EmergenciesNCT05221697
Effect of an ML Electronic Alert Management System to Reduce the Use of ED Visits and Hospitalizations
A study testing PRESAGE CARE in people with Emergencies, run by Presage at 2 sites in France.
Trial summary
Development, validation and impact of an alert management system using social workers' observations and machine learning algorithms to predict 7-to-14-day alerts for the risk of Emergency Department (ED) Visit and unplanned hospitalization. Read more about this study
- Who is it for?
- Ages 75 years and older
- Conditions the study is for
- Emergencies
- What is being tested?
- PRESAGE CARE
- Type of treatment
- Medical device
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 800 participants
- Where
- 2 sitesFrance
- Study dates
- 1 Sep 2020to 30 Jun 2024
- Phase
- Interventional
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 75 years and older
- All sexes
- No healthy volunteers
- Older adult
Who may be able to join
- age of 75 yo mini
- receiving the help of a social worker
- patient should give their consent
- patient should had seen their primary care professional within the past 12 months
Who cannot join
- People with severe dependence (French national instrument, which stratifies dependency level from group iso-resources (GIR) : 1 (very severe dependency) and 2 (severe dependency)
Trial locations and contacts
2 sites in 1 country.
About this study
Official title: Effect of an Electronic Alert Management System Using Caregivers' Observations and Machine Learning Algorithm to Reduce the Use of Emergency Department Visits and Unplanned Hospitalizations Among Older People
Development, validation and impact of an alert management system using social workers' observations and machine learning algorithms to predict 7-to-14-day alerts for the risk of Emergency Department (ED) Visit and unplanned hospitalization.
Multi-center trial implementation of electronic Home Care Aides-reported outcomes measure system among patients, frail adults >= 65 years living at home and receiving assistance from home care aides (HCA).
Read the detailed description
On a weekly basis, after home visit, HCAs reported on participants' functional status using a smartphone application that recorded 23 functional items about each participant (e.g., ability to stand, move, eat, mood, loneliness). Predictive system using Machine learning techniques (i.e., leveraging random forest predictors) was developed and generated 7 to 14-day predictive alerts for the risk of ED visit to nurses.
This questionnaire focused on functional and clinical autonomy (ie, activities of daily life), possible medical symptoms (eg, fatigue, falls, and pain), changes in behavior (eg, recognition and aggressiveness), and communication with the HA or their surroundings. This questionnaire is composed of very simple and easy-to-understand questions, giving a global view of the person's condition. For each of the 23 questions, a yes/no answer was requested. Data recorded by HAs were sent in real time to a secure server to be analyzed by our machine learning algorithm, which predicted the risk level and displayed it on a web-based secure medical device called PRESAGE CARE, which is CE marked. Particularly, when the algorithm predicted a high-risk level, an alert was displayed in the form of a notification on the screen to the coordinating nurse of the health care network center of the district. This risk notification was accompanied by information about recent changes in the patients' functional status, identified from the HAs' records, to assist the coordinating nurse in interacting with family caregiver and other health professionals.
In the event of an alert, the coordinating nurse called the family caregiver to inquire about recent changes in the patient's health condition and for doubt removal and could then decide to ask for a health intervention according to a health intervention model developed before the start of the study. In brief, this alert-triggered health intervention (ATHI) consisted of calling the patient's nurse (if the patient had regular home visits of a nurse) or the patient's general practitioner and informing them of a worsening of the patient's functional status and a potential risk of an ED visit or unplanned hospitalization in the next few days according to the eHealth system algorithm. This model of ATHI had been presented and approved by the Agences Régionales de Santé of the regions involved in our study
- Conditions
- Emergencies
- Being tested
- PRESAGE CARE
- Keywords
- Emergency Department visits,, machine learning, smartphone
- Medical subject headings
- Emergencies
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
PRESAGE CAREDevice
Participants in this arm will be followed by HCA and might benefit from Nurse health interventions
| Study group | Role | What they receive |
|---|---|---|
| Intervention | Experimental | PRESAGE Care ATIH + Nurse or GP consultation Receives: Device: PRESAGE CARE |
| Control group | No intervention | usual care |
What is being measured
The primary outcome is the main question the study is designed to answer.
Unplanned Hospitalization rate
Comparison between unplanned hospitalization ratio from 2 randomized groups (intervention and control arms).
P values <.05 will be considered statistically significant.
Measured: through study completion, an average of 1 year
Event-free survival (EFS)
Comparison average Time for first adverse event between intervention and control groups.
P values <.05 will be considered statistically significant.
Measured: through study completion, an average of 1 year
Impact on older adults and relatives' quality of life (European Quality of Life 5 Dimensions and 3 Lines scale)
Comparison of the average score of EQ5D-3L quality of life scale (European Quality of Life 5 Dimensions and 3 Lines) between intervention and control groups.
P values <.05 will be considered statistically significant.
Measured: through study completion, an average of 1 year
Cost-effectiveness
Incremental cost-effectiveness ratio (ICER), QALY. Willingness-to-pay thresholds of €30,000 per quality-adjusted life year (QALY) and €90,000 per QALY were used to define a very cost-effective and cost-effective strategy, respectively
Measured: through study completion, an average of 1 year
Secondary outcomes (6)
Impact on users : time needed to complete questionnaire
Time needed to complete questionnaire (minutes) : a time of less than 2 minutes will be considered acceptable
Measured: through study completion, an average of 1 year
Intervention rate
Part of alert which leads to interventions and intervention time (%). Rate of over 70% is considered acceptable.
Measured: through study completion, an average of 1 year
Intervention time
Mean of the duration between day of alert and day of intervention (in days). A delay of less than 4 days is considered acceptable.
Measured: through study completion, an average of 1 year
Time needed to analysis patient statut
Time needed to analysis patient statut (hours and minutes) : a time of less than 15 minutes by patient will be considered acceptable
Measured: through study completion, an average of 1 year
Impact on quality of care
Positive or very positive impact on quality of care : rate of over 80% is considered acceptable.
Measured: through study completion, an average of 1 year
Impact on Professional' Relationship and coordination
Positive or very positive impact on professionnal relationship and coordination :rate of over 80% is considered acceptable.
Measured: through study completion, an average of 1 year
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted3 Feb 2022
- Study start1 Sep 2020
- Primary completion31 Dec 2021
- Study completion30 Jun 2024 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (35)Papers the sponsor has linked to this study.
- Other trials for EmergenciesStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Prevention
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Enrolment
- 800 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Presage
- Sponsor type
- Industry (pharma, biotech, devices)
- Collaborators
- Assistance Publique Hopitaux De Marseille, Assistance Publique - Hôpitaux de Paris, University Hospital, Lille
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05221697
- Sponsor's protocol ID
- PRESAGE_2021-01
- Status last verified
- Aug 2023
- First submitted
- 7 Jan 2022
- Last updated
- 30 Aug 2023
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: anonymized Statistical data will be available
Publications
Clerencia-Sierra M, Calderon-Larranaga A, Martinez-Velilla N, Vergara-Mitxeltorena I, Aldaz-Herce P, Poblador-Plou B, Machon-Sobrado M, Egues-Olazabal N, Abellan-van Kan G, Prados-Torres A. Multimorbidity Patterns in Hospitalized Older Patients: Associations among Chronic Diseases and Geriatric Syndromes. PLoS One. 2015 Jul 24;10(7):e0132909. doi: 10.1371/journal.pone.0132909. eCollection 2015.
PubMed 26208112Kahn JH, Magauran BG Jr, Olshaker JS, Shankar KN. Current Trends in Geriatric Emergency Medicine. Emerg Med Clin North Am. 2016 Aug;34(3):435-52. doi: 10.1016/j.emc.2016.04.014.
PubMed 27475008Gasperini B, Cherubini A, Pierri F, Barbadoro P, Fedecostante M, Prospero E. Potentially preventable visits to the emergency department in older adults: Results from a national survey in Italy. PLoS One. 2017 Dec 21;12(12):e0189925. doi: 10.1371/journal.pone.0189925. eCollection 2017.
PubMed 29267333McCusker J, Verdon J. Do geriatric interventions reduce emergency department visits? A systematic review. J Gerontol A Biol Sci Med Sci. 2006 Jan;61(1):53-62. doi: 10.1093/gerona/61.1.53.
PubMed 16456194Inglis SC, Clark RA, McAlister FA, Ball J, Lewinter C, Cullington D, Stewart S, Cleland JG. Structured telephone support or telemonitoring programmes for patients with chronic heart failure. Cochrane Database Syst Rev. 2010 Aug 4;(8):CD007228. doi: 10.1002/14651858.CD007228.pub2.
PubMed 20687083Chen CC, Wang C, Huang GH. Functional trajectory 6 months posthospitalization: a cohort study of older hospitalized patients in Taiwan. Nurs Res. 2008 Mar-Apr;57(2):93-100. doi: 10.1097/01.NNR.0000313485.18670.e2.
PubMed 18347480Iwashyna TJ, Ely EW, Smith DM, Langa KM. Long-term cognitive impairment and functional disability among survivors of severe sepsis. JAMA. 2010 Oct 27;304(16):1787-94. doi: 10.1001/jama.2010.1553.
PubMed 20978258Camargo CA Jr, Tsai CL, Sullivan AF, Cleary PD, Gordon JA, Guadagnoli E, Kaushal R, Magid DJ, Rao SR, Blumenthal D. Safety climate and medical errors in 62 US emergency departments. Ann Emerg Med. 2012 Nov;60(5):555-563.e20. doi: 10.1016/j.annemergmed.2012.02.018.
PubMed 23089089Crane SJ, Tung EE, Hanson GJ, Cha S, Chaudhry R, Takahashi PY. Use of an electronic administrative database to identify older community dwelling adults at high-risk for hospitalization or emergency department visits: the elders risk assessment index. BMC Health Serv Res. 2010 Dec 13;10:338. doi: 10.1186/1472-6963-10-338.
PubMed 21144042Hu Z, Jin B, Shin AY, Zhu C, Zhao Y, Hao S, Zheng L, Fu C, Wen Q, Ji J, Li Z, Wang Y, Zheng X, Dai D, Culver DS, Alfreds ST, Rogow T, Stearns F, Sylvester KG, Widen E, Ling XB. Real-time web-based assessment of total population risk of future emergency department utilization: statewide prospective active case finding study. Interact J Med Res. 2015 Jan 13;4(1):e2. doi: 10.2196/ijmr.4022.
PubMed 25586600Takahashi PY, Heien HC, Sangaralingham LR, Shah ND, Naessens JM. Enhanced risk prediction model for emergency department use and hospitalizations in patients in a primary care medical home. Am J Manag Care. 2016 Jul;22(7):475-83.
PubMed 27442203Denis F, Krakowski I. How Should Oncologists Choose an Electronic Patient-Reported Outcome System for Remote Monitoring of Patients With Cancer? J Med Internet Res. 2021 Sep 9;23(9):e30549. doi: 10.2196/30549.
PubMed 34499046Bouazza YB, Chiairi I, El Kharbouchi O, De Backer L, Vanhoutte G, Janssens A, Van Meerbeeck JP. Patient-reported outcome measures (PROMs) in the management of lung cancer: A systematic review. Lung Cancer. 2017 Nov;113:140-151. doi: 10.1016/j.lungcan.2017.09.011. Epub 2017 Sep 23.
PubMed 29110842Schick-Makaroff K, Karimi-Dehkordi M, Cuthbertson L, Dixon D, Cohen SR, Hilliard N, Sawatzky R. Using Patient- and Family-Reported Outcome and Experience Measures Across Transitions of Care for Frail Older Adults Living at Home: A Meta-Narrative Synthesis. Gerontologist. 2021 Apr 3;61(3):e23-e38. doi: 10.1093/geront/gnz162.
PubMed 31942997Veyron JH, Friocourt P, Jeanjean O, Luquel L, Bonifas N, Denis F, Belmin J. Home care aides' observations and machine learning algorithms for the prediction of visits to emergency departments by older community-dwelling individuals receiving home care assistance: A proof of concept study. PLoS One. 2019 Aug 13;14(8):e0220002. doi: 10.1371/journal.pone.0220002. eCollection 2019.
PubMed 31408458
Study at a glance
From the registry record, last updated by the sponsor on 30 Aug 2023. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Effect of an Electronic Alert Management System Using Caregivers' Observations and Machine Learning Algorithm to Reduce the Use of Emergency Department Visits and Unplanned Hospitalizations Among Older People
- ClinicalTrials.gov ID
- NCT05221697
- Acronym / sponsor ID
- PRESAGE_2021-01
- Conditions
- Emergencies
- Keywords
- Emergency Department visits,, machine learning, smartphone
- Study type
- Interventional
- Recruitment status
- Unknown status
- Enrolment
- 800 participants (estimated)
- Lead sponsor
- PresageIndustry (pharma, biotech, devices)
- Collaborators
- Assistance Publique Hopitaux De Marseille, Assistance Publique - Hôpitaux de Paris, University Hospital, Lille
- Study start
- 1 Sep 2020 (actual)
- Primary completion
- 31 Dec 2021 (actual)
- Study completion
- 30 Jun 2024 (estimated)
- First posted
- 3 Feb 2022 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
Development, validation and impact of an alert management system using social workers' observations and machine learning algorithms to predict 7-to-14-day alerts for the risk of Emergency Department (ED) Visit and unplanned hospitalization.
Multi-center trial implementation of electronic Home Care Aides-reported outcomes measure system among patients, frail adults >= 65 years living at home and receiving assistance from home care aides (HCA).
Detailed description
On a weekly basis, after home visit, HCAs reported on participants' functional status using a smartphone application that recorded 23 functional items about each participant (e.g., ability to stand, move, eat, mood, loneliness). Predictive system using Machine learning techniques (i.e., leveraging random forest predictors) was developed and generated 7 to 14-day predictive alerts for the risk of ED visit to nurses.
This questionnaire focused on functional and clinical autonomy (ie, activities of daily life), possible medical symptoms (eg, fatigue, falls, and pain), changes in behavior (eg, recognition and aggressiveness), and communication with the HA or their surroundings. This questionnaire is composed of very simple and easy-to-understand questions, giving a global view of the person's condition. For each of the 23 questions, a yes/no answer was requested. Data recorded by HAs were sent in real time to a secure server to be analyzed by our machine learning algorithm, which predicted the risk level and displayed it on a web-based secure medical device called PRESAGE CARE, which is CE marked. Particularly, when the algorithm predicted a high-risk level, an alert was displayed in the form of a notification on the screen to the coordinating nurse of the health care network center of the district. This risk notification was accompanied by information about recent changes in the patients' functional status, identified from the HAs' records, to assist the coordinating nurse in interacting with family caregiver and other health professionals.
In the event of an alert, the coordinating nurse called the family caregiver to inquire about recent changes in the patient's health condition and for doubt removal and could then decide to ask for a health intervention according to a health intervention model developed before the start of the study. In brief, this alert-triggered health intervention (ATHI) consisted of calling the patient's nurse (if the patient had regular home visits of a nurse) or the patient's general practitioner and informing them of a worsening of the patient's functional status and a potential risk of an ED visit or unplanned hospitalization in the next few days according to the eHealth system algorithm. This model of ATHI had been presented and approved by the Agences Régionales de Santé of the regions involved in our study
Study design
- Primary purpose
- Prevention
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Intervention | Experimental | Device: PRESAGE CAREPRESAGE Care ATIH + Nurse or GP consultation |
| Control group | No intervention | –usual care |
Who may qualify
- Ages
- 75 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Older adult
Key inclusion criteria (4 listed)
- age of 75 yo mini
- receiving the help of a social worker
- patient should give their consent
- patient should had seen their primary care professional within the past 12 months
Key exclusion criteria (1 listed)
- People with severe dependence (French national instrument, which stratifies dependency level from group iso-resources (GIR) : 1 (very severe dependency) and 2 (severe dependency)
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| PRESAGE CAREParticipants in this arm will be followed by HCA and might benefit from Nurse health interventions | Device | Intervention |
Primary outcome
Unplanned Hospitalization rate
Time frame: through study completion, an average of 1 year
Comparison between unplanned hospitalization ratio from 2 randomized groups (intervention and control arms). P values <.05 will be considered statistically significant.
Event-free survival (EFS)
Time frame: through study completion, an average of 1 year
Comparison average Time for first adverse event between intervention and control groups. P values <.05 will be considered statistically significant.
Impact on older adults and relatives' quality of life (European Quality of Life 5 Dimensions and 3 Lines scale)
Time frame: through study completion, an average of 1 year
Comparison of the average score of EQ5D-3L quality of life scale (European Quality of Life 5 Dimensions and 3 Lines) between intervention and control groups. P values <.05 will be considered statistically significant.
Cost-effectiveness
Time frame: through study completion, an average of 1 year
Incremental cost-effectiveness ratio (ICER), QALY. Willingness-to-pay thresholds of €30,000 per quality-adjusted life year (QALY) and €90,000 per QALY were used to define a very cost-effective and cost-effective strategy, respectively
6 secondary outcomes
Impact on users : time needed to complete questionnaire
Time frame: through study completion, an average of 1 year
Intervention rate
Time frame: through study completion, an average of 1 year
Intervention time
Time frame: through study completion, an average of 1 year
Time needed to analysis patient statut
Time frame: through study completion, an average of 1 year
Impact on quality of care
Time frame: through study completion, an average of 1 year
Impact on Professional' Relationship and coordination
Time frame: through study completion, an average of 1 year
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 30 Aug 2023.
- Background: PubMed 26208112Clerencia-Sierra M, Calderon-Larranaga A, Martinez-Velilla N, Vergara-Mitxeltorena I, Aldaz-Herce P, Poblador-Plou B, Machon-Sobrado M, Egues-Olazabal N, Abellan-van Kan G, Prados-Torres A. Multimorbidity Patterns in Hospitalized Older Patients: Associations among Chronic Diseases and Geriatric Syndromes. PLoS One. 2015 Jul 24;10(7):e0132909. doi: 10.1371/journal.pone.0132909. eCollection 2015.
- Background: PubMed 27475008Kahn JH, Magauran BG Jr, Olshaker JS, Shankar KN. Current Trends in Geriatric Emergency Medicine. Emerg Med Clin North Am. 2016 Aug;34(3):435-52. doi: 10.1016/j.emc.2016.04.014.
- Background: PubMed 29267333Gasperini B, Cherubini A, Pierri F, Barbadoro P, Fedecostante M, Prospero E. Potentially preventable visits to the emergency department in older adults: Results from a national survey in Italy. PLoS One. 2017 Dec 21;12(12):e0189925. doi: 10.1371/journal.pone.0189925. eCollection 2017.
- Background: PubMed 16456194McCusker J, Verdon J. Do geriatric interventions reduce emergency department visits? A systematic review. J Gerontol A Biol Sci Med Sci. 2006 Jan;61(1):53-62. doi: 10.1093/gerona/61.1.53.
- Background: PubMed 20687083Inglis SC, Clark RA, McAlister FA, Ball J, Lewinter C, Cullington D, Stewart S, Cleland JG. Structured telephone support or telemonitoring programmes for patients with chronic heart failure. Cochrane Database Syst Rev. 2010 Aug 4;(8):CD007228. doi: 10.1002/14651858.CD007228.pub2.
- Background: PubMed 18347480Chen CC, Wang C, Huang GH. Functional trajectory 6 months posthospitalization: a cohort study of older hospitalized patients in Taiwan. Nurs Res. 2008 Mar-Apr;57(2):93-100. doi: 10.1097/01.NNR.0000313485.18670.e2.
- Background: PubMed 20978258Iwashyna TJ, Ely EW, Smith DM, Langa KM. Long-term cognitive impairment and functional disability among survivors of severe sepsis. JAMA. 2010 Oct 27;304(16):1787-94. doi: 10.1001/jama.2010.1553.
- Background: PubMed 23089089Camargo CA Jr, Tsai CL, Sullivan AF, Cleary PD, Gordon JA, Guadagnoli E, Kaushal R, Magid DJ, Rao SR, Blumenthal D. Safety climate and medical errors in 62 US emergency departments. Ann Emerg Med. 2012 Nov;60(5):555-563.e20. doi: 10.1016/j.annemergmed.2012.02.018.
- Background: PubMed 21144042Crane SJ, Tung EE, Hanson GJ, Cha S, Chaudhry R, Takahashi PY. Use of an electronic administrative database to identify older community dwelling adults at high-risk for hospitalization or emergency department visits: the elders risk assessment index. BMC Health Serv Res. 2010 Dec 13;10:338. doi: 10.1186/1472-6963-10-338.
- Background: PubMed 25586600Hu Z, Jin B, Shin AY, Zhu C, Zhao Y, Hao S, Zheng L, Fu C, Wen Q, Ji J, Li Z, Wang Y, Zheng X, Dai D, Culver DS, Alfreds ST, Rogow T, Stearns F, Sylvester KG, Widen E, Ling XB. Real-time web-based assessment of total population risk of future emergency department utilization: statewide prospective active case finding study. Interact J Med Res. 2015 Jan 13;4(1):e2. doi: 10.2196/ijmr.4022.
- Background: PubMed 27442203Takahashi PY, Heien HC, Sangaralingham LR, Shah ND, Naessens JM. Enhanced risk prediction model for emergency department use and hospitalizations in patients in a primary care medical home. Am J Manag Care. 2016 Jul;22(7):475-83.
- Background: PubMed 34499046Denis F, Krakowski I. How Should Oncologists Choose an Electronic Patient-Reported Outcome System for Remote Monitoring of Patients With Cancer? J Med Internet Res. 2021 Sep 9;23(9):e30549. doi: 10.2196/30549.
- Individual participant dataSharing planned: No. anonymized Statistical data will be available
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
2 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing PRESAGE CARE in people with Emergencies, run by Presage at 2 sites in France.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 30 Aug 2023. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 2 sites in France. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Unplanned Hospitalization rate (time frame: through study completion, an average of 1 year). The study lists 4 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT05221697, which also lists 35 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 30 Aug 2023.