Ignite Creation Date:
2024-07-17 @ 10:44 AM
Last Modification Date:
2024-10-26 @ 3:32 PM
Study NCT ID:
NCT06471036
Status:
RECRUITING
Last Update Posted:
2024-06-24
First Post:
2024-06-04
Brief Title:
Test 2 Treat Can we Improve the Testing and Treatment of High Cholesterol in Patients Who Have Been Hospitalized for a Cardiac Event by Providing Education to Doctors and Patients
Sponsor:
Duke University
Organization:
Duke University
Study Overview
Official Title:
Test 2 Treat A Randomized Implementation Trial to Improve LDL-C Management After Hospitalization for ASCVD Atherosclerotic Cardiovascular Disease
Status:
RECRUITING
Status Verified Date:
2024-07
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
T2T
Brief Summary:
The goal of this implementation trial is to learn if providing education to doctors and patients who have had a heart event works to prevent future heart problems The main questions it aims to answer are
1 Does educating the doctors in a health system improve how often patients in the hospital for a heart event have their cholesterol checked
2 Can a care champion who calls patients who have been discharged from the hospital after a heart event help patients to achieve their cholesterol goals
Researchers will compare the number of people who achieve their cholesterol goals with the help of the care champion to the number of people who did so without the intervention to see if the care champion works to help patients lower their cholesterol
Participants will
Complete two 15 minute surveys over the phone - 1 at enrollment and 1 at the end of the study 6 months later
1 Does educating the doctors in a health system improve how often patients in the hospital for a heart event have their cholesterol checked
2 Can a care champion who calls patients who have been discharged from the hospital after a heart event help patients to achieve their cholesterol goals
Researchers will compare the number of people who achieve their cholesterol goals with the help of the care champion to the number of people who did so without the intervention to see if the care champion works to help patients lower their cholesterol
Participants will
Complete two 15 minute surveys over the phone - 1 at enrollment and 1 at the end of the study 6 months later
Detailed Description:
Data Collection
Data will be collected from several sources
Clinical Research Coordinator CRC will enter patient data into the Case Report Form CRF Baseline clinical data from the electronic health record EHR
Baseline clinical data and patient reported outcomes PROs from patient
6-month clinical data from EHR 6-month clinical data and PROs from patient 8-month clinical data from EHR - only to collect post-study low-density lipoprotein cholesterol LDL-C values Care champion will record data around process ie number of calls to each patient etc Care champion will record data on adaptations to intervention at each site on monthly basis CRC will enter screening vs enrollment data into CRF
Data Protection Participants will be assigned a unique identifier by their enrolling site All participant data that are transferred to Duke will contain the identifier only participant names or any information which would make the participant identifiable will not be transferred
Safety Management and Reporting of Adverse EventsSerious Adverse Events As the intervention only promotes guideline adherence to care and no medications are being prescribed by study personnel this is a low-risk study and the investigators will not routinely collect safety or adverse events data Clinical event data including hospitalizations death MI stroke and coronary revascularization will be collected during the study period by health record check and by discussion with the patients However clinical events will not be formally adjudicated they will be reported and affirmed by site PI
Statistical Hypotheses Randomization and Sample Size Determination Hypotheses On average patients in the treatment arm with have a larger change in LDL level compared to the usual care arm
H_0 β_trt 0 H_a β_trt 0
Randomization Participants will be randomized with a 11 allocation at the site level
Sample Size Determination Sample size determination was done using a 2-level hierarchical mixed model design where patients level-1 are randomized within sites level-2 into two arms The arms are treatment and control arms Assuming a mean change in LDL of 189 mgdL with a standard difference of 388 mgdL ρ005 α005 and 6 clusters the planned overall sample size of n400 should be sufficiently powered
Total Subjects Group 1 Group 2 Clusters Subjects Per Cluster in Group 1 Subjects Per Cluster in Group 2 Mean Difference SD ICC Power N N1 N2 K M1 M2 δ σ ρ Alpha 099729 360 180 180 6 30 30 189 388 005 005 099871 396 198 198 6 33 33 189 388 005 005 099899 408 204 204 6 34 34 189 388 005 005 099921 420 210 210 6 35 35 189 388 005 005 099978 480 240 240 6 40 40 189 388 005 005 Power calculations were computed using PASS 2023 version 2302
Planned Statistical Analysis Patients admitted for MI andor coronary percutaneous revascularization who have an admission LDL level 70 mgdL and have a primary care clinician andor cardiologist within the same health system same EHR
Patients will be randomized 11 at the site level to either usual care or an interventional arm with a care champion to improve post-discharge LDL management Patients would be expected to get their LDL re-checked post-discharge as part of guideline recommended care However this does not always happen and the intervention is meant to increase the adherence to this standard as well as appropriate medication titration when indicated At 6 months post-discharge all patients who have not already had their LDL checked post-discharge will be prompted to do so At 8 months post-discharge the CRC will do an EHR review to obtain last LDL values
Primary Objectives The primary endpoint is within-patient change in LDL from admission LDL level to last LDL checked post-discharge within 8 months post-discharge 6 months of intervention and a 2-month post-study window to capture LDL The investigators will model the association between treatment group and last known LDL value using linear regression adjusting for admission LDL level age sex and race Random intercepts will be used to account for clustered data by site
Missing Final LDL Values The investigators expect some patients in each arm to never get their LDL checked within the 6-month follow-up window For these patients the CRC will contact both the patient and their primary providers PCP andor cardiologist at 6-months post-discharge to encourage them to get their LDL checked per standard of care The CRC will then do an EHR review at 8-months post-discharge to obtain any LDL values that have been recorded
For those who have been contacted but still do not have an LDL level recorded by 8 months post-discharge the investigators will assign their admission value as their final value if there have been no apparent lipid-lowering therapy LLT changes in the EHR If this group is 5 of either treatment arm the investigators will estimate temporal variability in LDL levels and further account for regression to the mean and chance variation The investigators will use our existing cohort to estimate this variability
For those who do not have an LDL level recorded by 8 months post-discharge but do have a record of LLT changes within 6 months post-discharge the investigators will conditionally impute final LDL based on other patients with similar LLT changes who have a final LDL level Clinically relevant LLT change categories eg increase from lowmoderate to high intensity statin addition of non-statin therapies such as PCSK9i mAb or siRNA ezetimibe or bempedoic acid will be created The investigators will use these change categories with other relevant covariates to impute final LDL levels Our imputation approach will be a model-based multiple imputation using the fully conditional specification method For patients whose LLT change category is not well represented in our data the investigators will use an expected reduction based on current literature The investigators will do sensitivity analyses varying the expected reduction thresholds
Proportions will be calculated by treatment arm and compared with logistic regression using random intercepts to account for clustered data by site Means will be calculated by treatment arm and compared with linear regression using random intercepts to account for clustered data by site Unadjusted and adjusted analyses will be done Adjusted models will adjust for age sex and race
Binary outcomes will be analyzed with logistic regression Time to event outcomes will be analyzed with Cox proportional hazards model The proportional hazards assumption will be assessed with Schoenfeld residuals For all models unadjusted and adjusted for age sex and race models will be calculated All models will also use random intercepts to account for clustered data by site
Data will be collected from several sources
Clinical Research Coordinator CRC will enter patient data into the Case Report Form CRF Baseline clinical data from the electronic health record EHR
Baseline clinical data and patient reported outcomes PROs from patient
6-month clinical data from EHR 6-month clinical data and PROs from patient 8-month clinical data from EHR - only to collect post-study low-density lipoprotein cholesterol LDL-C values Care champion will record data around process ie number of calls to each patient etc Care champion will record data on adaptations to intervention at each site on monthly basis CRC will enter screening vs enrollment data into CRF
Data Protection Participants will be assigned a unique identifier by their enrolling site All participant data that are transferred to Duke will contain the identifier only participant names or any information which would make the participant identifiable will not be transferred
Safety Management and Reporting of Adverse EventsSerious Adverse Events As the intervention only promotes guideline adherence to care and no medications are being prescribed by study personnel this is a low-risk study and the investigators will not routinely collect safety or adverse events data Clinical event data including hospitalizations death MI stroke and coronary revascularization will be collected during the study period by health record check and by discussion with the patients However clinical events will not be formally adjudicated they will be reported and affirmed by site PI
Statistical Hypotheses Randomization and Sample Size Determination Hypotheses On average patients in the treatment arm with have a larger change in LDL level compared to the usual care arm
H_0 β_trt 0 H_a β_trt 0
Randomization Participants will be randomized with a 11 allocation at the site level
Sample Size Determination Sample size determination was done using a 2-level hierarchical mixed model design where patients level-1 are randomized within sites level-2 into two arms The arms are treatment and control arms Assuming a mean change in LDL of 189 mgdL with a standard difference of 388 mgdL ρ005 α005 and 6 clusters the planned overall sample size of n400 should be sufficiently powered
Total Subjects Group 1 Group 2 Clusters Subjects Per Cluster in Group 1 Subjects Per Cluster in Group 2 Mean Difference SD ICC Power N N1 N2 K M1 M2 δ σ ρ Alpha 099729 360 180 180 6 30 30 189 388 005 005 099871 396 198 198 6 33 33 189 388 005 005 099899 408 204 204 6 34 34 189 388 005 005 099921 420 210 210 6 35 35 189 388 005 005 099978 480 240 240 6 40 40 189 388 005 005 Power calculations were computed using PASS 2023 version 2302
Planned Statistical Analysis Patients admitted for MI andor coronary percutaneous revascularization who have an admission LDL level 70 mgdL and have a primary care clinician andor cardiologist within the same health system same EHR
Patients will be randomized 11 at the site level to either usual care or an interventional arm with a care champion to improve post-discharge LDL management Patients would be expected to get their LDL re-checked post-discharge as part of guideline recommended care However this does not always happen and the intervention is meant to increase the adherence to this standard as well as appropriate medication titration when indicated At 6 months post-discharge all patients who have not already had their LDL checked post-discharge will be prompted to do so At 8 months post-discharge the CRC will do an EHR review to obtain last LDL values
Primary Objectives The primary endpoint is within-patient change in LDL from admission LDL level to last LDL checked post-discharge within 8 months post-discharge 6 months of intervention and a 2-month post-study window to capture LDL The investigators will model the association between treatment group and last known LDL value using linear regression adjusting for admission LDL level age sex and race Random intercepts will be used to account for clustered data by site
Missing Final LDL Values The investigators expect some patients in each arm to never get their LDL checked within the 6-month follow-up window For these patients the CRC will contact both the patient and their primary providers PCP andor cardiologist at 6-months post-discharge to encourage them to get their LDL checked per standard of care The CRC will then do an EHR review at 8-months post-discharge to obtain any LDL values that have been recorded
For those who have been contacted but still do not have an LDL level recorded by 8 months post-discharge the investigators will assign their admission value as their final value if there have been no apparent lipid-lowering therapy LLT changes in the EHR If this group is 5 of either treatment arm the investigators will estimate temporal variability in LDL levels and further account for regression to the mean and chance variation The investigators will use our existing cohort to estimate this variability
For those who do not have an LDL level recorded by 8 months post-discharge but do have a record of LLT changes within 6 months post-discharge the investigators will conditionally impute final LDL based on other patients with similar LLT changes who have a final LDL level Clinically relevant LLT change categories eg increase from lowmoderate to high intensity statin addition of non-statin therapies such as PCSK9i mAb or siRNA ezetimibe or bempedoic acid will be created The investigators will use these change categories with other relevant covariates to impute final LDL levels Our imputation approach will be a model-based multiple imputation using the fully conditional specification method For patients whose LLT change category is not well represented in our data the investigators will use an expected reduction based on current literature The investigators will do sensitivity analyses varying the expected reduction thresholds
Proportions will be calculated by treatment arm and compared with logistic regression using random intercepts to account for clustered data by site Means will be calculated by treatment arm and compared with linear regression using random intercepts to account for clustered data by site Unadjusted and adjusted analyses will be done Adjusted models will adjust for age sex and race
Binary outcomes will be analyzed with logistic regression Time to event outcomes will be analyzed with Cox proportional hazards model The proportional hazards assumption will be assessed with Schoenfeld residuals For all models unadjusted and adjusted for age sex and race models will be calculated All models will also use random intercepts to account for clustered data by site
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None