Clinical trial NCT06533254: Behavioral AI to Predict and Increase Peritoneal Dialysis Uptake
Official title: Use of Behavioral Artificial Intelligence to Predict and Increase Uptake of Peritoneal Dialysis [AI-Dialysis]
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Chronic Kidney Disease
Keywords: Chronic Kidney Disease
Artificial Intelligence
Dialysis
Lead sponsor: Scaled Insights
Sponsor class: INDUSTRY
Collaborators: name: Dallas Renal Group
name: The Leeds Teaching Hospitals NHS Trust
name: Suffolk and North East Essex NHS Trust
First posted: 2024-08-01
Enrollment: 3000
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: PROBABILITY_SAMPLE
Study population: There are two phases in this study.

Phase 1: 2000 participants in total. 1000 in the USA, and 1000 in the UK; 500 patients who predict they will choose peritoneal dialysis and 500 who predict they will choose hemodialysis in each country.

Patients who agree to participate in the study, will consent using an online survey disseminated to patients with Chronic Kidney Disease stage 4-5. Where needed, participants can be supported to complete the survey by a Research Nurse/Assistant to complete the survey.

Phase 2: 1000 Patients with Chronic Kidney Disease in total. 500 in the USA and 500 in the UK.

Patients who agree to participate in the study. They will be asked to access a link to an online web platform where the study will be hosted.

Brief summary: Scaled Insights Behavioral AI will be used to improve clinicians ability to detect and understand: The likelihood of patients with kidney failure opting for peritoneal dialysis and to select the right personalized nudge to get CKD patients to choose peritoneal dialysis

Scaled Insights' can utilize the data from this study to develop individualized nudges for healthcare professionals that can be broadly deployed within a digital platform so that clinics across the English speaking world can use it as a means of supporting patients with CKD in selecting peritoneal dialysis treatment.

Using Scaled Insights novel Behavioral Artificial Intelligence solution, the proposed project has two aims:

1. to identify personality attributes of patients with Chronic Kidney Disease and correlating their personality attributes with their subsequent choice of dialysis. Once correlated into personality based clusters, the AI Model will then be able will determine which personality cluster each net new patient will fall into and therefore predict their specific likelihood of opting for peritoneal dialysis or hemodialysis
2. once prediction is possible, customized nudges towards the selection of PD will be developed and tested to improve the likelihood that patients will opt for peritoneal dialysis

Hypotheses:

1. Scaled Insights Behavioral AI will identify the personality characteristics of patients with Chronic Kidney Disease who are likely to opt for both peritoneal dialysis and hemodialysis (Phase 1)
2. Scaled Insights Behavioral AI will identify the nudges that are most effective in encouraging or supporting patients with Chronic Kidney Disease in choosing peritoneal dialysis over hemodialysis (Phase 2)


Eligibility criteria: Inclusion Criteria:

* Phase 1: US and UK adult patients aged 18+ years, with Chronic Kidney Disease stage 4-5.
* Phase 2: US and UK adult patients aged 18+ years, with Chronic Kidney Disease stage 4-5.

Exclusion Criteria:

* Phase 1: Young people aged \&lt;18 years. People who do not have Chronic Kidney Disease. People who reside outside of the USA and UK.
* Phases 2: Young people aged \&lt;18 years. People who do not have Chronic Kidney Disease stage 4-5. People who reside outside of the USA and UK.

Primary outcomes:
measure: Choice of Dyalysis
timeFrame: From August 2024 to August 2025
Secondary outcomes:
Other outcomes:
Locations:
city: Dallas
country: United States
facility: Dallas Renal Group
state: Texas
Study officials:
affiliation: Scaled Insights
name: Stuart W Flint, PhD
role: PRINCIPAL_INVESTIGATOR
References:
citation: Flint SW, Brown A, Tahrani AA, Piotrkowicz A, Joseph AC. Cross-sectional analysis to explore the awareness, attitudes and actions of UK adults at high risk of severe illness from COVID-19. BMJ Open. 2020 Dec 29;10(12):e045309. doi: 10.1136/bmjopen-2020-045309.
citation: Flint SW, Leaver M, Griffiths A, Kaykanloo M. Disparate healthcare experiences of people living with overweight or obesity in England. EClinicalMedicine. 2021 Sep 15;41:101140. doi: 10.1016/j.eclinm.2021.101140. eCollection 2021 Nov.