DIAPI for Diabetes

Overseen By Lillian Ruiheng Chen, MD

Age: 18+

Sex: Any

Trial Phase: Academic

Sponsor: Centre hospitalier de l'Université de Montréal (CHUM)

Must be taking: Antidiabetics

Must not be taking: Insulin pump, Entuzity

Disqualifiers: Urgent endoscopy, Pregnancy, others

No Placebo Group All trial participants will receive the active study treatment (no placebo)

Compensation: Varies

Number of Visits: 2

173 Participants Needed

Trial Summary

Do I need to stop my current medications for the trial?

The trial does not specify if you need to stop your current medications. However, DIAPI will provide personalized instructions for managing your diabetes medications before and on the day of the endoscopy. If you are taking SGLT2 inhibitors, there are specific guidelines for discontinuation before the intervention.

What data supports the idea that DIAPI for Diabetes is an effective treatment?

The available research does not provide specific data on the effectiveness of DIAPI for Diabetes. Instead, it focuses on developing quality indicators for diabetes care and evaluating diabetes care protocols. These studies aim to improve diabetes management but do not directly assess DIAPI's effectiveness compared to other treatments. ^{1 2 3 4 5}

What safety data is available for the DIAPI treatment for diabetes?

The provided research does not contain specific safety data for the DIAPI treatment or its variations. The articles focus on diabetes management, medication adherence, and preparation for medical procedures in diabetic patients, but do not mention DIAPI or its safety data. ^{6 7 8 9 10}

Is the DIAPI Algorithm a promising treatment for diabetes?

Yes, the DIAPI Algorithm is a promising treatment for diabetes because it offers a personalized approach to managing blood sugar levels, which can help patients achieve better glucose control and meet their health goals. ^{11 12 13 14 15}

What is the purpose of this trial?

The peri-endoscopy management of diabetes mellitus creates significant challenges for both patients and healthcare professionals. These procedures require fasting and in certain situations, such as prior to a colonoscopy, the diet must be modified the day before the intervention and patients need to take a laxative. These factors put patients at high risk for hyperglycemia and hypoglycemia. Inadequate diabetes control or the continuation of certain medications during this period can be dangerous for the patient and lead to the cancellation of the procedure. DIAPI is a web application designed to generate orders for optimal and personalized treatment based on each patient's antidiabetic treatment, their glycemic control, their risk of hypoglycemia, and the intervention-related variables. DIAPI's algorithm is established on current evidence-based data when available, and experts' opinions. Information generated by DIAPI: * For the patient: Clear instructions regarding their diabetes medication management for the days preceding and the day of the endoscopy. * For the health care team: * Clear instructions regarding patient's diabetes medication management for the days preceding and the day of the endoscopy; * Individualized hyperglycemia protocol; * Hypoglycemia protocol; * Guidelines if SGLT2 inhibitors have not been discontinued pre-intervention; * Suggestion on whether an Endocrinology consultation is needed. DIAPI aims to simplify the complex task of peri-intervention diabetes management while ensuring patient safety. It is a cost-effective solution that can lead to a reduction of unnecessary Endocrinology consultations, a decrease in nurses' workload, a lessening of the risk of errors and a diminution of endoscopy cancellation. The validation study is divided into two main phases. * Phase 1 - Concordance. The investigators will assess the reproducibility of DIAPI orders when two different healthcare workers (an endocrinologist and a nurse) collect data for the same patient. The investigators hypothesize that DIAPI orders are concordant in 80%. Patients in this phase will be subjected to the mainstay management, which is using the treating-physician's recommendations instead of DIAPI's. This group will be the control arm for the non-inferiority study (Phase 2). * Phase 2 - Non-inferiority study. The investigators hypothesize that DIAPI's orders are not inferior to the recommendations issued by the treating-physician in terms of efficacy and security. Patients in this phase will be subjected DIAPI's orders. This group will be the intervention arm for the non-inferiority study.

Research Team

Jean-Marie Boutin, MD, PhD

Principal Investigator

Université de Montréal

Eligibility Criteria

This trial is for people who have had diabetes for at least 6 months, are on medication for it, and have an endoscopy scheduled in two weeks or more. It's designed to help manage their diabetes around the time of their procedure.

Inclusion Criteria

You are taking medication for diabetes.

You have had diabetes for at least 6 months.

You have an upcoming endoscopy scheduled in 2 weeks or more.

Timeline

Screening

Participants are screened for eligibility to participate in the trial

1-2 weeks

Concordance Phase

Assessment of the reproducibility of DIAPI orders by different healthcare workers. Patients receive usual care based on treating-physician's recommendations.

1 week

1 visit (in-person)

Non-inferiority Study

Patients receive DIAPI's orders to evaluate if they are not inferior to usual care in terms of efficacy and safety.

1 week

1 visit (in-person)

Follow-up

Participants are monitored for safety and effectiveness after treatment

2 weeks

Treatment Details

Interventions

• Peri-intervention Diabetes Management Algorithm (DIAPI)

Trial Overview The study tests a web app called DIAPI against usual care. DIAPI gives personalized instructions on managing diabetes meds before and during endoscopy procedures. The goal is to see if DIAPI can match or outperform standard doctor advice.

Participant Groups

Group I: DIAPI Experimental Treatment 1 Intervention

Patients will receive the care for their diabetes recommended by the DIAPI algorithm during their peri-procedural period.

Group II: Usual Care Active Control 1 Intervention

Patients will receive usual care for their diabetes during their peri-procedural period.

Who Is Running the Clinical Trial?

Centre hospitalier de l'Université de Montréal (CHUM)

Lead Sponsor

Trials

389

Recruited

143,000+

Findings from Research

A set of 36 quality indicators for perioperative diabetes care was developed using a modified Delphi method, aimed at improving care for patients undergoing major surgeries.

The practice test involving 389 patients revealed that while most indicators had good reliability, some were unmeasurable or inapplicable, highlighting the importance of testing indicators in clinical practice before implementing them for quality improvement.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Perioperative diabetes care: development and validation of quality indicators throughout the entire hospital care pathway. Hommel, I., van Gurp, PJ., Tack, CJ., et al.[2018]

Over a five-year period, a study of 318 diabetic patients showed an increase in complications, with microvascular issues rising from 33.4% to 42.1% and macrovascular issues from 22.3% to 37.2%, indicating a worsening of overall health despite increased insulin use and self-monitoring.

While some patients experienced a slight reduction in blood pressure, overall management of diabetes was ineffective as HbA1c levels worsened, highlighting the need for improved diabetes care protocols to better control blood sugar levels and reduce complications.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

[Chronic complications of type 2 diabetes mellitus. Clinical course after 5 years of follow-up]. Mundet Tudurí, X., Carmona Jiménez, F., Gussinyer Canabal, P., et al.[2020]

The study found that implementing system-level interventions in diabetes management led to significant improvements in clinic-controlled variables, such as the administration of pneumococcal vaccines, with 'yes' responses increasing from 8-24% to 16-95%.

Despite these improvements in clinic processes, the overall patient outcome measures did not show statistically significant changes, suggesting that while system changes are beneficial, influencing patient behavior remains a challenging aspect of diabetes care.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Quality improvement and changes in diabetic patient outcomes in an academic nurse practitioner primary care practice. Mackey, TA., Cole, FL., Lindenberg, J.[2022]

The quality improvement program significantly enhanced the adherence to diabetes care guidelines among 312 patients, with notable increases in care measures such as blood pressure monitoring (odds ratio of 12.08) and urine albumin testing (odds ratio of 2.43).

Despite improvements in the provision of care, the program did not lead to any measurable benefits in patient outcomes, such as blood pressure or HbA1c levels, over a follow-up period of 42 months.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Long-term effectiveness of a quality improvement program for patients with type 2 diabetes in general practice. Renders, CM., Valk, GD., Franse, LV., et al.[2022]

In a study of 8,733 diabetes patients across 68 provider groups, an increase in the number of documented diabetes care processes was linked to significantly lower LDL cholesterol levels, suggesting that better documentation may improve certain health outcomes.

The number of care processes also correlated with higher patient satisfaction and self-rated quality of care, indicating that thorough care practices can enhance the overall patient experience, although they did not significantly impact other cardiometabolic risk factors like A1C or blood pressure.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Is the number of documented diabetes process-of-care indicators associated with cardiometabolic risk factor levels, patient satisfaction, or self-rated quality of diabetes care? The Translating Research into Action for Diabetes (TRIAD) study. Ackermann, RT., Thompson, TJ., Selby, JV., et al.[2022]

People with diabetes face higher risks of serious complications during colonoscopy, such as hypoglycemia and acute kidney injury, mainly due to poor dietary changes and medication adjustments before the procedure.

This review offers updated guidelines for safely modifying diabetes medications, dietary preparations, and glucose monitoring to reduce these risks during the colonoscopy process.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Preparing for Colonoscopy in People with Diabetes: A Review with Suggestions for Clinical Practice. Chirila, A., Nguyen, ME., Tinmouth, J., et al.[2023]

The consensus algorithm for managing type 2 diabetes, originally published in 2006, is still endorsed by the authors, emphasizing the importance of careful updates based on new evidence rather than frequent changes without solid justification.

The 2008 update specifically highlighted safety concerns related to thiazolidinediones, and the current revision focuses on new medication classes that have emerged with more clinical data, indicating a commitment to incorporating the latest research into diabetes management.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Medical management of hyperglycaemia in type 2 diabetes mellitus: a consensus algorithm for the initiation and adjustment of therapy: a consensus statement from the American Diabetes Association and the European Association for the Study of Diabetes. Nathan, DM., Buse, JB., Davidson, MB., et al.[2022]

In a study of 26,713 diabetes patients, only 42% adhered to their oral antihyperglycemic drugs (OADs) over one year, highlighting a significant issue with medication adherence.

Adherent patients had a 7% lower risk of hospitalization and a 10% lower risk of mortality compared to nonadherent patients, indicating that better adherence can lead to improved health outcomes.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

One-year adherence to oral antihyperglycemic medication and risk prediction of patient outcomes for adults with diabetes mellitus: An observational study. Huber, CA., Rapold, R., Brüngger, B., et al.[2018]

The consensus algorithm for managing type 2 diabetes, originally published in 2006, is still endorsed by the authors, emphasizing the importance of careful updates based on new evidence rather than frequent changes without solid justification.

The 2008 update specifically highlighted safety concerns related to thiazolidinediones, and the current revision focuses on new medication classes that have emerged with more clinical data, indicating a commitment to incorporating the latest research into diabetes management.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Medical management of hyperglycemia in type 2 diabetes: a consensus algorithm for the initiation and adjustment of therapy: a consensus statement of the American Diabetes Association and the European Association for the Study of Diabetes. Nathan, DM., Buse, JB., Davidson, MB., et al.[2022]

In a study of 866,605 commercially insured patients with type II diabetes, 27.9% were pretreated with oral antidiabetics (OADs) before their diabetes diagnosis, which was linked to a modest delay in starting insulin therapy.

Patients who were pretreated had lower overall healthcare costs despite higher monthly diabetes-related pharmacy costs, suggesting that OAD pretreatment may lead to better long-term economic outcomes in diabetes management.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Evaluation of patients with type 2 diabetes mellitus receiving treatment during the pre-diabetes period: Is early treatment associated with improved outcomes? Chaudhari, P., Vallarino, C., Law, EH., et al.[2018]

Type 2 diabetes affects 30.3 million Americans, and timely intensification of rapid-acting insulin is crucial for managing rising blood sugar levels, especially when HbA1c exceeds 7%.

The paper emphasizes the importance of personalized treatment approaches using validated algorithms like FullSTEP and SimpleSTEP to improve glucose control while considering individual patient needs and minimizing risks like hypoglycemia and weight gain.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Approaches to rapid acting insulin intensification in patients with type 2 diabetes mellitus not achieving glycemic targets. Migdal, A., Abrahamson, M., Peters, A., et al.[2022]

The DirecNet Applied Treatment Algorithm (DATA) was effectively used by 30 children and adolescents with diabetes, showing that it provided clear guidance for insulin dosing and improved post-meal glucose levels over a 13-week period.

A high percentage of participants (86% at 3 weeks and 59% at 13 weeks) utilized the DATA for responding to glucose alarms and adjusting insulin doses, indicating its practical application in real-time continuous glucose monitoring.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Use of the DirecNet Applied Treatment Algorithm (DATA) for diabetes management with a real-time continuous glucose monitor (the FreeStyle Navigator). Buckingham, B., Xing, D., Weinzimer, S., et al.[2021]

In a six-month study involving 108 participants with type 1 diabetes, an AI-based decision support system (AI-DSS) for insulin dose adjustments was found to be as effective as physician-guided adjustments, with similar percentages of time spent in the target glucose range (70-180 mg/dL).

The AI-DSS group experienced fewer severe adverse events related to diabetes, with no cases of severe hypoglycemia or diabetic ketoacidosis reported, compared to three severe events in the physician group, suggesting a potential safety advantage of the AI-DSS approach.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Insulin dose optimization using an automated artificial intelligence-based decision support system in youths with type 1 diabetes. Nimri, R., Battelino, T., Laffel, LM., et al.[2021]

The DIALIN database and the Camit diabetes management system have been developed to enhance the monitoring and evaluation of diabetes care in hospitals, showing promising acceptance and utility in clinical settings.

The DIACONS expert system demonstrated a high accuracy of 96% in determining diabetes type and appropriate initial therapy based on patient history, indicating its potential effectiveness in improving diabetes management.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Analysis and processing of data in a hospital-based diabetes management system. Piwernetz, K., Renner, R., Möhrlein, A., et al.[2007]

The Diabetes Advisory System (DIAS) effectively identified issues like impaired insulin absorption and missed doses in four patients with poorly controlled type 1 diabetes, leading to better glucose management.

After retraining based on DIAS evaluations, three patients showed significant improvements in their glucose control, highlighting the system's potential to optimize insulin therapy for those struggling with high blood sugar levels.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Impaired absorption and omission of insulin: a novel method of detection using the diabetes advisory system computer model. Turner, BC., Hejlesen, OK., Kerr, D., et al.[2022]

References

1. England pubmed.ncbi.nlm.nih.gov

Perioperative diabetes care: development and validation of quality indicators throughout the entire hospital care pathway. [2018]

2. Spain pubmed.ncbi.nlm.nih.gov

[Chronic complications of type 2 diabetes mellitus. Clinical course after 5 years of follow-up]. [2020]

3. United States pubmed.ncbi.nlm.nih.gov

Quality improvement and changes in diabetic patient outcomes in an academic nurse practitioner primary care practice. [2022]

4. United States pubmed.ncbi.nlm.nih.gov

Long-term effectiveness of a quality improvement program for patients with type 2 diabetes in general practice. [2022]

5. United States pubmed.ncbi.nlm.nih.gov

Is the number of documented diabetes process-of-care indicators associated with cardiometabolic risk factor levels, patient satisfaction, or self-rated quality of diabetes care? The Translating Research into Action for Diabetes (TRIAD) study. [2022]

6. England pubmed.ncbi.nlm.nih.gov

Preparing for Colonoscopy in People with Diabetes: A Review with Suggestions for Clinical Practice. [2023]

7. Germany pubmed.ncbi.nlm.nih.gov

Medical management of hyperglycaemia in type 2 diabetes mellitus: a consensus algorithm for the initiation and adjustment of therapy: a consensus statement from the American Diabetes Association and the European Association for the Study of Diabetes. [2022]

8. United States pubmed.ncbi.nlm.nih.gov

One-year adherence to oral antihyperglycemic medication and risk prediction of patient outcomes for adults with diabetes mellitus: An observational study. [2018]

9. United States pubmed.ncbi.nlm.nih.gov

Medical management of hyperglycemia in type 2 diabetes: a consensus algorithm for the initiation and adjustment of therapy: a consensus statement of the American Diabetes Association and the European Association for the Study of Diabetes. [2022]

10. Ireland pubmed.ncbi.nlm.nih.gov

Evaluation of patients with type 2 diabetes mellitus receiving treatment during the pre-diabetes period: Is early treatment associated with improved outcomes? [2018]

11. England pubmed.ncbi.nlm.nih.gov

Approaches to rapid acting insulin intensification in patients with type 2 diabetes mellitus not achieving glycemic targets. [2022]

12. Denmark pubmed.ncbi.nlm.nih.gov

Use of the DirecNet Applied Treatment Algorithm (DATA) for diabetes management with a real-time continuous glucose monitor (the FreeStyle Navigator). [2021]

13. United States pubmed.ncbi.nlm.nih.gov

Insulin dose optimization using an automated artificial intelligence-based decision support system in youths with type 1 diabetes. [2021]

14. Germany pubmed.ncbi.nlm.nih.gov

Analysis and processing of data in a hospital-based diabetes management system. [2007]

15. United States pubmed.ncbi.nlm.nih.gov

Impaired absorption and omission of insulin: a novel method of detection using the diabetes advisory system computer model. [2022]