolactone on the proteomic profile, and spironolactone produced anti-fibrotic, anti-remodelling, blood pressure and natriuretic peptide lowering effects regardless of diabetes status.  Trial registration NCT02556450.

P2872Remote management of heart failure patients with the multisensor ICD alert: preliminary results from the Italian pilot experience

2019 ◽  
Vol 40 (Supplement_1) ◽  
Author(s):  
E De Ruvo ◽  
A Capucci ◽  
L Santini ◽  
D Pecora ◽  
S Favale ◽  
...  
Keyword(s):  
Heart Failure ◽  
Clinical Practice ◽  
Active Phase ◽  
Threshold Value ◽  
Cardiac Resynchronization ◽  
Office Visits ◽  
Resynchronization Therapy ◽  
Clinical Events ◽  
Remote Management ◽  
Alert Threshold

Abstract Background The HeartLogic (Boston Scientific) index combines data from multiple implantable cardioverter-defibrillator (ICD)-based sensors and has proved to be a sensitive and timely predictor of impending heart failure (HF) decompensation. Objective To describe a preliminary experience of remote HF management of patients who received a HeartLogic-enabled ICD or cardiac resynchronization therapy ICD (CRT-D) in clinical practice. Methods The HeartLogic feature was activated in 101 patients (74 male, 71±10 years, ejection fraction 30±7%). From implantation to activation (blinded phase), the HeartLogic index trend was not available, thus no clinical actions were taken in response to it. After activation (active phase), remote data reviews and patient phone contacts were performed monthly and at the time of HeartLogic alerts (when the index crossed the nominal alert threshold value of 16), to assess the patient decompensation status. In-office visits were performed when deemed necessary. Results During the blinded phase, the HeartLogic index crossed the threshold value 24 times (over 24 person-years, 0.99 alerts/pt-year) in 16 patients. HeartLogic alerts preceded all hospitalizations and unplanned in-office visits for HF (sensitivity: 100%, median early warning: 38 days for hospitalizations, 12 days for HF visits). No clinical events were detected during or within 30 days of recovery of 10 HeartLogic alerts (unexplained alert rate: 0.41 per patient-year). Thus, the positive predictive value was 58% (14/24). During the active phase, 44 HeartLogic alerts were reported (over 46 person-years, 0.95 alerts/pt-year) in 30 patients. 26 (59%) HeartLogic alerts were judged clinically meaningful (i.e. associated with worsening of HF and/or influenced the clinician's decision to make changes to the subject's management). Conclusions In this first description of the use of HeartLogic in clinical practice, the algorithm demonstrated its ability to detect gradual worsening of HF. The results of the blinded phase of our experience favorably compare with those reported in the validation study. In the active phase, the HeartLogic index provided clinically meaningful information for the remote management of HF patients.

Left Atrial Phasic Function Predicts NT-proBNP in Asymptomatic Community Patients at Risk for Heart Failure

2021 ◽  
Vol 30 ◽  
pp. S128-S129
Author(s):  
P. Crane ◽  
M. McGrady ◽  
L. Shiel ◽  
D. Liew ◽  
S. Stewart ◽  
...  
Keyword(s):  
Heart Failure ◽  
At Risk ◽  
Left Atrial ◽  
Patients At Risk ◽  
Community Patients

scholarly journals Remote heart failure management using a multiparameter implantable cardioverter-defibrillator alert: the multicentric RE-HEART registry

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
J De Juan Baguda ◽  
J.J Gavira Gomez ◽  
M Pachon Iglesias ◽  
L Pena Conde ◽  
J.M Rubin Lopez ◽  
...  
Keyword(s):  
Heart Failure ◽  
Implantable Cardioverter Defibrillator ◽  
Median Duration ◽  
Office Visit ◽  
Active Phase ◽  
Cardiac Resynchronization ◽  
Office Visits ◽  
Icd Implantation ◽  
Cardioverter Defibrillator ◽  
Clinical Events

Abstract Background The HeartLogic algorithm combines multiple implantable cardioverter-defibrillator (ICD)-based sensors into an index for prediction of impending heart failure (HF) decompensation. In patients with ICD and cardiac resynchronization therapy ICD remotely monitored at 13 Spanish centers, we analyzed the association between clinical events and HeartLogic alerts and we described the use of the algorithm for the remote management of HF. Methods The association between clinical events and HeartLogic alerts was studied in the blinded phase (from ICD implantation to alert activation – no clinical actions taken in response to alerts) and in the following active phase (after alert activation – clinicians automatically notified in case of alert). Results We enrolled a total of 215 patients (67±13 years old, 77% male, 53% with ischemic cardiomyopathy) with ICD (19%) or CRT-D (81%). The median duration of the blinded phase was 8 [3–12] months. In this phase, the HeartLogic index crossed the threshold value (set by default to 16) 34 times in 20 patients. HeartLogic alerts were associated with 6 HF hospitalizations and 5 unplanned in-office visits for HF. Five additional HeartLogic threshold crossings were not associated with overt HF events, but occurred at the time of changes in drug therapy or of other clinical events. The rate of unexplained alerts was 0.25 alert-patient/year. The median time spent in alert was longer in the case of HF hospitalizations than of in-office visits (75 [min-max: 30–155] days versus 39 [min-max: 5–105] days). The maximum HeartLogic index value was 38±15 in the case of hospitalizations and 24±7 in that of minor HF events. The median duration of the following active phase was 5 [2–10] months. After HeartLogic activation, 40 alerts were reported in 26 patients. Twenty-seven (68%) alerts were associated with multiple HF- or non-HF related conditions or changes in prescribed HF therapy. Multiple actions were triggered by these alerts: HF hospitalization (4), unscheduled in-office visits (8), diuretics increase (8), change in other cardiovascular drugs (5), device reprogramming (2), atrial fibrillation ablation (1), patient education on therapy adherence (2). The rate of unexplained alerts not followed by any clinical action was 0.13 alert-patient/year. These alerts were managed remotely (device data review and phone contact), except for one alert that generated an unscheduled in-office visit. Conclusions HeartLogic index was frequently associated with HF-related clinical events. The activation of the associated alert allowed to remotely detect relevant clinical conditions and to implement clinical actions. The rate of unexplained alerts was low, and the work required in order to exclude any impending decompensation did not constitute a significant burden for the centers. Funding Acknowledgement Type of funding source: None

scholarly journals Using Predictive Modeling and Supervised Machine Learning to Identify Patients at Risk for Venous Thromboembolism Following Posterior Lumbar Fusion

2021 ◽  
pp. 219256822110193
Author(s):  
Kevin Y. Wang ◽  
Ijezie Ikwuezunma ◽  
Varun Puvanesarajah ◽  
Jacob Babu ◽  
Adam Margalit ◽  
...  
Keyword(s):  
Machine Learning ◽  
At Risk ◽  
Venous Thromboembolism ◽  
Risk Stratification ◽  
Predictive Model ◽  
Predictive Modeling ◽  
Lumbar Fusion ◽  
Posterior Lumbar Fusion ◽  
Single Level ◽  
Patients At Risk

Study Design: Retrospective review. Objective: To use predictive modeling and machine learning to identify patients at risk for venous thromboembolism (VTE) following posterior lumbar fusion (PLF) for degenerative spinal pathology. Methods: Patients undergoing single-level PLF in the inpatient setting were identified in the National Surgical Quality Improvement Program database. Our outcome measure of VTE included all patients who experienced a pulmonary embolism and/or deep venous thrombosis within 30-days of surgery. Two different methodologies were used to identify VTE risk: 1) a novel predictive model derived from multivariable logistic regression of significant risk factors, and 2) a tree-based extreme gradient boosting (XGBoost) algorithm using preoperative variables. The methods were compared against legacy risk-stratification measures: ASA and Charlson Comorbidity Index (CCI) using area-under-the-curve (AUC) statistic. Results: 13, 500 patients who underwent single-level PLF met the study criteria. Of these, 0.95% had a VTE within 30-days of surgery. The 5 clinical variables found to be significant in the multivariable predictive model were: age > 65, obesity grade II or above, coronary artery disease, functional status, and prolonged operative time. The predictive model exhibited an AUC of 0.716, which was significantly higher than the AUCs of ASA and CCI (all, P < 0.001), and comparable to that of the XGBoost algorithm ( P > 0.05). Conclusion: Predictive analytics and machine learning can be leveraged to aid in identification of patients at risk of VTE following PLF. Surgeons and perioperative teams may find these tools useful to augment clinical decision making risk stratification tool.

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