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| ID | Type | Description | Link |
|---|---|---|---|
| NCI-2025-08258 | Registry Identifier | CTRP (Clinical Trial Reporting Program) | |
| S2424CD | Other Identifier | SWOG | |
| SWOG-S2424CD | Other Identifier | DCP | |
| S2424CD | Other Identifier | CTEP | |
| R37CA296075 | U.S. NIH Grant/Contract | View source | |
| UG1CA189974 | U.S. NIH Grant/Contract | View source |
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| Name | Class |
|---|---|
| National Cancer Institute (NCI) | NIH |
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This clinical trial evaluates an educational program called Algorithm-Enabled Patients Activated in Cancer Care Through Teams (A-PACT) for reducing unplanned hospital visits and improving goals of care conversations with providers among patients with solid cancers. A-PACT is an educational program where lay health workers (educators) help patients talk with their health care team about issues that matter most to them (goals of care). During A-PACT sessions, patients receive assistance in formulating health care and end of life care preferences, assistance in completing advance directives, guidance on how to engage in these conversations with family members, friends, and clinical teams, and encouragement to discuss these topics with their clinical team. A-PACT may reduce unplanned hospital visits and improve goals of care communication with providers among patients with solid cancers.
PRIMARY OBJECTIVE:
I. To compare the proportion of participants who have any hospitalizations within 12 months after randomization between those receiving A-PACT (intervention arm) and those receiving usual care alone (control arm).
SECONDARY OBJECTIVES:
I. To compare the proportion of participants who report anxiety at 3 months compared to baseline between participants receiving A-PACT and participants receiving usual care.
II. To compare the proportion of participants who report depression at 3 months compared to baseline between participants receiving A-PACT and participants receiving usual care.
III. To assess the proportion of participants who receive intensive end-of-life care between participants receiving A-PACT and those receiving usual care alone.
IMPLEMENTATION OBJECTIVES:
I. To quantitatively and qualitatively assess how patient, clinician, and organizational factors shape effectiveness and implementation of A-PACT.
II. To measure feasibility of machine learning (ML) algorithm, adoption of intervention (patient enrollment), and fidelity (% of patients completing A-PACT).
EXPLORATORY OBJECTIVES:
I. To assess the following in participants on the intervention arm versus the usual practice arm:
Ia. The proportion of participants who have any hospitalization or emergency department visits within 12 months from randomization between those receiving A-PACT and those receiving usual care; Ib. The mean number of hospitalizations within 12 months from randomization between participants receiving A-PACT and participants receiving usual care alone; Ic. The mean number of hospitalizations or emergency department visits within 12 months of randomization between participants receiving A-PACT and participants receiving usual care alone; Id. Anxiety at 6 months from baseline; Ie. Depression at 6 months from baseline; If. Heard and Understood measures at 6 months from baseline; Ig. Prognostic awareness and treatment preferences at 6 months from baseline; Ih. Presence of goals of care documentation in the electronic health record at 12 months; Ii. Presence of advance directive documentation in the electronic health record at 12 months; Ij. Presence of physician orders for life sustaining treatment documentation in the electronic health record at 12 months; Ik. The potential differential impact of sociodemographic factors on all outcomes.
OUTLINE: Patients are randomized to 1 of 2 groups.
GROUP 1: Patients attend an initial A-PACT education session over 30-45 minutes by phone and receive follow-up A-PACT phone calls over 15 minutes each twice monthly, or less frequently per patient preference, for up to 6 months, in addition to usual care.
GROUP 2: Patients receive usual care for 6 months.
After completion of study intervention, patients are followed up at months 9 and 12.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Group 1 (Usual care + A-PACT) | Experimental | Patients attend an initial A-PACT education session over 30-45 minutes by phone and receive follow-up A-PACT phone calls over 15 minutes each twice monthly, or less frequently per patient preference, for up to 6 months, in addition to usual care. |
|
| Group 2 (Usual care) | Active Comparator | Patients receive usual care for 6 months. |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Best Practice | Other | Receive usual care |
|
| Measure | Description | Time Frame |
|---|---|---|
| Proportion of patients with any hospitalization | The primary analysis will be conducted using multivariable logistic regression, adjusting for stratification factors as covariates. | Within 12 months of enrollment |
| Measure | Description | Time Frame |
|---|---|---|
| Proportion of patients with 0 versus >= 1 of the combined endpoint of hospitalizations or emergency room (ER) visits | Logistic, linear, and negative binomial regression models will be used, as appropriate for the given endpoint. Each regression analysis will include covariate adjustment for the stratification variables. | Up to 12 months |
| Measure | Description | Time Frame |
|---|---|---|
| Goals of care communication | Abstracted from the electronic health record. Will assess differential impact by income and race and ethnicity. | Up to 12 months |
| Emergency room visits | Will assess differential impact by income and race and ethnicity. |
Inclusion Criteria:
Participants must have a diagnosis of a solid tumor malignancy of any stage
Participants must be identified as high risk, defined as having 6 month mortality estimate from machine learning (ML) algorithm >= 20%
Participants must not be receiving or have pre-existing plans to enter hospice care at the time of study registration
Participant must be actively receiving or planning to receive systemic anti-cancer therapy (defined as any oral, injection, or intravenous therapy against cancer) within 3 months after registration
Participants must be >= 18 years of age at the time of study enrollment
Participants who can complete Patient Reported Outcome (PRO) questionnaires in English or Spanish must be willing to 1) complete PROs at all scheduled assessments; and 2) complete the pre-registration (baseline) PRO forms within 14 days prior to registration
Participants must be able to provide a valid telephone number for the purpose of being contacted by the lay health worker
NOTE: As a part of the Oncology Patient Enrollment Network (OPEN) registration process the treating institution's identity is provided in order to ensure that the current (within 365 days) date of institutional review board approval for this study has been entered in the system
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| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Patricia O'Kane | Contact | 2106148808 | 1011 | pokane@swog.org |
| Sarah Cantu | Contact | 2106148808 | scantu@swog.org |
| Name | Affiliation | Role |
|---|---|---|
| Ravi B Parikh | SWOG Cancer Research Network | Principal Investigator |
| Manali Patel, MPH MS FASCO | Stanford School of Medicine | Principal Investigator |
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Randomization assignment is not revealed to clinicians and general staff.
| Educational Intervention | Other | Receive A-PACT education |
|
|
| Electronic Health Record Review | Other | Ancillary studies |
|
| Interview | Other | Ancillary studies |
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| Questionnaire Administration | Other | Ancillary studies |
|
| Telephone-Based Intervention | Behavioral | Receive A-PACT phone calls |
|
| Number of hospitalizations as a continuous outcome |
Logistic, linear, and negative binomial regression models will be used, as appropriate for the given endpoint. Each regression analysis will include covariate adjustment for the stratification variables. |
| Up to 12 months |
| Number of hospitalizations or ER visits | Logistic, linear, and negative binomial regression models will be used, as appropriate for the given endpoint. Each regression analysis will include covariate adjustment for the stratification variables. | Up to 12 months |
| Intensive end-of-life care | Logistic, linear, and negative binomial regression models will be used, as appropriate for the given endpoint. Each regression analysis will include covariate adjustment for the stratification variables. | Up to 12 months |
| Anxiety | Logistic, linear, and negative binomial regression models will be used, as appropriate for the given endpoint. Each regression analysis will include covariate adjustment for the stratification variables. Baseline score will also be included as a model covariate. | At 3 months after enrollment |
| Depression | Logistic, linear, and negative binomial regression models will be used, as appropriate for the given endpoint. Each regression analysis will include covariate adjustment for the stratification variables. Baseline score will also be included as a model covariate. | At 3 months after enrollment |
| Up to 12 months |
| Patient-reported anxiety | Measured using Patient Reported Outcomes Measurement Information System (PROMIS) version 1.0 Short Form Anxiety. Will assess differential impact by income and race and ethnicity. | At baseline, 3 months, and 6 months |
| Patient-reported depression | Measured using PROMIS version 1.0 Short Form Depression. Will assess differential impact by income and race and ethnicity. | At baseline, 3 months, and 6 months |
| Patient-reported quality of communication with clinical staff | Assessed using the Heard and Understood questionnaire. Will assess differential impact by income and race and ethnicity. | At baseline, 3 months, and 6 months |
| Patient-reported experiences with end of life care discussions | Will be measured using the Prognostic Awareness and Treatment Preferences instrument. Will assess differential impact by income and race and ethnicity. | At baseline, 3 months, and 6 months |
| Hospitalizations alone | Will be evaluated as time-to-event analysis. Will assess differential impact by income and race and ethnicity. | Up to 12 months |
| Hospitalizations and ER visits | Will be evaluated as time-to-event analysis. Will assess differential impact by income and race and ethnicity. | Up to 12 months |
| ER visits | Will be evaluated as time-to-event analysis. Will assess differential impact by income and race and ethnicity. | Up to 12 months |
| Percentage of approached sites that can map the electronic health record query (Feasibility) | Feasibility goal for this implementation outcome is >= 90%. | Up to 6 years |
| Percentage of sites that can run the machine learning (ML) algorithm Python code on site (Feasibility) | Feasibility goal for this implementation outcome is >= 90%. | Up to 6 years |
| Percentage of sites where ML retraining is not necessary prior to trial activation (Feasibility) | Feasibility goal for this implementation outcome is >= 50%. | Up to 6 years |
| Percentage of eligible patients who consent to study (Adoption) | Adoption goal for this implementation outcome is >= 50%. | Up to 6 years |
| Percentage of patients enrolled in the intervention arm who engage with the lay health workers at least once a month for first 6 months, prior to death, or prior to transition to hospice (Fidelity) | Up to 6 years |
| Acceptability of intervention | Assessed during mixed-methods interviews among intervention participants. | At 6 months after trial enrollment |
| ID | Term |
|---|---|
| D017410 | Practice Guidelines as Topic |
| D059039 | Standard of Care |
| D018479 | Early Intervention, Educational |
| D004522 | Educational Status |
| D008722 | Methods |
| D007407 | Interviews as Topic |
| ID | Term |
|---|---|
| D017408 | Guidelines as Topic |
| D011785 | Quality Assurance, Health Care |
| D011787 | Quality of Health Care |
| D006298 | Health Services Administration |
| D017530 | Health Care Quality, Access, and Evaluation |
| D019984 | Quality Indicators, Health Care |
| D002662 | Child Health Services |
| D003153 | Community Health Services |
| D006296 | Health Services |
| D005159 | Health Care Facilities Workforce and Services |
| D011314 | Preventive Health Services |
| D012959 | Socioeconomic Factors |
| D011154 | Population Characteristics |
| D008919 | Investigative Techniques |
| D003625 | Data Collection |
| D004812 | Epidemiologic Methods |
| D017531 | Health Care Evaluation Mechanisms |
| D011634 | Public Health |
| D004778 | Environment and Public Health |
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