Medical Monitoring of Older Adults Patients After Discharged From Heart Failure Hospitalization

Heart Failure Clinical Trial

Official title:

Medical Monitoring of Older Adults Patients After Discharged From Heart Failure Hospitalization

Clinical Trial Details — Status: Not yet recruiting

Administrative data

• NCT number: NCT06018532
• Other study ID #: Presage_IC
• Status: Not yet recruiting
• Phase: N/A
• Start date: December 1, 2023
• Est. completion date: July 1, 2024

Study information

• Verified date: August 2023
• Source: Presage
• Contact: Jacques-Henry Veyron, MS
• Phone: 0622152004
• Email: jhveyron[@]presage.care
• Is FDA regulated: No
• Study type: Interventional

Clinical Trial Summary

Introduction: PRESAGE CARE has developed a solution for early detection of the risk of emergency department visits and unscheduled hospitalizations. This smartphone-based solution is used by homecare workers and family caregivers to predict the risk of emergency department visits and unscheduled hospitalization within one to two weeks.

Objective: To study the feasibility of a multicenter randomized trial to assess the effects of a healthcare intervention based on the alert from the Présage Care device after discharge from hospital for patients hospitalized for heart failure (MCO and SSR).

Background and participants: This is a feasibility study carried out on two hospital wards for heart failure patients aged 65 and over (MCO and SSR). Patient's relative will be asked to fill in a short questionnaire twice a week for each beneficiary, relating to the beneficiary's daily life (input time not to exceed 2 minutes). This information will be transmitted in real time to PRESAGE CARE's secure platform (health data host). The study will last 6 months, with a 3-month inclusion period and an expected average follow-up of 3 months. It is planned to include 50 to 100 patients, with the same number of caregivers, and to study recruitment rates, participant satisfaction (hospital doctors, patients, caregivers, general practitioners), drop-out rates and the usefulness of updating the PRESAGE CARE algorithms in the context of the risk of re-hospitalization heart failure patients.

Data collected: In addition to inclusion data to describe the participant population, informations on emergency department visits, hospitalizations, drugs prescribed and dispensed, and long-term care status will be extracted from the hospital information system .

The study will establish the usefulness of conducting a subsequent randomized multicenter study on the added value of the PRESAGE CARE system for this specific population.

Description:

Heart failure is a condition with a poor prognosis, especially in older adults with a difficult management. Overall, the mortality rate increases with age, rising by 27% per decade in men and 61% per decade in women.

The management and monitoring of heart failure rely on the appropriate prescription of drugs with beneficial effect on heart muscle remodeling (ACE inhibitors, beta-blockers), with a regular reevaluation for a best efficacy as well as a good bservance of hygienic-dietary rules such as regular weight control, simple nutritional rules (salt and water intake), and the maintenance of physical activity and training.

The main aim of ambulatory services is to detect episodes of decompensation early to treat it rapidly and prevent hospitalization. In the absence of improvement, hospitalization is necessary. Once discharged from the health care facility, coordination with the town or host structure is essential to ensure appropriate continuity of care.

Poor publications have already shown the benefits of Machine Learning (ML) for predicting hospitalization and death in heart failure patients. Predictive performance ranges from average (AUC: 0.55) to very good (AUC 0.8).

However, to work, these models require a large number of highly medicalized variables (typically over a hundred, including medication, medical history, ECG....), making them very difficult to apply in real life.

On the other hand, satisfactory predictive models (AUC>0.7) have large temporal prediction windows (1 to 3 years), making health actions difficult to implement.

To the best of our knowledge, no publication presents a short prediction window (a few weeks) based on simple models (less than 15 variables with little medicalization).

The PRESAGE Care medical device makes it possible to observe functional changes potentially heralding major medical events, and significantly improves predictions compared with conventional models.

• Caregivers fill in an easy-to-use application (less than 2 minutes), create to predict the risk of a serious event (hospitalization, loss of autonomy, etc.).

• An alert is sent to the end-users, who are the healthcare professionals, to trigger a healthcare intervention that could, in some cases, prevent the situation from deteriorating, or enable it to be managed in a non-emergency context.

This study is based on the hypothesize that the use of the PRESAGE CARE device coupled with a health intervention based on existing health networks could be associated with a lower incidence of unscheduled re-hospitalization, with no difference in mortality. This study is based on the hypothesize that the device will be well accepted by beneficiaries, their relatives and healthcare professionals (satisfaction > 80%), and that the intervention will not be associated with an increase in healthcare expenditure, as the additional costs associated with the use of PRESAGE CARE will be offset by the reduction in expenditure linked to avoided hospitalizations.

Recruitment information / eligibility

• Status: Not yet recruiting
• Enrollment: 100
• Est. completion date: July 1, 2024
• Est. primary completion date: March 1, 2024
• Accepts healthy volunteers: No
• Gender: All
• Age group: 65 Years and older

Eligibility

Inclusion Criteria:

• Person aged 65 or over.

• Living at home

• Moderately dependent, defined as GIR 3, 4 or 5.

• Be or have been hospitalized in the last 30 days for heart failure

• Have a relative volunteer to enter the PRESAGE questionnaire at least twice a week

• Agreeing to take part in the study, or not objecting to the study being carried out, and whose relative agrees to take part.

Exclusion Criteria:

• People under 65 years of age

• With severe dependency defined by a GIR 1 or 2 group.

• Not dependent as defined by a GIR 6 group.

• Refusing to take part in the study, or whose relative refuses to take part.

• Older adult benefiting from the Prado heart failure program on discharge from hospital.

Related Conditions & MeSH terms

• Frail Elderly Syndrome
• Heart Failure

Intervention

Device:
• PRESAGE CARE
PRESAGE Care informations from the application will be processed by the PRESAGE Care algorithm in real time, and may generate alerts indicating an increased risk of hospitalization or emergency department visits within 7 to 14 days. These alerts will be transmitted to a Care Manager platform, who will analyse the situation, initiate the healthcare intervention and coordinate its implementation. In practice, a care manager (coordinating nurse) will process the alert and carry out an initial assessment of the beneficiary's situation, using the information feedback platform, then by telephone, contacting the older adults beneficiary and the relative. The care manager may or may not implement preventive measures. If necessary, the patient may be offered an outpatient medical consultation, a home visit, a consultation with his or her GP and/or social or paramedical interventions.

Locations

Country	Name	City	State
n/a

Sponsors (2)

Lead Sponsor	Collaborator
Presage	Assistance Publique Hopitaux De Marseille

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Acceptability at inclusion by the patient-relative pair using PRESAGE Care	Rate of relatives participating in the study (%)	through study completion, an average of 6 months
Secondary	Acceptability of the relative	a regular follow-up rate, characterization of the caregiver (gender ratio (%), average age (years), status (% of children, % of spouse % of husband, % of others).; Validation : more than 50%	through study completion, an average of 6 months
Secondary	Relevance of follow-up	Rate of alerts leading to intervention (%) Satisfaction level of referring physicians	through study completion, an average of 6 months
Secondary	impact on emergency hospitalization	Tracking of re-hospitalization and emergency room admission rates (%) Lost to follow-up rate (%) Average length of stay for re-hospitalization in the facility (days) Time to re-hospitalization or emergency (days)	through study completion, an average of 6 months