HeartLogic is a multiparametric algorithm incorporated into implantable cardioverter-defibrillators (ICD). The associated alerts predict impending heart failure (HF) decompensations. Our objective ...was to analyze the association between alerts and clinical events and to describe the implementation of a protocol for remote management in a multicenter registry.
We evaluated study phase 1 (the investigators were blinded to the alert state) and phases 2 and 3 (after HeartLogic activation, managed as per local practice and with a standardized protocol, respectively).
We included 288 patients from 15 centers. In phase 1, the median observation period was 10 months and there were 73 alerts (0.72 alerts/patient-y), with 8 hospitalizations and 2 emergency room admissions for HF (0.10 events/patient-y). There were no HF hospitalizations outside the alert period. In the active phases, the median follow-up was 16 (95%CI, 15-22) months and there were 277 alerts (0.89 alerts/patient-y); 33 were associated with HF hospitalizations or HF death (n=6), 46 with minor decompensations, and 78 with other events. The unexplained alert rate was 0.39 alerts/patient-y. Outside the alert state, there was only 1 HF hospitalization and 1 minor HF decompensation. Most alerts (82% in phase 2 and 81% in phase 3; P=.861) were remotely managed. The median NT-proBNP value was higher within than outside the alert state (7378 vs 1210 pg/mL; P <.001).
The HeartLogic index was frequently associated with HF-related events and other clinically relevant situations, with a low rate of unexplained events. A standardized protocol allowed alerts to be safely and remotely detected and appropriate action to be taken on them.
HeartLogic es un algoritmo multiparamétrico incorporado a desfibriladores automáticos implantables (DAI). La alerta asociada predice descompensaciones de insuficiencia cardiaca (IC). Nuestro objetivo es analizar la asociación entre alertas y eventos clínicos bajo un protocolo de seguimiento común en un registro multicéntrico.
Se evaluaron la fase 1 (investigadores ciegos al estado de la alerta) y las fases 2 y 3 (tras la activación de HeartLogic, según práctica local y un protocolo común respectivamente).
Se incluyó a 288 pacientes en 15 centros. En fase 1, tras una media de observación de 10 meses, hubo 73 alertas (0,72 alertas/paciente-año), con 8 hospitalizaciones y 2 visitas a urgencias por IC (0,10 eventos/año-paciente). No hubo hospitalizaciones fuera del periodo de alerta. Las fases activas tuvieron una media de seguimiento de 16 (IC95%, 15-22) meses, con 277 alertas (0,89 alertas/año-paciente); 33 se asociaron con hospitalizaciones o muerte por IC, 46 con descompensaciones menores y 78 con otros eventos. La tasa de alertas inexplicables fue 0,39/año-paciente. Fuera del estado de alerta solo hubo una hospitalización y una descompensación menor. La mayoría de las alertas (el 82% en fase 2 y el 81% en fase 3; p=0,861) se gestionaron a distancia. La mediana de NT-proBNP fue superior en estado de alerta que fuera de él (7.378 frente a 1.210 pg/ml; p <0,001).
El índice HeartLogic se asoció con descompensaciones de IC y otros eventos relevantes, con baja tasa de alertas inexplicables. Un protocolo estandarizado permitió detectar y actuar a distancia con seguridad sobre las alertas.
Central adrenal insufficiency (AI) due to isolated adrenocorticotropic hormone (ACTH) deficiency (IAD) has been recently associated with immune checkpoint inhibitor (ICI) therapy. Our aim was to ...analyze the prevalence, clinical characteristics, and therapeutic outcomes in cancer patients with IAD induced by ICI therapy. A retrospective and multicenter study was performed. From a total of 4447 cancer patients treated with ICI antibodies, 37 (0.8%) (23 men (62.2%), mean age 64.7 ± 8.3 years (range 46–79 years)) were diagnosed with IAD. The tumor most frequently related to IAD was lung cancer (n = 20, 54.1%), followed by melanoma (n = 8, 21.6%). The most common ICI antibody inhibitors reported were nivolumab (n = 18, 48.6%), pembrolizumab (n = 16, 43.2%), and ipilimumab (n = 8, 21.6%). About half of the patients (n = 19, 51.4%) had other immune-related adverse events, mainly endocrine adverse effects (n = 10, 27.0%). IAD was diagnosed at a median time of 7.0 months (IQR, 5–12) after starting immunotherapy. The main reported symptom at presentation was fatigue (97.3%), followed by anorexia (81.8%) and general malaise (81.1%). Mean follow-up time since IAD diagnosis was 15.2 ± 12.5 months (range 0.3–55 months). At last visit, all patients continued with hormonal deficiency of ACTH. Median overall survival since IAD diagnosis was 6.0 months. In conclusion, IAD is a rare but a well-established complication associated with ICI therapy in cancer patients. It develops around 7 months after starting the treatment, mainly anti-PD1 antibodies. Recovery of the corticotropic axis function should not be expected.
The multiparametric implantable cardioverter-defibrillator HeartLogic index has proven to be a sensitive and timely predictor of impending heart failure (HF) decompensation. We evaluated the impact ...of a standardized follow-up protocol implemented by nursing staff and based on remote management of alerts.
The algorithm was activated in HF patients at 19 Spanish centers. Transmitted data were analyzed remotely, and patients were contacted by telephone if alerts were issued. Clinical actions were implemented remotely or through outpatient visits. The primary endpoint consisted of HF hospitalizations or death. Secondary endpoints were HF outpatient visits. We compared the 12-month periods before and after the adoption of the protocol.
We analyzed 392 patients (aged 69±10 years, 76% male, 50% ischemic cardiomyopathy) with implantable cardioverter-defibrillators (20%) or cardiac resynchronization therapy defibrillators (80%). The primary endpoint occurred 151 times in 86 (22%) patients during the 12 months before the adoption of the protocol, and 69 times in 45 (11%) patients (P<.001) during the 12 months after its adoption. The mean number of hospitalizations per patient was 0.39±0.89 pre- and 0.18±0.57 postadoption (P<.001). There were 185 outpatient visits for HF in 96 (24%) patients before adoption and 64 in 48 (12%) patients after adoption (P<.001). The mean number of visits per patient was 0.47±1.11 pre- and 0.16±0.51 postadoption (P<.001).
A standardized follow-up protocol based on remote management of HeartLogic alerts enabled effective remote management of HF patients. After its adoption, we observed a significant reduction in HF hospitalizations and outpatient visits.
Abstract
Background
The HeartLogic algorithm measures data from multiple implantable cardioverter-defibrillator-based (ICD) sensors and combines them into a single index. The associated alert has ...proved to be a sensitive and timely predictor of impending heart failure (HF) decompensation.
Objective
To analyze the association between HeartLogic alerts and clinical events and to describe the implementation in clinical practice of the algorithm for remote management of HF patients.
Methods
The association between HeartLogic alerts and clinical events has been analyzed in the blinded study Phase 1 (from ICD implantation to HeartLogic alert activation) and in the following unblinded Phase 2 and 3 (after HeartLogic activation). During Phase 1, patients were managed according to the standard clinical practice and physicians were blinded to the alert status. During Phase 2 physicians reacted to alerts according to their clinical practice, while during Phase 3 they followed a standardized protocol in response to alerts.
Results
We enrolled 288 patients who received HeartLogic-enabled ICD or CRT-D at 15 centers. 101 patients contributed to Phase 1. During a median observation period of 10 95% CI: 5 – 19 months, the HeartLogic index crossed the alert-threshold value 73 times (0.72 alerts/patient-year) in 39 patients. 8 HF hospitalizations and 2 emergency room admissions occurred in 9 patients (0.10 events/patient-year) during HeartLogic IN alert state. Other 10 minor events (HF in-office visits and non-HF hospitalization) were associated with HeartLogic alerts. During the active phases 267 patients were observed for a median follow-up of 16 95% CI: 15 – 22 months. 277 HeartLogic alerts (0.89 alerts/patient-year) occurred in 136 patients. Thirty-three HeartLogic alerts were associated with hospitalizations for HF or with HF death (n=6), and 46 alerts were associated with unplanned in-office visits for HF. In 78 cases, HeartLogic alerts were associated with other clinically relevant events. The rate of unexplained alerts was low (0.39 alerts/patient-year). During the active phases, one HF hospitalization and one unplanned in-office visit for HF occurred when patients were in OUT of alert state. Patient phone contacts or in-person assessments were performed more frequently in Phase 3 (85%) than in Phase 2 (73%; p=0.047), while alert-triggered actions were similar in the two phases. Most alerts in both Phases (82% in 2 and 81% in 3; p=0.861) were managed remotely. The total number of patient phone contacts performed during Phase 2 was 35 (0.65 contacts/patient-year) and during Phase 3 was 287 (1.12 contacts/patient-year; p=0.002).
Conclusions
HeartLogic index was frequently associated with HF-related clinical events, with a low rate of unexplained events. The HeartLogic alert and a standardize protocol of actions allowed to remotely detect impending decompensation events and to implement clinical actions with a low workload for the centers.
Funding Acknowledgement
Type of funding sources: None.
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
Abstract
Background
The HeartLogic algorithm measures data from multiple implantable cardioverter-defibrillator-based (ICD) sensors and combines them into a single index. The associated alert has ...proved to be a sensitive and timely predictor of impending heart failure (HF) decompensation.
Objective
To analyze the association between HeartLogic alerts and clinical events and to describe the implementation in clinical practice of the algorithm for remote management of HF patients.
Methods
The association between HeartLogic alerts and clinical events has been analyzed in the blinded study Phase 1 (from ICD implantation to HeartLogic alert activation) and in the following unblinded Phase 2 and 3 (after HeartLogic activation). During Phase 1, patients were managed according to the standard clinical practice and physicians were blinded to the alert status. During Phase 2 physicians reacted to alerts according to their clinical practice, while during Phase 3 they followed a standardized protocol in response to alerts.
Results
We enrolled 288 patients who received HeartLogic-enabled ICD or CRT-D at 15 centers. 101 patients contributed to Phase 1. During a median observation period of 10 95% CI: 5 – 19 months, the HeartLogic index crossed the alert-threshold value 73 times (0.72 alerts/patient-year) in 39 patients. 8 HF hospitalizations and 2 emergency room admissions occurred in 9 patients (0.10 events/patient-year) during HeartLogic IN alert state. Other 10 minor events (HF in-office visits and non-HF hospitalization) were associated with HeartLogic alerts. During the active phases 267 patients were observed for a median follow-up of 16 95% CI: 15 – 22 months. 277 HeartLogic alerts (0.89 alerts/patient-year) occurred in 136 patients. Thirty-three HeartLogic alerts were associated with hospitalizations for HF or with HF death (n=6), and 46 alerts were associated with unplanned in-office visits for HF. In 78 cases, HeartLogic alerts were associated with other clinically relevant events. The rate of unexplained alerts was low (0.39 alerts/patient-year). During the active phases, one HF hospitalization and one unplanned in-office visit for HF occurred when patients were in OUT of alert state. Patient phone contacts or in-person assessments were performed more frequently in Phase 3 (85%) than in Phase 2 (73%; p=0.047), while alert-triggered actions were similar in the two phases. Most alerts in both Phases (82% in 2 and 81% in 3; p=0.861) were managed remotely. The total number of patient phone contacts performed during Phase 2 was 35 (0.65 contacts/patient-year) and during Phase 3 was 287 (1.12 contacts/patient-year; p=0.002).
Conclusions
HeartLogic index was frequently associated with HF-related clinical events, with a low rate of unexplained events. The HeartLogic alert and a standardize protocol of actions allowed to remotely detect impending decompensation events and to implement clinical actions with a low workload for the centers.
Funding Acknowledgement
Type of funding sources: None.
INTRODUCTIONDiabetes mellitus (DM) is one of the most prevalent chronic diseases and has a significant health and social impact. Strict control of blood glucose levels and other risk factors for ...vascular disease reduces complications and mortality and is related to the quality of care received. Although care should be interdisciplinary, based on the coordination of primary care (PC) and hospital care (HC), little information is available on the effectiveness of the different existing intervention models. OBJECTIVETo assess, in a population with DM from a healthcare area, the impact on health, quality of care, and effectiveness in the use of resources of a specific model of shared management of patients with DM (Instrument for Evaluation of Models of Chronic Care in Diabetes Mellitus; IEMAC-DM). PATIENTS AND METHODSA quasi-experimental before-after intervention study in patients with DM in the Cádiz-San Fernando Healthcare Area (Andalusia, Spain) that allows for identifying the capacity of the program to improve the quality indicators both in the whole population with DM and in that referred to HC. For this, a working group consisting of healthcare professionals of different profiles and care levels was set up. An initial self-assessment was done using the IEMAC-DM tool and, after analysis of the preliminary results, improvement strategies were established and implemented. Finally, the clinical and resource management results were assessed before and two years after the implementation of the model. RESULTSDuring the study period, no significant changes were seen in process indicators related to laboratory practices or examinations in the health area. The proportion of patients with acceptable metabolic control glycosylated hemoglobin (HbA1c) level<8% was 49% in 2015 and 45% in 2017. The number of admissions related to acute myocardial infarction and stroke remained constant, but there was an increase in the standardized ratio of major lower limb amputations (1.5 vs. 1.9). Of the 295 patients referred from PC to HC, the proportion of adequate referrals increased from 40% in 2015 to 76% in 2017 (p=0.001). In the referred patients, a significant improvement was seen in the mean difference in glycosylated hemoglobin levels (HbA1c; 1.14±1.73%; 95% CI: 0.73-1.55; p=0.0001) and cholesterol (11.28±40mg/dL; 95% CI: 2.07-20.48; p=0.012). CONCLUSIONSThis study shows that an intervention based on a chronicity care model adapted to patients with DM improves certain aspects related to the quality of care and the degree of metabolic control. Improving health outcomes will require long-term evaluation and, probably, other additional interventions.
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