Behavioral AI to Predict and Increase Peritoneal Dialysis Uptake

NCT06533254 | Recruiting

• 1 other identifier: AI-Dialysis
• Study Type: observational
• Target: 3,000
• Locations: 1 country
• Sites: 1

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. 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. 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
3. 3.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)
4. 4.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)

Conditions

Chronic Kidney Disease

Keywords

Chronic Kidney Disease; Artificial Intelligence; Dialysis

Outcome Measures

Primary Outcomes (1)

• Choice of Dyalysis

  The Investigators will use AI to predict patients choice of dialysis. This choice will be recorded by their clinical provider as per normal routine care.

  From August 2024 to August 2025

Study Arms (2)

Patients who have Chronic Kidney Disease (Phase 1)

Patients who have Chronic Kidney Disease attending clinics managed by Dallas Renal Group in Texas USA, Leeds Teaching Hospitals NHS Trust UK and Suffolk and North East Essex NHS Trust UK will be contacted via their health care provider to invite them to participate.

Other: Predicting choice of dialysis

Patients who have Chronic Kidney Disease (Phase 2)

Patients who have Chronic Kidney Disease and are attending the units managed by Dallas Renal Group in Texas USA, Leeds Teaching Hospitals NHS Trust UK and Suffolk and North East Essex NHS Trust UK.

Behavioral: Behavioral nudges

Interventions

Predicting choice of dialysis OTHER

Language data collected using survey 1, will be analyzed using Scaled Insights Behavioral AI solution to identify personality attributes and values. The resulting individual scores will be plotted on a graph and be used to identify specific personality clusters of patients. Participants will respond to a survey that utilizes open and closed ended questions, asking broad questions about kidney failure and associated behaviors (e.g. diet, attitudes towards treatment). The open-ended questions will gather a natural language sample of 100-300 words whilst closed questions will capture data using Likert scale and multiple-choice responses. The open-ended questions will gather a natural language sample of 100-300 words for analysis using Scaled Insights Behavioral AI approach. This language sample will be analyzed using Scaled Insights Behavioral AI system to identify personality attributes and values. Patients will also be asked their likelihood of selecting PD over HD.

Patients who have Chronic Kidney Disease (Phase 1)

Behavioral nudges BEHAVIORAL

Patients with Chronic Kidney Disease, participants will be provided with their predicted personalized nudge to increase their likelihood of either maintaining their choice of peritoneal dialysis, or switching from hemodialysis to peritoneal dialysis. Participants will respond to a survey that utilizes open and closed ended questions, asking broad questions about kidney failure and associated behaviors (e.g. diet, attitudes towards treatment). The open-ended questions will gather a natural language sample of 100-300 words whilst closed questions will capture data using Likert scale and multiple-choice responses.

Patients who have Chronic Kidney Disease (Phase 2)

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• 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.

You may qualify if:

• 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.

You may not qualify if:

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

Sponsors & Collaborators

• Scaled Insights: lead
• Dallas Renal Group: collaborator
• The Leeds Teaching Hospitals NHS Trust: collaborator
• Suffolk and North East Essex NHS Trust: collaborator

Study Sites (1)

Dallas Renal Group

Dallas, Texas, 75230, United States

RECRUITING

Related Publications (2)

• 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.

  PMID: 33376185 BACKGROUND

• 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.

  PMID: 34585130 BACKGROUND

Related Links

• Website for the AI company that developed the AI solution that will be used in this study.

MeSH Terms

Conditions

Renal Insufficiency, Chronic

Condition Hierarchy (Ancestors)

Renal Insufficiency; Kidney Diseases; Urologic Diseases; Female Urogenital Diseases; Female Urogenital Diseases and Pregnancy Complications; Urogenital Diseases; Male Urogenital Diseases; Chronic Disease; Disease Attributes; Pathologic Processes; Pathological Conditions, Signs and Symptoms

Study Officials

• Stuart W Flint, PhD

  Scaled Insights

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Stuart W Flint, PhD

CONTACT

+44 7929404094; sflint@scaledinsights.com

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: INDUSTRY
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Dr Stuart W. Flint

Study Record Dates

• First Submitted: July 30, 2024
• First Posted: August 1, 2024
• Study Start: September 1, 2024
• Primary Completion: August 31, 2025
• Study Completion: December 31, 2025
• Last Updated: December 2, 2024

Record last verified: 2024-11

Data Sharing

• IPD Sharing: Will not share

Locations

US (1)